Yang P. Liu is an Assistant Professor at Carnegie Mellon University 's Department of Computer Science . He received his PhD from Stanford University under the supervision of Aaron Sidford and previously studied at MIT . Fields of Interest : Graph Algorithms, Optimization, High-Dimensional Geometry, Additive Combinatorics, Theoretical Computer Science. His research focuses on algorithmic design and analysis for graph problems, optimization, and combinatorics, with applications in machine learning and complexity theory. Recent work includes advancements in parallel repetition games , combinatorial lines , and dynamic graph algorithms . In 2024, his research spanned FOCS , STOC , and RANDOM conferences, addressing problems in k-CSPs , min-cost flow , and hypergraph sparsification . Earlier contributions (2023) included deterministic flow algorithms and spectral hypergraph techniques. Scientific Awards : NDSEG Fellowship (2018-2021), Google PhD Fellowship (2022-2023), FOCS Best Paper (2022), STOC Best Student Paper (2022), FOCS Best Student Paper (2021). He teaches CS 15-759 , a graduate course on convex optimization theory and applications, covering gradient descent, interior point methods, and algorithmic sparsification techniques.
Satish Rao is a Professor in the Computer Science Division at the University of California, Berkeley. He is affiliated with the Simons Institute for the Theory of Computing and the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB). His research focuses on algorithms, combinatorial optimization, graph theory, and theoretical computer science with applications to computational biology and machine learning. Rao has held teaching roles for courses such as CS 70 (Discrete Mathematics and Probability Theory) and CS 270 (Spring 2024). He has been recognized with prestigious awards including ACM Fellow (2013), the Delbert Ray Fulkerson Prize (2012), and the Okawa Research Grant (1999). Research Interests: Algorithm design, graph algorithms, combinatorial optimization, computational biology, and machine learning. Key Contributions: Pioneering work on metric embeddings, approximation algorithms, and network flow problems. Notable publications include foundational papers on tree metrics, distributed object location, and electrical flow-based optimization. Rao’s work bridges theoretical computer science with practical applications, including contributions to phylogeny estimation, anomaly detection, and parallel computing frameworks like the BSP model.
Venkatesan Guruswami is a Chancellor's Professor in the Department of EECS and a Senior Scientist at the Simons Institute for the Theory of Computing at UC Berkeley . He also holds a Professor position in the Department of Mathematics . His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras (1997) , followed by a Ph.D. in Computer Science from the Massachusetts Institute of Technology (2001) . After a Miller Research Fellowship at UC Berkeley (2001–02), he held faculty roles at the University of Washington and Carnegie Mellon University before returning to UC Berkeley in January 2022. Education : B.Tech, IIT Madras (1997) Ph.D., MIT (2001) Professional Affiliations : Chancellor's Professor, UC Berkeley (EECS) Senior Scientist & Interim Director, Simons Institute Professor, UC Berkeley (Mathematics) Guruswami's research spans multiple domains within Theoretical Computer Science , focusing on Error-Correcting Codes , Approximation Algorithms , Randomness in Computing , Probabilistically Checkable Proofs , and Computational Complexity . His groundbreaking work in List Decoding has enabled codes with minimal redundancy for correcting worst-case errors, while recent advancements include Polar Codes , Deletion-Correcting Codes , and Constraint Satisfaction Problems . He has also contributed to Quantum Coding Theory , Locally Recoverable Codes , and Approximation Hardness in various computational contexts. His publications reflect a deep engagement with interdisciplinary topics. Key trends include: Quantum Information Theory : Quantum LDPC codes, transversal gates, and quantum storage. Algebraic Coding : Reed-Solomon codes, AG codes, and polynomial-based constructions. Computational Complexity : Hardness of approximation, CSPs, and parameterized intractability. Data Transmission : Polar codes, deletion channels, and feedback mechanisms. Algorithmic Techniques : Spectral methods, semirandom models, and Lasserre hierarchy applications. Guruswami has received numerous accolades, including the Simons Investigator Award , Presburger Award , Packard Fellowship , Sloan Research Fellowship , ACM Doctoral Dissertation Award , and the IEEE Information Theory Society Paper Award . He is an ACM Fellow (2017) and IEEE Fellow (2019) , with recent honors like the Guggenheim Fellowship (2023) and AMS Fellow (2023) . As an advisor, he has mentored over 25 PhD and postdoctoral researchers , including Atri Rudra , Prasad Raghavendra , and Peter Manohar , whose work has won awards like the Edmund M. Clarke Doctoral Dissertation Award and CRA Outstanding Undergraduate Researcher Award . His research is supported by grants from the National Science Foundation , Packard Foundation , and Sloan Foundation . He also serves as Editor-in-Chief of the Journal of the ACM and holds leadership roles in IEEE and arXiv moderation. Guruswami is actively involved in Simons Institute programs and co-organized workshops on Coded Computation and Information Theory . His work bridges theoretical advancements with practical applications in Cloud Storage , Quantum Computing , and Group Testing , including pandemic-era contributions like AC-DC: Amplification Curve Diagnostics for SARS-CoV-2 .
