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Wolfgang MulzerView profile

Professor

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +3 more

Wolfgang Mulzer is a Professor in the Department of Computer Science at Freie Universität Berlin, within the Faculty of Mathematics and Computer Science. He leads the Theoretical Computer Science research group (AG Theoretische Informatik) and holds a PhD from Princeton University (2010), advised by Bernard Chazelle. His research focuses on computational geometry, algorithms, and discrete mathematics, with notable contributions to geometric algorithms, data structures, and combinatorial optimization. Education: PhD in Computer Science, Princeton University (2010) Advisor: Bernard Chazelle Research Interests: Mulzer's work spans theoretical computer science with an emphasis on computational geometry. Key areas include geometric algorithms (e.g., Voronoi diagrams, Fréchet distance), dynamic graph algorithms (e.g., disk graphs, connectivity problems), and algorithmic complexity. His research also addresses discrete geometry challenges like Tverberg theorems and geometric intersection graphs. Grants & Labs: Leading the Theoretical Computer Science group at FU Berlin Active in collaborative projects on geometric algorithms and algorithmic foundations Professional Activities: Mulzer has authored or co-authored over 100 peer-reviewed papers, with work appearing in top venues like Discrete & Computational Geometry , SIAM Journal on Computing , and Algorithmica . His research bridges theoretical foundations with practical algorithm design.

Kate LarsonView profile

Professor

• Artificial Intelligence
• Multiagent Systems
• Reinforcement Learning
• +3 more

Kate Larson is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo. Her research explores artificial intelligence with emphasis on multiagent systems, reinforcement learning, and AI applications for sustainability and climate challenges. She employs game-theoretic approaches to address coordination and cooperation in complex systems. Education Ph.D. in Computer Science from Carnegie Mellon University (2004), M.Sc. from Washington University in St. Louis (1999), and B.Sc. from Memorial University of Newfoundland (1997). Research Focus Professor Larson's work bridges theoretical AI and practical societal impacts. Key areas include: Designing cooperative AI frameworks for multiagent environments Developing value-alignment mechanisms for ethical AI Applying reinforcement learning to climate change mitigation Creating robust AI systems through game-theoretic guarantees Publication Trends Her recent publications (2024-2025) demonstrate strong focus on AI safety, game-theoretic foundations, and multimodal learning systems. Dominant themes include: algorithmic alignment techniques, multi-agent risk management, and interpretable AI architectures, with increasing emphasis on real-world climate and sustainability applications. Awards & Recognition No major scientific awards mentioned in source materials. Research Infrastructure No specific labs or teams detailed in available information.

Shikha SinghView profile

Assistant Professor

• Algorithmic Game Theory
• Algorithms & Data Structures
• Theoretical Computer Science
• +3 more

Shikha Singh is an Assistant Professor of Computer Science at Williams College. She holds a PhD from Stony Brook University (2018) and an Integrated BSc & MSc from IIT Kharagpur (2013). Her research focuses on algorithmic game theory, algorithms with predictions, and data structure optimization. She has received tenure at Williams College and was recognized for her work on adaptive filters at ESA 2021 and NeurIPS 2023. Education: PhD, Stony Brook University (2018); Integrated MSc & BSc, IIT Kharagpur (2013) Research Interests: Algorithmic game theory, adaptive and I/O-efficient algorithms, data structures with predictions. She explores how incentives influence algorithm outcomes and designs efficient algorithms for big data challenges. Key Contributions: Her work on online list labeling with predictions (NeurIPS 2023) and incremental shortest path algorithms (ICALP 2025) highlights her focus on predictive algorithms. Collaborations include visits to Carnegie Mellon University and Dagstuhl seminars. Awards: COCOA 2017 Best Paper Runner-Up, NeurIPS 2023 Spotlight Advising: Advised David Lee on adaptive filters (ESA 2021). Active in program committees for Euro-Par, TAMC, and ALENEX. Labs/Teams: Collaborates with Benjamin Moseley on scheduling and data structure research. Part of Williams College’s vibrant CS department.

