Find Academic Supervisors

• Theoretical Computer Science
• Computational Complexity
• Randomness Extraction
• +7 more

David Zuckerman is a prominent theoretical computer scientist known for his foundational contributions to computational complexity, pseudorandomness, and randomness extraction. His research spans over two decades, with consistent publication in top-tier venues under the Electronic Colloquium on Computational Complexity (ECCC). Although his current institutional affiliation is not specified in the text, his work is widely recognized and frequently collaborative with leading figures in the field. His research interests include: Randomness extraction and derandomization Computational complexity and circuit lower bounds Pseudorandom generators and hitting set constructions Error-correcting codes and combinatorial constructions Communication complexity and Boolean function analysis Applications in cryptography and distributed protocols Analysis of his recent publications reveals a sustained focus on improving the efficiency and scope of randomness extractors, especially for weak or structured sources such as Chor-Goldreich sources, interleaved sources, and algebraic sources. His work often bridges hardness and randomness, leveraging lower bounds to construct pseudorandom objects. A recurring theme is the derandomization of space-bounded and distributed computation, with implications for cryptography and coding theory. David Zuckerman has received significant recognition in the theoretical computer science community, though specific awards are not listed in the provided text. His collaborations with researchers like Salil Vadhan, Raghu Meka, and Eshan Chattopadhyay suggest leadership in major research initiatives. He has also contributed to foundational results in derandomization, such as optimal pseudorandomness from hardness assumptions and improved bounds for coin-flipping protocols. There is no mention of advising students or managing labs in the provided data, but his long-standing research output and mentorship through collaboration imply an academic leadership role. His continued publication into 2025 indicates active engagement in research.

• Applied Mathematics
• Random Walks
• Matrix Analysis
• +3 more

Martín Llopis Àlvar is an Associate Professor at the Department of Mathematics, School of Engineering of Barcelona East (EEBE), Universitat Politècnica de Catalunya. His research focuses on applied mathematics, particularly in random walks, matrix analysis, and network theory. He actively participates in international conferences and collaborative projects. Universitat Politècnica de Catalunya Department of Mathematics Associate Professor His work involves equilibrium measures, Kemeny's constant, and generalized inverses applied to random walks. He contributes to educational innovation projects and collaborates with research groups like MAPTHE and OMGRAPH. Recent publications analyze Schrödinger random walks, M-matrices, and potential theory applications in network analysis. His research intersects probability, linear algebra, and discrete mathematics. Collaborators include Angeles Carmona, Andres Encinas, Maria Jose Jimenez, and others in projects like "Red Temática de Álgebra Lineal" and "It's Your Day" educational initiatives.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +15 more

Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently serves as Department Head in the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science , within the Faculty of Mathematics and Computer Science . He is a leading researcher in theoretical computer science, particularly in the analysis of algorithms. Research Interests: His research focuses on Analysis of Algorithms , with deep expertise in Data Analysis and Massive Data Sets , Combinatorial Optimization , Approximation Algorithms , Hardness of Approximation , Embeddings of Finite Metrics , and Routing and Peer to Peer Networks . He also maintains a broad interest in Discrete Mathematics and High-Dimensional Geometry . His recent publications highlight work in graph algorithms, parameterized complexity, streaming algorithms, and metric embeddings. Publication Trends: His most recent work, including papers from SODA 2016, demonstrates a strong trend in the design and analysis of efficient algorithms for fundamental problems in graph theory, optimization, and data streams. Key themes include kernelization and sampling techniques for dynamic graph streams, subexponential parameterized algorithms, deterministic derandomization of the polynomial method, and structural results for graph modification problems. His research often bridges theoretical insights with applications in computational biology and network science. Service and Recognition: Journal Editorial: Editor-in-Chief of SIAM Journal on Computing (2019–2025), Associate Editor (2012–2017); Managing Editor of Theory of Computing (2007–2018), and current Editorial Board Member. Conference Leadership: Program Committee Chair for SODA 2016 and HALG 2018; Steering Committee member for SODA, ESA, and HALG; and committee member for the Gödel Prize (2019–2021). Workshops: Organizer of numerous workshops on sublinear algorithms, fine-grained complexity, and high-dimensional data. Teaching and Mentorship: He regularly teaches advanced courses such as Randomized Algorithms and Sublinear Time and Space Algorithms . He advises a large group of MSc and PhD students and hosts postdoctoral researchers, demonstrating a strong commitment to training the next generation of computer scientists. His former students have gone on to successful academic and research careers. Laboratories and Research Groups: He is a key member of the Foundations of Computer Science (theory) seminar at Weizmann and has organized the TheoryLunch and Reading Group in Algorithms, fostering a vibrant research community within the department.

