Find Academic Supervisors

Wei-Chiu MaView profile

Assistant Professor

• 3D Computer Vision
• Robotics
• AI Systems
• +4 more

Wei-Chiu Ma is an Assistant Professor of Computer Science at Cornell University, specializing in 3D/4D computer vision and robotics. His research focuses on developing AI systems for dynamic world modeling, sensor simulation, and autonomous systems. He holds a Ph.D. from MIT (2020) and an M.S. in Robotics from Carnegie Mellon University (CMU). Prior to Cornell, he was a Young Investigator at AI2/University of Washington and a Senior Research Scientist at Uber ATG and Waabi. Education: Ph.D. in Computer Science, MIT (2020) M.S. in Robotics, Carnegie Mellon University (CMU) Bachelor's degree (not explicitly stated, inferred from career timeline) Research Interests: Ma's work bridges computer vision and robotics, emphasizing robust computational tools for dynamic world modeling. Key areas include 3D reconstruction, sensor simulation (e.g., LiDAR), neural radiance fields (NeRF), and multimodal AI systems. His projects like BARF and Virtual Correspondence demonstrate innovations in extreme-view geometry and view synthesis. Key Achievements: Siebel Scholar (2022) Cyber-Physical Systems (CPS) Rising Star (2023) Rank 1 on KITTI Scene Flow Benchmark (2019) Featured research in MIT News and Vox Grants & Labs: His research is supported by industry collaborations (Uber, Waabi) and academic partnerships. He leads the Cornell vision and robotics group, focusing on projects like UniSim (sensor simulation) and Video2Game (real-time environment generation).

Gabriel SpadonView profile

Assistant Professor

• Spatiotemporal Analytics
• Time-Series Forecasting
• Complex Network Mining
• +13 more

Gabriel Spadon is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the MAPS Lab (Modeling and Analytics on Predictive Systems), focusing on spatiotemporal analytics, network science, and machine learning. His work is part of the Big Data Analytics, AI & Machine Learning research cluster. Education: PhD in Computer Science and Computational Mathematics, University of São Paulo (USP), 2021 MSc in Computer Science and Computational Mathematics, University of São Paulo (USP), 2017 BSc in Computer Science, São Paulo State University (UNESP), 2015 Research Interests: Gabriel Spadon’s research lies at the intersection of data science, machine learning, and geoinformatics . He specializes in spatiotemporal forecasting , complex network mining , and trajectory modeling , particularly in maritime and urban environments. His work integrates physics-informed AI, graph-based learning, and environmental data to improve predictive accuracy and decision support. Key domains include vessel movement prediction, environmental monitoring, and urban intelligence. Recent Publication Trends: His latest publications (2024–2025) focus on maritime mobility , using AIS data and physics-informed neural networks (PINNs) for vessel trajectory forecasting. He applies deep learning, clustering, and probabilistic fusion to model complex, multi-modal movement patterns. Other themes include fishing activity detection, port network analysis, and AI for public health and climate matching. Scientific Awards: Advising and Grants: Dr. Spadon supervises multiple graduate students and research assistants in the MAPS Lab, working on mobility data mining, physics-informed AI, and geospatial analytics. His research is supported by major grants from the Ocean Frontier Institute (OFI) , Canadian Space Agency (CSA) , Mitacs , and IMT Atlantique . He leads projects such as smartWhales , AISViz , and Physics-Informed Mobility Forecasting , involving international collaborations with institutions in Brazil and France. Labs and Teams: He is the Director of the MAPS Lab , which develops cutting-edge computational methods for predictive systems. The lab collaborates with the smartWhales initiative (DHI, WSP, Fisheries and Oceans Canada), MERIDIAN , and the Institute for Big Data Analytics . His team includes PhD and MCS students working on AI, data mining, and signal processing for real-world ocean and urban challenges.

