Maher Kayal is an Honorary Professor at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, affiliated with the School of Engineering (STI) and the Department of Mechanical and Process Engineering (STI-SMT). His primary role involves academic leadership in electronics and energy management research. He holds a Master's and PhD in Electrical Engineering from EPFL (1983 and 1989 respectively), and has been with the Electronics Laboratory (ELab) since 1990, directing the 'Energy Management and Sustainability' section. His work focuses on analog/mixed-signal IC design, energy harvesting, smart grid technologies, and sensor systems. Education: M.S. (1983), Ph.D. (1989) in Electrical Engineering, EPFL. Affiliations: EPFL’s Electronics Laboratory (ELab), School of Engineering. Research interests include ultra-low-power sensor interfaces, energy-efficient electronics for smart buildings, and real-time power grid emulation. He has authored/co-authored three textbooks on mixed-mode CMOS design and holds 11 patents. His work bridges semiconductor physics, circuit design, and energy systems. Recent articles focus on wearable biosensors (ANTIGONE project), blockchain-based smart-building energy management, and high-speed power system emulators. Notable awards include the Credit Suisse Teaching Award (2009) and multiple best-paper prizes at IEEE conferences. He has advised over 30 PhD students, with notable alumni working in semiconductor design and energy systems. His labs pioneer innovations in analog emulation for power systems and IoT-enabled smart infrastructure.
Prof. Bernd Gärtner is a Lecturer at the Department of Computer Science of ETH Zürich. His research focuses on algorithms, computational geometry, optimization, and theoretical computer science. He has contributed significantly to the study of unique sink orientations, combinatorial algorithms, and algorithm design. Gärtner teaches courses such as 'Algorithms, Probability, and Computing' and 'Geometry: Combinatorics and Algorithms,' reflecting his expertise in foundational computer science topics. His work bridges discrete mathematics and algorithmic theory, addressing challenges in linear programming, combinatorial optimization, and geometric algorithms. His recent research explores the realizability of structures in unique sink orientations, optimization techniques for symbolic visibility, and the analysis of opinion dynamics in networks. He has published extensively on topics including ARRIVAL game complexity, sampling algorithms, and high-dimensional learning models. His contributions also extend to the development of efficient algorithms for geometric problems and the study of cellular automata systems. Teaching: Courses include Algorithms, Probability, and Computing (252-0209-00L), Linear Algebra (401-0131-00L), and Geometry: Combinatorics and Algorithms. Research Interests: Algorithms, computational geometry, optimization, combinatorics, and theoretical computer science. Labs/Teams: Affiliated with the Institute of Theoretical Computer Science at ETH Zürich.
Dr. Surya Gupta is a PostDoc researcher at the University of Basel's Department of Environmental Sciences, Faculty of Science, working within the FG Alewell research group. He joined the university in April 2022 after completing his Ph.D. at ETH Zurich. His research focuses on the intersection of soil science, hydrology, and remote sensing applications, with particular emphasis on digital soil mapping and the relationship between soil properties and erosion processes. Education: Ph.D. in Environmental Sciences (2018-2021), ETH Zurich M.Tech in Remote Sensing and GIS (2013-2015), Indian Institute of Remote Sensing, Dehradun B.Tech in Agricultural Engineering (2009-2013), Punjab Agricultural University, Ludhiana Dr. Gupta's research primarily centers on soil hydraulic properties and their applications in environmental modeling. His work involves developing advanced methods for global and national digital mapping of soil properties, particularly saturated hydraulic conductivity and van Genuchten parameters. He investigates the complex relationship between soil erosion and soil hydraulic properties, examining how incorporating hydraulic properties changes soil erosion modeling outcomes. A significant portion of his research focuses on machine learning applications in soil science, where he works on reducing clustering and overfitting in algorithms while developing Pedo-Transfer Functions (PTFs) and Covariate-based GeoTransfer Functions (CoGTFs). His methodological approach combines extensive field data with remote sensing datasets and sophisticated computational techniques to address critical environmental questions related to soil health and water management. Analysis of Dr. Gupta's recent publications reveals a strong focus on global-scale soil property mapping using machine learning approaches. His research demonstrates increasing sophistication in integrating legacy soil data with modern environmental covariates to produce high-resolution global datasets. A notable trend is his work bridging soil physics with practical applications in erosion modeling and agricultural management, particularly in how soil hydraulic properties influence crop responses to climate variability. His publications span top-tier journals in soil science, hydrology, and environmental modeling, indicating strong recognition within these interdisciplinary fields. Dr. Gupta has demonstrated exceptional productivity with numerous first-author publications in high-impact journals. His collaborative network is extensive, working with researchers across multiple institutions in Switzerland, Europe, and India. While no specific major grants are mentioned in the provided text, his publication record suggests involvement in significant research projects addressing global soil and water challenges. As part of the Department of Environmental Sciences at the University of Basel, Dr. Gupta contributes to the institution's strong research profile in environmental systems science. His work aligns with the department's focus on understanding complex Earth system processes and human-environment interactions, particularly through the integration of field observations, remote sensing, and computational modeling approaches.
