Program of ML in PL Conference 2025

Konrad Staniszewski

NVIDIA, University of Warsaw

Contributed talk 7: Cache Me If You Can: Reducing Model Size and KV Cache Traffic for Faster LLM Inference

Thursday / 16 October 9:30 - 10:00 Hall B (CfC Session 3)

Abstract:

Large language models (LLMs) acquire impressive multi-step reasoning abilities. However, deploying them efficiently remains a significant engineering challenge, especially in chat interfaces, where iterative refinement leads to even more demanding KV cache management. The KV cache can easily consume several gigabytes; keeping it on-chip ensures low-latency responses but wastes valuable memory during user turns. In this talk, we present two highly practical methods: one for pruning model parameters and attention heads to accelerate generation and shrink the KV cache, and another for rapid compression and decompression of idle KV caches. Firstly, we show that attention heads exhibit task-specific activation patterns, a property that can be leveraged to create streamlined, domain- or problem-specific model variants. Then we demonstrate that the same algorithm which we have designed for careful pruning of attention heads, can be applied for ordinary parameter pruning, reaching close to state-of-the-art results. Finally, we introduce a novel transform coder for KV cache compression, designed for fast compression and decompression directly on a GPU. It achieves up to 20x compression with negligible accuracy loss on demanding tasks, which in some cases reaches as high as 80x. Inspired by classic media codecs, our method consists of a linear feature decorrelation, quantization, and entropy-based compression. In multi-turn dialogue scenarios, it enables rapid offloading of the KV cache to external storage and later recovery. Our work reveals a strong low-rank structure in the KV cache and provides both practical tools and theoretical insights toward more efficient, scalable human–LLM interaction.

Biography:

Konrad Staniszewski is a Ph.D. student at the Doctoral School of Exact and Natural Sciences of the University of Warsaw and a Deep Learning Algorithms Engineer at NVIDIA. His work focuses on optimizations for Large Language Models. His interests are machine learning, natural language processing, and algorithmics. He got his master’s degree at the Faculty of Mathematics, Informatics, and Mechanics at the University of Warsaw.

Paweł Cyrta

AGH Cyfronet / Stenograf.io

Contributed talk 8: Neural self-supervised audio representation for SpeechLLM: neural audio codecs for Polish language

Thursday / 16 October 10:00 - 10:30 Hall B (CfC Session 3)

Abstract:

This work investigates neural audio codecs as self-supervised speech representations for SpeechLLM architectures, with specific focus on Polish language optimization. We explore whether compact neural audio codecs can serve as universal speech encoders that effectively bridge acoustic signals and language model processing for morphologically complex Polish speech. We reviews various existing ssl speech representations and present benchmark results on diverse BIGOS speech dataset. We presents a systematic evaluation of neural audio codecs as self-supervised speech representations for Polish language. We compare widely used state-of-the-art SSL speech representations (wav2vec2.0, HuBERT, WavLM, w2v-bert-2.0, EnCodec, DAC, SoundStream, SpeechTokenizer, WavTokenizer, XCodec, FunCodec, Mimi) against our proposed Polish-optimized codec on the BIGOS benchmark dataset comprising of diverse Polish speech. Finally, we tackle the challenge of training high-quality SpeechLLM model based on Bielik LLM under severe data scarcity constraints through systematic synthetic data generation. Our approach leverages persona-based dialogue synthesis combined with thematic taxonomies to create semantically rich conversational datasets that preserve pragmatic qualities of natural Polish speech. The research provides practical guidelines for maximizing SpeechLLM training efficiency with minimal natural data requirements, showing comparable performance to models trained on substantially larger natural datasets. Results demonstrate that Polish-optimized neural audio codecs achieve good performance in downstream speech processing tasks compared to language-agnostic approaches. This work contributes to understanding how self-supervised audio representations can be specialized for speech encoding on linguistically complex languages while maintaining computational efficiency.

Biography:

Paweł Cyrta – Applied Research Scientist and Engineer specializing in speech and audio analysis, dedicated to transforming cutting-edge research into real-world applications. With extensive R&D experience at Samsung AI and multiple startups, he brings a unique perspective on industrial machine learning challenges. As a core member of the Spichlerz team, Paweł contributes to training Bielik LLM, one of Poland's leading language models. He developed Polish speech-to-text models powering Stenograf.io's transcription services. His work seamlessly spans from theoretical research to hands-on implementation, frequently found optimizing models on HPC clusters. Currently pursuing a Ph.D. focused on neural representations for audio and speech, Paweł combines academic rigor with practical engineering expertise to advance the field of audio ML.

Karolina Drożdż

Abstract:

Contemporary jailbreak attacks on Large Language Models (LLMs) employ sophisticated techniques with obfuscated content to bypass safety guardrails. Existing defenses either use computationally intensive LLM verification or require adversarial fine-tuning, leaving models vulnerable to advanced attacks. We introduce SafeQuant, a novel defense framework that leverages quantized gradient patterns to identify harmful prompts efficiently. Our key insight is that when generating identical responses like “Sure", LLMs exhibit distinctly different internal gradient patterns for safe versus harmful prompts, reflecting conflicts with safety training. By capturing these patterns through selective gradient masking and quantization, SafeQuant significantly outperforms existing defenses across multiple benchmarks while maintaining model utility. The method demonstrates particular effectiveness against sophisticated attacks like WordGame prompts and persuasive adversarial attacks, achieving high F1-score.

Biography:

/ Posters

Bartosz Marcinkowski

MIM Solutions

Co-authors:

Jakub Siuta, Ana Candela Celdran, Mena Nadum, Marek Wachnicki, Jerzy Orłowski

Poster 1: Towards Semantic Embeddings of Cardiological Signals with Diffusion Autoencoders

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

To support the development of wearable medical devices for remote monitoring and treatment of cardiovascular diseases, we tackle the data scarcity problem that hinders the application of machine learning methods. We propose a self-supervised approach applied to cardiological signals, which benefits from existing datasets despite differences between them and inconsistencies within them. We develop a specific implementation: a diffusion autoencoder with a semantic encoder based on linear recurrent units, trained on ECG signals (various leads mixed together) without any annotations. The semantic encoder is evaluated as a feature extractor by measuring classification metrics of a logistic regression on a dataset not included in the self-supervised training. We obtain promising results and propose future directions.

Biography:

Senior Data Scientist at MIM Solutions, with previous experience at Digital Science and RTB House. University of Warsaw graduate.

Grzegorz Rypeść

Warsaw University of Technology, RTB House

Poster 2: Gradient Free Continual Learning

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

Continual Learning (CL) seeks to train neural networks on sequential tasks without catastrophic forgetting. A key limitation of existing CL methods is their reliance on gradient-based optimization, which breaks down when data from previous tasks is no longer accessible - a common constraint in CL. In this work, we propose a paradigm shift: we hypothesize that the core issue in catastrophic forgetting is not data unavailability, but the inability to compute gradients for prior tasks. To address this, we introduce EvoCL, a novel gradient-free continual learning framework that leverages Evolution Strategies (ES) for optimization. EvoCL memorizes compressed latent features of past tasks and employs an adapter network to approximate their loss, enabling effective learning without relying on stored exemplars or backpropagation through old data. Our approach achieves promising results under the assumption of low number of trainable parameters and opens up new avenues for data-free and gradient-free continual learning.

Biography:

Grzegorz Rypeść obtained a double master's degree in computer science engineering from the Warsaw University of Technology and Kyungpook National University in Korea. He is currently a PhD student at IDEAS-NCBR and the Warsaw University of Technology, where he focuses on continual learning. He has published as the first author at the most prestigious machine learning conferences such as NeurIPS, ICLR, ECCV, and IJCAI.

