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Konstantin MakarychevView profile

Professor

• Approximation Algorithms
• Beyond Worst-Case Analysis
• Clustering Algorithms
• +3 more

Konstantin Makarychev is a Professor of Computer Science and Associate Chair for Graduate Studies at Northwestern University's McCormick School of Engineering. His research focuses on designing efficient algorithms for computationally hard problems, with an emphasis on approximation algorithms, beyond worst-case analysis, and applications of high-dimensional geometry. Before joining Northwestern, he was a researcher at Microsoft and IBM Research Labs, and earned his Ph.D. from Princeton University in 2007 under Moses Charikar. He holds a B.S. in Mechanics and Mathematics from Moscow State University and an M.S. from the Department of Mathematics at Moscow State University. His academic career includes roles at Microsoft Research, IBM Research, and teaching positions at the University of Washington. Research interests include approximation algorithms for constraint satisfaction problems, clustering algorithms, and algorithmic approaches to machine learning. He has published extensively in top conferences like SODA, ICML, and STOC, and his work often bridges theoretical computer science with practical applications in data storage and bioinformatics. Awards: IBM A-Level Accomplishment (2011), IBM Pat Goldberg Best Paper Award (2009), IBM PhD Fellowship (2006–2007). Grants: NSF Award CCF-1955351 (2020–2025), participation in IDEAL Institute (2019–2022). Teaching: Courses include Design and Analysis of Algorithms, Approximation Algorithms, and Advanced Algorithm Design. His work on correlation clustering, explainable k-means, and DNA data storage has led to impactful contributions in both theory and practical applications.

Fahiem BacchusView profile

Professor

• Artificial Intelligence
• Constraint Satisfaction Problems
• Satisfiability (SAT)
• +7 more

Fahiem Bacchus is a Professor in the Department of Computer Science at the University of Toronto, within the Faculty of Arts and Science. His research is centered on foundational problems in Artificial Intelligence, particularly in reasoning, representation, and algorithm design. Institution: University of Toronto School: Faculty of Arts and Science Department: Department of Computer Science Email: fbacchus@cs.toronto.edu His work spans key areas including constraint satisfaction, satisfiability (SAT), automated planning, Bayesian inference, and constraint optimization. He focuses on developing algorithms that exploit domain-specific knowledge and structural properties to improve performance. His research has led to significant contributions such as the TLPlan planning system, which won the AIPS2002 international planning competition, and the 2clseq SAT solver, which demonstrated that extensive binary clause reasoning can dramatically improve solver efficiency. His work on preprocessors like Hypre further advanced formula simplification techniques. The recent articles reflect a strong focus on improving search algorithms through richer reasoning mechanisms, particularly in SAT solving and non-clausal logic. His publications show a consistent trend toward enhancing DPLL-based solvers with advanced inference techniques, reducing search space through preprocessing, and leveraging structural knowledge in logical theories. No scientific awards are explicitly mentioned in the provided text. Bacchus has supervised research and developed educational materials, with involvement in teaching and academic conference organization. While specific grants are not listed, his software releases (2clseq, Hypre, NoClause) suggest externally supported research activity. He has contributed tutorials, talks, and online teaching resources, indicating an active role in academic dissemination and mentoring. His research group has produced several software systems available for non-commercial research use, including 2clseq, Hypre, and NoClause, reflecting a strong applied and experimental component to his work. These tools are used in SAT solving, preprocessing, and non-clausal reasoning, and are documented with detailed technical information and usage instructions.

Stefan GlockView profile

Assistant Professor

• Discrete Mathematics
• Extremal Combinatorics
• Probabilistic Combinatorics
• +4 more

