جست‌وجوی استاد و پژوهشگر

Michael Kapralovمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Sublinear Algorithms
• Big Data Analysis
• +۹ مورد دیگر

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.

Johannes Lenglerمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Evolutionary Algorithms
• Algorithm Design
• +۳ مورد دیگر

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 Wallraffمشاهده پروفایل

استاد

• Quantum Optics
• Quantum Information Processing
• Superconducting Circuits
• +۳ مورد دیگر

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 Nikeghbaliمشاهده پروفایل

استاد

• Quantitative Finance
• Probability Theory
• Random Matrix Theory
• +۳ مورد دیگر

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.

Damian Kozburمشاهده پروفایل

دانشیار

• Econometrics
• Machine Learning
• High-Dimensional Data Analysis
• +۳ مورد دیگر

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.

• Financial Econometrics
• Quantitative Finance
• Risk Management
• +۷ مورد دیگر

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

• Cardiovascular Health
• Exercise Physiology
• Hypertension Management
• +۳ مورد دیگر

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.

• Combinatorial Optimization
• Complexity Theory
• Approximation Algorithms
• +۵ مورد دیگر

Monaldo Mastrolilli is a Professor at the University of Applied Sciences of Southern Switzerland and holds a permanent position as Senior Researcher at IDSIA (Dalle Molle Institute for Artificial Intelligence), a joint institute of USI and SUPSI. He earned his PhD summa cum laude from the University of Kiel and a Computer Science Engineering degree from Politecnico di Milano. His research focuses on combinatorial optimization, complexity theory, approximation algorithms, and scheduling problems. He has authored over 30 publications in top journals/conferences such as FOCS, SODA, and Journal of Algorithms. Research interests include approximation algorithms, metaheuristics, scheduling theory, and computational complexity. His work bridges theoretical foundations with practical applications in operations research and artificial intelligence. Notable contributions include advancements in Sum-of-Squares proofs, scheduling algorithms, and combinatorial optimization techniques. He has supervised four PhD students and serves on program committees for ICALP, APPROX, and WAOA. Awards include the Whizzkids'97 competition prize and a prestigious PhD distinction. Education: PhD (summa cum laude) in Computer Science, University of Kiel (2002); Master's in Computer Science Engineering, Politecnico di Milano (1997) Key Research Areas: Complexity Theory, Approximation Algorithms, Scheduling, Metaheuristics

• Dermatology
• Nail Disorders
• Psoriasis
• +۳ مورد دیگر

Matilde Iorizzo is a Lecturer at the Faculty of Biomedical Sciences, Università della Svizzera italiana (USI). Her research focuses on dermatological conditions, particularly nail disorders, psoriasis, alopecia, and autoimmune skin diseases. She contributes to clinical guidelines through collaborations like the EADV Task Force on Hair Diseases and Dermatology for Cancer Patients. Her work emphasizes innovative diagnostics (e.g., AI-driven nail psoriasis assessment) and therapeutic interventions (e.g., JAK inhibitors, PRP therapy). Recent studies explore drug-induced autoimmune bullous diseases and telemedicine applications in dermatology. Education details are not provided in the source text, but her professional activities suggest advanced training in dermatology. Research interests include nail surgery outcomes, trichoscopy, and the intersection of dermatology with oncology. She collaborates internationally, contributing to consensus projects like the CONSONANCE Consensus on onychomycosis management and the International Dermoscopy Society’s trichoscopy task force. Key Projects: Delphi consensus on lichen planopilaris, systematic reviews on nail JAK inhibitors, multicenter studies on trichoteiromania. Technical Expertise: Dermoscopy, confocal microscopy, clinical trial design. Clinical Focus: Nail psoriasis, inflammatory nail dystrophies, chemotherapy-induced alopecia. Her publications span 2016–2025, demonstrating sustained contributions to both basic and applied dermatological research.

