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Rik SenguptaView profile

Part-Time Lecturer

• Graph Theory
• Algorithms
• Fair Allocation
• +3 more

Rik Sengupta is a Part-Time Lecturer at the University of Massachusetts Amherst, located in the Lederle Graduate Research Center. His research focuses on graph theory, algorithms, combinatorics, and fair allocation mechanisms. He has contributed to theoretical computer science, discrete mathematics, and algorithmic economics. His work spans dynamic graph streaming algorithms, fair division under structured constraints, and optimization problems with fairness metrics. He also explores graph reconstruction techniques, quantifier complexity in boolean functions, and Ramsey theory applications. Recent publications highlight trends in graphical allocation models, probabilistic graph analysis, and algorithmic solutions for resource distribution challenges. Notable areas include EFX allocations over graphs, time-aware fairness in knapsack problems, and approximation algorithms for housing markets. Rik holds no explicitly listed academic awards or grants. His advising activities are not detailed in the provided texts. He is affiliated with computational theory research groups but no specific labs or teams are mentioned.

Friedrich Martin SchneiderView profile

Researcher

• Topological Groups
• Functional Analysis
• Ergodic Theory
• +4 more

Friedrich Martin Schneider is a mathematician affiliated with the Institute of Discrete Mathematics and Algebra at the Faculty of Mathematics and Computer Science , Technische Universität Freiberg. His research focuses on topological groups, functional analysis, ergodic theory, and dynamical systems, often intersecting with algebra and geometry. His work explores exotic groups without non-trivial unitary representations, measure concentration phenomena, and the escape property in topological groups. He has contributed to the MacWilliams extension theorem for infinite rings, the Liouville property in random walks, and the classification of profinite algebras. Collaborations with Sławomir Solecki, Andreas Thom, and Vladimir Pestov highlight his interdisciplinary impact. Recent publications emphasize geometric group theory, invariant means, and applications to computer science, including constraint satisfaction problems and intrinsic dimensionality in data analysis. Funded by the German Research Foundation (DFG), he leads a research group with Paula Kahl, Josefin Bernard, and Yannik Höll.

• Theory of Machine Learning
• Randomized Algorithms
• Convex Optimization
• +4 more

Nicholas Harvey is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. He holds affiliations as an Associate Member in the Department of Mathematics and serves as Director of the Mathematics of Information, Learning, and Data (MILD) research cluster. His research focuses on randomized algorithms, convex optimization, and machine learning theory, with notable contributions to online learning, submodular functions, and algorithmic aspects of discrepancy theory. Harvey's academic journey includes significant work on problems such as prediction with expert advice, learning mixtures of Gaussians, and graph algorithms. He has supervised numerous graduate students, including notable researchers like Huang Fang and Victor Sanches Portella. His work has been recognized with awards such as the Best Paper Award at NeurIPS 2018 and the Excellence in Teaching Award in 2024. His research outputs span theoretical computer science, optimization, and machine learning, with key contributions in algorithms for submodular functions, online convex optimization, and probabilistic methods. He has also contributed to practical applications, such as algorithms for network coding and efficient failure notification systems in distributed networks. Harvey is actively involved in teaching, including pioneering courses on randomized algorithms and machine learning theory. His research group collaborates on projects related to the MILD cluster, focusing on interdisciplinary applications of data science and mathematics.

• Theoretical Physics
• Statistical Mechanics
• Condensed Matter Physics
• +3 more

Dr. Damien Barbier is a researcher specializing in theoretical physics, with a focus on statistical mechanics, condensed matter physics, and neural networks. His work bridges computational and theoretical approaches to understanding complex systems. Research Interests : Disordered systems, optimization algorithms, and quantum transport phenomena. Publications : Active in journals like SciPost Physics , with contributions to high-dimensional geometry and thermalization in harmonic models. Scientific Contributions : His recent articles explore anomalous transport in Anderson models, connected-solutions states in perceptrons, and generalized Gibbs ensembles. These works highlight interdisciplinary methods combining physics and machine learning.