Professor Stefan Glock is an Assistant Professor of Discrete Mathematics at the University of Passau's Faculty of Computer Science and Mathematics, a position he has held since September 2022. Prior to this appointment, he spent three years as a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich, following the completion of his doctorate at the University of Birmingham. Stefan Glock received his mathematics education at Technische Universität Ilmenau from 2009 to 2014, then pursued his PhD at the University of Birmingham, which he completed in 2018. His doctoral dissertation, "Decompositions of Graphs and Hypergraphs," was the runner-up for the Richard-Rado-Preis 2018. Professor Glock's research focuses on discrete mathematical structures, with particular emphasis on their asymptotic properties. His work spans several interconnected fields of combinatorics: Extremal Combinatorics : Investigating the maximum or minimum possible size of mathematical structures satisfying certain properties Probabilistic Combinatorics : Applying probability theory to solve combinatorial problems Graph Theory : Studying properties of graphs and networks Ramsey Theory : Examining conditions under which order must appear in large structures Design Theory : Creating arrangements of elements satisfying specific balance properties Discrete Geometry : Analyzing geometric problems with discrete structures Analysis of Professor Glock's recent publications reveals a consistent focus on solving long-standing open problems in combinatorics using innovative methods that combine probabilistic techniques with structural insights. His work often bridges theoretical mathematics with applications in theoretical computer science, particularly in the analysis of algorithms and network structures. A significant portion of his research addresses fundamental questions about graph and hypergraph decompositions, which have implications for coding theory, cryptography, and network design. Professor Glock has received notable recognition for his contributions to mathematics: Runner-up for the Richard-Rado-Preis 2018 for his dissertation "Decompositions of Graphs and Hypergraphs" Awarded funding through the prestigious DFG Emmy Noether Programme in 2024 for his research group on "the interplay of structure and randomness in mathematics" As a faculty member at the University of Passau, Professor Glock leads the Discrete Mathematics research group and actively collaborates with mathematicians worldwide. He has established a strong research program that has attracted funding for academic visitors and supports multiple research projects. His approach to mathematical problems emphasizes developing new methods that have far-reaching implications beyond the specific problems being solved. Professor Glock's research group at the University of Passau focuses on the interplay between structure and randomness in discrete mathematics. The group maintains active collaborations with leading institutions including ETH Zurich, University of Birmingham, and various research centers across Europe. Through the DFG Emmy Noether Programme funding, his group is expanding its research on combinatorial structures and their applications.

Julia ChuzhoyView profile

Professor

• Theoretical Computer Science
• Graph Theory
• Approximation Algorithms
• +5 more

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 ).

Sandy IraniView profile

Professor

• Quantum Computing
• Algorithm Design
• Computational Complexity Theory
• +5 more

Sandy Irani is a Full Professor at the University of California, Irvine (UCI) in the Department of Computer Science within the Donald Bren School of Information and Computer Sciences. She received her Ph.D. from UC Berkeley in 1991 and has been at UCI since 1992. Her research focuses on algorithm design, computational complexity theory, and quantum computing, with notable contributions to online algorithms and quantum complexity theory. She currently serves as Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley, a role she has held since 2022. This position allows her to collaborate with researchers across theoretical computer science and related disciplines. Irani’s teaching excellence is recognized through the UCI Distinguished Faculty Award for Teaching (2021), and she has contributed to education through her zyBook on Discrete Mathematics, used by over 94,000 students globally. Her work bridges foundational computer science with practical applications, including power management strategies and distributed computing algorithms. Notably, she has collaborated with industry leaders like Mike Luby on optimizing distributed systems. Her research in quantum computing explores computational problems inspired by condensed matter physics, aiming to understand quantum advantage over classical systems. She has also authored influential papers on topics like cache hierarchy design, scheduling algorithms, and the theoretical limits of electronic structure calculations. Awards: ACM Fellow (2022), UCI Distinguished Faculty Award for Teaching (2021). Key Roles: Associate Director, Simons Institute; Vice Chair, Computing Division at UCI. Recent Projects: Quantum algorithms for condensed matter systems, maximal independent set algorithms in distributed networks.

Michael MolloyView profile

Professor

• Graph Theory
• Probabilistic Method
• Random Graphs
• +2 more

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.