Benjamin Wesolowskiمشاهده پروفایل

پژوهشگر

• Cryptography
• Number Theory
• Algebraic Geometry
• +۶ مورد دیگر

Benjamin Wesolowski is a CNRS researcher (chargé de recherche) at the Unité de Mathématiques pures et appliquées (UMPA), École Normale Supérieure de Lyon (ENS Lyon), France. He previously held a CNRS researcher position at the Institut de Mathématiques de Bordeaux (IMB) and completed postdoctoral work at Centrum Wiskunde en Informatica (CWI) in the Netherlands. His research lies at the intersection of mathematics and cryptography, particularly in number theory and algebraic geometry. PhD, EPFL (2018) Master’s in Mathematics, EPFL (2014) Bachelor’s in Mathematics, EPFL (2012) Habilitation (HDR), ENS Lyon (2024) His research focuses on cryptologic algorithms rooted in number theory and algebraic geometry, especially isogeny-based cryptography , post-quantum signatures , and verifiable delay functions . He explores the structure of isogeny graphs of abelian varieties and their applications to blockchain and randomness generation. His work combines deep theoretical mathematics with practical cryptographic design. The most recent articles highlight a consistent trend in isogeny-based cryptography , particularly in constructing compact and secure post-quantum signature schemes like SQISign and PRISM. His contributions also extend to cryptanalysis, as seen in the key recovery attack on SIDH, and foundational work on verifiable delay functions. These works span top-tier venues such as Eurocrypt, Asiacrypt, and PKC, reflecting his leadership in advancing both theoretical and applied aspects of modern cryptography. CNRS 2025 Bronze Medal ERC Starting Grant AGATHA CRYPTY (2023) Secretary of the IACR (elected, 2023) Best paper award PKC 2025 Best paper award Eurocrypt 2024 Top-3 paper Eurocrypt 2023 Best paper award Asiacrypt 2020 Best young researcher paper Eurocrypt 2019 Benjamin Wesolowski has been supported by competitive grants including the ERC Starting Grant AGATHA CRYPTY . He has advised students during his tenure at EPFL as a teaching assistant and likely through doctoral supervision, though specific advisees are not listed. His academic service includes being elected Secretary of the IACR , a major recognition in the cryptology community. He has also received fellowships such as the Doctoral EDIC Fellowship and prizes including the Kudelski Prize and EPFL Prize . His research is conducted primarily within the Unité de Mathématiques pures et appliquées (UMPA) at ENS Lyon, a leading center for mathematical research in France. He was part of the Cryptology Group at CWI under Ronald Cramer during his postdoc and has collaborated extensively with institutions such as EPFL, UC Berkeley, and IMB. His work often bridges theoretical mathematics and practical cryptographic implementation.

Maximilien Drevetonمشاهده پروفایل

استادیار

• Network Analysis
• Community Detection
• Clustering
• +۳ مورد دیگر

Maximilien Dreveton is an Assistant Professor (Maître de Conférences) in Statistics at Université Paris Gustave-Eiffel and a member of LAMA (Laboratoire d'analyse et de mathématiques appliquées). Prior to his current position, he was a postdoctoral researcher in the INDY Lab at EPFL under the supervision of Patrick Thiran and Matthias Grossglauser. He earned his PhD in Computer Science in 2022 from Inria Sophia Antipolis with Konstantin Avrachenkov as his supervisor. His research focuses on developing innovative algorithms and understanding the theoretical limits of identifying meaningful patterns and structures within large datasets. He has particular expertise in network analysis, community detection, and clustering. Dreveton co-authored the open-access book Statistical Analysis of Networks , which provides a comprehensive introduction to network statistical analysis covering modeling, community detection, graph-based semi-supervised learning, and network sampling. His recent publications demonstrate a consistent focus on theoretical foundations of network analysis and community detection, with papers accepted at top venues including NeurIPS and COLT. His work on metric backbone preservation of community structure and universal lower bounds for clustering error represents significant theoretical contributions to the field. He also co-presented a tutorial on community detection at ACM SIGMETRICS / IFIP PERFORMANCE in June 2024. Dreveton maintains an active research profile with upcoming presentations at Netsci 2025. He has developed several open-source repositories related to clustering algorithms and graph analysis, reflecting his commitment to reproducible research and community engagement. He has also authored educational materials for mathematics preparation, including Leçons pour l'agrégation de mathématiques - Préparation à l'oral for French mathematics examination candidates.