• Computer Graphics
• Image Processing
• Scientific Visualization
• +2 more

Miquel Feixas is an Associate Professor in the Department of Computer Science, Applied Mathematics and Statistics at the University of Girona. He is a key member of the Graphics and Image Laboratory (GILab), where he contributes to cutting-edge research in computer graphics, image processing, and scientific visualization. His research focuses on the application of Information Theory to visual computing domains. This includes modeling visual attention, optimizing rendering pipelines, enhancing image quality assessment, and improving scientific data visualization through entropy and mutual information metrics. The analysis of his recent publications reveals a sustained focus on information-theoretic modeling across computer graphics and image analysis. His work bridges theoretical foundations with practical applications in medical imaging, 3D modeling, and data visualization, demonstrating consistent innovation over more than a decade. Scientific Contributions: Co-author of two books on information theory in graphics and image processing Over 100 peer-reviewed publications in top-tier venues Supervision of six doctoral theses Participation in over 20 publicly funded R+D projects Advising and Research Leadership: Miquel Feixas has directed six PhD theses and is actively involved in research management through GILab. His work is supported by multiple competitive grants, reflecting sustained funding and academic impact. He collaborates extensively within interdisciplinary teams focused on visual computing. Research Group: He is an integral member of the Graphics and Image Laboratory (GILab) at the University of Girona, a research group dedicated to advancing graphics, imaging, and visualization technologies through novel computational methods.

Taiji SuzukiView profile

Associate Professor

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +6 more

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.

Will MaView profile

Associate Professor

• E-commerce Optimization
• Supply Chain Management
• Revenue Management
• +5 more

Will Ma is the Roderick H. Cushman Associate Professor of Decision, Risk, and Operations at Columbia Business School, Columbia University. He is an affiliated member of the Data Science Institute's Foundations of Data Science and Financial and Business Analytics centers. His academic appointment involves full-time research and teaching responsibilities with no indication of part-time status or retirement. Ma's research centers on e-commerce optimization, addressing both supply-side challenges (inventory management, fulfillment logistics) and demand-side opportunities (personalized product assortments). His work specializes in designing real-time algorithms that emphasize simplicity and robustness, with applications spanning revenue management, online matching, dynamic pricing, and data-driven decision-making. Key methodologies include stochastic optimization, algorithmic game theory, and combinatorial design. Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in online optimization under uncertainty, including prophet inequalities, contention resolution schemes, assortment planning, and network revenue management. These contributions consistently bridge theoretical computer science and operations research, with practical applications in logistics, game design, and pricing strategies. Awards and Recognition: Operations Research Reviewer Meritorious Service Award (2025) While specific student advising relationships and grant details are not documented, Ma maintains an active research lab focused on algorithmic solutions for operational challenges. His industry background includes professional poker, video-game startups, and entrepreneurship, enriching his academic work with practical perspectives.

Sally AitkenView profile

Professor

• Population genetics
• Climate change adaptation
• Forest conservation
• +5 more

Dr. Sally Aitken is a Professor at the University of British Columbia's Faculty of Forestry , where she investigates genomic and ecological dynamics of tree adaptation to climate change . Her work spans multiple species including spruce, pine, and Douglas-fir, with a focus on hybrid zones, assisted gene flow, and forest conservation genetics . Lead researcher at Centre for Forest Conservation Genetics Principal investigator for the CoAdapTree Project Research Highlights Dr. Aitken's lab combines genomic tools with common garden experiments to study: Local adaptation in tree populations Hybridization as a mechanism for climate resilience Novel climate detection for forest management Genetic conservation in transitional ecosystems Awards & Recognition Canadian Forestry Scientific Achievement Award (2009) UBC Killam Teaching Prize (2010) IUFRO Scientific Achievement Award (2014) Wall Scholar (2014) Fellow of the Royal Society of Canada (2017) Genome BC Award For Scientific Excellence (2018) Training & Supervision Dr. Aitken has supervised over 12 doctoral projects and 4 master's theses, including work on: Adaptation of interior spruce to climate change Amphibian conservation in Haida Gwaii Lodgepole pine growth responses Douglas-fir stress tolerance genetics