Fabio FuriniView profile

Associate Professor

• Combinatorial Optimization
• Operations Research
• Integer Linear Programming
• +3 more

Fabio Furini is an Associate Professor at the Department of Computer Science, Automatics, and Management (DIAG) at Sapienza University of Rome since September 2021. Prior to this position, he served as a CNR researcher at IASI-CNR in Rome (2020-2021), Maître de Conférences at Université Paris-Dauphine, France (2013-2019), postdoctoral researcher at Université Paris-13, France (2012-2013), and research fellow at the University of Bologna (2011-2012). His educational background includes a Ph.D. in Control Engineering and Operations Research from the University of Bologna in 2011. He further obtained the Habilitation à Diriger des Recherches (HDR) in France in 2017 and the National Scientific Qualification for Full Professor in Operations Research in Italy in 2019. Fabio Furini conducts theoretical and methodological research on Combinatorial Optimization and Operations Research. His primary focus is on developing exact algorithms based on decomposition and reformulation techniques for integer linear programming problems. His research spans various applications including network optimization, graph theory, and combinatorial problems such as the maximum clique problem, bin packing problem, and vertex separator problem. His work often bridges theoretical developments with practical applications in transportation, logistics, and network security. His recent publications demonstrate a strong focus on exact algorithms for combinatorial optimization problems, particularly in network interdiction, bin packing with temporal constraints, and graph-based problems. His work consistently combines integer programming techniques with combinatorial search methods to develop novel formulations and efficient solution approaches that advance the state-of-the-art in these domains. Among his notable scientific awards are the Prime d'encadrement doctoral et de recherche (PEDR), which he received annually from 2014 to 2020, recognizing him among the top 15% of researchers in the French university system. He also holds the prestigious Habilitation à Diriger des Recherches from France (2017) and the National Scientific Qualification for Full Professor in Operations Research from Italy (2019). Fabio Furini has been actively involved in supervising PhD students and has served as principal investigator for numerous national and international research projects. His extensive network includes over 60 co-authors across European and American universities. He is also a member of the editorial boards for three prestigious international journals: Omega, Annals of Operations Research, and Discrete Applied Mathematics. His research activities include collaborations with various institutions across Europe and the United States, including Imperial College London and the University of Colorado. These collaborations have resulted in a robust research program focused on advancing the theoretical foundations and practical applications of combinatorial optimization.

Shuangping LiView profile

Assistant Professor

• Probability Theory
• High-Dimensional Statistics
• Theoretical Machine Learning
• +5 more

Shuangping Li is an Assistant Professor in the Department of Statistics and Data Science at Yale University. She was previously a Stein Fellow in the Department of Statistics at Stanford University (2022–2025). Her research lies at the intersection of probability theory, high-dimensional statistics, theoretical machine learning, and the theory of algorithms. Ph.D. in Applied and Computational Mathematics, Princeton University (2022) B.Sc. in Mathematics, University of Hong Kong Her research interests include probability theory , high-dimensional statistics , theoretical machine learning , and theory of algorithms . She investigates foundational aspects of random constraint satisfaction problems, neural networks, spectral methods, and phase transitions in high-dimensional models. Her work often draws from statistical physics and combinatorics to explain algorithmic behavior. The recent articles highlight a strong focus on binary perceptrons , clustering in network models , and algorithmic phase transitions . Keywords across publications include probability, theoretical computer science, machine learning, and statistical inference. Subfields reveal deep engagement with spin glass theory, discrepancy minimization, spectral embedding, and information-computation gaps. Scientific awards include: Stein Fellow, Department of Statistics, Stanford University (2022–2025) She has advised and taught at both Stanford and Yale, including courses such as Advanced Probability , Theory of Probability , and Stochastic Processes . She has organized seminars at Stanford and has delivered invited talks at institutions including Cornell, Duke, UC Berkeley, and Princeton. Her collaborative research involves prominent scholars such as Allan Sly, Emmanuel Abbe, and Tselil Schramm. There is no mention of external grants, but her postdoctoral fellowship suggests research funding support. She is involved in academic service through organizing the Stanford Statistics and Probability Seminars. She maintains an active research presence with publications in top venues like STOC, FOCS, COLT, ICLR, and journals such as Annals of Probability and Annals of Statistics .