Michael Unser is a Full Professor at EPFL’s School of Engineering and Academic Director of EPFL’s Center for Imaging in Lausanne, Switzerland. His research focuses on biomedical imaging, applied functional analysis, sampling theory, wavelets, splines, and computational bioimaging. Current Position: Full Professor, EPFL School of Engineering Academic Role: Director of EPFL Center for Imaging Key Research Areas: Inverse problems, sparse stochastic processes, machine learning for imaging Dr. Unser’s research bridges mathematical theory with practical biomedical imaging applications. He has pioneered the use of splines for image processing, developed advanced regularization frameworks for inverse problems, and contributed to computational bioimaging techniques like SMLM and optoacoustic tomography. His recent work explores connections between deep learning and classical kernel methods through variational formulations. He has published over 400 journal papers and authored the seminal book on sparse stochastic processes. His scientific contributions have been recognized with multiple awards including IEEE and EURASIP Technical Achievement Awards, five IEEE-SPS Best Paper Awards, and three consecutive ERC Advanced Grants (FUN-SP, GlobalBioIm, FunLearn). He has served on editorial boards for leading journals and founded the IEEE SPS Bio Imaging and Signal Processing Technical Committee.
Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) , where he leads research at the Theory Group . His work spans multiple departments including the Laboratory of Theory of Computation 4 and the Doctoral Program in Computer Science and Communications . Kapralov's research focuses on theoretical computer science , particularly sublinear algorithms for big data analysis , with applications in streaming , sketching , sparse recovery , and Fourier sampling . University: EPFL School: School of Computer and Communication Sciences Department: Theory Group Academic Rank: Associate Professor Education: Kapralov earned his Ph.D. in Computer Science from Stanford iCME under the supervision of Ashish Goel . He subsequently held postdoctoral positions at the Mit CSAIL Theory of Computation Group with Piotr Indyk and as a Herman Goldstine Postdoctoral Fellow at IBM T. J. Watson Research Center . Ph.D.: Stanford iCME (2012), advisor: Ashish Goel Postdoctoral: MIT CSAIL (2012-2014), IBM Watson (2014) Research Interests: Kapralov's work addresses fundamental challenges in processing large-scale data through rigorous mathematical models. His contributions include advancements in sublinear algorithms , streaming complexity , spectral sparsification , sparse Fourier transforms , and differential privacy . He has developed techniques for dimension-independent signal processing , kernel ridge regression , and graph spanners , with theoretical guarantees and practical implications for machine learning and data analysis. Scientific Awards: Kapralov received the ERC Starting Grant SUBLINEAR (2018-2023) and the Gene H. Golub Dissertation Award (2012). Advising: He has supervised numerous Ph.D. students and postdoctoral researchers, including Ekaterina Kochetkova , Grzegorz Gluch , Kshiteej Sheth , and Amir Zandieh , many of whom have taken academic or industry positions at institutions like UC Berkeley, National University of Singapore, and Google Zurich. Collaborations and Teaching: Kapralov co-organizes the Turing Course for high school students, leads the Reading Group on Foundations of Deep Learning , and contributes to academic initiatives such as Theory Coffee and the Swiss Winter School on Theoretical Computer Science . He teaches courses like Sublinear Algorithms for Big Data Analysis and Algorithms II , focusing on advanced algorithm design and analysis.
