Prof. Knut Drescher is an Associate Professor at the Biozentrum, University of Basel , leading a research group focused on bacterial biofilms , swarming , and microbial multicellularity . Previously, he served as a Professor of Biophysics and Max Planck Research Group Leader at Philipps-Universität Marburg (2015-2021) and conducted postdoctoral research at Princeton University. Research Interests: Physical and biological mechanisms of biofilm formation Cell-cell interactions in microbial communities Antibiotic resistance in biofilms Hydrodynamics of bacterial swarms Evolution of cooperation in multispecies biofilms Development of bioimaging software (BiofilmQ, BacStalk) Scientific Awards: 2023: SNSF Consolidator Grant 2019: Heinz Maier-Leibnitz Prize (DFG), VAAM Research Prize, IUPAP Young Scientist Prize 2016: ERC Starting Grant Advising & Grants: Advises PhD and Master's students in microbiology, biophysics, and bioinformatics Secured major grants from ERC , HFSP , and DFG
Christophe Ancey is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he serves in the School of Architecture, Civil and Environmental Engineering (ENAC), specifically in the Institute of Civil Engineering (IIC) and the Environmental Hydraulics Laboratory (LHE). He also holds teaching appointments in SGC-Teaching and EDME-Teaching departments at EPFL. His office is located at GC A1 401, Station 18, 1015 Lausanne, Switzerland. Dr. Ancey holds both a PhD and engineering degree from Ecole Centrale de Paris and Grenoble National Polytechnic Institute. After completing his doctoral work (1994-1997) on rheology of granular flows under Pierre Evesque, he worked as a researcher at Cemagref before joining EPFL in 2004. He directs the Environmental Hydraulics Laboratory and serves as associate editor for Water Resources Research, a leading journal in hydrology. Dr. Ancey's research spans fluid dynamics, rheology, and hydraulics with emphasis on geophysical flows and natural hazards. His work focuses on three interconnected themes: rheology of concentrated suspensions (particularly granular flows in avalanches and mudflows), inverse problems in rheology (determining microscopic behavior from macroscopic measurements), and particle entrainment in turbulent suspensions (erosion and sediment transport processes). His recent publications (2022-2025) demonstrate continued innovation in sediment transport modeling, experimental techniques like PIV for complex flows, and integration of machine learning approaches. The research shows progression from fundamental fluid mechanics to practical applications in natural hazard assessment and river engineering, with particular attention to granular segregation, avalanche-obstacle interactions, and river morphodynamics. Associate Editor, Water Resources Research Co-founder of Toraval, an engineering consulting firm specializing in avalanche risk management Dr. Ancey teaches courses on Fluid Mechanics, Flood and Dam Break Waves, Hydrological Risks and Structures, and Similarity and Transport Phenomena in Fluids. He has supervised numerous PhD students whose work spans environmental hydraulics, granular flows, and sediment transport, with current students including Chen Yanan, Farazande Sofi, and Giboulot Axel Loïc among others.
Manuel Kaufmann is a Lecturer in the Department of Computer Science at ETH Zürich. His work focuses on advanced 3D human motion capture, sensor-based systems, and computer vision applications. He is affiliated with the Institute of Informatics (inf.ethz.ch) and contributes to research in real-time motion tracking, dataset development, and machine learning integration for human-robot interaction. Research interests include holistic human-scene reconstruction from monocular videos, gaze estimation using EEG signals, and expressive avatar creation. His projects emphasize practical applications in robotics, sports analytics, and biomedical engineering, often leveraging electromagnetic and inertial sensors for high-precision data acquisition. His publications reflect a trend toward multi-modal data fusion, real-world dataset creation (e.g., WorldPose, ARCTIC), and addressing challenges in loose garment modeling (Reloo). These efforts aim to improve markerless motion capture, crowd analysis, and human-robot collaboration. No scientific awards or grants are explicitly listed. He has no documented advisees, though his research may involve collaborations with students or teams. His office is located at OAT X 23, Andreasstrasse 5, Zürich, Switzerland, and contact details include a phone number and professional email.