Fabrizio RiguzziView profile

Professor

• Logic Programming
• Statistical Relational Artificial Intelligence
• Probabilistic Logic Programming
• +3 more

Fabrizio Riguzzi is a Full Professor at the Department of Mathematics and Computer Science of the University of Ferrara, Italy. His academic career spans over two decades at the same institution, having served as Associate Professor (2014-2020) and Assistant Professor/Ricercatore (1999-2014). He is an active researcher in the fields of Logic Programming and Statistical Relational Artificial Intelligence with numerous publications and leadership roles in international conferences. His educational background includes: PhD in Electronic and Computer Engineering from the University of Bologna (1999) Laurea in Computer Engineering from the University of Bologna (1995) Riguzzi's research focuses on probabilistic approaches to artificial intelligence, particularly probabilistic logic programming and statistical relational AI. His work bridges symbolic reasoning with probabilistic methods, developing frameworks for uncertain knowledge representation and reasoning. He has made significant contributions to probabilistic answer set programming, neuro-symbolic integration, and applications in areas like network intrusion detection and knowledge graph completion. His research demonstrates how logical formalisms can be enhanced with probabilistic reasoning to tackle real-world problems with uncertainty. An analysis of his recent publications reveals a strong trend toward integrating neural and symbolic approaches in AI, with significant work on probabilistic answer set programming frameworks. His research spans theoretical foundations of probabilistic logic programming, practical implementations, and applications in cybersecurity, knowledge graphs, and decision-making under uncertainty. The interdisciplinary nature of his work connects computer science theory with practical AI applications. His notable awards include: Alain Colmerauer 10-Year Test-of-Time Award at ICLP 2021 Best Paper Award for "BUNDLE: A Reasoner for Probabilistic Ontologies" at RR-2013 Highly Commended Paper Award for "Probabilistic declarative process mining" at KSEM 2010 Riguzzi has supervised several PhD students to completion, including Elena Bellodi, Riccardo Zese, and Giuseppe Cota, who have gone on to win prestigious awards for their theses. He has served in numerous editorial roles, including Associate Editor of the Journal of Artificial Intelligence Research and Editor in Chief of Intelligenza Artificiale. His leadership extends to organizing major conferences like ILP 2018 and serving on program committees for top AI venues including IJCAI, AAAI, and ECAI. He is a member of the ML@unife research group and has developed several online systems including cplint, TRILL, and an Online AUC calculator. His work has fostered collaborations across the AI research community, particularly in the areas of probabilistic logic programming and neuro-symbolic AI.

Giorgia RamponiView profile

Assistant Professor

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +3 more

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

Fabio VitorView profile

Professor

• Integer Programming
• Operations Research
• Mathematical Optimization
• +2 more

Dr. Fabio Vitor is a faculty member in the Department of Mathematical and Statistical Sciences at the University of Nebraska at Omaha, College of Arts & Sciences. His research focuses on mathematical optimization and operations research with applications in integer programming. His research interests include: Integer Programming and Combinatorial Optimization Mathematical Optimization techniques Operations Research applications Algorithm development for complex optimization problems Cutting plane methods Knapsack constraints Dr. Vitor's publications demonstrate expertise in merging knapsack constraints with cover inequalities and developing advanced simplex methods. His work shows a consistent focus on improving computational efficiency in solving integer programming problems, with practical applications in resource allocation and decision-making systems. Research recognition includes: $600,000 Air Force Office of Scientific Research Grant through the Nebraska Deterrence Lab He actively collaborates with researchers in the mathematical optimization community, contributing to advancing methodologies in operations research and publishing in reputable journals such as Optimization.

• Optimization Algorithms
• Evolutionary Algorithms
• Machine Learning
• +4 more

Rafał Biedrzycki is an Assistant Professor at The Institute of Computer Science within Warsaw University of Technology's Faculty of Electronics and Information Technology. His research focuses on optimization algorithms, evolutionary computation, and machine learning applications. He holds a PhD in Information Science (2009) and a D.Sc. (2024). Key research interests include evolutionary algorithms (e.g., Differential Evolution, CMA-ES), optimization techniques for real-world problems (e.g., compressor scheduling, optical networks), and algorithm benchmarking. He has contributed to improving constraint-handling methods and hybrid algorithm designs. Received team awards for scientific achievements from Warsaw University of Technology (2019, 2023) and teaching excellence (2021, 2024). Active in interdisciplinary projects, including the DAFNE initiative for data fusion systems (2010-2011). Supervises research in optimization, machine learning, and computational electromagnetics. His work bridges theoretical algorithm development with practical applications in engineering and data analysis. Recent efforts include analysis of CEC competition algorithms and parameter-tuning methodologies.