Wolfgang KönigView profile

Professor

• Probability Theory
• Stochastic Processes
• Large Deviations
• +3 more

Wolfgang König is a full Professor of Probability Theory at the Institute of Mathematics, Faculty of Mathematics and Natural Sciences, Technische Universität Berlin, and leads the research group Stochastic Systems with Interaction at the Weierstraß Institute for Applied Analysis and Stochastics (WIAS Berlin). His research focuses on probability theory and its applications in statistical physics and telecommunications. Education 1984–1989: Diplom in Mathematik, Technische Universität Berlin 1994: Dr. Phil, Universität Zürich 2000: Habilitation, Technische Universität Berlin Research Interests Professor König's work spans: Stochastic processes and large deviations Parabolic Anderson model and random media Statistical mechanics of interacting particle systems Probabilistic methods in telecommunications Branching processes in random environments Publications His recent publications (2019–2025) explore large deviations in sparse networks, Bose gas physics, stochastic PDEs, and applications of probability in telecommunications. Works consistently demonstrate rigorous mathematical analysis of complex physical systems. Grants DFG Priority Programme 2265: Random Geometric Systems DFG Collaborative Research Center 1114: Scaling Cascades in Complex Systems DFG Research Unit 2402: Rough paths, stochastic partial differential equations Research Groups Leads the Stochastic Systems with Interaction group at WIAS Berlin and coordinates the IRTG 2544 Stochastic Analysis in Interaction graduate school.

• Microeconomics
• Econometrics
• Public Economics
• +3 more

Barry Ma is an Associate Professor at the Bert W. Wasserman Department of Economics and Finance, Zicklin School of Business, Baruch College, CUNY. He holds a PhD in Economics and an MS in Statistics from Stanford University, and a BA in Economics from the University of Toronto. Education: PhD in Economics (Stanford), MS in Statistics (Stanford), BA in Economics (University of Toronto) His research focuses on microeconomic theory, econometrics, and public economics. He has contributed to topics such as public good provision, Pareto efficiency, and statistical modeling. A recurring theme in his work involves analyzing preference representation and allocative efficiency in economic models. Recent publications span public economics, financial econometrics, and game theory, with emphasis on Pareto intervals, ARCH processes, and voting theory extensions. Notably, his 2024 paper explores voluntary public good supply mechanisms, while 2023 works address multivariate preference modeling. Service Roles: Coordinator of ECO 4000 course (2012), Graduate Assistant Coordinator (2010), Departmental Executive Committee member (2010)

Hessel WijkstraView profile

Professor

• Medical Imaging
• Contrast-Enhanced Ultrasound
• Prostate Cancer
• +5 more

Hessel Wijkstra is a Full Professor in the Signal Processing Systems Group at Eindhoven University of Technology and a principal investigator in the Department of Urology at Amsterdam University Medical Center (AMC). He bridges engineering and clinical medicine, focusing on advanced medical imaging technologies. Academic Background : MSc and PhD in Electrical Engineering from Twente University. Research Focus : Clinical validation of imaging technologies, particularly contrast-enhanced ultrasound for prostate cancer and renal cell carcinoma . Key Contributions : Development of dispersion imaging techniques, super-resolution ultrasound , and machine learning for cancer diagnostics. Collaborations : Long-standing partnership with Massimo Mischi since 2010, integrating engineering and urology. Scientific Awards : EFSUMB Young Investigator Award (2019) Media Engagement : Frequent expert commentary on nanobubbles , AI in imaging , and ultrasound advancements (2018-2023).

