Find Academic Supervisors

• Probability Theory
• Random Graphs
• Graph Limits
• +3 more

Backhausz Ágnes is a Assistant Professor at Eötvös Loránd University's Faculty of Science , specifically in the Department of Probability Theory and Statistics . She also holds a part-time Researcher position at the Alfréd Rényi Institute of Mathematics . Her academic journey includes habilitation and a PhD in Mathematics, focusing on random graph models and their asymptotic properties. Research Group: Struktúrák limeszei (since 2013, part-time since 2015) Grants: ERC Grant on 'Limits of Discrete Structures' (2014–2019) Ágnes specializes in Probability Theory and Random Graphs , with emphasis on graph limits , factor of i.i.d. processes , and spectral theory . Recent publications analyze epidemic spread on multilayer networks, entropy inequalities, and action convergence in graph operators. Her work bridges theoretical mathematics with applications in network science and stochastic processes. Notable awards include the Grünwald Géza Memorial Medal (2014) from the Bolyai János Matematikai Társulat. She actively contributes to academic service as a Supervisor and Training Lead for the Beyond The Edge Marie Curie Doctoral Network (2024–2027) and serves on program committees for conferences like Eurocomb and the European Girls' Mathematical Olympiad . Her teaching portfolio spans Probability Theory, Stochastic Processes, and Mathematical Statistics at both undergraduate and graduate levels.

Jemil Avers ButtView profile

Lecturer

• Geomatic Engineering
• Remote Sensing
• Structural Monitoring
• +3 more

Dr. Jemil Avers Butt is a Lecturer at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering (D-BAUG), specializing in Geomatics and Geodetic Engineering. His research focuses on advanced geospatial technologies, including terrestrial radar interferometry, LiDAR sensing, and MIMO-SAR for structural and environmental monitoring. He develops novel methods for improving sensor accuracy, data fusion, and deformation analysis in civil engineering applications. His work addresses challenges in monitoring infrastructure such as bridges, wind turbines, and dams, leveraging machine learning and probabilistic modeling to enhance data interpretation. Recent projects include RGB-guided 3D displacement estimation, self-calibration of laser scanners, and phase ambiguity resolution in electronic distance measurements. He teaches courses like 'Computational Methods for Geospatial Analysis' and 'Project Parameter Estimation,' reflecting his expertise in both theoretical and applied aspects of geomatic engineering.

Nina EffenbergerView profile

Researcher

• Climate Science
• Machine Learning
• Renewable Energy
• +3 more

Dr. Nina Effenberger is a Postdoctoral Researcher in the Climate Physics group at ETH Zurich, led by Professor Reto Knutti. She previously completed her Ph.D. at the University of Tübingen under Nicole Ludwig’s Machine Learning in Sustainable Energy Systems group and was affiliated with the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Her work bridges climate science, machine learning, and energy modeling to address challenges in renewable energy systems under climate change. Her research focuses on multi-decadal wind power forecasting using climate models (CMIP6) and Gaussian Processes, with a particular emphasis on turbine-location-aware downscaling techniques. She has also contributed to mental health advocacy in academia, including co-authoring a 2023 publication in Plos One on stress and well-being among Ph.D. students. Her recent work includes arXiv preprints on climate data selection and temporal resolution impacts in wind power predictions. Dr. Effenberger is an alumnus of the sustainAbility Ph.D. initiative and trained in mental health first aid. She can be contacted via email at nina.effenberger@env.ethz.ch .

