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Christoph KochView profile

Professor

• Database Theory
• Query Optimization
• Data Management Systems
• +4 more

Christoph Koch is a Full Professor of Computer Science at École Polytechnique Fédérale de Lausanne (EPFL), leading the Data Analysis Theory and Applications Laboratory (DATA). His academic journey includes previous roles as Associate Professor at Cornell University (2007-2010) and Saarland University (2005-2007), with academic foundations from TU Vienna (PhD 2001, Habilitation 2004) and research at CERN. Education: PhD in Artificial Intelligence (TU Vienna & CERN, 2001), Habilitation (TU Vienna, 2004) Appointments: EPFL (2010-present), Cornell University (2007-2010), Saarland University (2005-2007), TU Vienna (2003-2005) His research focuses on database theory and systems , with recent work spanning query optimization , transaction processing , and parallel data management . His publications address topics like robustness under SQL isolation levels , quantifier elimination for query optimization , and Datalog extensions . Articles trend toward high-dimensional data cubes , transactional concurrency , and embedded DSLs for performance-critical systems . Scientific accolades include: Best Paper Awards: PODS (2002) ICALP (2005) SIGMOD (2011) VLDB (2014) GPCE (2017) ERC Consolidator Grant (2011) Google Research Award (2009) ACM SIGLOG Alonzo Church Award (2021) He has advised numerous PhD students including Immanuel Trummer (now at Cornell), Milos Nikolic (Edinburgh), and Lionel Parreaux (HKUST). His lab contributes to query engine architectures , parallel processing systems , and quantum computing applications in database optimization.

Meisam RazaviyaynView profile

Associate Professor

• Optimization Algorithms
• Data Science
• Machine Learning
• +4 more

Meisam Razaviyayn is an Associate Professor at the University of Southern California (USC) with joint appointments in the Department of Industrial and Systems Engineering , Computer Science , Quantitative and Computational Biology , and Electrical Engineering . He serves as the Associate Director of the USC-Meta Center for Research and Education in AI and Learning and holds the Andrew and Erna Viterbi Early Career Chair . His academic background includes a Ph.D. in Electrical Engineering with a minor in Computer Science from the University of Minnesota, followed by a postdoctoral fellowship at Stanford University. Razaviyayn maintains active collaborations with industry partners like Google Research and Amazon. Current Roles : Faculty Visitor at Google Research Editorial Roles : Associate Editor for SIAM Journal on Optimization and IEEE Transactions on Signal Processing His research focuses on optimization algorithms for modern data science problems, particularly addressing privacy , fairness , and scalability in machine learning systems. Recent work includes differentally private optimization frameworks and fair learning algorithms applicable to large-scale and decentralized data scenarios. Key scientific awards include: 2022 NSF CAREER Award 2022 Northrop Grumman Excellence in Teaching Award 2021 AFOSR Young Investigator Award 2021 3M Nontenured Faculty Award 2020 ICCM Best Paper Award in Mathematics 2019 IEEE Data Science Workshop Best Paper Award He actively advises Ph.D. students and leads research initiatives in robust and private machine learning . His group has received funding from NSF, Google Research, and Amazon for projects related to generative AI training , differentially private algorithms , and distributed optimization . Razaviyayn also contributes to academic service as a conference chair and workshop organizer.

• Statistics
• Econometrics
• Algorithmic Decision Making
• +3 more

David Preinerstorfer is a Professor of Statistics and Econometrics at the Vienna University of Economics and Business (WU) and Director of the Swiss Institute for Empirical Economic Research (SEW-HSG) at the University of St.Gallen. His research focuses on the statistical foundations of algorithmic decision making, particularly in dynamic economic systems and uncertain environments. Director, SEW-HSG (2024–2025) Associate Professor, University of St.Gallen (2021–2024) Assistant Professor, Université libre de Bruxelles (2017–2021) PhD in Statistics and Operations Research, University of Vienna (2011–2015) His work integrates reinforcement learning, optimal control, stochastic programming, and optimization to develop robust and scalable algorithmic solutions. Key research areas include high-dimensional inference, moment-based methods, and optimal policy learning with distributional targets. Recent publications focus on moment-dependent phase transitions, power enhancements for testing moment equalities, and regularization in policy learning. He has contributed to journals like Econometrica and Econometric Theory . Scientific Awards Econometric Theory Multa Scripsit Award (2024) Tjalling C. Koopmans Econometric Theory Prize (2024) Adolphe Wetrems Prize (2022) Advanced ARC Grant (2018) University of Vienna Publication Awards (2016) He collaborates with institutions such as Universitat Pompeu Fabra, TU Graz, and Erasmus University Rotterdam. His research emphasizes both theoretical and practical advancements in data-driven decision making.

