Find Academic Supervisors

Illya V. HicksView profile

Professor

• Combinatorial Optimization
• Integer Programming
• Graph Theory
• +5 more

Illya V. Hicks is a Professor in the Computational and Applied Mathematics Department at Rice University. He holds a PhD from Rice University (2000) and a BS from Texas State University (1995). His research focuses on combinatorial optimization, integer programming, graph theory, and matroid theory, with applications in social networks, cancer treatment, and network design. He has advised numerous doctoral, post-doctoral, and masters students. Education: PhD and MA in Computational and Applied Mathematics, Rice University, 2000 BS in Mathematics, Texas State University, 1995 Research Interests: Utilizing graph decomposition techniques to solve NP-complete problems, including branch decompositions and matroid circuit problems. Applications include sensor network design, healthcare logistics, and algorithmic graph theory. Awards: Recognized with the 2015 Presidential Mentoring Award (Rice University), 2010 Forum Moving Spirit Award (INFORMS), and the 2005 Optimization Prize for Young Researchers. Grants and Projects: Includes NSF-funded research on branch decomposition techniques, submodular optimization, and healthcare service distribution. Active in promoting minority participation in operations research through travel grants and mentoring initiatives. Labs/Teams: Engaged in collaborative research on graph algorithms, combinatorial optimization, and interdisciplinary applications in healthcare and engineering.

Dmitri StrukovView profile

Professor

• Solid-State Nanoionics
• Emerging Electronics Devices and Circuits
• Non-Volatile Memories
• +5 more

Dmitri Strukov is a Professor at the University of California, Santa Barbara in the Department of Electrical and Computer Engineering. His work spans material science, electrical engineering, and computer science, focusing on novel computing paradigms using emerging memory devices. Education: PhD in Electrical and Computer Engineering from SUNY Stony Brook, MS in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests include neuromorphic computing , non-volatile memory applications , and mixed-signal circuits for machine learning and hardware security. His group develops memristive crossbar arrays and 3D NAND flash for energy-efficient systems. Scientific Leadership features Fellow of IEEE and Distinguished Lecturer roles. His work has been recognized with best paper awards at ASPLOS’19 and Computing Frontiers’13. Students: Mentored PhD graduates in neurocomputing, security, and memristor design including Z. Fahimi, S. Larimian, M.R. Mahmoodi, and X. Guo. Grants: Funded by AFOSR, ARO, DARPA, NSF, and industry leaders like Google and Samsung. Labs: Utilizes UCSB’s nanofabrication center and advanced tools for memristor characterization.

Chandra ChekuriView profile

Professor

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete and Combinatorial Optimization
• +6 more

Chandra Chekuri is the Paul and Cynthia Saylor Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, situated within the Grainger College of Engineering. He has been actively contributing to theoretical computer science for over two decades, with significant leadership roles including serving as Editor-in-Chief of the prestigious SIAM Journal on Computing since May 2025. His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras in 1993, followed by a Ph.D. in Computer Science from Stanford University in 1998. Prior to joining UIUC, he spent eight years as a Member of Technical Staff at Bell Labs, Lucent Technologies. Chekuri's research focuses on theoretical computer science with particular emphasis on the design and analysis of algorithms, discrete and combinatorial optimization, approximation algorithms, mathematical programming, and graph theory. His work explores fundamental connections between discrete structures and optimization problems, with applications spanning network design, data analysis, and computational complexity. His recent publications demonstrate a continued focus on hypergraph algorithms, submodular function optimization, and graph partitioning problems, showing how theoretical insights can yield practical algorithmic improvements. His approach often combines continuous relaxations with discrete rounding techniques to develop approximation algorithms for NP-hard problems. As Editor-in-Chief of SIAM Journal on Computing, Chekuri leads one of theoretical computer science's most respected publications, which covers analysis and design of algorithms, algorithmic game theory, computational complexity, and other mathematical aspects of computer science. His editorial leadership follows previous service as Associate Editor for several major journals including SIAM Journal on Computing, Mathematics of Operations Research, and Mathematical Programming. ACM Fellow (January 2024) Scott Fisher Teaching Award (for year 2022-23) from CS Department Chekuri has advised numerous PhD students to completion, including Kent Quanrud, Vivek Madan, Shalmoli Gupta, and Chao Xu, with several currently in progress such as Tanvi Bajpai, ElFarouk Harb, Rhea Jain, and Weihao Zhu. His teaching portfolio includes graduate courses on Randomized Algorithms, Approximation Algorithms, Algorithms for Big Data, and Combinatorial Optimization. He has served as Director of the Graduate Program in the Department of Computer Science from May 2014 to August 2017, demonstrating significant administrative leadership within the department.