Yang P. Liu is an Assistant Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. Previously, he was a Postdoctoral Member at the Institute for Advanced Study and earned his PhD from Stanford University under the supervision of Aaron Sidford. He completed his undergraduate studies at MIT, graduating in May 2018. His educational background includes: PhD in Computer Science, Stanford University (Advisor: Aaron Sidford) Bachelor's degree, Massachusetts Institute of Technology (graduated May 2018) Dr. Liu's research spans the intersection of mathematics and computer science, with particular focus on graph algorithms , optimization , high-dimensional geometry , and additive combinatorics . His work often develops novel algorithmic techniques that bridge theoretical insights with practical applications. He has made significant contributions to areas such as convex optimization, linear programming, and combinatorial problems. His teaching includes courses like "A Principled Approach to Optimization" (CS 15-759), which covers rigorous treatments of convex optimization topics including gradient descent, interior point methods, linear regression, linear programming, and sparsification. His extensive publication record in top-tier conferences (FOCS, STOC, SODA) demonstrates a consistent focus on developing almost-linear time algorithms for fundamental graph problems, optimization techniques, and combinatorial theorems. Recent work shows increasing emphasis on combinatorial lines, corners theorem, and k-CSP approximability, while maintaining strong connections to optimization theory and graph algorithms. Dr. Liu has received notable recognition for his work: National Defense Science and Engineering Graduate (NDSEG) Fellowship (2018-2021) Google PhD Fellowship (2022-2023) Best Paper award at FOCS 2022 for "Maximum Flow and Minimum-Cost Flow in Almost Linear Time" Best Student Paper at STOC 2021 for "Discrepancy Minimization via a Self-Balancing Walk" His research has been supported by prestigious fellowships including the NDSEG Fellowship and Google PhD Fellowship. His work on graph algorithms, optimization, and combinatorics involves collaborations with researchers across theoretical computer science and mathematics. His publications often involve co-authors from multiple institutions, suggesting active research collaborations across the field. Dr. Liu maintains an active research program with a focus on developing efficient algorithms for fundamental computational problems. His recent work continues to push the boundaries of what's computationally feasible in graph algorithms, optimization, and combinatorial mathematics, with particular emphasis on achieving almost-linear time complexity for challenging problems.
Julia Chuzhoy is the Manuel Blum Professor at the Toyota Technological Institute at Chicago (TTIC) and holds a part-time Professor appointment in the Department of Computer Science at the University of Chicago . She completed her Ph.D. at the Technion under the supervision of Seffi Naor , followed by postdoctoral positions at MIT , University of Pennsylvania , and the Institute for Advanced Study . She also served as a Weizmann Institute Weston Visiting Professor in 2018-2019. Her research in theoretical computer science focuses on graph-related optimization problems , including approximation algorithms, dynamic algorithms, fast graph algorithms, and hardness of approximation. She has received major funding through NSF grants (CCF-1318242, CCF-1616584, CCF-2006464, CCF-2402283) and the NSF HDR TRIPODS award (2216899). Her recent publications highlight advancements in approximation algorithms (e.g., maximum bipartite matching), dynamic graph algorithms (e.g., decremental shortest paths), and structural graph theory (e.g., excluded grid theorem). These works span both algorithmic improvements and theoretical lower bounds. Scientific recognition includes NSF Career Award (2013) Alfred P. Sloan Research Fellowship (2011) She has advised numerous TTIC and University of Chicago Ph.D. students, including Rachit Nimavat , Zihan Tan , and Parinya Chalermsook (now faculty at Aalto University ).