• Decentralized Systems
• Distributed Systems
• Privacy and Anonymous Communication
• +7 more

Prof. Bryan Alexander Ford is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Decentralized and Distributed Systems (DEDIS) lab within the School of Computer and Communication Sciences, Department of Computer Science. He also holds teaching positions in SIN (Systems and Networking) and SSC (Security and Software Composition) at EPFL, and serves on the Open Science Strategic Committee. His academic journey includes faculty positions at Yale University following his Ph.D. at MIT. Dr. Ford's research spans multiple domains with a primary focus on building secure decentralized systems. His work encompasses privacy and anonymous communication systems like Dissent, systems security, blockchain technology, and novel approaches to operating systems for deterministic parallel computing such as Determinator. He has made seminal contributions to parsing theory through his development of Parsing Expression Grammars (PEGs) and packrat parsing algorithms, which provide linear-time parsing with backtracking capabilities. His research also extends to networking protocols including the Unmanaged Internet Architecture and Structured Stream Transport, as well as virtualization technologies like VX32. Ford's publication record demonstrates a remarkable evolution from foundational work in parsing and programming languages to cutting-edge research in decentralized systems and security. His early career focused on operating systems theory, parsing algorithms, and language design, culminating in influential papers on packrat parsing and PEGs. More recently, his work has shifted toward practical decentralized systems, blockchain technology, and security architectures, while maintaining connections to programming language theory through projects like Matchertext and MinML. This trajectory reflects both continuity in his interest in system architecture and a strategic pivot toward emerging challenges in decentralized computing. As an educator and mentor, Ford advises multiple PhD students at EPFL through the EDIC program and welcomes prospective students and researchers to join his lab. His teaching includes courses on decentralized systems engineering and technologies for democratic society, reflecting his commitment to both technical rigor and societal impact of computing technologies.

Laurent LehmannView profile

Professor

• Evolutionary Biology
• Theoretical Population Genetics
• Social Evolution
• +5 more

Laurent Lehmann is a Full Professor at the University of Lausanne, holding a position in the Department of Ecology and Evolution within the Faculty of Biology and Medicine. He has served as director of the master's program 'Behaviour, Economics, and Evolution' since 2015. Previously, he was an Assistant Professor at the University of Lausanne (2011-2015) and the University of Neuchâtel (2009-2011). His academic journey includes postdoctoral research at Stanford University with Prof. Marc Feldman, Cambridge University with Dr. François Balloux, and the University of Helsinki with Prof. Hanna Kokko. Lehmann's research focuses on mathematical and simulation models to study the evolution of social behaviors, including cooperation, altruism, and learning. His work spans three primary areas: individual decision processes, life-history evolution, and the transition to large-scale human societies. His theoretical approach addresses fundamental questions about how social behaviors evolve through natural selection, with particular attention to the roles of kinship, spatial structure, and cultural transmission. Analysis of his recent publications reveals a strong emphasis on mathematical modeling of social evolution, with recurring themes including Hamilton's rule, kin selection, cultural transmission, and evolutionary game theory. His work bridges theoretical biology with anthropological questions about human social evolution, particularly examining how large-scale cooperation emerged in human societies. Recent papers demonstrate increasing integration of cultural evolution with traditional population genetic approaches. As an academic mentor, Lehmann has supervised doctoral students including Fumagalli E. (2014), with research focusing on information sharing and social network dynamics. His work has received funding from major research agencies including ERC and SNSF Starting Grants, as noted on his departmental profile. Lehmann leads a research group within the Department of Ecology and Evolution that develops mathematical models to understand social behavior evolution. His group collaborates across disciplinary boundaries, connecting evolutionary theory with economics, anthropology, and cognitive science to address fundamental questions about human sociality and cooperation.

• Additive Manufacturing
• Laser Machining
• Welding
• +4 more

Hannes Freiße is an Assistant Professor at the Geneva School of Landscape, Engineering and Architecture (HES-SO), specializing in Mechanical Engineering . His research focuses on Additive Manufacturing , Laser Machining , Welding , and Surface Treatment technologies. Education: BSc HES-SO in Mechanical Engineering from Geneva School of Landscape, Engineering and Architecture Key Competencies: Additive Manufacturing, Laser Machining, Industrial Welding, Quality Control, Surface Treatment Recent research highlights include: 2025: Development of a deep-learning segmentation model for automated detection of surface porosity defects in laser-deposited bronze coatings 2024: 3D COMSOL Multiphysics simulation of bimetallic alloy cooling processes tracking solidification dynamics and residual stresses 2024: Comparative studies on laser cladding techniques for tribological applications and bronze-on-steel deposition His work addresses critical challenges in metallurgical bonding , wear resistance , and process optimization across multiple manufacturing domains.