DERFOUL RatibaView profile

Lecturer

• Mathematical Imaging
• Partial Differential Equations (PDE)
• Approximation Theory
• +3 more

Ratiba Derfoul is affiliated with the Modal'X Laboratory at the University of Paris Ouest Nanterre La Defense. She held an ATER position (teaching and research assistant) from October 2013 before transitioning to a permanent engineer role at CGG VERITAS in May 2014. Her research focuses on mathematical imaging, partial differential equations, numerical analysis, and their applications in geosciences and medical imaging. She completed her PhD in 2013 under the supervision of Christian Gout and Carole Le Guyader, funded by IFP Energies Nouvelles. Educational Background: Master of Research in Mathematics and Applications, Pierre and Marie Curie University (Paris VI), 2010. Research Highlights: Her work bridges computational mathematics and applied geosciences, including image registration techniques for neuroanatomical mapping, 4D seismic data integration in reservoir models, and optimization methods for history matching. She co-authored patents on reservoir calibration techniques and presented at conferences like SIAM Imaging Sciences and Computational Geosciences. Awards: Finalist for the Labex AMIES 2014 Thesis Prize. Grants & Patents: French patent (2011): Method for oil field exploitation via calibration of production and seismic data. Funding from IFP Energies Nouvelles for her doctoral research. Professional Contributions: Her interdisciplinary approach combines numerical methods with practical applications in energy and biomedical fields, demonstrated through collaborations with institutions like INRIA and TOTAL.

Ioannis CaragiannisView profile

Professor

• Algorithmic Game Theory
• Social Choice Theory
• Fair Division
• +4 more

Ioannis Caragiannis is a Professor at the Department of Computer Science, Aarhus University. His research focuses on algorithmic game theory, fair division, mechanism design, and social choice theory. He explores computational aspects of fairness in resource allocation, voting systems, and multi-agent systems. His work often addresses theoretical challenges in designing efficient and fair algorithms with provable guarantees. Key research areas include social choice mechanisms, metric distortion in collective decisions, clustering algorithms with fairness constraints, and truthful mechanisms for budget allocation and facility assignment. He has contributed to understanding the complexity of Pareto-optimal and envy-free outcomes, as well as the efficiency of serial dictatorship mechanisms. His recent work investigates welfare-optimal allocations under computational constraints and the metric properties of sortition-based decision-making. Caragiannis has published extensively in top venues like AAAI, ICML, and SAGT. Notable contributions include studies on proportional fairness in non-centroid clustering, randomized learning-augmented auctions, and the distortion of sortition systems. His research bridges theoretical computer science with practical applications in fair resource distribution and collective decision-making processes.

Edwin van DamView profile

Professor

• Combinatorics
• Discrete Mathematics
• Operations Research
• +1 more

Edwin van Dam is a Full Professor in the Department of Econometrics and OR at Tilburg University, affiliated with the TS Economics and Management faculty and CentER. His research focuses on Combinatorics, Discrete Mathematics, and Operations Research, with contributions to eigenvalue studies, hypergraphs, and graph theory. He earned his PhD from CentER (1992–1996) and held academic roles since 1996, including postdoctoral fellowships at Queen’s University (1997–1998) and assistant/associate professorships at Tilburg. He co-authored textbooks like Linear and Dynamical Systems, Optimization and Games (2006, 2010). Recent research emphasizes spectral graph theory, hypergraphs, and combinatorial designs. He supervised four PhD students and contributed to projects on distance-regular graphs and black-box function approximation. Collaborations span global institutions, and he leads the Operations Research research group at Tilburg.

Peter Schneider-KampView profile

Professor

• Data Science
• Artificial Intelligence
• Machine Learning
• +5 more

Peter Schneider-Kamp is a Professor of Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Artificial Intelligence, Machine Learning, Privacy-Preserving Techniques, and Algorithms. He has led projects such as the Danish Foundation Models initiative and the PREPARE cardiovascular disease project. His work spans synthetic data frameworks (e.g., Syntheval), quantization-aware neural networks (BitNet), and autonomous drone systems (Drones4Safety). He has been honored with awards including the Researcher Award 2014 and the Friedrich-Wilhelm-Preis 2009. Key research interests include optimizing sorting networks, termination analysis, and UAV-based infrastructure inspection. He has contributed to over 112 publications and actively participates in academic activities like organizing the 13th ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming. Schneider-Kamp also engages in educational roles, teaching courses such as DS806 and DM564 on database systems. His projects highlight interdisciplinary impact, including cardiovascular disease risk estimation (PREPARE) and AI-driven health advice analysis. He collaborates internationally, with recent work featured in venues like the International Conference on Agents and Artificial Intelligence.