Piotr Ludynia

AGH University of Kraków

Co-authors:

Jakub Adamczyk, Wojciech Czech

Poster 3: Surpassing Complex Models With Simple Graph Feature Extraction

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

Peptides, short chains of amino acids, are crucial targets in drug discovery due to their therapeutic relevance in treating cancer, viral infections, and antibiotic-resistant bacteria. In machine learning, peptide datasets have been widely adopted as benchmarks for studying long-range dependencies in graph-based models, serving as testing grounds for increasingly complex long-range Graph Neural Network (GNN) architectures. We challenge this assumption by evaluating count-based molecular fingerprints across 126 datasets and six diverse benchmarks, including Long Range Graph Benchmark (LRGB), Antimicrobial Peptides (AMP) prediction tasks, and general peptide property benchmarks. We evaluate against GNNs, Graph Transformers, and complex multimodal models. Molecular fingerprints are domain-specific, short-range feature extraction methods that detect substructure occurrences in molecular graphs. We use count variants of ECFP, Topological Torsion, and RDKit fingerprints, paired with LightGBM classifier, to achieve state-of-the-art performance. Our results show that these inherently local representations outperform complex models explicitly designed to capture long-range interactions, including models with global attention mechanisms. This finding questions the presumed importance of long-range dependencies in peptide property prediction and demonstrates that simple, efficient models can capture essential biochemical patterns. In addition to improving predictive performance, our approach is highly scalable, requires minimal tuning, and is orders of magnitude faster than deep model training. These results emphasize the need to benchmark novel architectures against strong shallow baselines and reconsider assumptions about the role of long-range interactions in molecular graph learning.

Biography:

Piotr is a fresh master graduate in Machine Learning and Data Science at AGH University of Kraków. He's a member of AGH ML and Chemoinformatics Group where he conducts research on vectorization methods for graph data. He is also one of the creators and current maintainers of scikit-fingerprints, a molecular fingerprint Python library for efficient molecular vectorization.

Joanna Wiekiera

KP Labs, Silesian University of Technology

Poster 4: Continual learning for satellite data analysis

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

Data collected during a space mission can change over time, even slightly, due to factors such as sensor degradation or environmental conditions, potentially impacting model performance. To address these challenges, this work explores continual learning techniques for satellite data analysis, focusing on adapting models to evolving conditions without catastrophic forgetting. We implemented and evaluated state-of-the-art approaches, including regularization-based, replay-based, and architectural methods, under class-incremental scenarios for both telemetry and satellite imagery. Experimental results on benchmark datasets demonstrate improved adaptability and robustness compared to static models. These findings highlight the practical potential of continual learning for reliable AI-driven satellite operations under real mission constraints.

Biography:

Joanna Wiekiera holds a Bachelor’s degree in Computer Science from the Silesian University of Technology and is currently pursuing a Master’s degree in Data Science. She has served as President of the Scientific Student Association Data Science for two years, fostering interdisciplinary collaboration and organizing educational initiatives. Professionally, Joanna works as a Machine Learning Specialist at KP Labs, focusing on AI solutions for space-related applications. Her research interests include continual learning, anomaly detection, and AI for scientific discovery. She is particularly interested in applying AI to scientific challenges, such as those in physics.

Lucas Sancéré

University of Cologne

Co-authors:

Katarzyna Bozek

Poster 5: Context-aware skin cancer cell classification based on GNNs

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

Recent advancements in Graph Neural Networks (GNNs) have enabled classification tasks on data with graph structures. However, few GNN applications exist for large-scale medical data, particularly in handling million-node graphs. Here we develop a graph-based approach to classify cells in large microscopy megapixel images (whole slide images) of skin tumor samples. While there exist megapixel image segmentation methods for cell classification, these methods rely only on local information and fail to correctly classify cells that are morphologically similar but yet functionally different. In skin cancer, tumor cells can only be recognized from healthy skin cells based on their surrounding tissue structure. We encode tissue structure in 113 megapixel images of skin tumors as graphs where each node represents a cell, is characterized by its morphological and spatial features and is labelled according to its cell type. We use an individual tissue sample graph as a separate batch during training and train the network to infer cell types of different nodes in the graph. We compared our context-aware graph-based node classification model to state of the art segmentation model and to other GNNs tailored for large graph node classification tasks. Our study exemplifies how biological structures and their spatial features can be efficiently encoded as nodes within graphs, enabling GNNs to learn rich representations and perform accurate node-level classification.

Biography:

PhD student in AI applied to Medical Images, Bozek Lab CMMC Cologne Germany. Former Engineer in Curie Institute Paris in the same field. I grew up next to the esteemed corn fields of Les Landes, south-west France.

Eryk Zarębski

AGH University of Krakow

Poster 6: Vision Transformers for Enhanced Analysis of X-ray Fluorescence Spectra in Cultural Heritage

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

Elemental analysis of cultural heritage artifacts offers profound insights into their history, manufacturing techniques, and current state of preservation. X-ray Fluorescence (XRF) spectroscopy, a widely used non-invasive method, plays a central role in such investigations. However, interpreting XRF data, especially from instruments with moderate energy resolution remains a significant challenge. This is particularly true for spectra acquired using the DETART Full-Field XRF (FF-XRF) scanner, developed at AGH University in collaboration with the Polish National Museum in Krakow. The system’s Gas Electron Multiplier (GEM) detector, while effective, exhibits limited energy resolution, which leads to overlapping elemental peaks and spectral artifacts that complicate analysis. To address these challenges, I propose a deep learning-based approach, leveraging established computer vision models, specifically Vision Transformers (ViT), to analyze and interpret raw XRF spectra from the DETART system. This research centers on developing and evaluating algorithms for two key tasks: identifying the elements present (classification) and estimating their relative concentrations (regression). By applying these AI techniques directly to raw spectral data, the approach aims to automate and enhance the analytical process, enabling more accurate and detailed interpretations of the elemental composition of historical artifacts, even in the face of detector limitations. This work highlights the potential of deep learning to transform cultural heritage science.

Biography:

Fields medalists, machine learning researcher, 3000 ELO in chess - these words certainly don't describe me! I am a software engineer with a specialization in scalable, distributed backend systems. Currently, I am pursuing a Master's degree, with my research focusing on machine learning and its applications in physics.

Jan Małaśnicki

University of Warsaw

Co-authors:

Kamil Ciebiera, Mateusz Boruń, Maciej Pióro, Jan Ludziejewski, Maciej Stefaniak, Michał Krutul, Sebastian Jaszczur, Marek Cygan, Kamil Adamczewski, Jakub Krajewski

Poster 7: μ-Parametrization for Mixture of Experts

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

Recent years have seen a growing interest and adoption of LLMs, with μTransfer becoming a key technique for tuning hyperparameters in large-scale training. Meanwhile, Mixture-of-Experts (MoE) has emerged as a leading architecture in extremely large models. However, the intersection of these two advancements has remained unexplored. In this work, we derive a μ-Parameterization (μP) for MoE, providing theoretical guarantees for feature learning across model widths in both the router and experts. We empirically validate our parameterization and further investigate how scaling the number of experts and granularity affects the optimal learning rate.

Biography:

Aspiring researcher specializing in Large Language Models (LLMs), specifically pretraining and Mixture of Experts.

Piotr Wyrwiński

PCSS, PUT

Co-authors:

Wiktor Kamzela, Adam Dobosz, Jakub Kubiak, Wojciech Stefaniak

Poster 8: WICHER-M : Model for High-Resolution Weather Prediction

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

Accurate weather prediction offers immense benefits for humanity, enabling better preparation, planning, and decision-making across virtually every sector of society. In agriculture, precision farming relies on localized forecasts to guide irrigation, fertilization, and pest control with high spatial accuracy, helping to optimize yields and reduce input waste. In urban flood management, detailed short-range forecasts are essential to predict localized heavy rainfall and prevent flash flooding, especially in densely populated areas with complex drainage systems. For regional aviation and drone operations, fine-scale predictions of wind, fog, and turbulence are crucial for safety and operational efficiency. In the renewable energy sector, hyper-local forecasts allow grid operators to anticipate fluctuations in energy generation and balance supply and demand more effectively. For years weather prediction was solved using Numerical Weather Prediction (NWP) methods, resulting in appearance of global climate models such as GFS or ECMWF. Those models provide broad information about climate, however they are unable to give more detailed prognosis. This is why mesoscale models (e.g. WRF) are frequently applied on their outputs to provide more local and accurate predictions of weather. Unfortunately, not only are WRF simulations local, thus not representing the whole globe, but also NWP calculation is compute-intensive, especially for mesoscale models. These shortcomings lead to intensified interest in alternative methods for weather prediction, e.g. AI transformer models. In recent years there has been a surge of AI models able to match NWP results, such as GraphCast, Aurora or FourCastNet. They all excel in providing accurate weather predictions for whole globe at 0.25° scale, bounded by resolution of prevalent dataset used to train those models, ERA5 (available since 1940), delivering inference speeds thousands of times faster than traditional simulations, while maintaining state-of-the-art accuracy. Some of them tried to mitigate the problem of representing large areas like Poland by only a few pixels by bringing the resolution down to 0.1°. They proposed to fine-tune model on high-resolution IFS HRES dataset, however it is available only from year 2016, with scarce representation of mesoscale areas. Similarly to NWP approach, we believe that the solution of this issue lies in utilization of models such as WRF, which enable us to bring down resolution even more, preserving speed-ups achieved by aforementioned AI models. We propose WICHER-M (Weather Intelligence through Computational High-resolution Environmental Representation Model) to achieve this goal. We utilize both ERA5 dataset and WRF simulation outcomes matched in time and spatial domain. Using data from 7 years with granularity of 6-hour timesteps we fine-tune global prediction models already proficient in modelling climate to model at lower scale of 0.025°. We adapt decoder architecture of state-of-the-art Aurora to match the desired resolution and train the head to minimize loss on WRF simulation data. Utilizing the fact that Aurora model has a separate pretrained backbone, serving as a simulator of climate model we integrate it into our solution. To the best of our knowledge, predicting weather on the finer scale has only been achieved for single physical variables, whereas our approach attempts to leverage all available climate data.

Biography:

Piotr Wyrwiński is a PhD student at Poznan University of Technology and a Machine Learning Researcher at the Poznan Supercomputing and Networking Center. His research explores neurosymbolic learning, program synthesis, and graph-based deep learning. At PCSS, he develops and applies deep learning models in areas such as weather prediction, medical imaging, and satellite data analysis.

AGH University of Kraków

Poster 14: Benchmark for Ordinal Regression in pen & paper RPG game design

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

In recent years, the pen & paper RPG market has experienced significant growth. As a result, companies are increasingly exploring the integration of AI technologies to enhance player experience and gain a competitive edge. While most research on RPG monster design emphasizes emotional and narrative impact, the field of computer science has contributed little to tools for this field. One key challenge is estimating a monster’s challenge level, a task for which no automated solutions currently exist. To address this gap, we introduce a benchmark based on Pathfinder 2e, focused on predicting monster levels using ordinal regression techniques. The dataset consists of approximately 2,600 monsters sourced from official bestiaries, rulebooks, and supplements. Each instance contains a set of key monster’s attributes selected via domain expertise. The prediction target, monster level, is a discrete, ordered variable, positioning this task at the intersection of classification and regression, an ordinal regression problem. We evaluate a variety of modelling approaches, including human-inspired model, classical regression with rounding and dedicated ordinal methods. To account for the chronological nature of the data, we employ an expanding-window-inspired evaluation strategy. Tree-based models consistently outperformed other techniques, while neural approaches yielded disappointing results across all metrics. Model performance was assessed using a combination of classification, regression, and ordinal-specific metrics. Despite variations in absolute error and accuracy, all models demonstrated strong ordinal consistency according to Somer’s D. This benchmark provides practical applications for game design automation, monster creation, and educational use in ordinal modelling.

Biography:

I am a new graduate Data Science student from AGH University of Krakow. As part of my engineering and master’s thesis, I co-developed an application that supports the design of opponents in a pen & paper RPG game, using Machine Learning. For this reason, I have recently been spending my free time playing this type of game, and I also immerse myself in the fascinating world of animation.

Jakub Paszke

Adam Mickiewicz University in Poznań

Co-authors:

Daria Dworzyńska, Miłosz Rolewski, Michał Wujec

Poster 15: SARA: An LLM-Powered Agent for Scientific Collaborations and Grant Discovery

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Abstract:

SARA (Search and Research Agent): LLM-based Recommendation System for Scientific Collaborations and Grants In modern scientific environments, selecting appropriate collaborators and navigating the complex landscape of grant opportunities is both time-consuming and often inefficient. Existing methods heavily rely on informal networks, personal recommendations, and institutional familiarity, making them inherently biased, suboptimal, and inaccessible—especially for early-career researchers. To address these challenges, we propose SARA (Search and Research Agent), an intelligent recommendation system designed to support academic collaboration and funding processes using cutting-edge machine learning techniques. SARA utilizes a combination of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and vector databases to semantically analyze publication metadata, project information, and grant call descriptions. This allows the system to generate meaningful associations between researchers, disciplines, and funding sources, going beyond keyword matching by capturing latent thematic similarities and contextual knowledge. Our system transforms scattered, heterogeneous academic data into a structured, searchable knowledge graph enriched with embeddings. These embeddings enable high-precision recommendations that reflect both the scientific relevance and the collaboration potential of individual researchers. SARA acts as an interactive advisor, allowing users to ask natural language questions such as “I want to apply for a project on AI and sustainability—who should I work with?” or “Which calls in the next 6 months best match my expertise in deep learning?” The system responds with tailored suggestions, highlighting suitable collaborators, active and upcoming calls, and areas of strategic opportunity. Its architecture includes NLP-based entity recognition and classification, citation network analysis, and dynamic reranking based on topical overlap, project success likelihood, and institutional compatibility. Unlike existing academic search engines, our approach offers personalized, proactive guidance. The system not only retrieves data but interprets it in the user’s research context. It supports interdisciplinary queries, considers researchers’ grant histories, institutional affiliations, and scientific impact, and includes modules for grant-specific adaptation and call evaluation criteria modeling. A key innovation is the semantic integration of publication networks and grant programs into one framework, enabling robust matching even across different scientific domains. We present results from a functioning prototype tested on real-world academic and grant data. Preliminary evaluations demonstrate promising outcomes in recommendation quality, explainability, and usability. Our poster will illustrate the full pipeline—from data ingestion and vectorization, through model architecture and interaction design, to deployment challenges and ethical considerations (e.g., bias, data privacy, hallucination risks). We believe that SARA offers a scalable, generalizable model for enhancing the equity, efficiency, and transparency of research team formation and grant application planning.

Biography:

Jakub Paszke is a Master’s student in Artificial Intelligence at Adam Mickiewicz University in Poznań, Poland. His interests include natural language processing, semantic search, and AI-powered recommendation systems. He is currently developing SARA, an LLM-based system for improving scientific collaboration and grant discovery. As an Automation Engineer, Jakub builds intelligent workflows using scripts, RAG systems, AI chatbots, and no-code tools for automations.

UCT Prague

Co-authors:

Krystyna Waniová, Agnieszka Wojtuch, Wim Dehaen, Tomasz Danel, Martin Šícho

Poster 20: Explainable AI for Pharmacophore-Based Drug Activity Prediction

Thursday / 16 October 12:15 - 13:45 (Poster Session 1)

Marek Skiba

University of Warsaw

Redtone Solution OU

Poster 32: Contrastive Forecasting: Latent-Space Prediction for Time Series via Joint Embedding

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

This talk introduces Contrastive Forecasting, an unsupervised method for time series prediction that learns entirely in latent space. The approach is grounded in contrastive divergence and uses a joint-embedding predictive architecture (JEPA) to align predicted future states with actual outcomes while distinguishing them from carefully selected negative samples. We will detail the model architecture, which combines RWKV or Transformer-based forecasters with Residual encoders. The training objective encourages accurate representation learning by pulling forecasted embeddings toward future targets and pushing them away from dissimilar contexts. The talk will cover practical aspects of training, the design of contrastive losses for temporal data, and the handling of multivariate and long-horizon forecasting challenges. This session is intended for researchers and practitioners interested in self-supervised learning, representation learning, and time series modeling.