Mennatallah El-Assady serves as Assistant Professor at ETH Zurich's Department of Computer Science, where she leads the Interactive Visualization and Intelligence Augmentation Lab (IVIA). Her academic trajectory includes research fellowships at ETH's AI Center and doctoral work at University of Konstanz and OntarioTech University, establishing her expertise at the intersection of visualization and artificial intelligence. Her research focuses on advancing responsible data-driven decision-making through human-centered analytics, with particular emphasis on explainable machine learning systems. Dr. El-Assady combines data mining techniques with visual interfaces to create transparent AI workflows, specializing in text data analysis. Her work bridges computational linguistics, digital humanities, and information visualization to develop tools that make complex AI processes interpretable for end users. Recent publications demonstrate growing focus on generative AI's impact on visualization practices and sophisticated frameworks for interactive machine learning. Her research consistently addresses the challenge of maintaining human agency in increasingly automated systems, with applications spanning political debate analysis, musicology, and healthcare data interpretation. The trend shows increasing sophistication in evaluation methodologies for visual analytics systems. Best Paper Award: Honorable Mention for 'Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework' Dr. El-Assady actively shapes her field through workshop leadership including ArgVis (Argument Visualization), Vis4DH (Visualization for Digital Humanities), and VISxAI (Visualization for AI Explainability). Her teaching includes 'Interactive Machine Learning- Visualization and Explainability' at ETH Zurich, training next-generation researchers in human-centered AI development. She maintains strong industry connections with coverage in Forbes and ETH News regarding human-AI collaboration frameworks. The Interactive Visualization and Intelligence Augmentation Lab (IVIA) develops cutting-edge tools including explAIner for transparent machine learning, LingVis for linguistic analysis, VisArgue for debate structure visualization, and VALIDA for political deliberation analysis. These projects share a common thread of enhancing human understanding through carefully designed visual interfaces that expose AI decision processes.
Elaine M. Huang is an Associate Professor of Human-Computer Interaction at the Department of Informatics, University of Zurich, where she has served since 2010. She also leads the People and Computing Lab, focusing on the dynamic interplay between human practices and technological advancements. Her academic background includes: PhD in Computer Science, Georgia Institute of Technology (2006) Dr. Huang's research centers on human-computer interaction, examining the bidirectional relationship between technology and human practices. She is particularly known for her work on gender equality in technology, challenging assumptions about innate gender differences and investigating how AI systems may perpetuate biases. Her research also spans sustainable interaction design, mental health technologies, and chronic disease management, always emphasizing empirical data over intuition. Analysis of her recent publications (2023-2025) reveals a strong trajectory toward socially impactful HCI, with significant focus on health technologies (diabetes management, mental health), cultural sensitivity in design, and the ethical challenges of AI. Her methodology often involves field studies to understand real-world technology use, countering the industry's reliance on intuition. As head of the People and Computing Lab, Dr. Huang oversees a research group dedicated to designing and evaluating technologies that address complex human needs. The lab's work frequently involves co-design with diverse user communities to ensure relevance and inclusivity in technological solutions.
Prof. Dr. Julia Vogt is an Assistant Professor at the Department of Computer Science at ETH Zürich, leading the Professur für Medizin. Datenwiss. Her research focuses on medical machine learning, data science, and AI applications in healthcare. She specializes in developing interpretable AI systems for clinical decision support, particularly in pediatric diabetes management, medical imaging analysis, and anomaly detection. Her work bridges translational gaps by emphasizing causal approaches and clinical validation. Her academic role includes teaching courses like the Data Science Lab (263-3300-00L/10L) and Topics in Medical Machine Learning (263-5100-00L). Her lab's research spans predictive modeling for nocturnal hypoglycemia, echocardiogram analysis for pulmonary hypertension detection, and multimodal learning in radiology. She also contributes to national pediatric data initiatives like SwissPedHealth. Key technical areas include concept bottleneck models, stochastic AI frameworks, and generative models for medical signal denoising. Her work often emphasizes model interpretability, fairness, and robustness to distribution shifts. She collaborates on projects involving wearable devices for pediatric monitoring and AI-driven rehabilitation tools for post-stroke gait analysis.