Nicolas Macrisمشاهده پروفایل

استاد مدعو

• Statistical Mechanics
• Information Theory
• Error Correcting Codes
• +۳ مورد دیگر

Nicolas Macris is an Adjunct Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He is affiliated with multiple groups including the Information Processing group and the Lab for Statistical Mechanics of Inference in Large Systems . His academic career spans roles at the Faculty of Basic Science at EPFL and visiting appointments at institutions like the University College Dublin and ETH Zürich. Macris received his PhD in theoretical physics from EPFL and conducted postdoctoral research at the mathematics department of Rutgers University. His research bridges statistical mechanics , information theory , and quantum information , with a focus on error-correcting codes, graphical models, and learning theory. His work often employs methods from mathematical and statistical physics, probability, and discrete mathematics. His recent publications analyze phase transitions in high-dimensional models, quantum algorithms, and spatial coupling techniques. He has received recognition through IBM Quantum contest awards for his contributions. He actively teaches courses in quantum information processing and mentors PhD students in interdisciplinary research areas. 2019: 2nd place, IBM Quantum contest (Best Paper) 2018: 2nd place, IBM Quantum contest (Best Paper) Macris's work is supported by the Swiss National Foundation for Science , and he collaborates with labs such as the Communication Theories Laboratory and Information Processing group .

• Augmented Reality
• Eye Tracking
• Mixed Reality
• +۴ مورد دیگر

Dr. Kenan Bektas is a researcher at the University of St.Gallen, focusing on Augmented Reality , Eye Tracking , and Context-Aware Systems . His work bridges Mixed Reality and Human-Computer Interaction , with a particular emphasis on gaze-enabled interfaces, object identification, and activity recognition. University of St.Gallen His research explores how Augmented Reality can enhance cognitive offloading and improve user performance. He develops systems like NeighboAR and GEAR , which leverage gaze data and machine learning for object retrieval and activity recognition. Key article trends include: Integration of Eye Tracking with AR/MR systems Development of Context-Aware Interfaces Investigations into Responsible Personalization and privacy in MR Applications in Industrial Processes and remote living (Telelife) His work often involves interdisciplinary collaborations with researchers such as Simon Mayer and Jannis Rene Strecker, and has been published in venues including ACM, PLOS ONE, and Frontiers.

• Biomedical Imaging
• Applied Functional Analysis
• Sampling Theory
• +۴ مورد دیگر

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.

• Computer Vision
• Machine Learning
• Distributed Intelligent Systems
• +۷ مورد دیگر

Prof. Fabio Galasso heads the Perception and Intelligence Lab (PINLab) at the Department of Computer Science, Sapienza University of Rome. Previously, he founded and directed the Computer Vision Department at OSRAM in Munich, Germany, and conducted research at the University of Cambridge and Max Planck Institute for Informatics. His educational background includes a Master's Degree cum laude from RomaTre University and a PhD from the University of Cambridge, Department of Engineering. Prior to his academic career, he worked as a Researcher at Ericsson Laboratories and as a Project Engineer at Telecom Italia. Prof. Galasso's research focuses on fundamental aspects of computer vision and machine learning, with particular interest in distributed and multi-agent intelligent systems, perception tasks including detection, recognition, re-identification, and forecasting, and general intelligence encompassing reasoning, meta-learning, and domain adaptation. His work emphasizes sustainable AI frameworks with low-power consumption and constrained computational resources, as well as interpretable and verifiable AI systems. Earlier in his career, he conducted significant research on video analysis and segmentation, scene understanding, clustering, and 3D reconstruction from texture. His recent publications demonstrate strong contributions across multiple cutting-edge areas in computer vision, with a clear progression from fundamental research on video segmentation and texture analysis to practical applications in human motion forecasting, person search, and anomaly detection. His work consistently bridges theoretical computer vision with practical applications in smart lighting, retail, and city infrastructure. 2019 IoT/WT Innovation World Cup 2019 Digital Champions Award 2018 Deutscher Digital Award Prof. Galasso has coordinated a Marie Sklodowska-Curie Actions project (Horizon 2020) and served as Principal-Co-Investigator in multiple German-funded projects. He is actively involved in the academic community, serving as area chair for major conferences including NeurIPS, ECCV, and CVPR, and organizing workshops on specialized topics in computer vision. His leadership in the Perception and Intelligence Lab drives innovation in both theoretical understanding and practical implementations of computer vision technologies.