Marek M. KarpinskiView profile

Professor

• Theory of computation
• Design of efficient algorithms
• Computational complexity
• +10 more

Marek M. Karpinski is a Chair Professor of Computer Science at the University of Bonn and a founding member of the Hausdorff Center for Mathematics . He has held visiting or professorial positions at institutions such as Princeton University, Carnegie-Mellon University, and the University of Edinburgh. His affiliations also include the B-IT Research School on Applied Informatics and the Lab for Foundations of Computing . His research spans efficient algorithms , combinatorial optimization , computational complexity , randomized approximation techniques , and applications in network design , quantum computation , and molecular biology . Recent work focuses on approximation hardness for NP-hard problems, graph algorithms , and algebraic computational complexity . His scientific contributions include polynomial time approximation schemes for dense NP-hard problems and key publications in randomized algorithms , VC dimension , and network optimization . He has advised numerous researchers and received honors such as the Humboldt Research Award and the Max Planck Research Prize .

Paul L BendichView profile

Adjunct Professor

• Topological Data Analysis
• Machine Learning
• Computational Topology
• +4 more

Paul L Bendich is an Adjunct Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from Duke University (2008). His research focuses on adapting topological and geometric methods for data analysis, particularly in topological data analysis (TDA). He has pioneered TDA methodologies for applications in machine learning, sensor fusion, and environmental modeling. Current appointments include leading research initiatives in multi-modal data analysis and reinforcement learning optimization. Key areas of expertise include computational topology, persistent homology, and topological signal processing. He teaches courses on topological data analysis (COMPSCI 434, MATH 412) and has developed educational programs like Data+ at Duke. Grants include NSF-funded projects (BIGDATA: F: DKA: CSD) and Air Force Office of Scientific Research initiatives. Recent work emphasizes topological methods in AI safety (topological parallax), reinforcement learning efficiency, and geophysical feature tracking. Professional activities include conference presentations on TDA applications and editorial work for journals. His research bridges theoretical mathematics with practical data-driven challenges in science and engineering.

Ohad PerryView profile

Adjunct Associate Professor

• Stochastic Models
• Applied Probability
• Service Systems
• +4 more

Ohad Perry is an Adjunct Associate Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University under the McCormick School of Engineering . He holds a Ph.D. and M.S. in Operations Research from Columbia University and a B.Sc. in Mathematics and Statistics from the University of Haifa . Research Interests : Stochastic models and applied probability Queueing theory for service and healthcare systems Performance analysis and control of stochastic systems Inventory and contact center optimization Recent Publications : Focus on polling systems , patient flow dynamics , stability of parallel servers , and fluid-diffusion hybrids in priority queues. His work spans theoretical advancements (e.g., Stochastic Systems ) and applied implementations (e.g., Stroke ). Scientific Awards : 2020 APS Best Student Paper Award (as advisor for Hu Y. et al.) Advising and Grants : Co-advisor to doctoral graduates now at HKUST , Tsinghua University , and Stanford GSB . Recipient of multiple NSF grants (2014-2023) and Northwestern Memorial Hospital funding.

Jun KitagawaView profile

Associate Professor

• Optimal Transportation
• Partial Differential Equations
• Differential Geometry
• +1 more

Jun Kitagawa is an Associate Professor in the Department of Mathematics at Michigan State University (MSU). His research focuses on fully nonlinear elliptic PDEs, particularly Monge-Ampère equations, with applications in optimal transport, geometric optics, and fluid dynamics. Key interests include regularity properties of solutions and numerical methods for these equations. He has held a sabbatical during the 2022 semesters. Research Interests: Optimal Transportation, Partial Differential Equations, Differential Geometry, Numerical Methods. Publications: His work spans theoretical advancements and numerical algorithms in optimal transport, including studies on stability, geometric optics, and semi-discrete transport. Recent trends include exploring transport on manifolds, perturbative approaches, and algorithmic innovations like Newton methods for transport problems. Awards: None explicitly listed. Advising & Grants: Advised graduate students (e.g., Minh Nguyen) and postdocs (e.g., Farhan Abedin). Former students include Chamila Malagoda Gamage (U Florida) and Mohit Bansil (UCLA Ph.D., D.E. Shaw Analyst). Labs/Teams: Active research group at MSU focusing on optimal transport and PDEs.

