Kimberly Yu is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal’s Faculty of Arts and Sciences. Her research focuses on nonlinear programming, theoretical computer science, and optimization theory. She leads major research projects on mixed-integer nonlinear programming, funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). Her work bridges foundational algorithmic research and real-world applications, particularly in submodular optimization and computational methods. Dr. Yu’s academic background positions her at the intersection of computer science and mathematics. She teaches advanced courses such as IFT1575 (Operational Research Models) and IFT6551 (Integer Programming). Her research explores cutting-edge topics like DR-submodular minimization, integral invariants in computer vision, and polyhedral approaches to combinatorial optimization. Her research grants emphasize innovation in discrete optimization, with projects like "Theory and Algorithms for Mixed-Integer Nonlinear Programming" (2024–2030) advancing computational methods for complex decision-making systems. Kimberly Yu collaborates actively with researchers in computer science and operations research, contributing to both theoretical advancements and practical applications in fields like algorithm design and machine learning.
Dr. Ku Cheng Yeaw is a Senior Lecturer at the Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University (NTU). He holds a PhD from Queen Mary, University of London (2005) and has held prior academic positions at the California Institute of Technology (Harry Bateman Research Instructor, 2005–2008) and the National University of Singapore (Visiting Fellow/Lecturer/Senior Lecturer, 2008–2016). His research focuses on combinatorics, graph theory, and discrete mathematics, with notable contributions to eigenvalue analysis, intersecting families, and extremal set theory. He has an extensive publication record spanning over two decades, addressing topics such as hypergraphs, permutation patterns, and spectral properties of graphs. Dr. Ku has not been explicitly noted for awards or student advisement in the provided text.
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.
Zsuzsanna Lipták is an Associate Professor in the Department of Computer Science at the University of Verona, Italy, where she has been a faculty member since November 2011. Her research is centered on string algorithms, combinatorics on words, and algorithmic bioinformatics, with a particular focus on the Burrows-Wheeler Transform (BWT) and its applications in data compression and biological sequence analysis. She is an active member of the Algorithmic Bioinformatics and Natural Computing Group and the Algorithms Group at the university. She leads research within the PRIN-funded project 'PINC – Pangenome Informatics: From Theory to Practice' and collaborates internationally with institutions in South Africa, Finland, Chile, and the USA. Her research interests include string indexing, suffix trees, suffix arrays, data compression, computational biology, and combinatorial properties of permutations and BWT. She has made significant contributions to the theory and application of BWT variants, matching statistics, and de Bruijn sequence construction. Her recent publications reflect a consistent focus on improving the efficiency and understanding of text indexing and compression methods, particularly in the context of genomic data. These works often involve both theoretical analysis and experimental validation, bridging the gap between pure theory and practical implementation. Lipták actively supervises PhD and master’s students, including Davide Cenzato, Sara Giuliani, Francesco Masillo, and Martina Lucà. She teaches advanced courses such as 'Fundamental Algorithms for Bioinformatics,' 'Computational Analysis of Genome-Scale Sequences,' and 'Advanced Data Structures for Textual Data.' She has also supervised numerous bachelor’s theses and student projects. She has secured research funding through national projects like PRIN and has collaborated on international initiatives, including a Marie Curie IEF fellowship during her postdoctoral work. She is deeply involved in the academic community, having served as PC chair for SPIRE 2024, PC co-chair for CPM 2023, and a member of the Steering Committee of SPIRE since 2024. She has served on the program committees of major conferences such as ESA, DLT, WABI, and IWOCA. She co-organizes the weekly 'Monday Meetings' seminar series for the Algorithms Group and has co-edited special issues and conference proceedings in journals like Theory of Computing Systems , Discrete Applied Mathematics , and European Journal of Combinatorics . She earned her Diplom in Mathematics from Freie Universität Berlin and her PhD in Computer Science from Bielefeld University, Germany, where her thesis addressed algorithmic problems in mass spectrometry. She has held research positions at ETH Zurich, Bielefeld University, and Salerno University, and has been a visiting researcher at the Rényi Institute (Hungary), University of the Witwatersrand, and SANBI (South Africa). She is the scientific coordinator for Erasmus+ exchanges with Bielefeld and Jena Universities.