• Scheduling Problems
• Approximation Algorithms
• Combinatorial Optimization
• +5 more

Monaldo Mastrolilli is a Senior Researcher at IDSIA (Dalle Molle Institute for Artificial Intelligence) and a Professor at University of Applied Sciences of Southern Switzerland (SUPSI) . His research focuses on scheduling problems, approximation algorithms, combinatorial optimization, and complexity theory, with a particular emphasis on lift-and-project methods like the Sum-of-Squares Hierarchy and their applications to constraint satisfaction problems. Politecnico di Milano, Italy (Computer Science Engineering, 1997, with honors) University of Kiel, Germany (Ph.D. in Computer Science, 2002, summa cum laude) His work spans polynomial optimization , integrality gaps , and algebraic geometry approaches , as evidenced by his leadership in the Swiss National Science Foundation project "Ideal Membership Problems and the Bit Complexity of Sum of Squares Proofs" . He has mentored several Ph.D. students, including Ola Svensson, Nikos Mutsanas, Georgios Stamoulis, and Sam Leppanen. Prize in Whizzkids'97 competition on combinatorial optimization Summa cum laude Ph.D. degree (University of Kiel, 2002) Monaldo's publications highlight trends in approximation algorithms , scheduling theory , and constraint satisfaction , often intersecting with polynomial optimization and algebraic methods . His recent work explores the intersection of universal algebra, proof systems, and computational complexity. He supervises the Algorithms and Complexity Group at IDSIA, which is part of a collaborative environment involving the University of Lugano and SUPSI. The group includes two Ph.D. students, an IDSIA researcher, and collaborations with leading experts in theoretical computer science.

• Algorithms
• Counting and Sampling
• Random Structures
• +7 more

Mary Cryan is a Senior Lecturer in the School of Informatics at the University of Edinburgh, where she has been a faculty member since July 2003. She is affiliated with the Laboratory for Foundations of Computer Science (LFCS) and conducts research in theoretical computer science, with a focus on algorithms, counting and sampling, random structures, learning theory, and pseudorandom generators. Her educational background includes a joint undergraduate degree in Computer Science and Mathematics from University College Dublin (1993), an MSc by research at UCD (1995), and a PhD from the University of Warwick (awarded 2000) under the supervision of Leslie Ann Goldberg. She held postdoctoral positions at BRICS, University of Aarhus (1999–2001), and the University of Leeds (2001–2003) working with Martin Dyer. PhD, University of Warwick (2000) MSc, University College Dublin (1995) BSc (Joint Hons), Computer Science and Mathematics, University College Dublin (1993) Her research interests lie in the design and analysis of algorithms, particularly randomized algorithms for counting and sampling combinatorial structures. She has made significant contributions to the understanding of Markov chains for sampling matchings, contingency tables, Euler tours, and phylogenetic trees. Her work often bridges theoretical computer science with probability and discrete mathematics. The trend in her publications shows a sustained focus on fundamental algorithmic problems involving random structures and probabilistic methods. Her work spans exact and approximate counting, mixing times of Markov chains, and learning models in phylogenetics. Recent work includes analyzing log-Sobolev inequalities for log-concave distributions and exact counting on bounded-treewidth graphs. She has supervised several PhD students, including Veselin Blagoev ("Counting and Sampling Acyclic Orientations", 2018), Páidí Creed ("Counting and Sampling problems on Eulerian graphs", 2010), and James Matthews (co-supervised, "Markov chains for sampling matchings", 2008). She has also been active in academic service, serving as Deputy Director of the Informatics Graduate School since August 2020. Her research has been published in top-tier venues such as FOCS, STOC, SIAM Journal on Computing, and Journal of the ACM. She regularly lectures courses such as "Informatics 2 - Introduction to Algorithms and Data Structures".