Prof. Dennis Komm is an Associate Professor at ETH Zurich's Department of Computer Science, leading the group for Algorithms and Didactics. He chairs the Center for Computer Science Education (ABZ) and serves on committees such as the Swiss Maturity Board (Schweizerische Maturitätskommission) and the STEM Commission of the Swiss Academies. Previously, he held roles at RWTH Aachen University (Master's, 2008), ETH Zurich (PhD, 2012), University of Zurich (external lecturer, 2014–2020), and PH Graubünden (including department head and professor of 'Fachdidaktik Informatik'). Education: He completed a Master's in Computer Science at RWTH Aachen (2008), a PhD at ETH Zurich (2012), and studies in Information Technology at Queensland University of Technology (2006). His academic journey includes visiting roles at King's College, Stanford, and Comenius University. He has taught extensively across institutions, emphasizing Python and LOGO-based approaches for beginners. Research focuses on algorithm design, approximation algorithms, reoptimization, and advice complexity in theoretical CS. His work in education explores computational thinking, programming pedagogy (especially for K–12), and interdisciplinary approaches (e.g., robotics in math). Recent trends in his articles highlight advancements in online algorithms, optimization under dynamic conditions, and initiatives to integrate CS into Swiss school curricula sustainably. He actively promotes CS education through platforms like WebTigerPython and collaborates on projects such as CyberQuest and MINTerlink. His outreach includes organizing conferences (e.g., STIU 2025) and workshops on programming and cybersecurity for teachers and students. Despite no listed scientific awards, his contributions to education and theoretical CS are recognized through editorial roles in journals like Informatics in Education and contributions to the TigerJython Group. Grant-related advising includes co-supervising doctoral theses on robotics, USOs, and programming didactics. He advocates for equitable educational opportunities via the Passerelle exam and the Swiss Beaver Competition. His team's work spans teacher training, didactic certifications, and bridging university-school collaborations through initiatives like MINTerlink. Labs and teams: Head of ABZ (ETH's CS education center), collaborator with the Computational Robotics Lab, and part of the TigerJython Group. He also co-organizes the Colloquium on Mathematics, Computer Science, and Education with ETH's Mathematics Department.
Adam B. Badawi is a Professor of Law at the University of California, Berkeley - School of Law, where he maintains an active research agenda at the intersection of law, finance, and corporate governance. His scholarly work has established him as a leading expert in contract law, M&A transactions, securities litigation, and ESG compliance. Professor Badawi's research interests span multiple dimensions of corporate law and legal theory. His work on contractual complexity, particularly in debt agreements and M&A transactions, has provided significant insights into how legal drafting affects economic outcomes. He has extensively examined the business judgment rule, board authority, and the role of courts in corporate governance. More recently, his research has expanded into ESG-related topics, analyzing how environmental, social, and governance considerations intersect with traditional corporate law frameworks. His methodological approach often combines legal analysis with empirical techniques, including text analysis of legal documents and event studies of litigation outcomes. Professor Badawi's scholarly output demonstrates consistent high-impact research across multiple domains of corporate law. His early work focused on foundational aspects of contract theory and interpretation, while his more recent publications reflect growing interest in ESG metrics, executive compensation structures tied to sustainability goals, and the evolving regulatory landscape. The trajectory of his research shows a progression from theoretical legal analysis toward increasingly empirical and interdisciplinary approaches that bridge law with finance and data science. Professor Badawi has collaborated with numerous scholars across different institutions, including Duke University, Stanford Law School, and the University of Chicago Law School, reflecting the interdisciplinary nature of his work and its relevance to multiple academic communities.