Biography:

Jérémy Cochoy is an expert in technology with a strong academic background. Holding a PhD in Computer Science and Mathematics with a focus on Persistent Homology, he leveraged his expertise to co-found Symphonia, an app that creatively transforms voices into music. Currently, as CEO of Redstone Solutions, Cochoy applies his skills in deep learning to the field of financial market forecasting. His career is a testament to the fusion of advanced scientific knowledge and practical technological applications, underscoring his commitment to driving innovation in complex fields. Beyond his professional realm, Cochoy's interests in music and other artistic pursuits reflect a multifaceted personality, equally engaged in intellectual and creative endeavors.

Julia Kiczka

University of Wroclaw

Poster 33: Beyond Reconstruction Error: Leveraging Latent Representations for Anomaly Detection in Time Series

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

(Ongoing Master's Thesis Work) Anomaly detection in time series data is critical in domains such as healthcare, finance, and system monitoring. While many popular methods rely on reconstruction error from models like Autoencoders, Transformers, or Diffusion Models, my thesis investigates whether the latent space itself can carry meaningful information for this task beyond what reconstruction error reveals. I explore how to modify and structure latent representations to make them more informative, for example through contrastive learning. I also test whether simple statistics in the embedding space, such as distances to nearest neighbors, can already provide useful anomaly scores. Another key aspect of my work is the definition of meaningful anomalies - cases that are not just outliers in amplitude or easily detectable with simple statistics, but represent subtle or structural deviations. To handle such cases, I propose techniques for anomaly injection and analysis that go beyond standard benchmarks. Finally, I address challenges in modeling multivariate, correlated time series by introducing masked training and separate decoders per feature dimension, which improve the model’s capacity to capture diverse signal characteristics.

Biography:

Julia Kiczka obtained a Bachelor’s degree in Individual Studies in Mathematics and Computer Science at the University of Wrocław, currently pursuing Master’s in Computer Science and Mathematics.

Alessandro Crimi

AGH University of Krakow

Co-authors:

Szymon Mazurek, Stephen Moore

Poster 34: Graph Attention Networks for Detecting Epilepsy from EEG Signals Using Accessible Hardware in Low-Resource Settings

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

Nowadays, epilepsy is still heavily under-diagnosed in low-income countries due to the limited availability of neurologists and the high cost of diagnostic tools. To address this, in this study we propose a graph-based deep learning framework for detecting epileptic subjects using electroencephalogram (EEG) and artificial intelligence (AI) from low-cost accessible hardware, testing recordings from Nigeria and Guinea-Bissau. The contribution is focused both on fair and accessible automatic assessment of epilepsy and on explainability of the characteristics with the goal of shedding further light on biomarkers for epilepsy. Those goals are achieved by modeling EEG signals as spatio-temporal graphs, then by both classifying and identifying interchannel relationships and temporal dynamics by using graph attention networks (GAT). We employ the GAT approach, which is inherently node-based, to focus on edges to be more aimed at connectivity biomarkers. In this context of explainable AI, we identified the most discriminant connection using the portable EEG device as being within the frontal cortex and between the frontal and temporal cortex. In summary, we propose a combination of signal preprocessing techniques suitable for low-fidelity recordings and design a lightweight GAT architecture tailored for deployment on resource-constrained devices with full training on Google Colab Cloud and deployment on RaspberryPi devices. The approach achieves promising classification performance, outperforming a standard classifier based on Random Forest and graph convolutional networks in terms of accuracy and robustness over multiple sessions, but also highlighting specific connections in the fronto-temporal region. The results highlight the potential of GATs to provide insightful and scalable diagnostic support for epilepsy in underserved regions, paving the way for affordable and accessible neurodiagnostic tools. The innovative aspects for the ML community will be the investigation of GAT and GCN for brain connectivity analysis as we show what the attention weight represent in the brain.

Biography:

Alessandro Crimi, after completing his studies in engineering at the university of Palermo (Italy), obtained the Ph.D. in machine learning applied for medical imaging from the University of Copenhagen, and an MBA in healthcare management from the University of Basel. He is currently a professor at AGH Krakow and worked as a post-doctoral researcher at the French Institute for Research in Computer Science (INRIA), Technical School of Switzerland (ETH-Zurich), Italian Institute for Technology (IIT), and University Hospital of Zurich.

Jakub Adamczyk

AGH University of Krakow

Poster 35: ML in agrochemistry and ecotoxicology

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

Machine learning is widely used in the pharmaceutical industry, where molecular ML and chemoinformatics have supported safer and faster drug development for decades. In contrast, the equally important field of agrochemistry has received far less attention. Yet it has incredible potential with both predictive and generative models, e.g. predicting pesticide toxicity or generating novel, safer agrochemicals. With growing regulatory pressure and a shift toward more sustainable agriculture, there is a pressing need to accelerate the development of alternative pesticides, and ML can play a crucial role in meeting this challenge. In this talk, I will explore how and why machine learning can be applied in agrochemistry, with a particular focus on ecotoxicology - a critical and highly regulated aspect of modern agrochemical development. In particular, I will present ApisTox, a novel dataset on pesticides toxicity to honey bees, and demonstrate how the methods and workflows can be adapted to other molecular ML applications. We'll then review a range of predictive ML models suited to this kind of data, e.g. molecular fingerprints, graph kernels, and graph neural networks (GNNs). In particular, we will cover our insights for non-traditional molecular structures beyond typical pharmaceutical targets, such as pesticides. Finally, I will discuss potential opportunities for ML in agrochemistry and ecotoxicology areas.

Biography:

He is a PhD candidate in Computer Science at AGH University of Krakow and a member of the Graph ML and Chemoinformatics Group at the Faculty of Computer Science. His research focuses on fair evaluation, graph representation learning, graph classification, chemoinformatics, and molecular property prediction. He is also interested in time series, natural language processing (NLP), and MLOps, and teaches these subjects at AGH. He works at Placewise as a Data Science Engineer, where he addresses various machine learning challenges in tabular data, computer vision, and NLP, along with their end-to-end MLOps implementations. Outside of his professional work, he trains in Historical European Martial Arts (HEMA), specializing in messer and longsword, and enjoys reading and tabletop role-playing games.

Witold Drzewakowski

ELLIS Unit Warsaw, University of Warsaw

Co-authors:

Bartosz Piotrowski, Konrad Staniszewski, Piotr Miłoś

Poster 36: Lightweight Latent Verifiers for Efficient Meta-Generation Strategies

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

Verifiers are auxiliary models that assess the correctness of outputs generated by base large language models (LLMs). They play a crucial role in many strategies for solving reasoning-intensive problems with LLMs. Typically, verifiers are LLMs themselves, often as large (or larger) than the base model they support, making them computationally expensive. In this work, we introduce a novel lightweight verification approach, LiLaVe, which reliably extracts correctness signals from the hidden states of the base LLM. A key advantage of LiLaVe is its ability to operate with only a small fraction of the computational budget required by traditional LLM-based verifiers. To demonstrate its practicality, we couple LiLaVe with popular meta-generation strategies, like best-of-n or self-consistency. Moreover, we design novel LiLaVe-based approaches, like conditional self-correction or conditional majority voting, that significantly improve both accuracy and efficiency in generation tasks with smaller LLMs. Our work demonstrates the fruitfulness of extracting latent information from the hidden states of LLMs, and opens the door to scalable and resource-efficient solutions for reasoning-intensive applications.

Biography:

Zofia Hendrysiak

University of Warsaw

Poster 40: Teaching Small Models Big Math: A Curriculum Learning Approach for Bielik

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

SANO Poland; Warsaw University of Technology

Co-authors:

Weronika Smolak-Dyżewska, Dawid Malarz, Diego Dall'Alba, Przemyslaw Korzeniowski, Przemysław Spurek

Poster 49: PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstruction and novel-view synthesis from endoscopic images are promising tools for enhancing diagnosis. Moreover, integrating physiological deformations and interaction with the endoscope enables the development of simulation tools from real video data. However, constrained camera trajectories and view-dependent lighting create artifacts, leading to inaccurate or overfitted reconstructions. We present PR-ENDO, a novel 3D reconstruction framework leveraging the unique property of endoscopic imaging, where a single light source is closely aligned with the camera. Our method separates light effects from tissue properties. PR-ENDO enhances 3D Gaussian Splatting with a physically based relightable model. We boost the traditional light transport formulation with a specialized MLP capturing complex light-related effects while ensuring reduced artifacts and better generalization across novel views. PR-ENDO achieves superior reconstruction quality compared to baseline methods on both public and in-house datasets. Unlike existing approaches, PR-ENDO enables tissue modifications while preserving a physically accurate response to light, making it closer to real-world clinical use.