• Macroeconomics
• Labor Economics
• Business Cycle Theory
• +3 more

Toshihiko Mukoyama is a Professor in the Department of Economics at Georgetown University, specializing in macroeconomic theory with emphasis on labor market dynamics and business cycle fluctuations. His research integrates heterogeneous agent modeling to analyze structural economic forces. His primary research interests span Macroeconomics, Labor Economics, Business Cycle Theory, Economic Growth, Heterogeneous Agent Models, and Firm Dynamics. Mukoyama examines how labor market frictions—including job search behavior, occupational mismatches, and worker reallocation—drive aggregate fluctuations. His work also addresses policy impacts of financial deregulation, firing costs, and unemployment insurance within dynamic general equilibrium frameworks. Analysis of his 2021-2025 publications reveals persistent focus on labor reallocation mechanisms during economic shocks, with empirical attention to Japan's recession responses. Key trends include modeling export shock propagation, cyclical part-time employment patterns, and welfare implications of Kaldor-Hicks efficiency in policy reforms. Scientific Awards: None listed in the provided information. Advising and Grants: The source material contains no mention of graduate students, postdoctoral advisees, or grant funding details. His RePEc Genealogy and IDEAS page suggest academic engagement without explicit advising records. Labs and Teams: No affiliated research laboratories, collaborative teams, or institutional research groups are referenced in the scraped content.

Jennifer HuntView profile

Professor

• Labor Economics
• Immigration Economics
• Wage Inequality
• +6 more

Jennifer Hunt is a Professor of Economics at Rutgers University, specializing in labor market analysis with a particular focus on immigration economics. She maintains an affiliation as a DIW Fellow with the German Socio-Economic Panel study at the German Institute for Economic Research (DIW Berlin). Education: Ph.D. in Economics, Harvard University Bachelor's degree in Electrical Engineering, Massachusetts Institute of Technology Professor Hunt has established herself as a leading researcher in labor economics, with particular expertise in immigration effects on labor markets, wage inequality, and innovation. Her work spans multiple continents and addresses both developed and developing economies. She has conducted significant research on the German labor market, particularly during the post-reunification transition period in East Germany. Her current research focuses on the geographic spread of technology, immigration and wage inequality, while her past research has encompassed unemployment, the science and engineering workforce, the transition from communism, crime and corruption. Her publication record demonstrates consistent high-impact research across several major economic journals. Hunt's work shows particular strength in analyzing how immigration affects innovation through patenting activity, why women disproportionately leave engineering fields, wage inequality dynamics, and the economic transition in East Germany. Her research methodology typically combines rigorous empirical analysis with careful consideration of policy implications. Professor Hunt has held significant government positions, serving as Chief Economist of the U.S. Department of Labor (2013-2014) and Deputy Assistant Secretary for Microeconomic Analysis at the U.S. Department of the Treasury (2014-2015). Prior to joining Rutgers in 2011, she held academic positions at McGill University, the University of Montreal, and Yale University. Her work has gained international recognition, as evidenced by her recent interview with Dagens Nyheter (Swedish newspaper) where she discussed her research findings that "the entire economy benefits from immigration" and that "it is an advantage if migrants have low education."

Mikel VandeloiseView profile

Researcher

• Computer Science
• Software Engineering
• Data Flow Analysis
• +4 more

Mikel Vandeloise is an active Researcher affiliated with the Faculty of Computer Science. His work focuses on algorithm optimization, data flow analysis, and software engineering paradigms, with recent contributions to educational tools and collaborative metrics in software development. Research Interests: Vandeloise explores the intersection of Data Flow Analysis , Abstract Interpretation , and Automated Video Generation to enhance educational methodologies. He also investigates Bipartite Matching Optimization and Inter-paradigm Synergy in software engineering, developing novel metrics for collaborative efficiency. Recent Publications: His 2025 outputs highlight algorithm visualization, heuristic evaluation, and paradigm integration.