Prof. Dr. Johannes Lengler is a Lecturer at the Department of Computer Science at ETH Zürich. He focuses on theoretical computer science with specialties in evolutionary algorithms, algorithm design, and network analysis. His research explores the theoretical foundations of optimization heuristics, random graph models, and stochastic processes in computational systems. Lengler has contributed to understanding population diversity in evolutionary algorithms, network connectivity in scale-free models, and algorithmic performance in dynamic environments. His work bridges theoretical insights with practical applications in manufacturing and AI safety. Key research areas include evolutionary computation theory, algorithmic analysis of complex networks, and optimization under uncertainty. His studies often address fundamental questions in computational complexity, such as the efficiency of self-adjusting algorithms and the challenges posed by multimodal landscapes. Lengler frequently collaborates on interdisciplinary projects, applying theoretical methods to real-world problems like laser metal deposition and AI ethics. Publications highlight his expertise in crossover mechanisms, graph traversal algorithms, and rumor spreading dynamics. He has explored the interplay between population diversity and algorithmic efficiency, demonstrating how genetic drift can accelerate optimization processes. His work on expander graphs and scale-free networks contributes to network science, analyzing average distances and connectivity thresholds in complex systems.
Andreas Wallraff is a Full Professor in the Department of Physics at ETH Zurich, where he leads cutting-edge research in quantum optics and quantum information processing using superconducting electronic circuits. His work focuses on large-bandwidth microwave techniques at ultra-low temperatures, leveraging ETH's FIRST laboratory clean room facilities for device fabrication. He actively collaborates within the Quantum Systems for Information Technology (QSIT) program and teaches advanced courses such as 'Quantum Science with Superconducting Circuits' (Autumn 2025). Education: Imperial College London and RWTH Aachen (B.Sc. equivalent in Physics, 1994) RWTH Aachen (Diploma in Physics/M.Sc. equivalent, 1997) University of Erlangen-Nuremberg (Ph.D. in Physics, 2000) Wallraff's research centers on quantum-coherent phenomena in superconducting circuits, with emphasis on quantum optics implementations, qubit control, and quantum information processing. His group pioneers experimental techniques for observing quantum effects like energy level quantization and tunneling in macroscopic systems, building on his early work with Josephson vortex oscillators. Current efforts integrate microwave engineering with quantum error correction, multi-qubit architectures, and hybrid quantum systems involving semiconductors and graphene quantum dots. His recent publications reveal a strong trend toward scalable quantum computing solutions, particularly in quantum error correction (surface codes, lattice surgery), multi-module processor integration, and real-time feedback control. Work spans fundamental quantum optics (photon-qubit coupling) to engineering challenges (flux control calibration, leakage reduction), with increasing focus on practical implementations for fault-tolerant systems. Scientific Awards: Nicholas Kurti European Science Prize (2006) for 'decisive and innovative experiments on quantum mechanical effects in superconducting circuits' Wallraff leads an active research group integrated into ETH's QSIT initiative, securing substantial grants for quantum processor development and cryogenic infrastructure. His team maintains collaborations across ETH on semiconductor quantum dots, atomic cavity QED, and single-molecule spectroscopy, while developing novel fabrication techniques like polymer spacer processes for 3D-integrated circuits. Future work targets loophole-free Bell tests, quantum networks, and real-time reinforcement learning for quantum control. The group operates within ETH's Laboratorium für Festkörperphysik (HPF D 9), utilizing advanced cryogenic setups for 100-qubit-scale systems. They maintain close ties with Yale University (where Wallraff was a postdoc) and contribute to international quantum computing roadmaps through publications in high-impact journals.
Ashkan Nikeghbali is a Professor of Quantitative Finance at the University of Zurich's Department of Finance, holding a part-time academic position. His research focuses on advanced topics in probability theory, random matrix theory, and their applications to quantitative finance, particularly in credit risk modeling and stochastic processes. He has contributed to areas such as Mod-Gaussian convergence, asymptotic analysis of random matrices, and statistical mechanics. His work often bridges pure mathematics and practical financial applications, addressing challenges in portfolio risk management and optimization. Research interests include the interplay between probability theory and algebraic structures, with notable studies on characteristic polynomials of random matrices, stochastic processes, and their implications in mathematical physics. He explores topics like high-dimensional optimization, graphons, and permutons, contributing to both theoretical advancements and applied financial models. His publications emphasize precise deviation analysis, approximation schemes, and the application of advanced mathematical techniques to understand complex systems. Despite no explicit mention of awards or grants, his extensive publication record highlights sustained contributions to probability and financial mathematics. No specific advising roles or student mentorship details are provided in the available texts.