• Power electronics
• Energy conversion
• Digital controller
• +4 more

Mauro Carpita is a Full Professor at the School of Engineering and Management of the Canton of Vaud (HEIG-VD), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). He serves as Director of the Energy Institute, leading research and development in power electronics and energy conversion systems. His work bridges academic research with industrial applications through numerous collaborative projects across Switzerland. Professor Carpita's research focuses on power electronics and energy conversion systems, with expertise in digital controller design, system simulation, grid integration of renewable energy sources, and medium-voltage DC distribution technologies. His work addresses critical challenges in modern power grids, particularly the integration of renewable energy sources and maintenance of grid stability in low-inertia systems. His research spans from fundamental power electronics to applied grid integration solutions for applications ranging from electric aviation to satellite systems. His recent publications demonstrate a strong focus on grid-forming inverter technologies and advanced modeling techniques for modern power systems. His work on direct voltage control for grid-forming inverters provides innovative solutions for maintaining grid stability as renewable energy penetration increases. His research on medium-voltage DC distribution systems explores continuous time simulation methodologies that enable more efficient grid operation and planning. These publications reflect his commitment to developing practical solutions for real-world power system challenges with significant industrial relevance. Professor Carpita has secured substantial research funding through numerous projects with both academic and industry partners. His projects include 'Soft Open Point with Storage' (CHF 194,445), 'SCCER 2 Phase 2 - WP3' (CHF 257,888), 'Power and Data Contactless Transmission System' (CHF 137,100), and 'Development of the propulsion chain for high-altitude electric aviation' (CHF 5,000,000). Funding sources include the HES-SO Rectorate, Innosuisse, EOS Holding SA, and various industrial partners including DEPsys, RUAG Aerospace, and ABB Sécheron. As Director of the Energy Institute, Professor Carpita oversees a research environment that fosters innovation in energy technologies. His institute maintains strong connections with industry through projects ranging from satellite power systems to smart grid technologies. The Energy Institute under his direction collaborates extensively with other research groups within HES-SO and serves as a hub for power electronics research with practical applications in electric transportation, renewable energy integration, and advanced grid technologies.

• Power Systems
• Smart Grids
• Renewable Energy Integration
• +5 more

Bozorg Mokhtar is an Associate Professor at the School of Engineering and Management of the Canton of Vaud (HEIG-VD) , part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). His research focuses on power systems, smart grids, and renewable energy integration. BSc and MA in Electrical Engineering from HES-SO Key projects: SCCER FURIES Phase 2 (WP3 on hybrid AC-DC grids), funded via CTI and HES-SO Research Interests include grid stability, power electronics, and energy storage optimization. He explores probabilistic flexibility indices , grid-forming inverters , and digital twin platforms for distribution networks. His work addresses challenges like peak shaving , carbon trading mechanisms , and dynamic state estimation . Scientific Contributions span 15 recent articles on DER aggregation, rail-to-EV energy recycling, and voltage control algorithms. His collaborations include researchers like Rachid Cherkaoui (EPFL) and Mauro Carpita (HEIG-VD).

• Power Electronics
• Smart Grids
• Energy Conversion Systems
• +3 more

Mauro Carpita is a full Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland), serving as Director of the Institute of Energy . His work focuses on power electronics, smart grids, and energy conversion systems , with extensive collaboration in industrial and academic projects . Research Interests: Power Electronics for renewable energy integration Smart Grid Stability and control systems Energy Conversion technologies for high-altitude aviation and industrial applications Digital Controllers and system simulation Recent Publications address grid forming inverters, MVDC distribution modeling, residential energy consumption causality, and stochastic optimization for active distribution networks. These works emphasize grid stability, converter efficiency, and renewable energy integration. Project Leadership includes Innosuisse-funded initiatives like Limitless and Prisys , as well as collaborations with industry partners DEPsys, RUAG Aerospace, and ABB Sécheron . Projects span soft open point systems, fuel cell converters, and electromagnetic compatibility in smart grids.