Gary ChenView profile

Assistant Professor

• Corporate Social Responsibility
• Financial Accounting
• Information Systems
• +6 more

Gary Chen is an Assistant Professor at the School of Accountancy & MIS, DePaul University. He holds a PhD in Accounting from Northwestern University and dual master’s degrees in Computer Science and Electrical Engineering from the University of Illinois at Urbana-Champaign. Research Interests: His work bridges accounting with corporate social responsibility, information systems, labor relations, and supply chain management, focusing on economic forces shaping accounting disclosures and decision-makers' information search. He has published in top journals like Journal of Accounting and Economics and Contemporary Accounting Research . Scientific Awards: Dean’s Recognition for Outstanding Teaching at University of Illinois at Chicago FARS Excellence in Reviewing Award Articles Overview: His publications analyze topics such as XBRL adoption's impact on financial transparency, CSR's role in information dynamics, tax incentives in hedge funds, and investor behavior in fast-paced markets. Key subfields span data privacy, regulatory compliance, and stakeholder communication. Professional Involvement: Chen serves as a referee and conference discussant, with research nominated for best conference paper awards. He previously taught advanced financial accounting at the University of Illinois at Chicago.

Cristina ToninelliView profile

Researcher

• Probability
• Mathematical Statistical Mechanics
• Interacting Particle Systems
• +4 more

Cristina Toninelli is a CNRS Researcher at Ceremade (Applied Mathematics department of University Paris Dauphine - PSL) and an Adjunct Professor at the Mathematics Department (DMA) of Ecole Normale Supérieure since 2022. She supervises the ENS Math-Physics bachelor program and Master 2 Applied/ Theoretical Mathematics (MATH) since 2021. Her research focuses on interacting particle systems , bootstrap percolation , and glassy/jamming transitions . She has developed mathematical frameworks for kinetically constrained models (KCM), linking cooperative dynamics to phase transitions and mode-coupling theory. Her work spans probability, statistical mechanics, and mathematical physics, with applications to glass-forming liquids and non-equilibrium systems. She has led the ERC MALIG grant (2016-2022) on mathematical approaches to liquid-glass transitions. Her editorial roles include Chief Editor of Electronic Journal of Probability (2024) and associate editorships in Probability and Statistical Physics journals. She has advised PhD students like Oriane Blondel, Laure Marêché, and Ivailo Hartarsky.

Vincent TassionView profile

Associate Professor

• Probability Theory
• Stochastic Processes
• Percolation Theory
• +5 more

Vincent Tassion is an Associate Professor at the Department of Mathematics , ETH Zurich , Switzerland. His research focuses on probability theory and stochastic processes , with particular emphasis on percolation theory , Ising models , and random walks on transitive graphs . He supervises Master's theses in discrete probability, requiring a strong background in probability and attendance in advanced probability courses. Over the years 2017-2024, he taught courses such as Probability Theory , Stochastic Processes , and Percolation Theory , often collaborating with colleagues like J. Bertoin, A.-S. Sznitman, and W. Werner. His research explores critical phenomena in statistical physics, geometric properties of random structures, and probabilistic methods in mathematical physics. He leads a research group within the department and offers thesis opportunities in topics like branching processes, random matrices, and Boolean functions.

