Find Academic Supervisors

Yiannis GiannakopoulosView profile

Senior Lecturer

• Algorithms
• Algorithmic Game Theory
• Computational Complexity
• +6 more

Yiannis Giannakopoulos is a Senior Lecturer (equivalent to Associate Professor) in Algorithms and Complexity at the University of Glasgow and a Turing Fellow. He previously held faculty positions at FAU Erlangen-Nürnberg and TU Munich, and was a postdoctoral researcher at TU Munich and the University of Liverpool. PhD (DPhil) in Computer Science, University of Oxford, advised by Elias Koutsoupias MSc in Logic, Algorithms and Computation (MPLA), University of Athens BSc in Mathematics, University of Athens His research centers on algorithms, complexity, and optimization, with a strong emphasis on algorithmic game theory. Key topics include auction design, mechanism design, equilibrium computation, congestion games, and computational economics. His work bridges theoretical computer science and economic theory, focusing on the algorithmic aspects of strategic behavior. The recent publications highlight a consistent focus on equilibrium computation in auctions and congestion games, optimal mechanism design, and complexity analysis. Trends include smoothed complexity, approximation algorithms for equilibria, robust revenue maximization, and structural analysis of inefficiency in games. His work frequently appears in top venues such as EC, SAGT, ICALP, SICOMP, and MOR. Scientific Awards: Best Paper Award at ICALP 2015 for 'Selling Two Goods Optimally' He is actively supervising PhD students, including Johannes Hahn, and has mentored several others such as Charalampos Kokkalis and Alexander Grosz. He is involved in academic service, including organizing the 1st UK Workshop for Junior Researchers in Economics and Computation (2025) and serving on the program committee of SAGT 2025. PhD positions are currently available with funding for UK students. He is affiliated with research groups in Algorithms & Complexity and has collaborated extensively with leading researchers in the field, including Aris Filos-Ratsikas, Elias Koutsoupias, and Diogo Poças. His work is supported by institutions such as the Simons Foundation.

Pinar HeggernesView profile

Professor

• Algorithms
• Graph Theory
• Combinatorics
• +3 more

Pinar Heggernes is a Professor at the Department of Informatics at the University of Bergen , specializing in algorithms and graph theory . Her research focuses on solving NP-hard graph problems through polynomial-time and fixed parameter tractable algorithms , particularly on chordal , split , and outerplanar graphs . She served as the elected deputy rector of the University of Bergen (2021–2025) with responsibility for education and digital knowledge, and previously as head of the Department of Informatics until 2021. Her career spans leadership in externally funded projects, editorial roles in international journals, and active participation in national research policy, including co-founding the Norwegian Artificial Intelligence Research Consortium (NORA). Education: PhD in Informatics from the University of Bergen. Research Interests: Algorithms for graph classes, computational complexity, parameterized algorithms, and combinatorial optimization. Collaborations: Extensive partnerships with academia, businesses, and public administration, including advisory roles in the Bergen Chamber of Commerce and the Norwegian Cognitive Center . Grants: Project leader for four Research Council of Norway FRIPRO grants (CLASSIS, SCOPE, MIST, and earlier projects) and participant in EU, Trond Mohn Foundation, and National Security Authority grants. Students: Supervised Paloma T. Lima (PhD, 2019) and co-supervised numerous advisees through collaborative projects. Labs/Teams: Coordinated interdisciplinary ICT research and education initiatives at UiB, with international collaborations in the USA, France, and Turkey.