• Oenology
• Wine Microbiology
• Fermentation Technology
• +5 more

Benoit Bach is a full professor at Changins - High School of Viticulture and Oenology , part of HES-SO - University of Applied Sciences and Arts Western Switzerland . Holding a BSc and MSc in Life Sciences from HES-SO, he leads research at the intersection of oenology, wine microbiology, and sustainable production technologies. Education : BSc and MSc from HES-SO institutions Primary Research Areas : Wine protein stability, membrane contactors for gas management, bioprospecting indigenous yeast strains, chitosan applications in food preservation His work spans analytical method development and industrial implementation, focusing on reducing environmental impact while preserving wine quality. Projects include INTERREG Ecofass-Vin for carbonated beverage distribution, and collaborations with Agroscope and French research institutes. Funded by HES-SO Rectorat, OFAG, and Innosuisse, his teams develop tools for microbial biodiversity management and wine stabilization. Key contributions include standardized haze potential tests, automated distillation sensors using impedance spectroscopy, and valorization of grape byproducts through Valuxtract and DistiMatu-2 initiatives. His research directly impacts winemaking practices, particularly in biological acidification, fining agent alternatives, and sensory space definition for distilled beverages.

Mohammad Hossein MoradiView profile

Researcher

• Sustainable Transportation
• Renewable Energy
• Machine Learning Applications
• +3 more

Mohammad Hossein Moradi is a researcher at ETH Zürich affiliated with Prof. Christopher Onder's research group. His work focuses on sustainable transportation and renewable energy systems, with contact via moradim@ethz.ch. His research integrates machine learning with environmental engineering to optimize energy infrastructure across transportation and power sectors. Key interests include emission reduction in maritime and urban transit systems, thermo-electrical analysis of photovoltaic technologies, and pollution control through intelligent systems. His interdisciplinary approach bridges mechanical engineering, data science, and sustainability science. Recent publications (2021-2024) reveal consistent application of reinforcement learning and AI for route optimization, CO2 reduction, and renewable energy integration. Dominant themes include electrified public transportation infrastructure, maritime vessel efficiency, solar power plant allocation, and advanced thermography for PV systems. This work demonstrates strong cross-disciplinary connections between transportation engineering, environmental science, and computational modeling.

• Graph Machine Learning
• Signal Processing
• Higher-Order Networks
• +5 more

Elvin Isufi is an Associate Professor at the Delft University of Technology (TU Delft) , where he co-founded and co-directs AIdroLab , one of the 24 TU Delft AI Labs. His research focuses on fundamental and applied graph-based data processing with applications to water networks, flood modeling, infrastructure systems, and recommender systems. Elvin obtained his Ph.D. from TU Delft in graph signal processing and completed his master's and bachelor's studies at the University of Perugia in Italy. He has been mentored by renowned academics including Prof. Alejandro Ribeiro (postdoc), Prof. Geert Leus (Ph.D.), and Prof. Paolo Banelli (master's/bachelor's). His research integrates signal processing , machine learning , and mathematical modeling to develop techniques for graph signal processing, graph neural networks (GNNs), and higher-order network analysis. Key application domains include water distribution networks , flood modeling , and recommender systems . His work addresses critical challenges such as stability analysis of GNNs, dynamic graph processing , and physics-informed machine learning . Recent publications highlight advancements in multi-scale hydraulic GNNs for flood prediction, carbon footprint-aware recommender systems , and simplicial vector autoregressive models for edge flow analysis. His research group includes Ph.D. students like Bishwadeep Das and M.Sc. students exploring topics such as online edge flow prediction and topological signal processing . 2021: Audience Choice Award, IEEE Data Science and Learning Workshop 2022: Best Student Paper Award, IEEE DSLW 2023: Top 3% Recognition Award, ICASSP Elvin actively supervises students in graph machine learning projects, emphasizing requirements like Python and PyTorch/TensorFlow expertise . He provides structured thesis project themes covering dynamic graph analysis , physics-informed GNNs , and self-supervised learning for networked data.

Luiz ChamonView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Constrained Optimization
• +3 more

Luiz Chamon is an Assistant Professor (tenure-track) and Hi!PARIS Chair holder at the Center for Applied Mathematics (CMAP) , École polytechnique, Paris, France. His research bridges machine learning with control theory and signal processing, focusing on intelligent systems that extract, process, and act on information under constraints. Research Interests: He specializes in constrained learning frameworks for machine learning, with applications to reinforcement learning, statistical signal processing, and optimization. His work includes adversarial robustness, safe policy design, and duality analysis in constrained settings. Article Trends: Recent publications span differential equation solving (ICLR 2025), primal-dual sampling (NeurIPS 2024), and resilient reinforcement learning (IEEE Trans. Autom. Control 2024). Common themes include constrained optimization, robustness, and bridging theory with practical applications in wireless systems and graph signal processing. Scientific Awards: Hi!PARIS Chair (2025) ELLIS Scholar (2025) Best Student Paper Award (ICASSP 2020) Best Paper Award (ICASSP 2020) Top 50 Most Accessed Articles in IEEE TSP (2019) Grants & Projects: Currently leads a 4-year ANR JCJC project on "Beyond (Robust) Learning: Striking Compromises in Machine Learning" (2024). Previously involved in technical reports on aircraft vibroacoustics and wireless power systems. Labs & Teams: Heads a research group at École polytechnique developing tools for constrained learning, collaborating with institutions like University of Pennsylvania and IMPRS-IS. Organizes tutorials and panels on constrained learning and career development.