Sami DaviesView profile

Researcher

• Theoretical Computer Science
• Combinatorial Optimization
• Algorithm Design
• +5 more

Sami Davies is a Research Scientist in Theoretical Computer Science at the Simons Institute and the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He earned his PhD in Mathematics from the University of Washington in 2021 under the supervision of Thomas Rothvoss, with work spanning the Department of Mathematics and the Allen School theory group. Prior to Berkeley, he was a postdoctoral researcher in the theory group at Northwestern University. His research lies at the intersection of theoretical computer science and combinatorial optimization. Key interests include scheduling algorithms, clustering, hypergraph matchings, approximation algorithms, and algorithmic applications in machine learning. His work often addresses fundamental problems in algorithm design with practical implications in resource allocation, data analysis, and network optimization. The most recent publications reflect a strong trend in developing efficient combinatorial algorithms for clustering and scheduling problems, particularly under complex constraints such as communication delays or fairness objectives. There is a growing integration of machine learning concepts into traditional algorithmic frameworks, as seen in works on predictive flows and trace reconstruction. His research is consistently published in top-tier venues including NeurIPS, ICML, SODA, FOCS, and COLT, indicating high impact and recognition within the theoretical CS community. Sami is actively engaged in academic service and outreach. He has served as a mentor in the Washington Directed Reading Program and co-taught the 'Math in Society' course through FEPPS, which provides education to incarcerated individuals. He has also taught yoga at the University of Washington. He enjoys bouldering, running with his dog, vegan cooking and baking, and practicing yoga. He completed a 200-hour yoga teacher training in 2019. Contact: Email: samidavies@berkeley.edu , davies@berkeley.edu

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.

Michael PriesView profile

Professor

• Macroeconomics
• Labor Economics

Michael Pries is a Professor of Economics and Associate Dean for the Social Sciences at the University of Notre Dame. He holds a Ph.D. in Economics from Stanford University (1999) and a B.A. in Economics from Notre Dame (1993). As a faculty fellow at the Kellogg Institute for International Studies, his research bridges macroeconomics and labor economics. His research examines labor market dynamics, including shock amplification, cross-country labor performance, and policy impacts on employment and wages. Key interests include: Labor market volatility and uncertainty Job flows and search frictions Social security reforms Worker heterogeneity Pries' publications in top economics journals (e.g., Journal of Political Economy , Review of Economic Studies ) analyze labor fluctuations, welfare implications of volatility, and institutional impacts on hiring. His work employs quantitative modeling to study recurring job loss, intertemporal smoothing, and matching frictions.

Kevin WayneView profile

Lecturer

• Algorithms
• Data Structures
• Computer Science Education
• +4 more

Kevin Wayne serves as the Phillip Y. Goldman '86 University Lecturer in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. His teaching responsibilities include foundational courses in data structures, algorithms, optimization, and introductory computer science, specifically COS 126, COS 226, and COS 423. Dr. Wayne's research interests center on algorithms, data structures, and computer science education. His work emphasizes practical applications across science, engineering, and commerce, with particular focus on making complex computational concepts accessible through interdisciplinary approaches. He has pioneered numerous educational methodologies that bridge theoretical computer science with real-world applications. His educational materials, particularly the Algorithms textbook series and associated MOOCs, have established new standards in computer science education globally. These resources have demonstrated exceptional impact during the pandemic era, with Algorithms Part I ranking among the top online courses worldwide. The textbook Algorithms, 4th Edition was named one of The 25 Best Programming Books of All-Time in 2020 The MOOC Algorithms, Part I was recognized as one of the Top 25 Most Popular Courses During the Pandemic Named the 2019 Outstanding Teacher in Computer Science at a Ph.D. Granting Institution in New Jersey Recipient of the 2014 ACM Distinguished Educator award Winner of Princeton University's 2013 Phi Beta Kappa Teaching Award Dr. Wayne has developed numerous innovative educational programs including the Summer Programming Experiences for first-year students and the Course Development Program. His 'Nifty Assignments' collection, designated by ACM SIGCSE, has been adopted by institutions worldwide and includes projects ranging from WordNet analysis to particle collision simulations. He maintains active development of educational software tools including the widely-used algs4 GitHub repository containing Java implementations of fundamental algorithms.