Tatsuya Terao is a Research Fellow at Kyoto University's Research Institute for Mathematical Sciences, holding a prestigious JSPS DC1 fellowship from April 2024 to March 2027. He operates within the Discrete Optimization Group under the supervision of Professor Yusuke Kobayashi, having completed both his Bachelor of Science (2022) and Master of Science (2024) at Kyoto University. His research focuses on theoretical computer science with particular expertise in matroid theory , quantum query algorithms , and discrete optimization . Terao has developed novel approaches for matroid intersection approximation, parameterized quantum algorithms for graph problems, and submodular maximization with matroid constraints. His work demonstrates a consistent pattern of improving query complexities and developing innovative algorithmic techniques that advance theoretical boundaries in combinatorial optimization. His recent publications reveal a strong trend toward optimizing independence oracle queries in matroid problems while expanding into quantum computing applications for fundamental graph problems. The research shows increasing sophistication in handling complex constraints while reducing computational requirements. Research Fellow of the Japan Society for the Promotion of Science (DC1) Terao collaborates extensively with leading researchers in theoretical computer science, particularly with his advisor Yusuke Kobayashi, as evidenced by multiple co-authored publications in top-tier conferences. His work on the shortest disjoint paths problem demonstrates effective interdisciplinary collaboration with Hirai and Namba's research on (A+B)-paths. While no formal grants are explicitly mentioned beyond his JSPS fellowship, his research direction suggests strong institutional support for theoretical algorithm development. Working within Kyoto University's Research Institute for Mathematical Sciences, Terao contributes to Japan's strong tradition in theoretical computer science research, particularly in combinatorial optimization and discrete mathematics. His research group appears focused on pushing the boundaries of what's computationally feasible in matroid theory and quantum algorithm design.
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.
Bhaskar DasGupta is a Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC), with an additional affiliation as Adjunct Professor in the Bioengineering Department. His research spans multiple interdisciplinary domains including theoretical computer science, computational biology, and network analysis. Primary affiliation: College of Engineering, UIC Secondary affiliation: Bioengineering Department, UIC Research Interests: Theoretical Computer Science (algorithms, complexity) Computational Biology and Biomedical Networks Computational Geometry and Finance Network privacy and security analysis Gerrymandering detection algorithms Scientific Awards: NSF CAREER award (2004) UIC College of Engineering Teaching Award (2012) Grant Support: Research supported by National Science Foundation (NSF) grants. Current work includes network anomaly detection, privacy measures evaluation, and computational approaches to gerrymandering mitigation. Publication Trends: Recent publications focus on computational challenges in network security (2017-2019), with specialized work on efficiency gap analysis in electoral districting patterns using machine learning and geometric algorithms. Interdisciplinary applications span biomedical networks, social network privacy, and transportation optimization.
Jesper Nederlof is an Associate Professor in the Algorithms and Complexity group at the Department of Information and Computing Sciences, Faculty of Science, Utrecht University. His research focuses on designing efficient algorithms for computationally hard problems, particularly in the areas of parameterized complexity, graph algorithms, and NP-complete problems. He received his M.Sc. in Applied Computing Science from Utrecht University in 2008 and his Ph.D. from the University of Bergen in 2011 with the thesis 'Space and Time Efficient Structural Improvements of Dynamic Programming Algorithms' under supervision of Pinar Heggernes. Nederlof's research interests span multiple areas of theoretical computer science, with a particular focus on designing algorithms for NP-complete problems with small exponential worst-case run time. His work extends to algorithmic game theory, information theory, representation theory, approximation algorithms, and online algorithms. He has made significant contributions to parameterized complexity, particularly in developing algorithms parameterized by structural graph parameters like treewidth and cutwidth. His publication record shows a consistent output of high-quality research in top theoretical computer science venues. His recent work demonstrates trends toward tighter bounds for exponential-time algorithms, improved space complexity, and connections between different complexity hypotheses like ETH. Many papers focus on structural parameters of graphs to develop more efficient algorithms for fundamental problems like Hamiltonian cycle, Steiner tree, and subset sum. EATCS-IPEC Nerode Prize (2023) WG best paper award (2020) Nederlof has been involved in teaching courses on algorithms, (non)-linear optimization, graph theory, (vector) calculus, modeling, and management and product development. His research has been supported by various grants including an NWO open competition project during his postdoctoral period and an EU ERC Starting Grant for the project 'Finding Cracks in the wall of NP-Completeness' (2020-2025). As a member of the Algorithms and Complexity group at Utrecht University, Nederlof collaborates with researchers working on foundational aspects of computing, contributing to the group's reputation in theoretical computer science research.
