Find Academic Supervisors

• Theoretical Computer Science
• Computational Complexity
• Data Structures
• +3 more

Prof. Michal Koucký is a faculty member at the Institute of Informatics, Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on theoretical computer science, particularly computational complexity, data structures, algorithms, and combinatorics. He leads significant research projects including EPAC (supported by GA CR) and LBCAD (funded by ERC). His educational background includes a Ph.D. from Rutgers University (2003) and a Master's degree from Charles University (1998). Prof. Koucký's research spans multiple areas of theoretical computer science with emphasis on computational complexity , data structures , algorithms , and combinatorics . His work explores fundamental questions about computational efficiency, lower bounds, and algorithmic techniques. He has made significant contributions to edit distance algorithms, string processing, and circuit complexity. His research often bridges theoretical foundations with practical algorithmic applications, particularly in areas like dynamic data structures and approximation algorithms. An analysis of his recent publications (2019-2024) reveals a strong focus on string algorithms, particularly edit distance problems. His work develops novel approximation techniques, sketching methods, and sublinear-time algorithms for pattern matching. There's also significant work on data structure lower bounds and connections to popular complexity conjectures. His research demonstrates a consistent pattern of tackling fundamental problems with innovative approaches that push theoretical boundaries while maintaining practical relevance. Antonin Svoboda award for best dissertation in 2014 (awarded to Jan Bulánek) Best paper award at SAGT 2016 for 'The Big Match in Small Space' Best paper award at FOCS 2018 for 'Approximating Edit Distance Within Constant Factor in Truly Sub-Quadratic Time' He has successfully advised numerous graduate students including Pavel Dvořák, Karel Král, Debarati Das, and Jan Bulánek who have gone on to positions at prestigious institutions like Bristol, Google (Zurich), and Penn State. His research is supported by significant grants including the EPAC project (GA CR) and LBCAD project (ERC), demonstrating the impact and relevance of his work in theoretical computer science. Prof. Koucký is an active member of the theoretical computer science community, regularly presenting at major conferences including STOC, FOCS, ICALP, and SODA. His work often involves collaborations with researchers from institutions worldwide, contributing to a vibrant research environment at Charles University's Institute of Informatics.

Peter ManoharView profile

Researcher

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +4 more

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

Huseyin TopalogluView profile

Professor

• Revenue Management
• Pricing Analytics
• Operations Research
• +4 more

Dr. Huseyin Topaloglu is the Howard and Eleanor Morgan Professor at the School of Operations Research and Information Engineering at Cornell University and Cornell Tech . He holds a B.S. in Industrial Engineering from Bogazici University (1997), an M.A. in Operations Research from Princeton University (1999), and a Ph.D. in Operations Research from Princeton University (2001). Education: B.S. Industrial Engineering (1997) – Bogazici University M.A. Operations Research (1999) – Princeton University Ph.D. Operations Research (2001) – Princeton University His research focuses on revenue management , pricing analytics , assortment optimization , and stochastic dynamic programming . Recent work includes advancements in multinomial logit models , network revenue management , and dynamic inventory allocation . He has published extensively in leading journals such as Operations Research , Management Science , and M&SOM , often collaborating with researchers like Y. Bai , P. Rusmevichientong , and O. El Housni . His 15 most recent publications (2024–2003) demonstrate expertise in revenue management using multinomial logit models , dynamic programming , and stochastic optimization . Key subfields include assortment planning , network revenue , pricing under uncertainty , and approximation algorithms for complex systems like ambulance redeployment and airline capacity control . He has also authored books like Revenue Management and Pricing Analytics (2019) and Fundamentals of Linear Optimization (2021).

Fernando Granha JeronimoView profile

Assistant Professor

• Complexity Theory
• Pseudorandomness
• Quantum Computing
• +6 more

Fernando Granha Jeronimo is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign. His research explores theoretical computer science with emphases on coding theory, expander graphs, quantum computing, and optimization. He holds a PhD from the University of Chicago, M.Sc./B.Sc. degrees from Unicamp (Brazil), and an engineering degree from Telecom Paris. His work investigates interactions between complexity theory, pseudorandomness, quantum algorithms, and high-dimensional expanders. Recent studies focus on explicit code constructions near information-theoretic bounds, quantum-classical complexity separations, and efficient decoding algorithms leveraging expander properties. Publications demonstrate consistent focus on coding theory (explicit codes, list decoding), quantum complexity (unentangled proofs, pseudoentanglement), and optimization (LP/SDP hierarchies). A trend toward quantum applications is evident in recent works on quantum LDPC codes and property testing. Awards & Fellowships: Simons-Berkeley Fellow Google Research Fellow TA Prize, University of Chicago (awarded twice) Advising & Grants: Actively recruits graduate students for his research group. Previously supported by Simons Institute and Google Research Fellowship during postdoctoral work at IAS. Current courses include quantum computing (CS 498) and advanced topics in codes/optimization (CS 598). Leads the Local-to-Global TCS Mentorship Program and research groups focused on coding theory, quantum complexity, and expander applications.