Jens KoberView profile

Associate Professor

• Robotics
• Motor Skill Learning
• Reinforcement Learning
• +5 more

Jens Kober is an Associate Professor in the Cognitive Robotics department at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science, and a key member of the TU Delft Robotics Institute. His research bridges machine learning and robotics for real-world applications. His research focuses on Motor Skill Learning, Reinforcement Learning, and Interactive Learning for robotics, with applications spanning robot manipulation, locomotion, surgical assistance, and wind turbine control. Key areas include: Developing robust learning algorithms for compliant robots Creating interactive learning frameworks for human-robot collaboration Applying deep learning to robot control under uncertainty Transferring machine learning techniques to medical robotics His recent publications reveal strong trends in interactive imitation learning and reinforcement learning for dynamic tasks , particularly in robot manipulation (e.g., pick-and-place ambiguity resolution) and locomotion (explosive jumping with soft quadrupeds). The work increasingly integrates vision systems and focuses on real-world applicability in retail, dentistry, and renewable energy. Scientific recognition includes: Robotics: Science and Systems Early Career Award (2022) IEEE-RAS Early Academic Career Award (2018) Georges Giralt PhD Award for best European Robotics thesis (2013) Kober actively supervises PhD students and leads major collaborative projects including the NWO-funded DL-foRCe (Deep Learning for Robust Robot Control), Robot-Assisted Tooth Removal, and the industry-backed AI for Retail Lab with Ahold Delhaize. His work connects academic research with industrial applications through grants from NWO, TKI Wind op Zee, and industry partnerships. He directs the Cognitive Robotics group within the TU Delft Robotics Institute, collaborating closely with the Delft Center for Systems and Control and the ELLIS Unit Delft. His team develops foundational learning algorithms while deploying them in applied settings like the AIR Lab Delft test site for retail robotics.

• Optimization
• Statistics
• Operations Research
• +7 more

Yifan Hu is an Assistant Professor in the Department of Statistics at Rutgers University (New Brunswick, NJ). His research focuses on optimal decision-making under uncertainty, with expertise spanning optimization, statistics, and operations research. He works on problems arising from reinforcement learning, large language models, causal inference, and operations management. Dr. Hu received his PhD in Operations Research from the University of Illinois at Urbana-Champaign in 2022, advised by Prof. Xin Chen and Prof. Niao He. Prior to his PhD, he earned a Bachelor of Mathematics from Nankai University. He also completed postdoctoral work with Prof. Daniel Kuhn from EPFL and Prof. Andreas Krause from ETH Zurich. Dr. Hu's research centers on developing optimization methods to solve complex problems with provable guarantees. His work addresses challenges such as nonconvexity, noisy observations due to distribution shifts, interactions between multiple decision makers, and limited data samples. He has developed approaches for structured nonconvex optimization problems that can be solved efficiently to global optimality, despite their apparent computational intractability. His research spans several key areas including global optimality in structured nonconvex optimization, large-scale causal inference, contextual bilevel optimization, and robust reinforcement learning with large language models. Dr. Hu has published numerous papers in top-tier venues including NeurIPS, ICML, ICLR, AISTATS, and journals like SIAM Journal on Optimization and Operations Research. His recent work shows a strong trend toward integrating optimization theory with practical applications in machine learning, causal inference, and decision-making systems. He has made significant contributions to understanding the landscape of nonconvex optimization problems, developing efficient algorithms for causal discovery, and creating robust frameworks for reinforcement learning and language model alignment. Dr. Hu serves as an area chair for ICLR and as a reviewer for numerous statistics, optimization, and operations research journals (e.g., JASA, JMLR, OR, MS, MSOM, MP, SIAM OPT, Math OR) as well as machine learning conference proceedings (e.g., NeurIPS, ICML, ICLR, AISTATS, COLT). He is actively seeking motivated PhD students and research interns with backgrounds in mathematics, statistics, operations research, or computer science. Outside of academia, Dr. Hu enjoys skiing, swimming, hiking, and playing tennis.