Raffaella BurioniView profile

Professor

• Statistical Physics
• Complex Networks
• Random Walks
• +5 more

Raffaella Burioni is a Full Professor of Theoretical Physics in the Department of Mathematics, Physics and Computer Science at the University of Parma. She serves as Chair of the Non-Linear and Statistical Physics Division of the European Physical Society (EPS) and co-founded the Italian Society of Statistical Physics (SIFS), where she holds the position of Vice President. Her academic leadership includes directing the Ph.D. School in Physics and previously managing quality assurance for the Master's program in Physics (2017-2023). Her educational background includes an MS in Physics with distinction from the 2nd University of Rome and a PhD in Theoretical Physics from the University of Rome 'La Sapienza'. Postdoctoral experience spans the Theoretical Physics Laboratory of the École Normale Supérieure in Paris, the University of Milan, and the National Institute for the Physics of Matter (INFM). Prof. Burioni's research centers on equilibrium and non-equilibrium statistical physics, with deep expertise in graph theory, complex networks, random walks, and stochastic processes. She pioneers interdisciplinary applications to biological systems, neuroscience, and machine learning. Her work on rare events and anomalous diffusion has established fundamental principles like the 'single big jump' mechanism in transport phenomena. Recent investigations bridge statistical physics with neural network theory, examining kernel renormalization and feature learning in deep architectures. Analysis of her 15 most recent publications (2022-2025) reveals three dominant trends: (1) Theoretical advances in rare event statistics for jump processes and extreme value theory, (2) Network-based epidemic modeling incorporating adaptive temporal dynamics and simplicial structures, and (3) Machine learning physics connecting neural network theory to statistical mechanics through Bayesian effective actions and kernel methods. Her work consistently demonstrates how statistical physics principles solve complex problems across disciplines. Her scientific accolades include: Two-time recipient of the Enrico Persico Prize from the Accademia Nazionale dei Lincei Fellow of the Institute for Scientific Interchange (ISI) since 2014 American Physical Society Outstanding Referee (2018) Fellow of the European Centre for Living Technology (2020) As Director of the Ph.D. School in Physics, she mentors doctoral candidates while serving on editorial boards for Physical Review E, JSTAT, and Journal of Physics A. Her research is supported by international collaborations with institutions including the Kavli Institute for Theoretical Physics, Max Planck Institute, and Ben-Gurion University, evidenced by frequent keynote invitations at major conferences like StatPhys, ECCS, and APS March Meetings. Prof. Burioni leads collaborative research networks through her EPS division chairmanship and SIFS leadership, fostering cross-institutional projects on statistical physics applications. Her campus-based studies leverage Wi-Fi data from Parma University to model pedestrian dynamics and epidemic spreading in real-world constrained environments.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +6 more

Francisco Javier Escolano Ruiz is a Professor at the University of Alicante's Department of Computer Science and Artificial Intelligence, affiliated with the Higher Polytechnic School II. He holds a Doctorate in Computer Systems (1997) and a Graduate degree in Computer Science (1992) from the Polytechnic University of Valencia. His academic roles include teaching courses like Machine Learning, Discrete Mathematics, and Artificial Vision. Research Interests: Prof. Escolano's work spans computer vision, Bayesian inference, information theory, and assistive technology. He founded the Robot Vision Group (2001) and co-founded the Mobile Vision Research Lab (2012), developing Navilens—a breakthrough navigation system for visually impaired individuals using mobile vision. Since 2020, he has collaborated on COVID-19 data science initiatives, contributing to pandemic forecasting models. Publication Trends: Recent research focuses on graph theory applications in medical diagnostics (Alzheimer's detection), pandemic mobility modeling, and computer vision for accessibility. His team's work earned the XPRIZE Pandemic Response Challenge in 2022. Awards: First Prize, XPRIZE Pandemic Response Challenge (2022) Students & Grants: He has supervised 8 PhD students and mentored ELLIS doctoral candidates. Currently coordinates national projects including: Rewiring de Grafos Unificado y Escalable (2023-2027) Reconocimiento Estructural de Patrones con Teoría de la Información (2019-2021) Labs & Teams: Leads the Mobile Vision Research Lab (MVRLab) and collaborates with ELLIS Alicante on AI initiatives.

• Economics
• Finance
• Exchange Rate Policy
• +4 more

Douglas Mbululu is a Lecturer at the School of Economics and Finance within the University of the Witwatersrand . His research focuses on economics and finance, with a strong emphasis on exchange rate policy, stock market anomalies, and macroeconomic influences on financial systems. Qualifications: BCom, BComHons, MCom (Rhodes University) Mr. Mbululu's research explores topics such as exchange rate dynamics, day-of-the-week effects in African stock markets, capital structure adjustments, and policy impacts on credit extension. His work often intersects macroeconomic conditions with financial market behavior, providing insights into market efficiency and regulatory impacts. Recent publications highlight trends in exchange rate analysis, stock market anomalies, and macroeconomic policy evaluation. Key disciplines include economics, finance, and econometrics.