Cesim ErtenView profile

Associate Professor

• Algorithms
• Bioinformatics
• Computational Biology
• +3 more

Cesim Erten serves as an Associate Professor of Practice in the Department of Computer Science at the University of Arizona, maintaining an active research program in algorithm development for computational biology. His office is located in GS 845 with contact information 520-626-1350 and cesim@cs.arizona.edu. Education: Ph.D. in Computer Science, University of Arizona, 2004 Research Interests: Dr. Erten specializes in designing algorithms for biological network analysis with emphasis on cancer genomics and single-cell data integration. His work bridges theoretical computer science and biomedical applications through network alignment techniques, mutation impact prediction, and driver pathway identification. Recent publications demonstrate consistent innovation in applying graph-theoretic methods to precision oncology challenges, particularly in developing tools for personalized cancer driver detection and multi-omics data integration. Scientific Awards: No scientific awards mentioned in source materials Advising and Grants: While graduate student advising details are unavailable in provided texts, Dr. Erten's extensive publication record since 2004 indicates sustained research activity. His work shows strong alignment with NIH-funded initiatives in computational oncology, though specific grant awards are not documented in the source materials. Labs and Teams: Research focuses on computational biology algorithm development within the University of Arizona's Computer Science ecosystem, with particular emphasis on cancer genomics applications. Collaborative work with biomedical researchers is evident through publications, though dedicated lab names or formal team structures are not specified in available information.

Youngtak SohnView profile

Assistant Professor

• Probability Theory
• High-Dimensional Statistics
• Random Graphs
• +3 more

Youngtak Sohn is an Assistant Professor in the Division of Applied Mathematics at Brown University. His research bridges probability theory with statistical physics, machine learning, and theoretical computer science. Current investigations focus on high-dimensional statistical inference, random constraint satisfaction problems, and phase transitions in disordered systems. Previously, he was a postdoctoral researcher at MIT and earned his PhD in Statistics from Stanford University under Amir Dembo. Key research areas include: Phase transitions in random constraint satisfaction problems Statistical-computational gaps in high-dimensional inference Replica symmetry breaking in spin glass models Sharp thresholds in graph inference and community detection His publications demonstrate deep mathematical rigor, with recent work establishing fundamental limits in statistical estimation using low-degree polynomials and characterizing exact phase transitions in stochastic block models. The research consistently develops new mathematical frameworks for understanding computational thresholds in high-dimensional statistics and statistical physics. He mentors students through programs like MIT PRIMES, guiding projects on hypergraph coloring thresholds. His teaching portfolio includes graduate courses in probability theory and seminars on statistical learning theory.

Yang LiuView profile

Assistant Professor

• Theoretical Computer Science
• Graph Algorithms
• Optimization
• +4 more

Yang P. Liu is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. 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. His research focuses on theoretical computer science and mathematics, emphasizing graph algorithms, optimization, high-dimensional geometry, and additive combinatorics. He has received notable awards including the A.W. Tucker Prize and Google PhD Fellowship, alongside multiple best paper recognitions at major conferences like FOCS, STOC, and ITCS. Education: PhD in Computer Science from Stanford University (2023); BS from MIT (2018). Research Interests: Graph Algorithms Optimization (especially convex and high-dimensional) Algorithmic Techniques in Additive Combinatorics Geometric and Structural Aspects of Computation Teaching: Currently instructing CS 15-759: A Principled Approach to Optimization (Spring 2025), covering topics like gradient descent, interior-point methods, and sparsification techniques. Course emphasizes rigorous mathematical foundations. Awards: Recognized for contributions to optimization theory and algorithmic complexity. His work bridges discrete mathematics and continuous optimization paradigms.

• Distributed Computing
• Quantum Computing
• Cellular Automata
• +3 more

Augusto Modanese is a Postdoctoral Researcher in the Department of Computer Science at Aalto University. His research focuses on distributed computing, quantum computing, and computational models like cellular automata and fungal automata. He investigates local problems in tree networks, quantum advantage limitations, and sublinear-time algorithms. Research Trends: Recent publications emphasize distributed quantum computing, algorithmic equivalence between quantum and classical models, and computational complexity in constrained topologies. Key themes include probabilistic cellular automata, graph coloring, and bio-inspired computing frameworks.