Phong NguyenView profile

Research Professor

• Cryptography
• Cryptanalysis
• Post-quantum Cryptography
• +5 more

Phong Nguyen is a Research Professor at Inria (Directeur de recherche) and a part-time professor at the Computer Science Department (DI ENS) of École Normale Supérieure (ENS), PSL University in Paris. He leads the ENS Crypto Team (Inria Equipe Projet Cascade) and serves as the principal investigator for the ERC Advanced Grant PARQ (2020) focused on lattices in parallel and quantum computing. He holds a PhD (1999) and Habilitation (2007) from ENS-Lyon, with an agrégation de mathématiques (1997). His research integrates cryptography, algorithmic number theory, and lattice-based computations, emphasizing: Cryptanalysis : Deconstructing cryptographic protocols, especially lattice-based systems Post-quantum cryptography : Developing quantum-resistant solutions Lattice algorithms : Optimization of reduction, enumeration, and sieving techniques Real-world applications : Bridging theoretical constructs with practical security implementations His publications (spanning Eurocrypt, Asiacrypt, and Journal of Cryptology) demonstrate deep expertise in lattice cryptography, with recurring themes in algorithm efficiency, cryptanalysis of NTRU/GGH systems, and theoretical advancements in lattice reduction. Recent work (2024) continues this trajectory with improved BKZ analysis and hypercubic lattice optimizations. Awards include : ERC Advanced Grant (2020) for PARQ project Best Paper Award at EUROCRYPT 2006 Cor Baayen Award (2001) He advises PhD students (e.g., Henry Bambury, Leo Ducas) and interns from institutions like École Polytechnique and ENS. He directs the ENS Crypto Team and previously held leadership roles as: French Director of the Japanese-French Laboratory for Informatics (2015-2019) European Director of LIAMA (Sino-European Computer Science Lab, 2013-2015) Coordinator of ECRYPT II virtual labs (2008-2012)

Alastair KayView profile

Lecturer

• Quantum Computation
• Quantum Information Theory
• Quantum Cryptography
• +13 more

Dr. Alastair Kay is a Lecturer in the Department of Mathematics at Royal Holloway, University of London. His research focuses on theoretical quantum computation, quantum information theory, and quantum cryptography, particularly addressing challenges in quantum state transfer, error correction, and networked quantum systems. He holds a PhD from the University of Cambridge under Prof. Artur Ekert and a physics degree from Keble College, University of Oxford. His research spans topics such as Quantum state transfer protocols using engineered Hamiltonians Quantum error correction mechanisms for experimental systems Entanglement properties in graph states and spin networks Applications of quantum computing in cryptography and information theory The articles listed reflect his work in quantum information science, computational physics, and theoretical cryptography. Key trends include advancements in fault-tolerant quantum communication, optimization of spin chain dynamics, and foundational studies in quantum correlations and nonlocality. Alastair actively develops software tools like quantikz for quantum circuit diagrams and ConTeXi for LaTeX equation integration in Microsoft Office. He also emphasizes open science principles and reproducibility in quantum research through personal commentary and collaboration with his fiancée, a Panton Fellow in open research practices.

• Intelligent Transportation Systems
• Traffic Control
• Machine Learning
• +3 more

Dr. Maha Elouni is an Assistant Professor of Computer Science at Randolph-Macon College since 2022, with expertise in Intelligent Transportation Systems , Machine Learning , and Traffic Control . She earned her PhD in Computer Engineering (2021) and MSc in Applied Mathematics (2015) from Virginia Polytechnic Institute and State University, and her BS and ME in Computer Science Engineering from the National School of Computer Science in Tunisia. PhD : Computer Engineering, Virginia Tech, 2021 MSc : Applied Mathematics, Virginia Tech, 2015 BS/ME : Computer Science Engineering, National School of Computer Science, Tunisia, 2012 Her research focuses on Intelligent Transportation Systems , integrating machine learning and control theory to optimize traffic flow and develop adaptive controllers for urban road networks. She has contributed to studies on sliding mode control , game-theoretic decentralized systems , and connected vehicle technologies . Selected publications highlight her work on network perimeter control , dynamic freeway speed controllers , and weather-adaptive traffic systems . Recent articles explore applications of machine learning and clustering algorithms in transportation engineering. Student of the Year , Urban Mobility and Equity Center (UMEC), 2020 Best Paper Award , VEHITS Conference, 2018 Dr. Elouni teaches foundational and advanced courses, including Introduction to Computer Science , Data Structures , Object-Oriented Programming , and Android App Development . Her interdisciplinary background bridges computer engineering , mathematics , and transportation systems .