Daniel Karapetyan is an Assistant Professor at the School of Computer Science , University of Nottingham, and a member of the Computational Optimisation and Learning Lab . His research spans Artificial Intelligence , Data Science , Operational Research , and Optimization , with applications in transportation , logistics , satellite mission planning , and access control . Education: PhD in Computer Science, Royal Holloway, University of London (2007–2010) BSc/MSc in Computer Science, Bauman Moscow State Technical University (2001–2007) His research interests focus on the automated design of heuristic algorithms , multi-objective optimization , and parameter tuning for machine learning and combinatorial problems. He integrates Large Language Models (LLMs) and hyperparameter optimization into practical systems, addressing real-world challenges in airport sequencing , TV ad scheduling , and electric vehicle range prediction . Notable scientific contributions include the CORS Practice Prize (2014) for ferry scheduling work and pioneering fixed-parameter algorithms in security-related optimization problems. His publications emphasize combinatorial optimization , Markov Chain methods , and automated algorithm configuration . Teaching roles include module convener for COMP3008: Knowledge Representation and Reasoning , personal tutoring for COMP1002 , and supervision of COMP2002: Software Engineering Group Project . He also offers PhD supervision in areas like AI for optimization , interpretability in ML , and emergency department coordination .
Jiarui Gan is a Lecturer in the Department of Computer Science at the University of Oxford, where they conduct research at the intersection of computational game theory, multi-agent systems, and artificial intelligence. Their work focuses on understanding and shaping interactions among intelligent agents in complex real-world scenarios, with applications spanning transportation systems, digital platforms, and societal ecosystems. Dr. Gan's academic journey includes: PhD in Computer Science from the University of Oxford, supervised by Edith Elkind and Michael Wooldridge Postdoctoral research at the Max Planck Institute for Software Systems (MPI-SWS) with Rupak Majumdar Dr. Gan's research program centers on computational approaches to game theory and multi-agent systems. They investigate how to design incentive mechanisms that effectively coordinate autonomous agents toward organizational objectives, while addressing critical issues of fairness, security, and sustainability. Their work spans several interconnected themes: Principal-agency problems and dynamic mechanism design Stackelberg games and robust equilibrium concepts Fair resource allocation and envy-freeness in multi-agent settings Bayesian persuasion and information design Applications to security, transportation, and societal challenges Analysis of Dr. Gan's publication record reveals a consistent pattern of bridging theoretical insights with practical applications. Their work demonstrates sophisticated mathematical modeling combined with algorithmic innovations, resulting in computationally tractable solutions for complex multi-agent problems. The research shows particular strength in developing frameworks that unify previously disparate problem domains, such as their generalized principal-agency model that encompasses contract design, information design, and Bayesian Stackelberg games. Dr. Gan is actively involved in the academic community, mentoring students and collaborating with researchers across institutions. They are currently seeking motivated PhD students interested in computational game theory and multi-agent systems, with opportunities to work on both theoretical foundations and practical applications that address societal challenges.
Domagoj Matijević is an Associate Professor at the School of Applied Mathematics and Informatics within Josip Juraj Strossmayer University of Osijek . His academic career spans computational geometry, optimization algorithms, and bioinformatics applications. PhD in Computer Science (Algorithms and Complexity), Max-Planck-Institute for Computer Science, Saarbrücken (2007) MS in Computer Science, Saarland University (2002) BS in Mathematics and Computer Science, University of Osijek (2001) Research interests focus on Machine Learning , Computational Geometry , and Bioinformatics , particularly through software tools like Fortuna for RNA splicing analysis and Trajan for comparing single-cell trajectories. His work bridges theoretical computer science with practical implementations in C++ , Python , and CUDA for high-dimensional data processing. 2023 Best Paper Award at MIPRO's Artificial Intelligence Systems track Key contributor to Neural Network-Based Pollen Prediction and Well-Separated Pair Decomposition implementations Developed Trajan for dynamic pseudotime warping and Fortuna for novel splicing event detection Currently teaches Algorithm Complexity , Computational Geometry , and Embedded Systems . Past projects include NVIDIA-funded GPU implementations and German-Croatian collaborations on kinetic spanners.