• Discrete Mathematics
• Theoretical Computer Science
• Random Discrete Structures
• +4 more

Prof. Dr. Konstantinos Panagiotou is a faculty member at the Department of Mathematics, Ludwig Maximilian University of Munich. His research focuses on Discrete Mathematics and Theoretical Computer Science, particularly Random Discrete Structures, Random Graphs, Analytic Combinatorics, and Randomized Algorithms. He leads the workgroup on Stochastics and Financial Mathematics. His research spans theoretical and applied domains, including Random Graph Models Combinatorial Optimization Stochastic Processes in Networks Algorithmic Analysis Financial Risk Modeling His recent publications analyze critical phenomena in random constraint satisfaction, dispersion processes, and combinatorial enumeration, with applications in networks and financial systems. Key projects funded by the ERC, Volkswagen Stiftung, and DFG reflect his interdisciplinary impact. Scientific Awards: ERC Consolidator Grant (2018-2023) for 'Phase Transitions in Random Constraint Satisfaction Problems' He has advised numerous PhD and postdoctoral researchers, including Leon Ramzews, Matija Pasch, and Benedikt Stufler, and contributed extensively to teaching core courses like Discrete Mathematics, Optimization, and Probability Theory.

Petr KolmanView profile

Associate Professor

• Theoretical Computer Science
• Combinatorial Optimization
• Graph Theory
• +7 more

Assoc. Prof. Petr Kolman is an academic at the Department of Applied Mathematics , Charles University , where he serves in advisory bodies of the faculty management. His research focuses on theoretical computer science , combinatorial optimization , and graph algorithms , particularly in approximation techniques and network flow problems . He has taught courses such as Linear Algebra 1/2 and Mathematical Programming and Polyhedral Combinatorics . Recent publications highlight his work on spanning tree congestion (2025), length-bounded cuts (2020), and treewidth-based extended formulations (2020). His research spans algorithms , flow theory , and graph modification problems . He maintains contact through kolman@kam.mff.cuni.cz and office hours in Prague Malostranské nám. 2/25, 2nd floor, room S225 .

Yexiang XueView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Computational Science and Engineering
• +6 more

Yexiang Xue is an Assistant Professor in the Department of Computer Science at Purdue University, part of the College of Science. He joined Purdue in Fall 2018. His research focuses on integrating machine learning and probabilistic reasoning to enable optimal decision-making in high-dimensional, uncertain environments. Key areas include computational sustainability, materials science, robotics, and medical AI. Education: PhD in Computer Science, Cornell University (2018), advised by Carla Gomes and Bart Selman. B.Sc. in Computer Science, Peking University, China (2011). Research Interests: Xue develops cross-cutting computational methods for scientific discovery, constraint-embedded machine learning, and AI-driven sustainability. His work spans symbolic regression, probabilistic models, and applications in robotics, healthcare, and materials science. Recent efforts include end-to-end physics model discovery and integrating decision diagrams into neural networks. Awards: NSF CAREER Award (2024). IAAI Innovative Application Award (2017) for Phase-Mapper AI platform in materials discovery. Advising & Grants: Mentored multiple undergraduates (e.g., Luming Tang, Runzhe Yang) pursuing graduate studies. His NSF CAREER grant supports AI-driven scientific discovery. Teaching & Service: Taught courses like Statistical Machine Learning (CS 578). Serves on AAAI, UAI, and IJCAI program committees. Media coverage includes NSF News, Science, and MIT Technology Review.

Ioana-Oriana BerceaView profile

Assistant Professor

• Theoretical Computer Science
• Data Structures
• Randomized Algorithms
• +6 more