• Building Energy Simulation
• Thermal Engineering
• Renewable Energy Systems
• +4 more

Andrii Zakovorotnyi serves as Senior Research Associate at the Institute of Building Technology and Energy within the School of Engineering and Architecture at Lucerne University of Applied Sciences and Arts (HSLU). His research focuses on advanced building energy systems and thermal simulations. He holds a Bachelor's and Master's degree in Thermal Physics from Kiev Polytechnic Institute (2013, 2015), following which he joined HSLU as an intern before becoming a research assistant and later research fellow. His educational background directly informs his current research trajectory in energy-efficient building technologies. His research interests span Building Energy Simulation , Thermal Engineering , and Renewable Energy Systems , with particular expertise in CFD modeling of building environments and energy monitoring systems. Current projects address critical challenges like climate-resilient urban design through AI-based Mixed Reality (KlimaCoLab), seasonal thermal energy storage (ITC Hochleistung-Wärmedämmung), and optimization of photovoltaic-integrated energy storage systems. Analysis of his 15 most recent publications reveals strong emphasis on practical applications of building energy modeling, with recurring themes in geothermal cooling systems, cloud-based simulation platforms for self-sufficient buildings, and advanced data analytics for energy profiling. His work consistently bridges computational methods with real-world building performance validation. Project leadership includes significant contributions to national initiatives like BFE-Projekt CoolShift+, SCCER FEEB&D work packages, and Minergie 2017 development. His technical approach integrates Monte Carlo uncertainty analysis, clustering algorithms for energy data, and climate change adaptation strategies into building design frameworks. Based at the Center for Integrated Building Technology (Room C312), his laboratory work focuses on experimental validation of thermal building models and development of monitoring protocols for building automation systems. Current work includes the COOL2REGEN project investigating active building cooling with heat pumps and geothermal probes for high regeneration grades.

Adway GirishView profile

Academic

• Information Theory
• Coding Theory
• Machine Learning
• +9 more

Adway Girish is a third-year Ph.D. candidate and Doctoral Assistant at the School of Computer and Communication Sciences (IC), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is affiliated with the Information Processing Group (IPG) and the Laboratory for the Theory of Information (LTHI), working under the supervision of Prof. Emre Telatar. He has also collaborated with leading researchers including Michael Gastpar, Hyeji Kim, and Shlomo Shamai. His educational background includes a B.Tech. in Electrical Engineering with honors and a minor in Mathematics from the Indian Institute of Technology Bombay (IITB), completed in 2022. Adway's research centers on information theory and its applications in communication, security, and machine learning. He is particularly interested in foundational aspects of information measures, entropy optimization, and the theoretical underpinnings of modern deep learning architectures such as transformers and large language models. His recent work explores rate-distortion frameworks for prompt compression, learning dynamics in transformers, and entropy-constrained communication channels. His research bridges theoretical rigor with practical implications in AI and communication systems. The trend in his recent publications shows a shift from classical signal processing and micro-Doppler analysis during his undergraduate years to advanced topics in information theory and machine learning during his Ph.D. His contributions are published in top-tier venues such as ISIT, NeurIPS, ICLR, and ICML workshops, indicating a strong trajectory in theoretical computer science and applied mathematics. Scientific Awards and Recognitions: ICLR 2025 Spotlight Paper (awarded to top 5% of accepted papers) Oral presentation at ICML 2024 Workshop on Theoretical Foundations of Foundation Models (selected as one of top 4 out of 58 submissions) Adway advises no students currently, as he is himself a doctoral candidate. However, he plays an active role in collaborative research projects involving multiple co-authors across institutions. He has not received specific mention of external grants in the provided text, but his position as a Doctoral Assistant at EPFL suggests institutional funding. His collaborations with renowned researchers suggest involvement in larger research initiatives and potential access to grant-supported projects. He is a core member of the Information Processing Group (IPG) at EPFL, a research lab focused on theoretical and applied aspects of information science, including coding, communication, learning, and data analysis. The group fosters interdisciplinary research and hosts regular seminars, candidacy reviews, and internal presentations, all of which Adway actively participates in.