Vadim LozinView profile

Professor

• Graph theory
• Combinatorics
• Discrete Mathematics
• +4 more

Vadim Lozin is a Professor of Mathematics at the University of Warwick, affiliated with the Department of Mathematics within the School of Mathematics. His research interests span graph theory, combinatorics, and discrete mathematics, focusing on areas such as clique-width, Ramsey numbers, and structural graph theory. He has held visiting positions at institutions including the Université Paris-Dauphine, EPFL, and KAUST. Lozin has received several accolades, including the Best Paper Award for 'Linear Ramsey numbers' in 2018 and a 2024 award at the International Symposium on Algorithms and Computation. His work involves collaborations with global researchers and contributions to conferences like IWOCA and WG. Lozin serves on editorial boards for journals such as Discrete Applied Mathematics and Electronic Notes in Discrete Mathematics . His research explores foundational problems in graph theory, with applications in algorithm design and complexity analysis. Lozin’s publications include studies on union-closed sets, functional graph properties, and algorithmic approaches to graph parameters. He has also contributed to books like Words and Graphs , bridging formal language theory with graph structures. His grants focus on clique-width and stability in graphs, reflecting his commitment to advancing theoretical and applied discrete mathematics.

• Quantum Computation
• Quantum Algorithms
• Computational Complexity
• +3 more

Ashley M R Montanaro is a Professor of Quantum Computation at the School of Mathematics, University of Bristol . Active in quantum computing research since at least 2014, they lead projects at the intersection of quantum algorithms , computational complexity , and quantum information theory , affiliated with the Bristol Quantum Information Institute. Research interests focus on quantum algorithm design , computational complexity analysis , and quantum simulation . Key work includes developing variational quantum algorithms for phase transition detection, Hamiltonian simulation techniques, and quantum-classical hybrid methods for solving complex problems in physics and optimization. Recent publications demonstrate expertise in: Quantum phase diagram simulation with low-depth circuits Quantum speedups for constraint satisfaction problems Quantum communication complexity of machine learning tasks Quantum-enhanced optimization heuristics Hamiltonian simulation with time-dependent product formulas Quantum algorithm complexity analysis Scientific awards include: EPSRC Fellowship (2014-2019) - "New insights in quantum algorithms and complexity" Active in quantum software development through projects like: "Quantum Algorithms from Foundations to Applications" (ERC-2018-COG) "Quantum Computing and Simulation Hub" (2019-2024) "Prosperity Partnership in Quantum Software" (2019-2023)

• Computer Networks
• Data Center Networking
• Software Defined Networking (SDN)
• +5 more

Yashar Ganjali is a Professor in the Department of Computer Science at the University of Toronto , leading the Systems and Networking Group . His research spans computer networks , with a focus on data center networking , software-defined networking (SDN) , and congestion control . Education : Not explicitly detailed, but inferred from academic rank as a Professor. His work on flow consolidation , load migration in SDN controllers , and machine learning for network management has been influential. Recent projects include FORESIGHT (2025) for ML-driven scheduling and Meta-Migration (2023) to reduce switch migration latency. Scientific Awards include the IFIP Networking 2025 Best Paper Award . Collaborations with institutions like Google (2024) and Facebook (2019) highlight his industry impact. Advisees include Sepehr Abbasi Zadeh (PhD, 2024). Current projects integrate optical packet switching and eBPF-based network augmentation , aiming to address scalability, micro-bursts, and resource allocation efficiency in cloud environments.