Prof. Damian Kozbur is an Associate Professor of Econometrics at the University of Zurich's Department of Economics, affiliated with the Digital Society Initiative. He holds a PhD from the University of Chicago (2014) and has been at UZH since 2016. His research focuses on integrating economic theory with machine learning tools, particularly in high-dimensional econometric models. Key areas include estimation techniques for weak instruments, panel data analysis, and clustering methods for statistical inference. He has contributed to journals like Econometrica and the Journal of Business and Economic Statistics. His work addresses applications such as market dynamics, causal inference in complex data, and policy analysis. Education: PhD in Econometrics from the University of Chicago Booth School of Business (2014), BA in Mathematics (2008). Previous roles include ETH Zurich Fellow (2014–2016). Teaching includes advanced econometrics and machine learning courses for doctoral students. Research Interests: High-dimensional econometrics, model selection, statistical inference, and applications of machine learning to economic problems. His recent articles tackle spatial dependence robustness, debiased machine learning, and forecast hedging with random forests. Professional Service: Associate Editor of the Journal of Business and Economic Statistics since 2022. Active in reviewing for top econometric outlets like Journal of Econometrics and Econometric Theory.
Prof. Margit Osterloh holds the title of Permanent Visiting Professor at the University of Basel and is an Emeritus Professor at the University of Zurich's Department of Business Administration. She is Research Director at the Center for Research in Economics, Management and the Arts (CREMA) and affiliated with CREMA Vermögensverwaltung & Research GmbH. Her research focuses on organizational theories, gender economics, corporate governance, and innovation management, with a particular emphasis on exploring paradoxes like the Gender Equality Paradox in STEM fields. Her career spans over four decades, including leadership roles at universities in Germany, Switzerland, and the UK, alongside advisory positions in corporate and policy arenas. Osterloh has received honorary doctorates from Leuphana University (2007) and Friedrich-Alexander-Universität Erlangen-Nürnberg (2024), reflecting her impactful contributions to behavioral economics and institutional innovation. She advocates for randomized selection processes in leadership roles to enhance diversity and reduce systemic biases. Her recent publications address societal challenges such as academic freedom, gender pay gaps, and migration policies. Osterloh frequently contributes opinion pieces to Swiss media, engaging public discourse on topics like cancel culture in academia, leadership dynamics, and the intersection of gender roles and career choices. Her work combines empirical research with practical policy recommendations, emphasizing the role of post-materialist values in shaping gender norms and occupational segregation. She serves on multiple academic and corporate boards, including Swiss Post and SV Group AG, and is a vocal advocate for institutional reforms to promote fairness and efficiency in education and governance systems.
Aleksey Kolokolov is an Associate Professor at the Manchester Business School, University of Manchester, specializing in financial econometrics and market microstructure analysis. His research focuses on high-frequency data analysis, jump detection in financial markets, liquidity modeling, and statistical methods applied to financial time series. His research interests span several key areas in modern finance: Developing statistical methods for analyzing high-frequency financial data Studying market microstructure and price formation processes Investigating liquidity dynamics and market stability during extreme events Applying econometric techniques to cryptocurrency markets, particularly Bitcoin Creating robust estimators for financial volatility and jump activity His work bridges theoretical econometrics with practical financial applications, often addressing methodological challenges in analyzing discontinuous trading patterns and irregular sampling schemes. Analysis of his publication record reveals a strong focus on methodological innovations in financial econometrics, with particular emphasis on jump detection, price staleness, and liquidity measurement. His most cited work, 'Nonstandard Errors' (Journal of Finance, 2024), represents a major collaborative effort addressing statistical challenges in finance research. His recent publications show increasing attention to cryptocurrency markets and the application of traditional financial econometric methods to these emerging asset classes. Kolokolov maintains an active research agenda with frequent collaborations, particularly with researchers like Roberto Renò, Federico M. Bandi, and Kim Christensen. His work appears in top finance journals and working paper series, demonstrating his significant contribution to the field of financial econometrics.