Biography:

Joanna Kaleta is a Computer Science graduate from Warsaw University of Technology who is currently a Ph.D. student at Sano - Center for Computational Medicine. Her research interest lies in the exploration of innovative Computer Vision applications for computed assisted surgery and diagnosis.

Michał Sala

Uniwersytet Warszawski

Co-authors:

Karol Kuźniak

Poster 50: PAST LIME: Explaining Code Generation via AST-Based Perturbations

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

Code-generation models such as Stable-Code-3B have shown strong performance in tasks like code completion, bug fixing, and refactoring. However, their black-box nature limits users’ ability to understand the factors that influence the model’s output. This lack of interpretability poses challenges in software engineering, where trustworthy and semantically coherent explanations are essential. We propose PAST LIME (Perturbed Abstract Syntax Tree LIME), a model-agnostic interpretability method that identifies which code fragments affect the probability assigned to a fixed continuation. Most existing approaches either operate at the token level without considering code structure, or attempt to align token probabilities with semantic representations without using perturbation-based analysis. In contrast, PAST LIME assigns importance scores to code fragments -- corresponding to a subset of nodes in the abstract syntax tree -- indicating whether the presence of each fragment increases or decreases the likelihood of the given continuation. PAST LIME perturbs semantically meaningful, non-overlapping code segments and queries the model’s conditional probability distribution, without requiring access to model gradients or internal parameters. Its output is independent of the sampling strategy used during generation and reflects the sensitivity of the continuation probability to structural changes in the code. Despite operating locally, the method scales to large codebases and remains computationally efficient. Beyond standard explainability, PAST LIME enables analysis of arbitrary continuations, offering new opportunities for uncovering hidden biases, evaluating model behavior, and supporting model debugging. Through experiments on Stable-Code-3B, we show that PAST LIME yields explanations more aligned with code semantics than token-level baselines, providing developers with interpretable insights into the model’s decision-making process.

Biography:

A human being. Interested in ML, distributed systems, programming languages & formal verification.

Pawel Struski

University of Warsaw

Poster 51: Competitive Market Behaviour of LLMs

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

This study explores the competitive market behaviour of Large Language Models (LLMs). We find that LLM agents exhibit systematic biases in economic decision-making tasks. In a simulated competitive market, we find that LLM buyer agents frequently bid their maximum reservation price, despite being instructed to buy at the lowest possible price. This leads to transaction prices above the competitive market equilibrium – a striking divergence from human behaviour in the same setting. We arrive at this result by replicating Smith’s (1962) classic behavioural economics experiment using LLM agents (GPT-4.1-mini-2025-04-14). We simulate a market for a fictitious good, populated by two types of agents: buyers and sellers, each with a predefined reservation price. Buyers are tasked with purchasing at the lowest possible price (but not above their reservation price), and sellers with selling at the highest possible price (but not below their reservation price). Reservation prices are structured so as to create a downward-sloping demand curve and an upward-sloping supply curve. Economic theory, supported by Smith’s original experiments, predicts that such a market should converge to a competitive equilibrium where supply equals demand. In our experiments, however, transaction prices and traded quantities consistently converge to levels above the competitive market equilibrium. Our preliminary analysis indicates that this deviation is primarily driven by non-competitive behaviour among buyer agents. Despite being instructed to maximize profits, buyers frequently bid their maximum price when asked to submit a bid, thus violating the standard assumption that agents seek to maximise utility (profits). Interestingly, this behaviour is not observed among sellers, despite near-identical (role-adjusted) prompts. This asymmetry suggests that the LLM encodes a role-based behavioural bias: associating sellers with competitive behaviour and buyers with cooperative behaviour. When this bias is accounted for, the observed price deviation aligns with theoretical predictions from economics. To our knowledge, this is the first study to apply economic theory as a tool for analysing LLM behaviour. Economic theory provides objective, consistent and tractable predictions about equilibrium outcomes in multi-agent systems. This offers a new lens for diagnosing and understanding LLM biases and decision-making patterns. Looking ahead, we plan to extend our analysis to other models (including open-source models) and apply mechanistic interpretability techniques to identify the internal representations that drive this behaviour. Ultimately, this research could inform the design and control of LLM-powered multi-agent systems, guided by principles from mechanism design – a field at the intersection of economics and game theory. References Smith, V. L. (1962). An Experimental Study of Competitive Market Behavior. Journal of Political Economy, 70(2), 111–137. https://doi.org/10.1086/258609.

Biography:

Pawel Struski is a PhD student at the University of Warsaw. His research lies at the intersection of economics and machine learning. He holds an MPhil degree in Economic Research from the University of Cambridge (2019) and a BSc degree in Economics from UCL (2018). He has previously worked as research assistant at the Institute for Fiscal Studies and as an economist in the financial sector.

Katarzyna Woźnica

Warsaw University of Technology

Co-authors:

Piotr Wilczyński, Przemysław Biecek

Poster 52: SeFNet: Linking tabular datasets with semantic feature nets

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

Tabular datasets play a significant role in wide range of machine learning applications. However, although they often address similar problems, tabular datasets are typically treated as standalone tasks. The opportunities of using previously solved problems are limited due to the lack of structured contextual information about their features and the lack of understanding of the relations between them. To overcome this limitation, we propose a new methodology called Semantic Feature Net (SeFNet), capturing the semantic meaning of the analyzed tabular features. By leveraging existing ontologies and domain knowledge, SeFNet opens up new opportunities for sharing insights between diverse predictive tasks. One such opportunity is the Dataset Ontology-based Semantic Similarity (DOSS) measure, which quantifies the similarity between datasets using relations across their features. In this paper, we present an example of SeFNet’s application prepared for a collection of predictive tasks in healthcare, with the features’ relations derived from the SNOMED-CT ontology. The proposed SeFNet methodology and the accompanying DOSS measure address the issue of limited contextual information in tabular datasets. By incorporating domain knowledge and establishing semantic relations between features, we enhance the potential for meta-learning and enable valuable insights to be shared across different predictive tasks.

Biography:

Katarzyna is a researcher at Warsaw University of Technology with a PhD in Computer Science. Her work centers on AutoML, hyperparameter optimization, and meta-learning, with a focus on human-in-the-loop methods and Automated Data Science. She collaborates with physicians to develop machine learning solutions for medical applications.

Stanisław Pawlak

Warsaw University of Technology

Co-authors:

Jan Dubiński, Daniel Marczak, Bartłomiej Twardowski

Poster 53: Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

Model merging (MM) recently emerged as an effective method for combining large deep learning models. However, it poses significant security risks. Recent research shows that it is highly susceptible to backdoor attacks, which introduce a hidden trigger into a single fine-tuned model instance that allows the adversary to control the output of the final merged model at inference time. In this work, we propose a simple framework for understanding backdoor attacks by treating the attack itself as a task vector. Backdoor Vector (BV) is calculated as the difference between the weights of a fine-tuned backdoored model and fine-tuned clean model. BVs reveal new insights into attacks understanding and a more effective framework to measure their similarity and transferability. Furthermore, we propose a novel method that enhances backdoor resilience through merging dubbed Sparse Backdoor Vector (SBV) that combines multiple attacks into a single one. We identify the core vulnerability behind backdoor threats in MM: inherent triggers that exploit adversarial weaknesses in the base model. To counter this, we propose Injection BV Subtraction (IBVS) -- an assumption-free defense against backdoors in MM. Our results show that SBVs surpass prior attacks and is the first method to leverage merging to improve backdoor effectiveness. At the same time, IBVS provides a lightweight, general defense that remains effective even when the backdoor threat is entirely unknown.