Michael Molloy is a Professor in the Department of Computer Science at the University of Toronto, with a cross-appointment to the Department of Computer and Mathematical Sciences at the University of Toronto Scarborough (UTSC). He teaches courses in Discrete Mathematics and the Probabilistic Method, including CSC/MAT A67 and CSC2427/MAT1500 . Research Focus: Graph Theory, Probabilistic Methods, Random Graphs, Constraint Satisfaction Problems, and Markov Chain analysis. His work includes foundational contributions to graph coloring, such as adaptable/conflict coloring and correspondence coloring, and exploring phase transitions in random graphs. He has supervised numerous graduate students, including Lora Hrisch, Jurgen Aliaj, and Hamed Hatami, advancing combinatorial and algorithmic research. Recent publications analyze random graph processes, the freezing threshold for k-colorings, and the resolution complexity of constraint satisfaction problems. These studies intersect theoretical computer science, combinatorics, and probabilistic modeling, often revealing deep structural insights through rigorous mathematical proofs.
Standa Živný is a Professor of Computer Science at the University of Oxford and a Fellow and Tutor at Merton College. He has been a faculty member at Oxford since 2013 and was promoted to full professor in 2021. His research spans theoretical computer science and discrete mathematics, with a focus on algorithms, computational complexity, and constraint satisfaction problems (CSPs) in various forms, including optimisation, counting, and approximation. His research interests include the power and limitations of convex relaxations, sparsification, submodularity, and the algebraic and logical foundations of tractability in combinatorial problems. He has made significant contributions to understanding when and why certain problems can or cannot be efficiently solved using linear programming and other algorithmic paradigms. The recent trends in his publications show a deep engagement with approximation algorithms, hardness results, sparsification techniques, and the complexity of counting and promise problems. His work often lies at the intersection of algebra, logic, and optimisation, demonstrating the power of interdisciplinary approaches in theoretical computer science. ERC Consolidator Grant (NAASP, 2022–2027) ERC Starting Grant (PowAlgDO, 2017–2022) Royal Society University Research Fellowship (2013–2021) He actively supervises a large cohort of postdoctoral researchers and students, including PhD candidates, master’s, and undergraduate students. His leadership extends to academic service, where he serves as Editor-in-Chief of the SIAM Journal on Discrete Mathematics and holds editorial and committee positions in major journals and funding bodies. He has organised numerous workshops and research programmes at institutions such as Dagstuhl, the Isaac Newton Institute, and AIM. He is involved in major research initiatives, including a Simons Programme on symmetry in computation and an American Institute of Mathematics SQuARE on relaxations for promise CSPs.
Andrei Krokhin is a Professor in the Department of Computer Science at Durham University, UK. His academic roles include being a member of the Algorithms and Complexity Research Group. He holds a PhD in Mathematics from Ural State University (Russia) and has held positions at Warwick University and Oxford University. His research focuses on computational complexity, constraint satisfaction problems (CSP), universal algebra, and combinatorics. Education: PhD in Mathematics, Ural State University, 1990s Research Interests: Professor Krokhin investigates the mathematical and algorithmic foundations of CSP, emphasizing complexity classification and approximation. His work bridges universal algebra, logic, combinatorics, and graph theory. Key themes include algebraic approaches to CSP, constraint optimization, and the interplay between computational complexity and structural mathematics. Awards: EPSRC Advanced Research Fellowship (2006) Principal organizer of the 2006 Oxford Workshop on Mathematics of Constraint Satisfaction Invited plenary speaker at ISMVL 2003 (Tokyo) Invited lectures at NATO ASI Summer School (2003) Advising & Grants: Supervises PhD students (e.g., Yiming Qiu) and leads EPSRC-funded projects like 'Promise Constraint Satisfaction Problems: Structure and Complexity.' He recruits students for research on CSP complexity and approximation. Labs/Teams: Member of the Algorithms and Complexity Research Group at Durham University, collaborating internationally on CSP theory and applications.
Madhur Tulsiani is a Professor at the University of Chicago's Department of Computer Science and a researcher at the Toyota Technological Institute at Chicago (TTIC). His research focuses on theoretical computer science, particularly complexity theory and algorithm design, with applications in coding theory and information theory. He has been supported by NSF grants 1254044, 1816372, and 2326685. Education: Bachelor’s in Computer Science, IIT Kanpur (2001-2005) Ph.D. in Computer Science, UC Berkeley (2005-2009), advised by Luca Trevisan Postdoctoral fellowships at the Institute for Advanced Study (IAS) and Princeton University Research Interests: Mathematical foundations of computation Complexity theory and algorithm design Coding theory and error-correcting codes Sum-of-Squares hierarchies and approximation algorithms Recent Contributions: Pioneering work on list decodable codes and expander-based constructions Advances in approximation algorithms for high-dimensional expanders Lower bounds for Sum-of-Squares algorithms using high-dimensional expanders Teaching: Information and Coding Theory Mathematical Toolkit (linear algebra/probability) Summer REU programs in theoretical computer science Students: Advised PhD students including Fernando Granha Jeronimo, Goutham Rajendran, and Shashank Srivastava Co-advised students with Sasha Razborov, Janos Simon, and others Labs/Groups: Member of the Theoretical Computer Science Group at TTIC and UChicago, contributing to cross-disciplinary research in algorithms and complexity.