• Privacy
• Cybersecurity
• Machine Learning
• +8 more

Mathias Humbert is an Associate Professor at the University of Lausanne, with affiliations at the Department of Information Systems in the Faculty of Business and Economics. Previously, he held roles as a Scientific Project Manager at the Cyber-Defence Campus, Senior Data Scientist at the Swiss Data Science Center (SDSC) at ETHZ and EPFL, and Postdoctoral Researcher at CISPA in Saarbrücken. He earned his Ph.D. in 2015 from EPFL (Switzerland) after completing B.Sc./M.Sc. studies at EPFL and UC Berkeley. His research focuses on privacy, cybersecurity, and machine learning, particularly examining privacy risks in MLaaS, online social networks, wearable devices, and spectrum monitoring, while developing novel privacy-preserving frameworks for biomedical data and social graphs. Key research areas: Privacy in data sharing, interdependent privacy, genomic privacy, location privacy, and graph-based machine learning Notable contributions: GraphEraser for graph unlearning, KGP Meter for genomic privacy awareness, and SVT² for differential privacy in methylation data His recent publications explore machine unlearning vulnerabilities, privacy risks in DNA methylation data, and usability challenges in web security mechanisms, with a focus on empirical studies and cryptographic solutions. He received a Distinguished Paper Award at NDSS 2019 for his work on MBeacon and has contributed extensively to privacy research across ACM CCS, IEEE EuroS&P, and USENIX Security venues.

Jean-Marc OdobezView profile

Adjunct Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +6 more

Jean-Marc Odobez is a Senior Scientist at the IDIAP Research Institute and Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he is affiliated with the School of Engineering and serves on the Electrical Engineering Doctoral committee (EDEE). He leads the Perception & Activity Understanding Group at Idiap and has extensive teaching responsibilities across multiple departments. Dr. Odobez received his PhD in Computer Science from Rennes University in 1994. His research focuses on multimodal perception systems combining computer vision, statistical machine learning, and deep learning for activity recognition, behavior understanding, and human-robot interaction. His work spans diverse application domains including human health assessment, social robotics, and media content analysis. His recent research shows strong trends in gaze estimation, human activity recognition, and multimodal processing. His team has developed innovative solutions for gaze tracking, head pose estimation, and activity recognition using depth sensors and neural networks. His work increasingly bridges computer vision with digital humanities, particularly in the analysis of ancient Maya glyphs. IEEE member Associate Editor of Machine Vision and Applications journal Dr. Odobez has supervised numerous PhD students and serves as a committee member for the Electrical Engineering Doctoral program. He has been principal investigator for over 16 European and Swiss research projects and has worked on 10 technology transfer projects with SMEs. He co-founded Klewel SA and Eyeware SA, focusing on eye tracking and attention modeling technologies. His research group actively collaborates with industry partners and maintains strong connections with the computer vision and human-computer interaction research communities.

• Control Systems
• Optimization
• Machine Learning
• +5 more

Prof. Maryam Kamgarpour is a faculty member at ETH Zurich's Automatic Control Laboratory within the Department of Information Technology and Electrical Engineering. Her research focuses on control systems, optimization, and machine learning, with applications in robotics, power systems, and multi-agent systems. She has contributed significantly to safe reinforcement learning, stochastic control, and distributed optimization frameworks. Her work bridges theoretical foundations and practical implementations in areas like safe trajectory planning, market mechanisms for energy systems, and algorithmic guarantees for greedy methods. Key technical areas include model-based multi-agent reinforcement learning, robust control policies under uncertainty, and parameterization techniques in control theory. Collaborations with institutions like ETH Zurich's Automatic Control Lab and researchers such as Andreas Krause highlight her interdisciplinary approach. Her research has been supported by grants from the Swiss National Science Foundation (NCCR Automation) and the European Union (Reliable Data-Driven Decision Making in Cyber-Physical Systems). Publications span top journals and conferences in control systems, machine learning, and robotics, emphasizing rigorous analysis and practical scalability. Recent work addresses challenges in safe black-box optimization, stochastic hazard management in robotics, and market-based resource allocation in power grids.