• Numerical Analysis
• Numerical Linear Algebra
• Optimization
• +3 more

Dr. Jan Van Lent is a Senior Lecturer in Applied Mathematics at the University of the West of England , affiliated with the Faculty of Engineering and Technology and its Engineering, Design and Mathematics department. His academic focus spans numerical methods, computational mathematics, and interdisciplinary applications in biological modeling. His research interests include Numerical Analysis , Numerical Linear Algebra , Optimization , Scientific Computing , Machine Learning , and Optimal Transport . He has contributed extensively to numerical methods for PDEs and multigrid techniques, alongside interdisciplinary work in ant colony behavior modeling. His publications reflect a strong emphasis on multigrid methods for partial differential equations (e.g., energy-minimizing coarse spaces , implicit Runge–Kutta discretizations ), with applications in both traditional numerical analysis and biological systems. His 2020 work on Python in teaching highlights his pedagogical contributions to computational education.

Jack M. GuralnikView profile

Professor

• Epidemiology of Aging
• Disability
• Functional Status
• +6 more

Jack M. Guralnik serves as Professor in the Department of Epidemiology & Public Health at the University of Maryland, where he conducts pioneering research on aging and disability. Previously, he spent 25 years at the National Institute on Aging (NIH) as Chief of the Laboratory of Epidemiology, Demography and Biometry, establishing foundational frameworks for studying functional decline in older adults. His educational background includes: M.D. from Jefferson Medical College (1971) M.P.H. from University of California, Berkeley (1982) Ph.D. from University of California, Berkeley (1985) Guralnik's research focuses on the epidemiology of aging, with particular emphasis on physical functioning, disability mechanisms, and the protective effects of physical activity. His work establishes critical links between socioeconomic status and disability outcomes, anemia prevalence in elderly populations, and the validation of functional assessment tools like the Short Physical Performance Battery. He has demonstrated how physical activity interventions prevent mobility disability through landmark studies like the LIFE trial. Analysis of his 15 most recent publications reveals consistent focus on mobility disability prevention (60%), with strong emphasis on physical activity interventions (40%), anemia-aging relationships (20%), and socioeconomic determinants of disability (20%). His work increasingly integrates cognitive outcomes with mobility research and utilizes sophisticated longitudinal modeling of disability transitions. Professional recognition includes: 2005 Honorary Doctorate from University of Tampere, Finland 2009 NIH Director's Mentoring Award Guralnik has secured major NIH funding for longitudinal cohort studies and multi-center clinical trials. His leadership in the LIFE study—an $18M NIH-funded trial across 8 centers—demonstrates expertise in large-scale geriatric research infrastructure. He actively mentors junior investigators through formal NIH training grants and informal collaborations. Current research integrates with the Maryland Aging and Disability Research Center, where he co-leads projects examining physiological mechanisms linking chronic conditions to functional decline. His international collaborations span Northwestern University, University of Florida, Johns Hopkins, and University of Montreal.

Greg PlaxtonView profile

Professor

• Theoretical Computer Science
• Algorithm Design
• Scheduling
• +5 more

Greg Plaxton is a Professor of Computer Science at the University of Texas at Austin. He holds a Ph.D. from Stanford University (1989) and a B.A.Sc. from the University of Toronto (1985). His research focuses on the design and analysis of efficient algorithms for computational problems related to matching, scheduling, sorting, and clustering, with applications in distributed systems and algorithmic game theory. Key research areas include Theoretical Computer Science Matching and Scheduling Algorithms Sorting Networks and Distributed Computing Algorithmic Game Theory and Resource Allocation Recent publications analyze three-dimensional stable matchings, egalitarian resource sharing over multiple rounds, and obnoxious facility location games. His work frequently appears in top conferences like ACM-SIAM Symposium on Discrete Algorithms and International Conference on Game Theory. He is affiliated with the Algorithms and Computational Theory Group at UT Austin. Contact: plaxton@cs.utexas.edu , (512) 471-9751, GDC 4.512.