Olivier Scaillet is a Research Fellow at the Swiss Finance Institute, affiliated with the University of Geneva. His work spans financial econometrics, quantitative finance, and risk management, with a focus on stochastic volatility models, copulas, and high-frequency data analysis. Research Interests : Financial econometrics and nonparametric estimation Stochastic volatility and jump-diffusion models Asset pricing and factor models in large panels Systemic risk and recovery rate density estimation Machine learning applications in finance Market microstructure and high-frequency data dynamics Scientific Contributions : Developed methodologies for nonstandard error analysis in multi-analyst studies Advanced techniques for testing stochastic dominance efficiency and latent factor models Explored copula-based goodness-of-fit tests and threshold effects in time series Innovated in American option pricing under complex market conditions
Marcel Zeelenberg is a Professor at Tilburg University's Faculty of Social and Behavioural Sciences, specializing in behavioral economics and decision-making processes. His research focuses on greed, scarcity effects, consumer behavior, and health-related interventions. He has contributed to understanding market dynamics through experimental asset market studies, the psychological mechanisms behind decision regret, and the application of commitment strategies in promoting physical activity. Research interests include exploring how emotions and cognitive biases influence choices, particularly in economic contexts like trading behavior and consumer preferences. He also investigates the design of behavioral economic incentives to foster health behavior change, emphasizing long-term outcomes and emotional responses to interventions. Notable work includes analyzing 'option attachment' in decision-making and the role of scarcity in consumer choices. His experimental studies often bridge psychology and economics to explain real-world behaviors in markets and health contexts. No scientific awards are explicitly mentioned in the provided text. Collaborative projects involve interdisciplinary teams addressing public health challenges through behavioral insights. While no advising details or grants are listed, his research frequently involves randomized trials and experimental methods. Zeelenberg’s work is characterized by a focus on translating theoretical insights from behavioral science into practical applications, such as improving health promotion strategies or understanding financial market behaviors.
Prof. Hanssen Henner is Deputy Head of the Department of Prevention, Sports Medicine & System Physiotherapy at the University of Basel's Medical Faculty. He holds a faculty position in the Department of Sport, Exercise and Health (DSBG), focusing on cardiovascular health, hypertension management, and exercise physiology. His research bridges clinical practice and population health, emphasizing early intervention strategies, vascular biomarkers, and exercise-based therapies. Key areas include pediatric cardiovascular risk, post-COVID-19 recovery, and microvascular dysfunction in chronic diseases. Research interests span hypertension pathophysiology, retinal vessel analysis as a biomarker, and the role of physical activity in disease prevention. He leads trials like VascuFit and HyperVasc, evaluating exercise interventions for cardiovascular risk reduction. Collaborations include international guidelines (ESC) and interdisciplinary projects on metabolic profiling and epigenetic influences on health. His work addresses translational challenges such as standardizing retinal imaging protocols and developing personalized exercise prescriptions. Recent studies highlight the metabolic signatures of cardiorespiratory fitness and the impact of dietary components (e.g., AGEs) on vascular health. He also investigates pandemic-related occupational stress in healthcare workers and long-term post-COVID-19 sequelae. Grants and projects involve randomized controlled trials (e.g., HIT-GLAUCOMA, SphingoFIT), biomarker validation initiatives, and public health strategies targeting obesity trajectories in children. His expertise is reflected in contributions to clinical consensus statements on cardiovascular prevention and sports medicine guidelines for post-infection athlete management.
Paolo Perona is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Laboratory of Environmental Hydraulics (PL-LCH). He holds the title of Professeur titulaire and serves as the Academic Director of the PL-LCH. His roles also include membership in the ENAC Faculty Council. His research focuses on the eco-morphodynamics of rivers, integrating fluid mechanics, ecology, and environmental engineering. Key areas include riparian vegetation dynamics, sediment transport, hydropower management, and sustainable water allocation. He examines how vegetation interacts with flow and sediment, particularly in restored river corridors and alpine environments. His recent publications reveal a strong trend in experimental and modeling work on vegetation uprooting, flow-vegetation interactions, environmental flow optimization, and the ecological impacts of hydropower. These studies span disciplines such as hydrology, geomorphology, ecology, and environmental engineering, often using a combination of laboratory experiments, field data, and stochastic modeling. Scientific Awards: No specific awards were mentioned in the provided text. Perona has advised several PhD students, including Katharina Maria Edmaier, Lorenzo Gorla, Amin Niayifar, and Samuel Vorlet. He teaches courses such as Water Resources Engineering and Management, River Eco-Morphodynamics and Bioengineering, and Irrigation and Drainage Engineering. He leads a research group focused on environmental hydraulics and river restoration, contributing to both fundamental science and practical water management solutions.