Akanksha AgrawalView profile

Assistant Professor

• Parameterized Complexity & Algorithms
• Graph Algorithms
• Computational Geometry
• +3 more

Akanksha Agrawal is an Assistant Professor and Veena and Induprakas Keri Faculty Fellow at the Department of Computer Science and Engineering, Indian Institute of Technology Madras. Her research focuses on Parameterized Complexity & Algorithms, Graph Algorithms, Computational Geometry, Exact Algorithms, and Fine Grained Algorithms & Complexity. She has held postdoctoral positions at Ben-Gurion University of the Negev (Israel) and the Hungarian Academy of Sciences, funded by a PBC Fellowship. She earned her Ph.D. from the University of Bergen under Professors Saket Saurabh and Daniel Lokshtanov. Education: Ph.D., University of Bergen, Norway (2017-2021) Postdoctoral Researcher, Ben-Gurion University of the Negev, Israel (2021-2022) Postdoctoral Researcher, Hungarian Academy of Sciences, Hungary (2022-2023) Research Interests: Her work emphasizes parameterized complexity, graph algorithms, and computational geometry. She explores exact algorithms, fine-grained complexity, and algorithmic approaches to NP-hard problems. Her research bridges theoretical insights with practical algorithmic solutions. Recent Activities & Awards: ACM-India Eminent Speaker (2024-2026) Invited Talks: COMSNETS 2025, IISc Bengaluru (2024), Bali Parameterized Graph Algorithms (2024) Program Committee Roles: IPEC 2025 (Co-Chair), WG 2025, CALDAM 2025 Grants & Academic Services: Organizing Dagstuhl Seminar (Jan. 2025) with Maria Chudnovsky, Daniel Paulusma, and Oliver Schaudt Contributions to international conferences and workshops Teaching: Courses include Combinatorial Objects, Parameterized Algorithms, Design & Analysis of Algorithms, Approximation Algorithms, and Advanced Data Structures.

Lane A. HemaspaandraView profile

Professor

• Computational Complexity Theory
• Structural Complexity Theory
• Computational Social Choice Theory
• +4 more

Lane A. Hemaspaandra (formerly Hemachandra) is a Professor at the Department of Computer Science, University of Rochester, New York. His academic career spans over three decades, with research focusing on computational complexity theory (especially structural complexity) and computational social choice theory . He holds a Ph.D. in Computer Science from Cornell University (1987) and has been recognized with prestigious awards such as the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation and NSF Presidential Young Investigator (1989–1995). Education: B.S. in Computer Science and Mathematics & Physics, Yale University (1981) M.S. in Computer Science, Stanford University (1982) M.S. in Computer Science, Cornell University (1984) Ph.D. in Computer Science, Cornell University (1987) Hemaspaandra's research bridges theoretical computer science with political science and economics , particularly analyzing the computational complexity of election systems. His work includes foundational studies on Carroll/Dodgson voting , control complexity , and manipulative attacks in single-peaked societies. He has pioneered the use of complexity as a shield against election manipulation and control. The 15 most recent articles (2021–2024) span topics like backbone opacity , electoral control dichotomies , iterative constant-setting for complexity , and online bribery in sequential elections . These works often intersect with parameterized complexity , multi-agent systems , and game-theoretic models . Scientific Awards: AAAI Senior Member (2020–...) ACM Distinguished Scientist (2007–...) Alexander von Humboldt Foundation Renewed Research Stay (2018–2019) SIGACT Distinguished Service Prize (2013) Edward Peck Curtis Award for Undergraduate Teaching (2012) Hertz Foundation Fellowship (1982–1987) He has advised 15 Ph.D. students and postdocs, including prominent researchers like Prof. Piotr Faliszewski (AGH University) and Dr. Curtis Menton (Google). His NSF-funded projects explore complexity-theoretic approaches to election systems, and he has collaborated with institutions in Germany, Japan, and Poland.