Biography:

Stanisław Pawlak is a Ph.D. student and AI researcher working at Warsaw University of Technology. He received a M.Sc. degree in data science and a B.Sc. in applied computer science from the Warsaw University of Technology. Stanisław coauthored multiple publications on world top-tier AI conferences, including NeurIPS 2023 and CVPR 2024. He also worked as a programmer, AI engineer building ML-powered applications, and AI consultant. His research focuses on continual learning, generative models, and ML security. His latest efforts aim to understand backdoors in model merging learning paradigm.

Bartosz Jezierski

Warsaw University of Technology

Co-authors:

Mateusz Jarosz, Vladimir Zaigrajew

Poster 54: Comparing Different Jailbreak Detection Methods

Friday / 17 October 12:15 - 13:45 (Poster Session 2)

Abstract:

As Large Language Models (LLMs) are increasingly deployed in production, their vulnerability to novel 'jailbreak' attacks poses a significant security risk. Popular safeguards often fail to generalize to these unseen threats, creating a critical security gap. We evaluate this generalization failure by comparing prominent open-source safeguards against targeted, data-efficient fine-tuning of an encoder only (ModernBERT) and a decoder only (Gemma 3) model. Our experiments on the established benchmark indeed confirm the critical generalization gap against unseen jailbreaks. Guardrails like Llama Guard 2 and 3 achieve less than 55% accuracy on the unseen RedTeams2K dataset. We discover that resource-efficient fine-tuning models yield superior performance compared to baseline open-source safeguards. While even simple head-tuning of ModernBERT surpasses the baseline, more comprehensive LoRA fine-tuning on Gemma yields the best results. However, the largest performance gain, which boosts detection accuracy to over 95%, comes from introducing a small, curated set of just 200 similar jailbreak examples. By breaking down performance by category, our analysis exposed a significant, shared blind spot. We found that detecting prompts designed to violate privacy was a critical challenge that affected every model we tested. Our findings demonstrate that effective LLM safety is found not in large, static guardrails, but in an agile strategy that prioritizes rapidly fine-tuning smaller, general-purpose models on new, similar attack patterns as they appear.

Biography:

Łukasz Janisiów is a first-year doctoral student in the Group of Machine Learning Research at Jagiellonian University and a participant in the ELLIS PhD Program. His research focuses on the application of explainable artificial intelligence (XAI) in drug discovery.

/ Tutorials

Oleksii Furman

Wrocław University of Science and Technology

Łukasz Lenkiewicz

Wrocław University of Science and Technology

Marcel Musiałek

Wrocław University of Science and Technology

Tutorial 1: Counterfactual Explanations: From Theory to Implementation

Saturday / 18 October 9:00 - 13:00

Description:

This tutorial demystifies Counterfactual Explanations, a powerful technique for making black-box AI models transparent and actionable. Unlike traditional XAI methods that simply highlight important features, counterfactuals provide practical, user-friendly answers to the essential question: “What needs to change to get a different outcome?” In this tutorial, participants will progress from theoretical foundations to implementing counterfactual methods and using them. You’ll learn how to generate explanations that help end-users understand not just why they were denied a loan, but precisely what they need to change to get approved next time. The tutorial balances theory with extensive coding exercises. We’ll cover both basic implementations and advanced techniques using modern methods. By the end, you’ll understand how to use counterfactual explanations for your AI systems to improve transparency. You’ll leave with ready-to-use code and practical implementation strategies.

Knowledge Prerequisites

Intermediate Python programming skills (comfortable with functions, classes, and common data structures)
Basic understanding of machine learning concepts (classification, features, training/testing)
Familiarity with common libraries like NumPy, Pandas, and Matplotlib
Basic knowledge of classification models (particularly decision trees and random forests)
Exposure to the concept of model explainability (though expertise is not required)

Technical Setup: Participants should have the following installed prior to the tutorial: Python 3.10+ environment (Anaconda distribution recommended) and Jupyter Notebook or JupyterLab.

Biography:

Oleksii Furman is a Ph.D. student in Artificial Intelligence at Wrocław University of Science and Technology, specializing in generative models and explainable AI (XAI). His research develops innovative approaches to enhance the transparency and interpretability of black-box machine learning models, particularly through counterfactual explanations. He aims to bridge the gap between complex AI systems and human understanding, making machine learning more accessible and trustworthy. He has co-authored papers at prominent machine learning conferences.

Łukasz Lenkiewicz is pursuing his Ph.D. in Artificial Intelligence at Wrocław University of Science and Technology, where he investigates new directions in explainable AI. His doctoral research focuses on developing counterfactual explanations at the local, global, and group-wise level, aiming to capture both individual decisions and broader model behavior. Beyond tabular data, he explores explainability in computer vision, developing techniques that clarify how visual recognition models operate and can be put to use. His research highlights practical integration, focusing on turning theoretical advances in explainability into real-world applications.

Marcel Musiałek is a third-year undergraduate student at Wrocław University of Science and Technology, with a strong interest in computer vision, particularly in the medical sector and explainable AI (XAI). He actively develops his research interests through scientific projects and throughout the university’s science club. His focus is on expanding his knowledge and building solid foundations for future education and research.

Maura Pintor

University of Cagliari

Giorgio Piras

University of Cagliari

Tutorial 2: Where ML Security is Broken and How to Fix it

Saturday / 18 October 9:00 - 13:00

Description:

Adversarial robustness is a critical concern for modern machine learning systems, yet reliably evaluating model resilience to adversarial attacks remains a major challenge. In practice, robustness is often measured using gradient-based methods that optimize perturbations to simulate worst-case inputs. These empirical evaluations might provide an inaccurate picture of model security, as small flaws in the attack setup can lead to overly optimistic results. Without systematic testing and diagnostic tools, even well-intentioned evaluations risk repeating past mistakes.

This tutorial will guide participants through the fundamentals of adversarial robustness evaluation, emphasizing the importance of rigorous and reproducible methods. We will present a comparison framework for benchmarking gradient-based attacks that helps identify optimization failures, standardize evaluation protocols, and support fair comparisons across models and datasets. Through practical examples, attendees will learn how to design, test, and debug adversarial attacks in a principled way. Looking forward, we discuss how these challenges extend to the evaluation of foundation models and multimodal systems, where conventional adversarial techniques may be adapted and still produce significant impact. As machine learning models continue to grow in scale and complexity, there is a growing need for robust and efficient evaluation methodologies. We conclude with a discussion of open problems and future research directions aimed at strengthening the reliability and trustworthiness of machine learning under adversarial conditions.

Prerequisites: Participants should have a basic understanding of machine learning and deep learning concepts, including neural network training and evaluation. Familiarity with PyTorch is strongly recommended, as examples and hands-on exercises will be based on it. Some background in adversarial machine learning (e.g., knowledge of adversarial examples or gradient-based attacks) is helpful but not strictly required, as essential concepts will be introduced during the tutorial.

Software Requirements: All hands-on exercises will be conducted using Google Colab, so no local installation is necessary. Participants only need a Google account and a modern web browser. We will provide pre-configured Colab notebooks with all dependencies already listed, and they will be installed during the tutorial. Access to a GPU runtime in Colab is recommended for optimal performance but not strictly required.

Biography:

Maura Pintor (PhD 2022, honors) is an Assistant Professor at the PRA Lab, University of Cagliari, Italy. Her research focuses on optimizing and debugging adversarial robustness evaluations. She has held visiting positions at the University of Tuebingen (Germany, 2020), SCCH (Austria, 2021), and the Computer Vision Center (Spain, 2024). Maura serves as Area Chair for NeurIPS, Associate Editor for Pattern Recognition, and regularly reviews for top-tier conferences and journals, including ACM CCS, ICLR, ECCV, and ICCV. She is co-chair of the ACM Workshop on Artificial Intelligence and Security (AISec), co-located with ACM CCS, and contributes to several EU Horizon projects, including ELSA, Sec4AI4Sec, and CoEvolution. She is the main maintainer of the open-source SecML-Torch library.