Anna Huber is a Senior Lecturer in Mathematics and Computing at Teesside University's Department of Computing & Games Centre for Sustainable Engineering. She holds a PhD from Saarland University (2010) and completed postdoctoral research at Durham University (2010-2013) on EPSRC-funded submodular optimization projects. Her research focuses on algebraic structures, graph theory, and combinatorial algorithms with applications in discrete optimization. She has published influential work in SIAM journals on bisubmodular functions and valued constraint satisfaction problems. Education: PhD in Mathematics and Informatics, Saarland University & Max Planck Institute (2010) Previously affiliated with University of Derby as Lecturer in Mathematics Research Themes: Explores algebraic methods for solving combinatorial optimization problems, particularly through randomized algorithms and graph-based approaches. Current work emphasizes tractability analysis in constraint satisfaction frameworks and bisubmodularity applications. Grants & Projects: EPSRC Project: 'Submodular Optimization, Lattice Theory and Maximum Constraint Satisfaction Problems' (2010-2013) Labs/Teams: Active in Teesside University's Computing & Games research groups, collaborating on sustainable engineering computing applications.
Dr. Barnaby Martin is an Associate Professor in the Department of Computer Science at Durham University, part of the Algorithms and Complexity Group (ACiD). Previously, he held a lectureship at Middlesex University and undertook postdoctoral roles at Durham and Paris institutions. His research focuses on Complexity Theory, including Proof Complexity and Computational Complexity, as well as Finite Model Theory and links between logic and complexity. Current work emphasizes Constraint Satisfaction Problems (CSP), Proof Complexity, and Algorithmic Graph Theory. His recent publications explore forbidden subgraphs, edge subdivision, Steiner Forest problems, and H-free graphs. Dr. Martin’s articles often address computational challenges in graph theory and algorithm design, with a recurring theme of analyzing tractability and hardness in specific graph classes. He has collaborated extensively with researchers like Daniel Paulusma, Siani Smith, and others on topics ranging from graph coloring to quantified constraint satisfaction problems. He supervises postgraduate students Tala Eagling-Vose and Yiming Qiu. His work has been published in venues such as Algorithmica , SIAM Journal on Computing , and ACM Transactions on Computational Logic . His research bridges theoretical computer science and discrete mathematics, contributing to foundational understanding of computational limits in structured domains.
Dr. Jakub Opršal is a Research Fellow at the University of Birmingham specializing in the theoretical foundations of computer science and mathematics. His research focuses on the computational complexity of constraint satisfaction problems (CSPs) and their variants. Previously, he held postdoctoral positions at ISTA (2022-23), Oxford (2021-22), Durham University (2018-21), TU Dresden (2016-18), and Jagiellonian University (2016), establishing a strong international research profile. Dr. Opršal received his PhD from Charles University in Prague in 2016 under the supervision of Libor Barto. His doctoral work laid the foundation for his ongoing contributions to universal algebra and computational complexity theory. Dr. Opršal's research integrates advanced mathematical frameworks including universal algebra , homotopy theory , category theory , and mathematical logic to analyze constraint satisfaction problems. He has made significant contributions to understanding the computational complexity of CSPs, particularly through topological methods and algebraic structures. His work often establishes dichotomy theorems and proves hardness results for various CSP variants, with increasing focus on promise constraint satisfaction problems. Analysis of Dr. Opršal's publication record reveals a sophisticated evolution in methodology, with recent work increasingly incorporating category-theoretic approaches and topological techniques to tackle fundamental questions in computational complexity. His research demonstrates a consistent pattern of bridging abstract mathematical concepts with concrete computational problems, particularly in establishing connections between algebraic structures and computational hardness. Dr. Opršal actively seeks PhD students and has organized significant academic events including the Birmingham CSP Meeting (2024). He serves on program committees for major theoretical computer science conferences and collaborates extensively with researchers across Europe, including prominent institutions like Oxford, Durham, and ISTA. As a key organizer of the Birmingham Constraint Satisfaction research group, Dr. Opršal contributes to a vibrant international research community. He participates in the CSP World Congress series and maintains active collaborations that advance the theoretical foundations of constraint satisfaction problems through interdisciplinary approaches combining computer science, mathematics, and logic.