Zhuang LiuView profile

Assistant Professor

• Deep Learning
• Computer Vision
• Natural Language Processing
• +3 more

Zhuang Liu is an Assistant Professor of Computer Science at Princeton University, where he leads a research group focused on deep learning and computer vision. His work spans vision and language, unified by a focus on deep learning methods, representations, and architectures. Prior to joining Princeton, he was a Research Scientist at Meta AI Research (FAIR) in New York City. He received his Ph.D. from UC Berkeley and his B.E. from Tsinghua University, both in Computer Science. His educational background includes: Ph.D. in Computer Science, University of California, Berkeley, 2022 B.E. in Computer Science, Tsinghua University (Yao Class) Liu's research focuses on empirical approaches to understanding how deep learning models work and behave. He explores simple approaches to gain empirical insights into neural networks, often challenging existing beliefs in architectures, training, pruning, and datasets. His work spans multiple domains including computer vision, natural language processing, and multimodal learning. He has made significant contributions to the field, including DenseNet and ConvNeXt architectures, which have influenced modern neural network design. His recent publications reveal a strong focus on understanding and improving large language models and vision-language systems. His work examines idiosyncrasies in LLMs, pruning approaches for efficient inference, visual shortcomings of multimodal systems, and bias in large-scale visual datasets. He also investigates fundamental questions about neural network architectures, exploring the relationship between ConvNets and Transformers, and developing normalization-free transformer variants. His research consistently bridges theoretical insights with practical applications, as evidenced by his numerous conference publications and industry collaborations. His notable scientific achievements include: CVPR Best Paper Award NeurIPS'18 Compact Neural Networks Workshop Best Paper Award Professor Liu actively mentors students and postdoctoral researchers, currently advising seven Ph.D. students including Wenhao Chai, Tony Chen, Sachin Konan, Taiming Lu, Zhuorui Ye, David Yin, and Boya Zeng. He has successfully guided graduates such as Jiachen Zhu (now at Skild AI) and Mingjie Sun (now at Thinking Machines Lab). His research has practical implications for improving the efficiency, interpretability, and fairness of deep learning systems, with applications across multiple domains. His research group maintains active collaborations with industry partners including Meta FAIR, NVIDIA Research, and Adobe, and regularly hosts research interns. The group's work has established Zhuang Liu as a leading voice in empirical deep learning research, with invitations to speak at top academic institutions including Harvard, Stanford, Columbia, and Boston University.

Jiabao HeView profile

Researcher

• Control Theory
• System Identification
• Information Theory
• +5 more

Jiabao He is a Ph.D. Student in the Division of Decision and Control Systems at KTH Royal Institute of Technology , supervised by Prof. Håkan Hjalmarsson. He holds a Master's degree in Control Engineering (2021) and a Bachelor's degree in Mechanical Engineering (2018) from Tsinghua University. Education M.S. in Control Engineering (Tsinghua University, 2021) B.S. in Mechanical Engineering (Tsinghua University, 2018) His research interests lie in systems, control theory, and information theory , with current focus on subspace identification, finite-sample analysis , and data-driven control . Past work explored fuzzy control and descriptor systems . His publications reflect expertise in subspace methods , statistical learning , and Markov parameter identification . Conference Participation includes Reglermötet 2025 (Lund) , 63rd IEEE CDC 2024 (Milan) , 32nd ERNSI Workshop 2024 (Venice) , and SYSID 2024 (Boston) . He is a reviewer for journals and conferences like IEEE Transactions on Automatic Control and IFAC Symposium on System Identification . Scientific Awards IEEE TC SIAC Student Paper Award (2025) Travel Scholarship from Karl Engvers Foundation (2024) Travel Scholarship from Björns stiftelse (2024) He has served as a Teaching Assistant for courses such as EL1020 Automatic Control and EL2520 Control Theory and Practice , and supervised Bachelor theses on Learning in Dynamical Systems (2023-2024). His work bridges theoretical control methods with practical applications in descriptor systems and space robotics.

Andrej BogdanovView profile

Professor

• Cryptography
• Computational Complexity
• Pseudorandomness
• +6 more

Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.

Joy MorrisView profile

Professor

• Combinatorics
• Algebraic Graph Theory
• Group Theory
• +4 more

Joy Morris is a Professor at the Department of Mathematics & Computer Science at the University of Lethbridge. She earned her BSc from Trent University in 1992 and her PhD from Simon Fraser University in 2000 under Brian Alspach. Her academic journey includes tenure in 2005, promotion to Associate Professor, and full Professor status in 2015. Research Focus: Interactions between group theory and graph theory, with emphasis on Cayley graphs and automorphisms Key Contributions: Solving the distinguishing number problem for various graph families, advancing DCI/CI group theory Research Overview Her work bridges abstract algebra with graph theory through Cayley graphs. She investigates automorphism groups, Hamilton cycles, and graph symmetry properties. Notable contributions include resolving the DCI property for specific groups, analyzing Praeger-Xu graphs, and studying color-preserving automorphisms. Academic Leadership As a co-author of two influential open-access textbooks ( Proofs and Concepts with Dave Morris and her own Combinatorics text), she has shaped curriculum development and mathematical education outreach programs for parents in Alberta. Her teaching portfolio includes foundational courses like Math 2000 and advanced combinatorial theory instruction. Collaborative Impact With over 30 publications since 1996, her collaborations span international researchers including C. Praeger, P. Spiga, and E. Dobson. Current projects involve hypercube distinguishing costs and automorphism group analysis of vertex-transitive digraphs. She maintains active research into graphical regular representations and graph isomorphism problems.