Raquel Urtasun is a Full Professor in the Department of Computer Science at the University of Toronto and a co-founder of the Vector Institute for AI. She is also the Founder and CEO of Waabi, an autonomous vehicle company. Previously, she was Chief Scientist and Head of R&D at Uber ATG (2017–2021) and held faculty positions at the Toyota Technological Institute at Chicago (TTIC) and as a visiting professor at ETH Zurich. Her research spans machine learning, computer vision, robotics, and AI with a strong focus on autonomous driving and 3D perception. Education: Bachelor's degree, Universidad Pública de Navarra, 2000 Ph.D., Computer Science, École Polytechnique Fédérale de Lausanne (EPFL), 2006 Postdoctoral studies, MIT and UC Berkeley Raquel Urtasun's research focuses on developing AI systems for self-driving cars, emphasizing efficient perception using minimal sensors. Her work includes 3D scene understanding, stereo vision, optical flow, semantic segmentation, and object detection. She has developed the KITTI benchmark suite, widely used in autonomous driving research. Her lab is an NVIDIA NVAIL lab, reflecting its leadership in AI innovation. Her recent publications show a consistent trend in deep learning for visual perception, particularly in stereo matching, optical flow, 3D object detection, and semantic segmentation. These works integrate deep neural networks with structured models like CRFs and MRFs, pushing the boundaries of accuracy and efficiency in scene understanding for autonomous systems. Scientific Awards: NSERC E.W.R. Steacie Fellowship NVIDIA Pioneers of AI Award Google Faculty Research Awards (multiple) Amazon Faculty Research Award Connaught New Researcher Award Fallona Family Research Award Best Paper Runner Up at CVPR 2013 and 2017 UPNA Alumni Award Chatelaine 2018 Woman of the Year Adweek 2018 Toronto's Top Influencers Urtasun has advised numerous PhD and Master’s students, many of whom now hold faculty or research scientist positions at institutions like UIUC, NYU, UBC, MIT, and companies including Google, Amazon, NVIDIA, and Apple. She has secured significant research grants from NSERC, Google, Amazon, and NVIDIA. Her leadership extends to organizing workshops and serving as Area Chair and Program Chair at top conferences like CVPR, ICML, and NeurIPS. Labs and Teams: She leads a research group at the University of Toronto focused on AI for autonomous systems. Her team has been recognized as an NVIDIA NVAIL lab, and she continues to mentor students and postdocs working on cutting-edge problems in robotics and machine learning, both at UofT and through her company Waabi.
Kumar Varoon Agrawal is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding the Gaznat Chair for Advanced Separations. He is affiliated with the School of Basic Sciences (SB), the Institute of Chemical Sciences and Engineering (ISIC), and the Laboratory of Advanced Separations (LAS) in Sion, Switzerland. Additionally, he contributes to the Swiss Doctoral School in Chemical and Bioengineering (SCGC) and serves as Vice President of the Confédération des Chimistes et des Génie Chimique (CCE). Research Focus: Material Chemistry & Engineering at the Ångström scale for high-performance inorganic and hybrid membranes, emphasizing energy-efficient molecular separations. Teaching: Courses include Fundamentals of separation processes , Diffusion and mass transfer , and Chemical engineering product design . Scientific Contributions: His 15 most recent publications (2025-2020) span topics like graphene pore engineering , 2D material synthesis , carbon capture , and gas separation membranes , with keywords such as Nanotechnology , Materials Science , and Molecular Transport . Subfields include Atomic-Scale Pores , Membrane Stability , and Industrial Scalability . Students and Collaborations: He advises 10 current PhD students and has mentored 9 past PhD candidates in areas like graphene membranes , ion separation , and MOF films . He is an Academic Referent for the EPFL Carbon Team and a committee member for the EDCH Doctoral Program in Chemistry and Chemical Engineering.
Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.