Mahdi Jafari SiavoshaniView profile

Assistant Professor

• Machine Learning
• Optimization
• Data Science
• +14 more

Mahdi Jafari Siavoshani is an Assistant Professor in the Department of Computer Science and Engineering at Sharif University of Technology, where he leads the Information, Network, and Learning (INL) Lab. He holds a PhD and MSc from the Swiss Federal Institute of Technology (EPFL), and a BSc in Electrical Engineering and Physics from Sharif University of Technology. Prior to his current role, he was a postdoctoral fellow at the Institute of Network Coding, The Chinese University of Hong Kong. His research spans fundamental problems in information processing, transmission, and analysis, with a focus on theoretical limits and practical algorithm design. Key areas include Machine Learning, Optimization, Data Science, Information and Communication Theory, and Computer Networks. His work integrates probabilistic modeling, network coding, and deep learning to address challenges in secure communication, traffic classification, and distributed systems. The recent publications highlight a strong trend in network coding, information theory, and machine learning applications. His work explores coding-theoretic solutions for secrecy, load balancing in cache networks, belief propagation algorithms, and encrypted traffic classification using deep learning. These contributions reflect a blend of theoretical depth and practical relevance in modern communication systems. Assistant Professor, Department of Computer Science and Engineering, Sharif University of Technology PhD and MSc, Swiss Federal Institute of Technology (EPFL) BSc in Electrical Engineering and Physics, Sharif University of Technology Postdoctoral Fellow, Institute of Network Coding, The Chinese University of Hong Kong He advises research in the INL Lab and teaches courses such as Computer Networks, Engineering Probability and Statistics, Network Coding, and Stochastic Processes. While no formal students are listed, his lab environment fosters collaborative research. There is no mention of external grants or funding in the provided texts. The INL Lab serves as the primary research unit, focusing on information theory, network algorithms, and machine learning applications. The lab integrates theoretical research with practical implementations, particularly in networked systems and data analysis.

Andrea CarronView profile

Senior Lecturer

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +7 more

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

Dmitry VetrovView profile

Professor

• Bayesian Deep Learning
• Diffusion Models
• Loss Landscape Analysis
• +5 more

Dmitry Vetrov serves as a Professor of Computer Science at Constructor University in Bremen, where he founded and leads the Bayesian Methods Research Group. His academic foundation includes graduation from Moscow State University in 2003 and completion of his PhD in 2006, establishing a career centered on advancing probabilistic machine learning methodologies. His educational trajectory features: Undergraduate studies at Moscow State University (2003) Doctoral degree (PhD, 2006) Vetrov's research program critically bridges Bayesian statistics with deep learning architectures, with his group pioneering efficient diffusion model algorithms, loss landscape characterization in neural networks, scalable stochastic optimization tools, tensor decomposition applications for large-scale ML systems, and enhanced conditional text generation frameworks. This work manifests practical implementations across generative AI domains while maintaining theoretical rigor in probabilistic modeling. Analysis of his 2024-2025 publications reveals a concentrated research thrust toward diffusion model innovation, spanning text generation (token embedding smoothing, language model encoding properties), image synthesis (hair transfer, gesture generation), and scientific applications (protein modeling, genetic fine-mapping). Key thematic threads include sampler acceleration, theoretical property analysis of diffusion processes, and robust evaluation frameworks for generative systems. No scientific awards were documented in the source materials. Mentorship outcomes demonstrate significant impact, with three recent PhD students securing research positions at DeepMind. While specific grant details remain undisclosed, the group's prolific output across NeurIPS, ICML, and CVPR indicates sustained research funding. The Bayesian Methods Research Group operates as an integrated innovation hub within Constructor University's academic ecosystem. The research collective he directs maintains active development of Bayesian-deep learning fusion techniques, with current projects emphasizing diffusion model efficiency, theoretical foundations of optimization landscapes, and cross-domain applications in computational biology and multimodal generation.