• Heuristic Search
• Combinatorial Search
• Artificial Intelligence
• +2 more

Nathan Sturtevant is a Professor at the University of Alberta's Department of Computing Science, an Amii Fellow, and Canada CIFAR Chair. His research spans heuristic and combinatorial search problems, with applications in game AI and pathfinding algorithms. He collaborates with the games industry to implement his research in commercial products. Dr. Sturtevant's research explores search algorithms for single and multiple agents, covering areas such as bidirectional search, meta-learning for game theory, procedural content generation, and multi-agent pathfinding. His work integrates machine learning techniques with classical search algorithms to solve complex problems in game environments. Recent publications demonstrate innovations in search optimization, including novel frameworks for suboptimal bidirectional search, new puzzle difficulty metrics, and applications of transformer models to card game planning. His FarmQuest player telemetry dataset provides resources for studying player behavior in farming simulations.

Eren C KızıldağView profile

Assistant Professor

• Statistics
• Data Science
• Probability Theory
• +5 more

Eren C. Kızıldağ is an Assistant Professor in the Department of Statistics at the University of Illinois Urbana-Champaign, with additional affiliations in the Department of Electrical and Computer Engineering. He holds a PhD in Electrical Engineering and Computer Science from MIT, where he was part of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS). Previously, he was a Distinguished Postdoctoral Fellow at Columbia University. His research bridges probability, statistics, and computer science, focusing on statistical-computational trade-offs in random models such as optimization problems and statistical inference. Key interests include understanding algorithmic barriers in high-dimensional problems, discrepancy theory, and neural network theory. His work often explores connections to statistical physics and combinatorial structures. Recent publications highlight contributions to topics like low-rank tensor recovery, algorithmic obstructions in partitioning problems, and geometric barriers in discrepancy minimization. His research also addresses theoretical foundations of overparameterized neural networks and hardness results for partition functions in spin glass models. Kızıldağ’s academic journey includes a Master’s from MIT and a B.S. (summa cum laude) from Bogaziçi University, with early work in MRI technology at ISMRM. His research is supported by collaborations with institutions like LIDS and IDSS, and his work frequently appears in top venues such as Annals of Applied Probability, Mathematics of Operations Research, and IEEE ISIT.

Yangjun ChenView profile

Professor

• Computer Science
• Database Systems
• Graph Databases
• +6 more

Dr. Yangjun Chen is a Full Professor in the Department of Applied Computer Science at the University of Winnipeg, Canada. He holds a Ph.D. from the University of Kaiserslautern, Germany (1995). His research focuses on database systems, graph algorithms, computational complexity, and theoretical computer science. Key areas include Federated Databases, Deductive Databases, DNA Databases, and the P vs NP problem. Education: Ph.D. in Computer Science, University of Kaiserslautern, Germany (1995). Research interests span graph query processing, algorithm design, big data optimization, and NP-completeness. Recent work includes polynomial-time solutions for 2-MAXSAT and advancements in string matching algorithms for DNA databases. Publications highlight contributions to graph indexing, efficient reachability queries, and algorithmic efficiency in databases. His work often bridges theoretical foundations with practical database applications. Awards: Excellent Merit Award (2022-2023, 2021-2022) - University of Winnipeg Best Article, ACTA Scientific Computer Sciences (2022) Multiple Best Paper Awards at conferences like DBKDA 2016 and CyberC Summit Teaching includes Advanced Databases, Distributed Database Systems, and Algorithms courses at both undergraduate and graduate levels. Active in supervising research in database systems and theoretical computer science. Laboratory and team focus on database innovation, including projects on graph databases and efficient query processing techniques.