Andrei Bulatov is a Professor of Computing Science at Simon Fraser University (SFU), affiliated with the School of Computing Science within the Faculty of Applied Sciences. His research focuses on computational complexity, constraint satisfaction problems (CSP), combinatorics, and universal algebra. Bulatov earned his Ph.D. and M.Sc. in Mathematics from Ural State University, Russia, in 1995 and 1991, respectively. His work bridges theoretical computer science and algebra, with notable contributions to the complexity classification of CSPs. He has received a Best Paper Award at FOCS 2002 for his dichotomy theorem on three-element set constraints. Bulatov’s research also explores counting CSPs, algorithms for satisfiability, and applications of algebraic methods in discrete mathematics. He is part of the Algorithms & Theory Group and the Computational Logic Laboratory at SFU. Bulatov’s publications span journals like Journal of Computer and System Sciences, Theoretical Computer Science, and SIAM Journal on Computing, addressing topics from graph theory to randomized algorithms. His academic service includes roles in professional organizations and editorial work. Bulatov’s teaching includes courses such as Discrete Mathematics (MACM 101) and Directed Reading (CMPT 894).
Michał Wrona is a Professor in the Department of Algorithmics at the Faculty of Mathematics and Computer Science, Jagiellonian University. His research focuses on constraint satisfaction problems (CSPs), computational complexity, universal algebra, and model theory, particularly concerning finite and omega-categorical structures. He holds a habilitation and has held postdoctoral positions at École Polytechnique (France) and Linköping University (Sweden). His current grant (2021–2026) investigates constraint satisfaction problems for infinite homogeneous structures. Education: M.Sc. in Computer Science (2004, University of Wrocław), Ph.D. in Mathematics (2009, University of Wrocław). Key Roles: Principal Investigator of the National Science Centre grant on infinite-domain CSPs. His work bridges theoretical computer science and mathematical logic, addressing foundational questions in algorithmic complexity and constraint satisfaction. Notable contributions include advances in quantified CSPs, tractability frontiers, and symmetries in infinite-domain structures. Research has been published in top venues like SIAM Journal on Computing, IEEE LICS, and ACM Transactions on Computational Logic. He actively contributes to the algorithmics research group and collaborates internationally on topics like universal algebra and computational complexity.
Michael Pinsker is a full professor and head of the Research Unit Algebra at the Vienna University of Technology. He is a leading researcher in universal algebra, model theory, and theoretical computer science, with a strong focus on constraint satisfaction problems (CSPs), particularly over infinite domains. He is deeply involved in the Vienna School of Mathematics and serves on the steering committees of the Workshop on General Algebra and the CSP World Congress (CWC), which he regularly co-organizes. Principal Investigator, ERC Synergy Grant POCOCOP (2023–2029) Principal Investigator, FWF-NCN Project on Constraint Satisfaction (2022–2026) Associate Editor, Algebra Universalis (Springer) Member, Executive Board, Vienna School of Mathematics His research lies at the intersection of algebra, logic, and computation, emphasizing the algebraic and model-theoretic analysis of infinite structures to understand the complexity of CSPs. He investigates how symmetry, topology, and polymorphisms govern tractability and hardness. His work often connects Ramsey theory and homogeneous structures with computational problems. His recent publications reveal a sustained focus on infinite-domain CSPs, particularly through algebraic methods like polymorphisms, smooth approximations, and topological clones. Trends include collapsing width hierarchies, establishing hardness criteria for infinite digraphs, and developing new algorithms based on symmetry and consistency. His work bridges finite and infinite model theory, aiming to unify complexity classification frameworks. ERC Synergy Grant POCOCOP (2023) Distinguished Paper Award, LICS 2023 Pinsker actively mentors PhD students and postdoctoral researchers, including current advisees like Johanna Brunar, Moritz Schöbi, Roman Feller, and Christoph Spiess. He has advised former PhD students Clemens Schindler, Tomas Nagy, and Michael Kompatscher. He leads major research projects funded by the European Research Council and national science foundations, indicating significant grant leadership. His collaborative network includes Libor Barto, Manuel Bodirsky, and Marcin Kozik. He leads the Research Unit Algebra at TU Wien, which includes faculty, postdocs, PhD students, and project managers working on universal algebra and CSPs. He co-organizes major annual events like the CSP World Congress and the Early Student Award meetings of the Austrian Mathematical Society, fostering community and collaboration.