Ioana-Oriana Bercea is an Assistant Professor at the Division of Theoretical Computer Science (EECS), KTH Royal Institute of Technology, and a member of the Digital Futures Faculty. She holds a PhD in Computer Science from the University of Maryland, with prior postdoctoral research at the IT University of Copenhagen and Tel Aviv University. Her research focuses on Theoretical Computer Science, including Data Structures (e.g., Bloom filters), Randomized Algorithms (e.g., hashing), and Computational Geometry (e.g., TSP). Education : PhD in Computer Science, University of Maryland (advisor: Samir Khuller) Master's in Computer Science, University of Maryland (advisor: Aravind Srinivasan) Bachelor of Science in Mathematics (Honors) and Computer Science, University of Chicago Research Interests : She explores scalable data management (e.g., Bloom filters), algorithm design for clustering and hashing, and geometric optimization problems. Her work emphasizes theoretical foundations with practical applications. Key Achievements : VR Starting Grant (2024) for the project "DataTech" Best Artifact Award at ACM SIGMOD 2023 for the InfiniFilter paper Advising & Grants : Advises PhD student Jonas Østergaard Klausen (joint with Mikkel Thorup and Jacob Holm) Lead researcher on grants exploring data storage and algorithmic efficiency Labs & Teams : Active in KTH's Theoretical Computer Science group and collaborates with the Digital Futures initiative to advance cross-disciplinary digital technologies.

Vladimir KolmogorovView profile

Professor

• Discrete Optimization
• Combinatorial Optimization
• Global Optimization
• +3 more

Vladimir Kolmogorov is a Professor at the Institute of Science and Technology Austria (IST Austria), leading the Kolmogorov Group focused on Discrete Optimization. He previously held positions as Assistant Professor at IST Austria (2011–2014), Lecturer at University College London (2005–2011), and Associate Researcher at Microsoft Research (2003–2005). His research spans algorithm development for graphical models, combinatorial optimization, and applications in computer vision. Education: M.S. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology, and Ph.D. in Computer Science from Cornell University (2003). Research interests include complexity classifications, algorithm design for discrete optimization problems (e.g., max-flow, min-cost matching), and applications in computer vision. Notable contributions include the 'Boykov-Kolmogorov' max-flow algorithm and 'Blossom V' for minimum cost perfect matching. Honors include the ERC Consolidator Grant (2014–2019), Koenderink Prize (2012), and best paper awards at CVPR and ECCV. His work on optimization algorithms and theoretical complexity has significantly impacted computer vision and combinatorial optimization fields. Advising and grants: Supervised multiple PhD students and postdocs, with current advisees including Martin Dvořák and Pavel Arkhipov. His team explores inference in graphical models, combinatorial optimization, and discrete optimization theory.

• Distributed Algorithms
• Quantum Computing
• Graph Theory
• +2 more

Henrik Lievonen is a Doctoral Researcher at Aalto University's Department of Computer Science. His work focuses on distributed algorithms and their connections to mathematics, computer science theory, and quantum computing. University: Aalto University Role: Researcher in distributed algorithms and quantum computing Research interests center on distributed systems, quantum advantage analysis, and algorithmic complexity in graph problems. Key areas include LOCAL model constraints, quantum derandomization, and theoretical limits of distributed quantum computing. Scientific Awards: Nokia Scholarship 2024 (Nokia Foundation)

• Formal Methods
• Model Checking
• Satisfiability (SAT) Solving
• +4 more

Tommi Junttila serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, Finland, where he conducts cutting-edge research at the intersection of formal methods and computational logic. His academic profile demonstrates sustained contributions to theoretical computer science with practical applications in system verification and blockchain technology. His research program centers on advancing formal verification techniques, with core expertise in: Symmetry reduction algorithms for state space explosion SAT/SMT solving with specialized parity and XOR reasoning Bounded model checking of timed and asynchronous systems Blockchain protocol verification (notably DeFi lending pools) Canonical labeling tools for graph automorphism detection Recent work shows increasing focus on decentralized finance applications while maintaining foundational contributions to solver technology. Analysis of his publication trajectory (2011-2022) reveals consistent innovation in SAT solving methodologies, with symmetry reduction and parity reasoning forming persistent research threads. His 2022 work on DeFi lending pools represents a strategic expansion into blockchain verification, leveraging established formal methods expertise for emerging financial technologies. Tool development (bliss, PySMT) demonstrates commitment to practical research impact. No scientific awards were documented in the source materials. Similarly, no information regarding student supervision, grant funding, or laboratory affiliations was present in the provided text. His independent tool development (including bliss for graph canonical labeling and PySMT for SMT solver interfaces) indicates significant technical leadership within the formal methods community.