• Quantum Computing
• Quantum Foundations
• ZX-calculus
• +7 more

John van de Wetering is an Assistant Professor at the Theoretical Computer Science group of the Informatics Institute, University of Amsterdam, working with the QuSoft research center. He co-authored the open-access book Picturing Quantum Software and developed the PyZX quantum compiler. His research spans quantum computation and quantum foundations, focusing on diagrammatic methods like the ZX-calculus and ZH-calculus. Quantum circuit optimization and verification Quantum foundations via algebraic/compositional methods Co-creator of PyZX His recent publications explore multi-qutrit systems, completeness of graphical calculi, and quantum state representations. Supervises students in quantum computing, including Lia Yeh and Sarah Li. Directs the new Master's program in Quantum Computer Science at UvA. Actively contributes to open-source projects and international conferences. Notable collaborations include Aleks Kissinger, Neil J. Ross, and QuSoft researchers. Uses GitHub for DiZX development (qudit extension of PyZX). No explicit scientific awards mentioned.

Stuart KurtzView profile

Professor

• Theoretical Computer Science
• Computational Complexity Theory
• Type Theory
• +4 more

Stuart Kurtz is a Professor in Computer Science and the College at the University of Chicago, and serves as Master of the Physical Sciences Collegiate Division. He holds the endowed position of George and Elizabeth Yovovich Professor. His research focuses on theoretical computer science, including computational complexity theory, randomness in computation, type theory, and formal logic. He has contributed to foundational areas such as the Berman-Hartmanis Isomorphism Conjecture and the computational properties of random sets. Kurtz is affiliated with the Theoretical Computer Science and Programming Languages Groups at the University of Chicago. He has been recognized with the 2009 Quantrell Award for teaching excellence. His service roles include Director of Undergraduate Studies and Department Chair in Computer Science. He actively mentors Ph.D. students and has advised multiple graduates in complexity theory and related fields. His academic background includes a Mathematics Ph.D. from the University of Illinois, supervised by Carl Jockusch. He approaches type theory as an intersection of formal logic and functional programming. Research interests span measure-theoretic randomness, computational logic, and complexity class separations. His recent work explores connections between theoretical computer science and interdisciplinary fields like physics and statistics. In teaching, Kurtz has instructed courses such as Formal Language Theory, Discrete Mathematics, and Honors Intro Programming. His service contributions include roles in the Computation Institute and Toyota Technological Institute at Chicago. His lab affiliations and collaborative work reflect a strong commitment to advancing theoretical foundations in computer science.

Valentine KabanetsView profile

Professor

• Computational Complexity
• Pseudorandomness
• Circuit Lower Bounds
• +4 more

Valentine Kabanets is a Professor in the School of Computing Science at Simon Fraser University. He holds a Ph.D. in Computer Science from the University of Toronto (2000), M.Sc. from Simon Fraser University (1996), and B.Sc. from the National University of Kiev (1993). His research focuses on computational complexity theory, pseudorandomness, circuit lower bounds, and cryptography. He has received prestigious awards including the Nerode Prize (2013) and multiple best-paper awards at conferences like STOC and CCC. Teaching includes courses on computability and complexity (CMPT 308), data structures and algorithms (CMPT 307), and advanced complexity theory. His research explores foundational questions such as derandomization of algorithms, connections between upper/lower bounds in complexity, and cryptographic hardness assumptions. He leads the Algorithms and Complexity Theory Lab and has supervised numerous graduate students and postdocs. Kabanets' work bridges theoretical insights with practical algorithmic advancements, contributing to both foundational computer science and applied cryptography.

Damien WoodsView profile

Professor

• DNA Computing
• Molecular Programming
• Self-Assembly
• +3 more

Professor Damien Woods is a faculty member at Maynooth University's Faculty of Science & Engineering, specifically affiliated with the Department of Computer Science and the Hamilton Institute. He leads groundbreaking research in DNA computing, molecular programming, and optical computing, focusing on self-assembly, algorithmic design, and computational complexity. ERC Consolidator Grant: 'Computationally Active DNA Nanostructures' SFI ERC Support Award EIC Pathfinder Challenge Grant: 'DISCO - DNA Infrastructure for Storage and Computation' His research projects explore programmable DNA storage, molecular robotics, and robust self-assembly systems. Recent publications span diverse topics like algorithmic DNA tile assembly, thermodynamic stability, and computational universality in nanosystems. Awards include ERC and SFI grants, with a focus on bridging theoretical computer science and experimental molecular biology. Scientific Contributions include: 2022: 'Turning Machines' - Molecular Robotics 2019: 'Diverse Molecular Algorithms' in Nature 2017: 'A Cargo-Sorting DNA Robot' in Science