Alexandre AmiceView profile

Researcher

• Optimization
• Control Theory
• Robotics
• +7 more

Alexandre Amice is a fourth-year PhD candidate at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS), advised by Professor Pablo Parrilo and Professor Russ Tedrake. His research focuses on optimal and efficient decision-making for complex dynamical systems, particularly leveraging polynomial and graph structures to develop tractable algorithms for control theory and certification. Doctoral Student, MIT (2020–Present) Research Intern, Boston Dynamics AI Institute (May–September 2023) Open Source Developer, Drake Robotics Toolbox (2022–Present) Masters and Undergraduate Researcher, University of Pennsylvania (2018–2020) His contributions include work on collision-free configuration space certification, sums-of-squares optimization for controller synthesis, and robust actuator scheduling under matroid constraints. He is also an active contributor to the open-source Drake simulator. His publications highlight applications in robotics, including certified polyhedral decompositions, efficient trajectory certification, and optimization techniques for motion planning and resilient control. His work intersects robotics, optimization, and mathematical structures like posets and matroids. He has collaborated with researchers such as Peter Werner, Tobia Marcucci, and Alejandro Ribeiro on projects related to control theory, motion planning, and collision detection. His technical experience spans institutions like MIT, the University of Pennsylvania, and Boston Dynamics AI Institute.

• Combinatorial optimization
• Statistical physics
• Epidemic inference
• +7 more

Alfredo Braunstein is an Associate Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino, and a member of SmartData@PoliTO laboratory. His research spans combinatorial optimization, statistical physics of complex systems, epidemic inference, and computational biology . He leads the Simultaneous Inference from Multiscale Biological Data (SIMBAD) project (2023-2027) and previously led Statistical Inference via Belief Propagation for Epidemics (SIBYL) (2015-2017). He has supervised multiple PhD students in Physics of Complex Systems . Research Interests : Statistical physics, machine learning, network science, epidemic modeling, and optimization. Key Collaborations : Collegio Carlo Alberto, Human Genetics Foundation, and interdisciplinary teams. His recent publications focus on epidemic risk estimation , network dismantling , and message-passing algorithms . He received the La Ricerca dei Talenti award from Fondazione CRT (2015).

• Optimization
• Renewable Energy Systems
• Metaheuristics
• +3 more

Pandian Vasant is a prominent researcher in optimization, renewable energy systems, and metaheuristic algorithms. His work focuses on applying computational intelligence techniques to solve complex engineering problems, particularly in energy systems, manufacturing, and supply chain optimization. Collaborating with institutions globally, he has extensively published in reputable journals and conferences, including Computers & Industrial Engineering , Applied Soft Computing , and Wireless Networks . Key research areas include hybrid renewable energy optimization, nature-inspired algorithms (e.g., Grey Wolf Optimizer, Bat Algorithm), and fuzzy logic applications in decision-making under uncertainty. Major contributions include optimizing biogas plant processes, solar photovoltaic systems, and electric vehicle charging infrastructure. He has also explored industrial applications such as green sand mould systems and biofuel supply chains. His methodologies often integrate metaheuristics with fuzzy logic to handle real-world uncertainties. Vasant frequently collaborates with researchers like Timothy Ganesan, Igor Litvinchev, and José Antonio Marmolejo Saucedo, co-authoring over 100 peer-reviewed articles between 2002 and 2024. Notable trends in his publications include multi-objective optimization, energy efficiency, and smart grid technologies. Recent works emphasize Industry 4.0 integration, intelligent social systems for visually impaired individuals, and advanced machine learning models for stress detection in drivers.