Kevin J LiangView profile

Researcher

• Deep Learning
• Computer Vision
• 3D Computer Vision
• +5 more

Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.

Annemarie FriedrichView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Computational Syntax
• +7 more

Annemarie Friedrich is a tenured University Professor for Natural Language Understanding (Computational Linguistics) at the Faculty of Applied Computer Science, University of Augsburg. She also holds membership in the Faculty of Philology and History. Previously, she worked as a Senior Expert on Natural Language Processing and Computational Linguistics at the Bosch Center for Artificial Intelligence. Currently, she serves as president of the German Society for Computational Linguistics (GSCL), the primary scientific association for NLP research in German-speaking regions, and is a member of the ACL Special Interest Group for Annotation (ACL SIGANN). University: University of Augsburg School: Faculty of Applied Computer Science Department: Institute of Computer Science Position: University Professor (tenured) for Natural Language Understanding Professor Friedrich's research focuses on computational linguistics and natural language processing with emphasis on semantics and information extraction from text. Her work spans both machine-learning oriented approaches to text mining for scientific text, syntactic and semantic parsing, and uncertainty in deep learning for NLP, as well as corpus-linguistic research on syntax-semantics interface, discourse, pragmatics, aspect, genericity, and modal verbs. She has particular expertise in annotation and corpus creation, recognizing that machine learning models depend fundamentally on underlying data quality. Her research group at Augsburg actively contributes to computational linguistics through numerous publications and datasets. Analysis of Professor Friedrich's recent publications reveals a strong focus on table question answering, patent text processing, uncertainty modeling, and multimodal scientific document understanding. Her work consistently bridges theoretical linguistics with practical NLP applications, with increasing emphasis on robust evaluation methodologies and domain-specific adaptations of language models. Notably, her research group has produced significant resources including the AnnoCTR dataset for cyber threat reports, PAP2PAT for patent generation, and FREB-TQA for evaluating table QA robustness. Professor Friedrich actively mentors multiple PhD students working on diverse topics including document-level patent processing (Valentin Knappich), temporal processing (Timo Schrader), document-level text modeling (Wei Zhou), and topic modeling for digital forensics (Jenny Maria Felser). Her collaborative approach is evident in co-supervision arrangements with researchers from institutions including Bosch Center for Artificial Intelligence, TU Dresden, and Hochschule Mittweida. She has also successfully guided previous PhD students including Sophie Henning (Uncertainty Modeling), Stefan Grünewald (Syntactic Dependencies), and Subhash Pujari Chandra (Neural Patent Classification). Her teaching responsibilities include courses such as Introduction to Natural Language Processing, Introduction to Python Programming, and specialized seminars on Natural Language Understanding for both Bachelor's and Master's students. These courses reflect her commitment to both theoretical foundations and practical implementation skills in computational linguistics.

• Bayesian Probabilistic Models
• Machine Learning
• Optimization
• +3 more

Dr. Thomas Oskar Weinmann is the Head of the Research Focus Area Scientific Computing & Algorithmics at ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW). His research interests include Bayesian probabilistic models, machine learning, optimization, and visual computing. He holds a PhD in Mathematics from ETH Zürich (2003–2007) and completed a CAS in Visual Computing at ETH Zürich in 2011. Education: PhD in Mathematics, ETH Zürich (2003–2007) CAS Visual Computing, ETH Zürich (2011) His current research projects include: Smart Acquisition for Ultra-High field NMR Spectroscopy (Deputy project leader, ongoing) Raman for Process Analytics (Team member, ongoing) Target Recognition using Artificial Intelligence (TRAI) (Project leader, ongoing) Completed projects include machine learning applications in NMR spectroscopy, varroa mite counting in honeybee colonies, football motion data analysis, and NoSQL database systems. His publications span Bayesian spectral analysis and query optimization in NoSQL systems.

Paolo FavaroView profile

Professor

• Computer Vision
• Computational Photography
• Machine Learning
• +4 more

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.