Ross KingView profile

Professor

• Automation of Science
• Artificial Intelligence
• Machine Learning
• +5 more

Professor Ross King is a faculty member at the University of Cambridge, affiliated with the Department of Chemical Engineering and Biotechnology. His research focuses on the automation of scientific discovery, machine learning applications in biology and chemistry, and DNA computing. Developed the first autonomous 'Robot Scientist' systems (Adam, Eve, Genesis) capable of hypothesis generation, experimental design, and execution using AI Pioneer in DNA computing, demonstrating the first physical Nondeterministic Universal Turing Machine (NUTM) 35+ years of expertise in machine learning, particularly relational learning for complex biological/chemical data Organizer of the international 'Nobel Turing Grand Challenge' for AI scientists His work in computational biology spans eukaryotic cell modeling, cancer signaling pathways, and AI-driven drug discovery for neglected tropical diseases like malaria and Chagas disease. The Genesis system aims to automate 10,000 simultaneous closed-loop experiments using micro-chemostats to model cellular complexity. The DNA computing research demonstrates exponential theoretical advantages over classical and quantum computing architectures for NP-complete problems, utilizing Thue string rewriting systems and polymerase chain reaction techniques. This work has significant implications for computer science, physics, and practical computing resource utilization. King's machine learning contributions include active learning strategies for compound selection in drug design and meta-learning approaches to optimize ML applications in bioinformatics and chemoinformatics.

Christoph HertrichView profile

Assistant Professor

• Discrete Mathematics
• Theoretical Computer Science
• Machine Learning
• +3 more

Christoph Hertrich is a tenure-track professor for Applied Discrete Mathematics at University of Technology Nuremberg, where he conducts research at the intersection of discrete mathematics, theoretical computer science, and machine learning. His work particularly focuses on applying polyhedral geometry and combinatorial optimization techniques to neural network theory, with significant contributions to understanding the computational complexity and expressivity of neural networks. Hertrich received his BSc and MSc degrees from TU Kaiserslautern (2013-2018) working with Sven O. Krumke, followed by his PhD at TU Berlin (2018-2022) under the supervision of Martin Skutella. His doctoral thesis, titled "Facets of Neural Network Complexity," laid foundational work for his current research direction. Prior to joining UTN, he held postdoctoral positions at Université libre de Bruxelles (2023-2024) with a Marie Skłodowska-Curie fellowship under Samuel Fiorini, and at LSE London (2022-2023) with László Végh. He also served as a substitute professor for discrete mathematics at Goethe-Universität Frankfurt during the winter semester of 2023/24. Hertrich's research interests center on the mathematical foundations of neural networks, with particular emphasis on polyhedral geometry approaches. His work explores computational complexity questions related to neural network training and architecture, expressivity bounds, and connections to combinatorial optimization problems. He has made significant contributions to understanding the relationship between neural network depth and function representation, the complexity of counting linear regions in ReLU networks, and the application of extended formulations to neural network theory. His approach combines rigorous theoretical analysis with practical implications for neural network design and optimization. His recent publication record reveals a strong trend toward establishing fundamental theoretical limits and connections between deep learning and discrete mathematics. A significant portion of his work examines computational complexity of various neural network problems, often proving hardness results or establishing bounds on expressivity. He has also developed novel connections between polyhedral combinatorics and neural network architecture, demonstrating how techniques from operations research can inform deep learning theory. Marie Skłodowska-Curie fellowship Since February 2025, Hertrich has been supervising PhD student Moritz Stargalla at UTN. His research has been supported by prestigious fellowships including a Marie Skłodowska-Curie fellowship during his postdoctoral period in Brussels. He is organizing a workshop on "Polyhedral Geometry for Neural Networks" in March 2026 in Nuremberg, highlighting his leadership in this emerging interdisciplinary field.

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.