• Innovation
• Innovation Policy
• Strategy
• +4 more

Cindy Lopes Bento serves as an Associate Professor in the Department of Management, Strategy and Innovation at KU Leuven's Faculty of Economics and Business. Her office is located at Naamsestraat 69, box 3535, 3000 Leuven (room 03.151), with an ORCID ID: 0000-0002-4969-3755. She actively contributes to academic research and teaching, including the course 'Introduction to Management and Strategy' (D0R78A). Her research spans critical domains including Innovation, Innovation Policy, Strategy, Policy evaluation, Applied Econometrics, Economics of Science, and Industry-science links. These interconnected interests drive investigations into how organizations innovate, the effectiveness of science funding mechanisms, and knowledge transfer dynamics between academia and industry. Her methodological approach combines rigorous econometric analysis with policy-relevant frameworks. Analysis of her 15 most recent publications (2017-2025) reveals consistent focus on empirical evaluation of science policy interventions. Key trends include microfoundations of scientific teams, asymmetric information in funding allocation, R&D subsidy effectiveness, and export-induced innovation. Her work frequently examines how research funding mechanisms, industry-science collaborations, and knowledge transfer programs impact innovation outcomes through large-scale data analysis. No scientific awards were explicitly mentioned in the provided sources. Dr. Lopes Bento currently leads multiple competitive research projects demonstrating significant grant acquisition success. These include 'Do Narrative CVs Reduce the Asymmetric Information Problem in Science Funding?' (2023-2027, Co-promotor), 'The Economics of Science – Random Selection in the Awarding of Research Funding' (2022-2026, Promotor), and dual projects on scientific team microfoundations (2021-2025, Co-promotor). These initiatives address fundamental questions about research evaluation, funding allocation equity, and team science dynamics.

Gilles AudemardView profile

Professor

• Satisfiability (SAT)
• Constraint Satisfaction Problems
• eXplainable AI (XAI)
• +3 more

Gilles Audemard is a Professor at Artois University in France, specializing in satisfiability (SAT), constraint satisfaction problems, and eXplainable AI (XAI). His research focuses on developing efficient solvers such as Glucose and CoSoCo , and tools like PyXAI for interpretable machine learning. He leads the XCSP3 format for combinatorial problem representation and the SAT Heritage project to archive SAT solvers. Key achievements include the 2023 Skolem Award for influential work on hybrid SAT solving, a 2021 CAV Award for foundational contributions to SMT, and multiple best paper awards. His solvers have won medals in SAT and XCSP competitions. Audemard collaborates on projects like the French ANR-funded EXPECTATION AI Chair and contributes to open-source tools like pFactory and PyCSP3 . Research spans formal methods, constraint programming, and AI explainability, with a focus on practical applications in combinatorial optimization and machine learning interpretability. His work bridges theoretical advancements with scalable solver implementations, impacting both academic and industrial problem-solving.

Yap RolandView profile

Associate Professor

• Constraint Programming
• Optimization
• Combinatorial Problems Modelling
• +5 more

Yap Roland is an Associate Professor at the School of Computing, National University of Singapore (NUS), and a member of the Institute of Operations Research and Analytics (IORA). His research focuses on constraint programming and solving, optimization, combinatorial problems, vehicle routing, and big data applications. He actively contributes to advancing methodologies in computational logic, software security, and algorithm design. His work spans theoretical foundations of constraint satisfaction (CSP/SAT) and practical applications in database systems, graph neural networks, and transportation logistics. Notable contributions include benchmarking tools like OSS-Bench for coding LLMs, novel approaches to detecting logic bugs in graph databases, and enhancing security through randomized pointer techniques. His research bridges algorithmic theory and real-world challenges in AI, cybersecurity, and efficient resource allocation. Yap Roland has published extensively in top-tier venues such as the International Conference on Principles and Practice of Constraint Programming (CP) and the International Conference on Theory and Applications of Satisfiability Testing (SAT). His recent work emphasizes scalability, fairness in machine learning, and robustness against memory errors in software systems.

Ryan O'DonnellView profile

Professor

• Algorithms and Complexity
• Security and Privacy
• Theoretical Computer Science
• +6 more

Ryan O'Donnell is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He specializes in theoretical computer science, with a focus on algorithms, complexity theory, quantum computing, and probability. His research includes Fourier analysis of Boolean functions, constraint satisfaction problems, and quantum information theory. He teaches courses such as 15251 (Fall 2025), 15754 (Spring 2025), and others. His work spans quantum tomography, pseudorandomness, and algorithmic design. Recent publications explore topics like quartic quantum speedups, uniformity testing, and explicit expanders. He advises PhD students William He, Noah Singer, and Jingxun Liang. His research interests bridge foundational theory and practical applications, including healthcare informatics and cryptographic tools. He actively contributes to academic conferences and program committees, reflecting his leadership in the field.