Dominique Foray is a Professor at Ecole Polytechnique Fédérale de Lausanne (EPFL) and holds the Chair of Economics and Management of Innovation (CEMI). He is a member of the Swiss Council for Science, chairman of the KOF Swiss Economic Research Institute's Advisory Board, and a foreign member of Columbia University's Center of Capitalism and Society. His career includes extensive advisory roles, such as with the European Commission's 'Knowledge for Growth' group and Germany's Expert Commission for Research and Innovation. Education: Ph.D. in Economics, University Lumière of Lyon (1984) Habilitation à diriger des recherches, University Lumière of Lyon (1992) Foray's research focuses on the economics of innovation, knowledge-based economy dynamics, technology policy, smart specialization strategies, and institutional frameworks for innovation. He has authored seminal works including The Economics of Knowledge (MIT Press, 2004) and Smart Specialisation (Routledge, 2015). His recent publications explore mission-oriented innovation policies, smart specialization in EU contexts, and knowledge diffusion mechanisms. Foray's work bridges academic theory with practical policy design, influencing regional and national innovation strategies globally. Advising: Past EPFL PhD students: Aouinait Camille Marguerite, Ayoubi Charles Chadi, Baruffaldi Stefano Horst, Bednyagin Denis, Bogers Marcellus, Coffano Monica, Conti Annamaria, Cristelli Gabriele, Gaulé Patrick, Hamdan Livramento Intan Maizurah, Marino Marianna, Muñoz Tellez de Kieffer Viviana Carolina, Pellegrin Claudia, Simeth Markus Foray's institutional roles include leadership in European policy initiatives and contributions to the World Bank's studies on innovation's role in economic growth. His interdisciplinary approach integrates economics, policy analysis, and technology management to address grand societal challenges through innovation.
André Bardow is a Full Professor at the Department of Mechanical and Process Engineering, ETH Zürich. His research focuses on energy systems optimization, life cycle assessment, computer-aided molecular design, and CO2 capture/utilization. Professor (ETH Zürich, 2020–present) Head of Institute of Technical Thermodynamics (RWTH Aachen University, 2010–2020) Visiting Professor (University of California, Santa Barbara, 2015/16) Part-time Director (Forschungszentrum Jülich, 2017–2022) Associate Professor (TU Delft, 2007–2010) Research Interests: His work spans energy and process systems engineering, with emphasis on sustainable technologies. Key areas include: Computer-aided molecular and process design Machine learning for chemical engineering Carbon capture and utilization (CCU) Life cycle assessment (LCA) of industrial processes Thermo-economic modeling of energy systems Multiphase equilibrium analysis Publication Trends: Recent articles focus on integrating machine learning with process design, optimizing CO2 capture in steel production, and advancing electrochemical cooling technologies. Subfields include sustainable plastics, ORC working fluids, and solvent mixture design. Scientific Awards: Fellow of the Royal Chemical Society Recent Innovative Contribution Award (EFCE, 2019) PSE Model-Based Innovation Prize (2018) Covestro Science Award (first recipient) Arnold-Eucken-Award (VDI-GVC) Highly Cited Researcher (Clarivate, 2024) Advising and Grants: Professor Bardow mentors students in process optimization and leads projects like Systemic expansion of territorial CIRCULAR Ecosystems for end-of-life FOAM (Grant 101036854, EC).
Konstantinos Karapiperis is a Tenure Track Assistant Professor at EPFL's Laboratory of Multiscale Modeling of Materials (LMD), within the School of Architecture, Civil and Environmental Engineering (ENAC). His research integrates mechanics , multiscale modeling , and data science to study geomaterials and structural materials. PhD in Applied Mechanics (minor in Applied Mathematics), Caltech Postdoctoral Researcher & Lecturer, ETH Zürich (Marie Skłodowska-Curie Fellowship) Research focuses on granular materials , architected materials , and nonlocal modeling using techniques like Level-Set Discrete Element Method (LS-DEM) and machine learning . Recent work explores fracture control via graph neural networks and thermodynamics-informed models. Selected scientific award: Marie Skłodowska-Curie Fellowship Teaches courses in Soil Mechanics and Multiscale Modeling . PhD students include Thomas Henzel and Hrishikesh Gopakumar Menon. His Data-Driven Mechanics Laboratory (LMD) develops predictive tools for granular and structured material behavior.
Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.