Assoc. Prof. Petr Gregor is a faculty member at the Department of Theoretical Computer Science and Mathematical Logic , Faculty of Mathematics and Physics , Charles University in Prague . His research focuses on algorithmic and structural problems in interconnection networks, leveraging tools from extremal combinatorics, graph theory, and coding theory. Research Interests : Interconnection networks, Gray codes, hypercube structures, symmetric graph decomposition, fault-tolerant combinatorial algorithms. Teaching : Offers courses in propositional/predicate logic, computational complexity, hypercube structures, automata theory, and data structures. Awards : Best paper award at MFCS 2022 . Contact : Petr.Gregor@mff.cuni.cz | gregor@ktiml.mff.cuni.cz | Personal Website
Martin Klazar is an Associate Professor at the Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University. He has been affiliated with the university since 1995, initially as an assistant professor before becoming an associate professor in 2004. Since 2000, he has also worked at the Institute for Theoretical Computer Science (ITI). His education includes a Ph.D. (1995) under Jiří Nesetril at Charles University, following undergraduate studies at the same institution (1984–1989). Klazar's research spans multiple areas of discrete mathematics, with primary interests in enumerative and extremal combinatorics, number theory, power series, generating functions, and elementary mathematical analysis. His work frequently bridges combinatorial methods with analytic techniques, particularly in asymptotic enumeration and extremal problems. His publications demonstrate a strong focus on combinatorial structures (permutations, set partitions, matchings), graph theory algorithms, and combinatorial number theory. Recent trends include applications of combinatorial duality, growth rate classification of discrete structures, and interdisciplinary topics linking physics-inspired models (e.g., Potts model) with graph invariants. Honors include the Alexander von Humboldt Stiftung fellowship (1997/98) and a prize from the Rector of Charles University for co-editing the book Topics in Discrete Mathematics . He has supervised Ph.D. students including Vít Jelínek and Jaroslav Hančl. Klazar contributes to the ITI research group, focusing on theoretical computer science and combinatorial mathematics. His current teaching includes courses in mathematical analysis, combinatorial counting, and number theory.
Hubert Tsz-Hong Chan is an Associate Professor in the Department of Computer Science, School of Computing and Data Science at The University of Hong Kong. He earned his PhD in 2007 from Carnegie Mellon University under the supervision of Anupam Gupta, followed by post-doctoral research at the Max-Planck-Institut für Informatik (2007-2009). Education PhD in Computer Science, Carnegie Mellon University, 2007 Research Interests Dr Chan's research lies at the intersection of algorithms , combinatorial optimisation , discrete metric spaces , and security & privacy . A recurring theme is the design of provably efficient approximation algorithms for geometric and graph-theoretic problems under realistic or adversarial settings. Representative contributions include polynomial-time approximation schemes (PTAS) for TSP and Steiner Forest in doubling metrics, spectral analysis of hypergraph Laplacians, and foundational work on differential obliviousness and oblivious RAM. Publications & Trends With more than 80 peer-reviewed papers in premier venues such as JACM , SIAM Journal on Computing , Algorithmica , FOCS , SODA , EUROCRYPT , ASIACRYPT , CCS , and WWW , his recent output (2018-2021) demonstrates a shift toward privacy-preserving algorithms, differential obliviousness, and socially-aware optimisation models, often combining rigorous theory with practical datasets like Netflix and Twitter. Scientific Awards IPDPS 2019 Best Paper Award WWW 2018 Honorable Mention Students & Mentoring Dr Chan has successfully graduated 17 PhD and MPhil students and currently mentors 7 PhD candidates and 1 MPhil student. His graduates have secured academic and industry positions worldwide, and their theses frequently build on his funded projects. Research Funding & Labs Since 2012 he has been Principal Investigator on 12 competitive grants from the Hong Kong Research Grants Council (RGC), totalling more than HK$8 million, spanning topics from privacy-preserving aggregation to Byzantine-resilient federated learning. While no dedicated laboratory name is advertised, his group operates within the Security & Privacy and Algorithms Labs in the Department of Computer Science.
Dr. Stefan Canzar leads the Algorithmic Bioinformatics research group at Ludwig-Maximilians-Universität München's Gene Center since 2016, where he develops computational methods for genomic data analysis. His lab focuses on reconstructing biological patterns from fragmented sequencing data using combinatorial optimization and machine learning techniques. Research interests span: Transcriptome reconstruction through methods like CIDANE and Ladder-seq that improve RNA-seq accuracy Single-cell genomics including Sphetcher for cellular heterogeneity analysis and multimodal integration Algorithm engineering for high-dimensional biological data processing Combinatorial optimization applied to omics data integration and network analysis Canzar's publications demonstrate consistent focus on developing scalable algorithms for genomic data challenges, particularly in RNA reconstruction, single-cell analysis, and multimodal integration. His recent work emphasizes dimensionality reduction techniques and transcriptome quantification improvements. Currently funded projects include BAYHOST and DFG collaborative research center LETSIMMUN. As group leader, he mentors doctoral researchers including Alice Descoeudres and Shuang Li. The Canzar Lab develops open-source software tools that have contributed to discoveries in neural development, leukemia research, and immune system regulation through collaborations with medical institutions.