David D. GamarnikView profile

Professor

• Discrete Probability
• Optimization and Algorithms
• Quantum Computing
• +3 more

David D. Gamarnik is a Professor of Operations Research at the MIT Sloan School of Management, affiliated with the Operations Research and Statistics Group. He holds a BA in Mathematics from New York University (1993) and a PhD in Operations Research from MIT (1998). Prior to joining MIT in 2005, he was a Research Staff Member at IBM T.J. Watson Research Center. His research focuses on discrete probability, optimization, quantum computing, machine learning, and stochastic processes. He serves as an Area Editor for Mathematics of Operations Research and has held editorial roles in journals such as Operations Research and Annals of Applied Probability . Education: Bachelor of Arts in Mathematics, New York University (1993) PhD in Operations Research, MIT (1998) His honors include the Erlang Prize, Best Publication Award from INFORMS’ Applied Probability Society, and recognition as a finalist for the Franz Edelman Prize. He is a Fellow of the Institute for Mathematical Statistics, Institute for Operations Research and Management Science, and the American Mathematical Society. His academic contributions span theoretical computing, algorithm design, and applications in healthcare (e.g., kidney exchange models). Recent work explores algorithmic barriers in optimization, quantum computing, and high-dimensional statistics.

Mikkel ThorupView profile

Professor

• Algorithms
• Data Structures
• Hash Functions
• +3 more

Mikkel Thorup is a Professor at the Department of Computer Science, University of Copenhagen, and a VILLUM Investigator heading the Basic Algorithms Research Copenhagen (BARC) Center supported by a €5.3 million grant from the VILLUM Foundation. Born in 1965, he holds a D.Phil. from Oxford University (1993). Prior roles include positions at the University of Copenhagen (1993–1998) and AT&T Labs-Research (1998–2013). His research focuses on algorithms, data structures, and hashing, with a particular emphasis on unifying theoretical and practical aspects. Awards include the ACM Fellowship, Villum Kann Rasmussen Award, and Fulkerson Prize. Education: D.Phil. in Computer Science, Oxford University (1993). Earlier positions: University of Copenhagen (1993–1998), AT&T Labs-Research (1998–2013). Research interests encompass algorithm design, data structure optimization, and hashing techniques. His work bridges theory and practice, addressing problems like graph clustering, optimization, and efficient sampling. He is known for contributions to correlation clustering, hash function analysis, and dynamic graph algorithms. Awards include the 2011 MAA Robbins Award, 2015 Villum Kann Rasmussen Award (Denmark’s top research prize), and 2021 AMS-MOS Fulkerson Prize. His Erdős number is 2, and he boasts an H-index above 60 with over 200 publications in top venues like STOC, FOCS, and SODA. Advising and grants: Leads BARC since 2017, funded by VILLUM Foundation. His research has influenced both academia and industry, particularly in telecommunications and algorithmic efficiency. Collaborates internationally, with a focus on foundational algorithmic challenges. Labs/Teams: Founder and head of BARC, fostering research in algorithms and their applications. Engages in interdisciplinary work combining theory with practical implementations.

Karthik SrikantaView profile

Assistant Professor

• Complexity Theory
• Discrete Geometry
• Hardness of Approximation
• +4 more

Karthik C. S. is an Assistant Professor in the Department of Computer Science at Rutgers University, specializing in complexity theory, discrete geometry, and parameterized complexity. He is supported by the NSF CAREER Award Simons Foundation Junior Faculty Fellowship National Science Foundation grants . His research explores hardness of approximation, fine-grained complexity, and algorithm design in metric spaces.

• Discrete Optimization
• Algorithm Design
• Computer Vision
• +3 more

Prof. Vladimir Kolmogorov is a faculty member at the Institute of Science and Technology Austria (IST Austria), specializing in discrete optimization and algorithm design. He holds a Ph.D. in Computer Science from Cornell University and has held positions at Microsoft Research and University College London. His research focuses on combinatorial optimization, MAP inference in graphical models, and applications in computer vision. Educations: M.S. in Applied Mathematics and Physics, Moscow Institute of Physics and Technology Ph.D. in Computer Science, Cornell University Research Interests: Dr. Kolmogorov's work spans algorithmic optimization, including complexity analysis of constraint satisfaction problems, graph algorithms, and machine learning applications. His contributions include foundational work on graph cuts for computer vision and the development of efficient optimization methods for discrete problems. Publications: His recent work includes advancements in parallel algorithms for Gibbs distributions, semidefinite programming, and combinatorial optimization. These contributions highlight his expertise in bridging theoretical computer science with practical applications. Awards: Royal Academy of Engineering/EPSRC Research Fellowship (2006–2011) ERC Consolidator Grant (2014–2020) Best Paper Award at ECCV 2002 Outstanding Student Paper Award (NIPS 2007) Best Paper Honorable Mention (CVPR 2005) Advising and Grants: He has advised multiple PhD students and leads a research team at IST Austria. His grants include significant funding for exploring optimization in machine learning and discrete systems. Labs/Teams: His lab focuses on theoretical and applied discrete optimization, collaborating with institutions globally. Current projects include developing faster algorithms for graph problems and advancing Gibbs distribution analysis.