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Daniel SussmanView profile

Assistant Professor

• Probability and Statistics
• Network Analysis
• Graph Theory
• +3 more

Dr. Daniel Sussman is an Assistant Professor at Boston University, affiliated with the Probability and Statistics research group. He also serves as the Associate Director of the Program in Statistics. His work focuses on statistical methodologies for network analysis, including graph matching, latent variable models, and causal inference under network interference. Dr. Sussman develops computational tools for analyzing complex networks, such as the iGraphMatch R package for graph matching analysis. His research bridges theoretical foundations with practical applications in neuroscience, epidemiology, and computational social science. Key research interests include network dynamics, stochastic processes on graphs, and statistical estimation in noisy network data. He explores geometric and algebraic properties of random graphs, as well as scalable algorithms for large network datasets. His contributions span theoretical results (e.g., asymptotic consistency of embeddings) to applied methods (e.g., unbiased estimation in exposure models). Dr. Sussman’s publications highlight advancements in graph matching techniques, including matched filter approaches and multi-scale heat diffusion methods. His work emphasizes robustness to noise and scalability for real-world networks. While no explicit awards are listed, his contributions reflect significant impact in statistical network analysis. His professional role as Associate Director involves overseeing statistical training programs and curricula. Though no advising details are provided, his research group likely supports graduate students focused on network science and computational statistics.

Nikos NtarmosView profile

Senior Lecturer

• Distributed Systems
• Big Data Management
• NoSQL Databases
• +3 more

Nikos Ntarmos is a Senior Lecturer at the University of Glasgow's School of Computing Science, with affiliations to the IDEAS research lab. He holds a PhD in Computing Engineering & Informatics from the University of Patras and specializes in distributed systems and large-scale data management. Research focuses on: Distributed data storage and indexing for NoSQL systems Efficient query processing for graph databases Scalable algorithms for big data analytics Recommendation systems and collaborative filtering Publications demonstrate evolving interests from distributed hash tables to contemporary graph processing and recommendation systems. Recent work increasingly addresses practical applications in education technology alongside core database research.

Alexandre RefregierView profile

Professor

• Cosmology
• Astrophysics
• Dark Energy
• +5 more

Prof. Alexandre Refregier is a Full Professor at the Department of Physics, ETH Zürich. He leads research in cosmology and astrophysics, focusing on dark energy, dark matter, and large-scale structure analysis using observational, theoretical, and instrumental approaches. His work spans gravitational lensing, cosmic microwave background, and galaxy cluster studies. He has contributed to major projects like the Dark Energy Survey and Euclid mission, leading instrumental development and data analysis efforts. Refregier holds a PhD from Columbia University (1997) and has held positions at institutions including Princeton University and the University of Cambridge. Education: PhD in Physics, Columbia University, 1997 M.Phil. and M.A. in Physics, Columbia University, 1992–1993 B.S. in Physics (Summa cum Laude), University of Texas at Austin, 1991 Research Interests: Dark energy/dark matter dynamics, weak lensing, baryon acoustic oscillations, cosmic microwave background analysis, and cosmological probes. His interdisciplinary methods combine theoretical modeling with cutting-edge observational techniques and instrument design. Key Contributions: Over 200 published papers (e.g., on Euclid mission instrumentation, joint lensing-CMB analyses, and baryonic feedback modeling). Active in developing simulation tools like PyCosmo and GalSBI for cosmological inference. Awards/Grants: Not explicitly listed, but his leadership in major collaborations implies significant funding and recognition. Active in Simons Foundation-supported research. Labs/Teams: Leads the Cosmology Group at ETH Zurich, involved in Euclid's science and instrument teams, and collaborates on projects like HIRAX and SKA simulations.