Giorgio Piras (PhD 2025, honors) is a Postdoctoral Researcher at the PRA Lab, University of Cagliari, Italy. His research interests broadly cover adversarial machine learning, with a particular focus on adversarial pruning methods and LLM security. During his PhD, he was a visiting student at the Karlsruhe Institute of Technology, Germany. He regularly serves as a reviewer for Pattern Recognition, Neurocomputing, and IEEE TIFS journals, and AAAI, USENIX, ACM CCS conferences. He is now involved with the University of Cagliari in the EU Horizon Projects Sec4Ai4Sec and CoEvolution.

Maciej Żelaszczyk

Samsung AI Center Warsaw

Tutorial 3: [Generative AI] The Noise, the flow, the images.

Saturday / 18 October 9:00 - 13:00

Description:

A brief introduction to flow matching. We will cover the idea and intuition behind flow matching, how it relates to diffusion models, and what training a flow matching model looks like in practice. We will introduce the flow matching setup for continuous data, show how this setup can be modified with the use of optimal transport and close out with a discrete flow matching example.

Prerequisites: Basic knowledge of linear algebra, probability theory, vector fields, physics, and neural networks. Familiarity with Python, PyTorch, Jupyter Notebook.

Biography:

Maciej is a Senior Research Scientist at the Samsung AI Center Warsaw, where he researches topics related to safety and alignment in neural networks. He holds a Ph.D. in computer science from the Warsaw University of Technology with a dissertation on representation learning. Prior to joining Samsung, he worked in a variety of fields, including a stint as Software Engineer at Cardinal Cryptography, a Senior Data Scientist position at Yosh.AI with focus on recommendation systems and as Machine Learning Engineer at Daftcode developing a matchmaking system for mobile games. Apart from that, he also has experience in finance, particularly quantitative investments.

Patryk Wielopolski

Independent Researcher, AI Safety Poland

Taras Kutsyk

Jagiellonian University, AI Safety Poland

Tutorial 4: From Superposition to Sparse Autoencoders: Understanding Neural Feature Representations

Saturday / 18 October 9:00 - 13:00

Description:

Modern neural networks often learn representations that are difficult to interpret, with individual neurons responding to multiple unrelated features - a phenomenon called polysemanticity. This workshop explores the theoretical foundations and practical implications of how neural networks represent features through the lens of Anthropic’s influential “Toy Models of Superposition” paper. Participants will gain hands-on experience with the fundamental concepts of mechanistic interpretability by building and analyzing simple neural networks that demonstrate superposition - the ability of models to represent more features than they have dimensions. Through interactive exercises attendees will train toy models with varying sparsity levels and observe how networks organize features into geometric structures like antipodal pairs and pentagons when forced to compress high-dimensional feature spaces. The workshop concludes with an exploration of Sparse Autoencoders (SAEs) as a promising solution for disentangling features. Participants will implement and train their own SAEs on the toy models, visualizing how dictionary learning techniques can recover interpretable feature representations from seemingly incomprehensible neural activations. This workshop bridges theory and practice, providing both the mathematical intuition behind superposition phenomena and practical tools for investigating real neural network behavior - essential knowledge for anyone interested in AI safety, interpretability research, or understanding the inner workings of modern ML systems.

Prerequisites: Participants should have a basic understanding of deep learning and PyTorch. For this tutorial, we will be using Google Colab, so only a web browser with internet access is required.

Biography:

Patryk Wielopolski is an AI researcher with a Ph.D. in probabilistic modeling and publications at AAAI, ECAI, and TPAMI. He previously served as Solution Innovation Leader at DataWalk, leading the company’s AI research agenda on Knowledge Graphs, Large Language Models, and unstructured data processing. As an active member of Poland’s AI community, he co-founded the genwro.ai research group and contributed to the Polish AI Olympics. At previous ML in PL conferences, he presented on knowledge graphs (2024) and TreeFlow (2023), receiving the Best Contributed Talk Award for the latter. Patryk is currently transitioning his research focus to AI Safety.

Taras Kutsyk is a Ph.D. student specializing in AI Interpretability and Safety. He began his career in machine learning with research on satellite imagery enhancement, before shifting his focus to AI Safety after completing the AI Safety Fundamentals course. Since then, he has completed research internships in mechanistic interpretability of language models, including the MATS program under Neel Nanda and the AI Safety Camp (AISC). His work has led to front-page–promoted blog posts on new insights into Sparse Autoencoders (SAEs). Taras is currently investigating model organisms of misalignment and developing mechanistic approaches to prevent it.

Anna Kozak

Warsaw University of Technology

Katarzyna Woźnica

Warsaw University of Technology, Systems Research Institute, Polish Academy of Sciences

Antoni Zajko

Warsaw University of Technology

Tutorial 5: Introduction to Automated Machine Learning (AutoML)

Saturday / 18 October 9:00 - 13:00

Description:

AutoML aims to democratise machine learning by automating key stages of the model development pipeline - making it accessible to experts and domain specialists. This tutorial offers a comprehensive overview of the AutoML field, including its motivations, history, and technical foundations. We will explore the major components of an AutoML system: algorithm selection, hyperparameter optimisation, meta-learning, and ensemble methods. The human expert’s role in the AutoML pipeline and challenges such as interpretability and monitoring will also be discussed. The practical component of the tutorial includes hands-on experience with popular open-source AutoML frameworks in Python, such as AutoGluon and MLJAR. Participants will build complete AutoML pipelines, compare tools, and understand their strengths and limitations. This tutorial is ideal for ML practitioners, researchers, and data scientists who want to understand how AutoML works under the hood and how to use it effectively in real-world projects.

Prerequisites: Basic knowledge of machine learning concepts (supervised learning, model evaluation, overfitting). Familiarity with Python programming and standard ML libraries (scikit-learn, pandas). Participants should install the following Python-based AutoML libraries in a virtual environment: auto-gluon, mljar. A Jupyter notebook environment (e.g., Anaconda, JupyterLab, or Google Colab) is recommended for hands-on exercises.

Biography:

Anna Kozak is a data scientist with over eight years of experience. She conducts research in Automated Machine Learning at the Warsaw University of Technology, and lectures data visualisation, machine learning, and statistics.

Katarzyna Woźnica holds a PhD in Computer Science with a specialization in Machine Learning. Her work centers on AutoML, hyperparameter optimization, and meta-learning, with a focus on human-in-the-loop methods and Automated Data Science. She has experience applying machine learning methods in medical research and clinical practice.

Antoni Zajko is a PhD student at Warsaw University of Technology with both research and commercial experience in machine learning.

Maciej Draguła

Tenstorrent

Artem Yerofieiev

Tenstorrent

Tutorial 6: Next Generation Hardware for ML - Hands on with Tenstorrent🤘

Saturday / 18 October 9:00 - 13:00

Description:

This workshop offers a guided, hands‑on introduction to running AI workloads on Tenstorrent hardware. Participants will connect to provided environments, experiment with example models, and explore profiling tools. By the end, they’ll have a practical overview of tt-metal and know how to continue developing on Tenstorrent platforms.

This tutorial is free of charge but also requires registration. Please register here.

Biography:

Maciej Draguła is a Senior Engineer at Tenstorrent, where he develops tt-train, a framework for training large language models on Tenstorrent’s cutting-edge AI hardware, with a focus on optimising operations to reduce training time. Previously, he worked on computer vision applications for avionics at Daedalean, and his experience includes internships at CERN, Google, and Goldman Sachs. He holds a Bachelor’s degree in Computer Science from the University of Wrocław.

Senior Principal at Tenstorrent, Artem Yerofieiev leads software engineering for TT-NN — the open-source, scaleout-native and tile-based ML framework for Tenstorrent’s next-gen accelerators. Currently building autonomous AI systems to generation software and support hardware-software co-design. Previously led AR-creation tools at Snap and analytical software in a defense R&D lab.

Tudor Coman

Adobe

Tutorial 7: Migrating Python AI Prototypes to Cross-Platform Solutions

Saturday / 18 October 14:00 - 18:00

Description:

This tutorial helps active and aspiring developers and engineers understand how to migrate Python-based AI proof-of-concepts into production-ready, cross-platform systems. It focuses on practical strategies for translating NumPy-based computations, model inference, and LLM integrations into Java/Kotlin environments using technologies such as ONNX, Multik, LangChain4j, pgvector and Kotlin coroutines. Learners will gain skills to preserve performance, ensure interoperability, and future-proof AI-driven applications.

Prerequisites:

Proficiency in Python and NumPy
Familiarity with AI model inference and APIs
Some experience with Java (or Kotlin as a bonus)
Software installed: Python 3 with an editor of their choice, Java 21+, IntelliJ IDEA Community Edition (preferably), Gradle, Kotlin

Tomasz Steifer is an adiunkt at the Institute of Fundamental Technological Research of the Polish Academy of Sciences and external collaborator at the Centro Nacional de Inteligencia Artificial (Chile). After obtaining his PhD from the Institute of Computer Science of the Polish Academy of Sciences, he was a postdoc at the Pontificia Universidad Católica de Chile and, through various fellowships, spent time at the Laboratoire Bordelais de Recherche en Informatique, at the University of California, Berkeley and at the University of Bristol. He works on theoretical foundations of machine learning and AI, including learning theory and the expressivity of transformer architecture, as well as on topics in mathematical logic and computational social choice.

Przemysław Wałęga is an Associate Professor (Senior Lecturer) in Queen Mary University of London, Centre for Fundamental Computer Science. He was a Senior Researcher in University of Oxford, Department of Computer Science and received PhD in Logics in the Institute of Philosophy at the University of Warsaw. His research is devoted to designing formal logical languages for AI, studying their computational properties, and developing efficient reasoning algorithms for them. He is especially interested in methods for complex reasoning about time which brings together computer scientists, mathematical logicians, and philosophers.

Mieszko Rutkowski

Allegro

Tutorial 11: Machine Translation in the LLM Era: A Practical Guide

Saturday / 18 October 14:00 - 18:00

Description:

In the LLM era, one might think that the problem of Machine Translation is solved. But it’s certainly not, especially at scale and in domain-specific contexts. This tutorial provides a practical overview of modern MT. I will start with the core concepts like parallel data, system architectures and evaluation methods, and then will move to more advanced topics like quality-aware decoding or applications of reinforcement learning to MT. During the hands-on session, we will implement and evaluate state-of-the-art MT techniques on our own!

Prerequisites: No prior knowledge in Machine Translation is required, but familiarity with basic deep learning concepts is beneficial. Hands-on session will be run in a Jupyter notebook. To actively participate in the tutorial, you need to have Python 3 installed together with libraries transformers and trl.

Biography:

Mieszko Rutkowski is a Research Engineer at Allegro, where he contributes to developing Allegro Translation Engine. His interests include Machine Translation, especially with reinforcement learning and multimodal methods. Previously he worked on risk management at UBS and on astrophysics at the Jagiellonian University.

This tutorial is free of charge but also requires registration. Please register here.

Wojtek Czekalski

CEO at Dialo

Patryk Wojnarowski

Dialo

Adam Cholewiński

Dialo

Tutorial 12: End "It Works On My Machine": A Practical Guide to Nix and uv

Saturday / 18 October 14:00 - 18:00

Description:

Welcome to dependency hell; this workshop is your way out. Learn to replace the chaos of pip, apt, and docker with modern tools used by elite firms like Jane Street and Anduril to build reliable and isolated environments. To achieve this, we’ll first go back to basics, teaching you the Linux fundamentals—how PATH, environment variables, and linking really work—that cause these problems in the first place. Armed with that knowledge, you will learn to use Nix to declaratively define and build perfectly self-contained environments from a single file. The result is true isolation, giving you the power to reliably set up any project and switch between conflicting toolchains instantly. Finally, you’ll turbocharge your Python workflows with the uv package manager and see how these principles scale up, from ensuring dev/prod parity to managing entire, reproducible operating systems with NixOS.

This tutorial is free of charge but also requires registration. Please register here.

/ Table of Contents

/ Agenda

Day 1: Wednesday / 15 October

Copernicus Science Centre

Wybrzeże Kościuszkowskie 20, 00-390 Warsaw

Student Research Workshop (open event, no ticket required)

Attribute-Regularized VAEs for Controlled and Interpretable Antimicrobial Peptide Design

by Aleksandra Gwiazda (Warsaw University of Technology)

DetoxAI - Python Package for Debiasing Neural Networks

by Łukasz Sztukiewicz (Independent Researcher)

OMENN: One Matrix to Explain Neural Networks

by Mikołaj Janusz (Jagiellonian University)

From Blurry to Sharp: Enabling High-Frequency Detail in Implicit Neural Representations

by Tomasz Dądela (Jagiellonian University)

Semantic Label Reconstruction: How to breach privacy in Federated Learning

by Rafał Malcewicz (Carnegie Mellon University, GHOST Day)

Filling in the Blanks: Redefining Inpainting with a Novel Geometrical Latent Space VAE Architecture

by Nikodem Świerkowski (Wrocław University of Science and Technology)

GraphaFold: Graph Neural Network for predicting non-canonical RNA base pairing

by Paulina Hładki (Poznan University of Technology)

Enhancing Chemical Explainability Through Counterfactual Masking

by Łukasz Janisiów (Jagiellonian University)

Registration

(Also open after 12:00)

Opening remarks

Opening remarks

Discussion Panel 1: PL in ML

(conducted in Polish)

Invited Talk 1: Causal representation learning in temporal settings with actions

by Sara Magliacane (University of Amsterdam)

Invited Talk 2: TBA

by Shreya Pathak (Google DeepMind)

Lunch

Sponsor Talk: Sequential Representation Learning for Real-Time Bidding

by Mateusz Błajda and Maciej Zdanowicz (RTB House)

Sponsor Talk: Phoenix from the ashes - IDEAS Research Institute

by Tomasz Trzciński (IDEAS Research Institute)

Sponsor Talk: Evolving Search and Recommendations at a Leading E-commerce Platform

by Aleksandra Chrabrowa and Mateusz Marzec (Allegro)

Invited Talk 3: TBA

by Adel Bibi (University of Oxford / Kellog College / Softserve)

Invited Talk 4: Bridging perception and causality with causal representations

by Francesco Locatello (ISTA)

Conference Party

Bolek Pub & Restaurant, al. Niepodległości 211, 02-086 Warszawa

Remember to take your badges and ID cards with you, they are required to enter

Day 2: Thursday / 16 October

Copernicus Science Centre

Wybrzeże Kościuszkowskie 20, 00-390 Warsaw

Registration + Breakfast

(Also open after 09:30)

Contributed Talks Session 1

Learning Dynamic Segmentation and Compression of Sequences in Transformer LLMs

by Adrian Łańcucki (NVIDIA)

State-of-the-Art Document AI on a Single 24GB GPU

by Łukasz Borchmann (Snowflake)

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights

by Jakub Krajewski (University of Warsaw)

Contributed Talks Session 2

FlySearch: Exploring how vision-language models explore

by Adam Pardyl (Jagiellonian University; IDEAS NCBR)

How Good Are Open-Source Models for Robot Learning?

by Gracjan Góral (University of Warsaw)

Shaping Robotic Actions with Fourier Flow Matching

by Mateusz Wyszyński (University of Warsaw)

Contributed Talks Session 3

Cache Me If You Can: Reducing Model Size and KV Cache Traffic for Faster LLM Inference

by Konrad Staniszewski (NVIDIA, University of Warsaw)

Neural self-supervised audio representation for SpeechLLM: neural audio codecs for Polish language

by Paweł Cyrta (AGH Cyfronet / Stenograf.io)

Entity Tracking as a Microcosm of Semantic Abilities in LLMs and Humans

by Karolina Drożdż (IDEAS Research Institute)

Discussion Panel 2: Open models, open data

Invited Talk 5: Beyond the screen: capturing, understanding and generating 3D scenes

by Federico Tombari (Google / Technical University of Munich)

Invited Talk 6: Reliable and Sustainable AI: From Mathematical Foundations to Next Generation AI Computing

by Gitta Kutyniok (DLR / University of Tromsø / LMU Munich)

Poster Session 1 + Coffee

with posters: 1-30

You can continue presenting your posters during lunch