Robert D. KleinbergView profile

Professor

• Algorithms
• Theoretical Computer Science
• Economic Aspects of Algorithms
• +5 more

Robert D. Kleinberg is a Professor of Computer Science at Cornell University's Department of Computer Science and a member of the field of Information Science. He received his Ph.D. from MIT in 2005 and previously worked at Akamai Technologies designing internet content delivery networks. His research bridges theoretical computer science and practical applications in electronic commerce, networking, and information systems. Research Interests: Kleinberg's work focuses on the design and analysis of algorithms, with special emphasis on economic aspects of algorithms, online learning, random processes in networks, and applications to electronic commerce and information retrieval. His research combines theoretical rigor with real-world impact, exploring how algorithmic principles can optimize complex systems. Publication Trends: Kleinberg's recent publications demonstrate a consistent focus on online optimization, algorithmic fairness, network design, and learning algorithms. His work frequently appears in top-tier venues spanning theoretical computer science (STOC, FOCS), machine learning (NeurIPS, COLT), and networking (NSDI). The publications reflect interdisciplinary approaches combining computer science, economics, and applied mathematics. Awards and Honors: Microsoft Research New Faculty Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Best Paper Award at ACM EC 2014 Advising and Academic Leadership: Kleinberg actively mentors Ph.D. students and postdoctoral researchers, with current advisees including Raunak Kumar, Princewill Okoroafor, and Tegan Wilson. He has supervised numerous graduates who now hold academic positions worldwide. His teaching includes core algorithm courses (CS 4820, CS 6820) and specialized topics at both undergraduate and graduate levels.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Eunji LimView profile

Associate Professor

• Operations Management
• Supply Chain Management
• Business Analytics
• +2 more

Eunji Lim is an Associate Professor in the Department of Decision Sciences and Marketing at the Robert B. Willumstad School of Business, Adelphi University, located in Garden City, New York. She has been a faculty member at Adelphi since 2018, initially as an Assistant Professor and promoted to Associate Professor in 2023. Her prior academic appointments include Assistant Professor roles at Kean University (2013–2018) and the University of Miami (2008–2013). Education Ph.D. in Operations Research (implied by department) , Stanford University (2008) Research Interests Lim’s research lies at the intersection of operations research, statistics, and management science. She focuses on simulation-based decision making for operations and supply-chain systems, leveraging large-scale stochastic simulation to optimize complex processes. A second major theme is nonparametric function estimation under shape constraints , where she develops statistically rigorous methods to estimate convex, monotonic, or smooth functions from noisy data. These methodologies find applications in business analytics, revenue management, inventory control, and engineering design. Publication Trends Her recent articles (2013–2025) reveal a consistent emphasis on convex and isotonic regression , simulation analytics , and stochastic optimization . A clear trajectory emerges from theoretical foundations—convergence rates, consistency, and asymptotic distributions—toward practical algorithms for high-dimensional and noisy environments. Interdisciplinary impacts are evident in publications targeting both statistics and operations-research audiences, with cross-cutting keywords such as simulation optimization, shape-restricted inference, and robust estimation. Scientific Awards No specific awards or fellowships are listed in the provided materials. Advising & Grants No information on current or former PhD/Master’s students, postdocs, or funded research grants is provided. Labs & Teams No laboratory or research-group affiliations beyond her departmental appointment are mentioned.

Roie LevinView profile

Assistant Professor

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +5 more

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

• Networked Systems
• Computer Networking
• Communications Engineering
• +4 more

Professor Mark Handley is a Professor of Networked Systems in the Department of Computer Science at University College London. His research focuses on network architecture, protocols, and systems with a particular emphasis on low-latency networking, datacenter networks, and network security. Education: Doctor of Philosophy, University College London (1997) Bachelor of Science (Honours), University College London (1988) Professor Handley's research spans multiple areas of computer networking with a focus on practical, deployable solutions. His work addresses fundamental challenges in network architecture, including low-latency routing, congestion control, network security, and datacenter networking. He has made significant contributions to Multipath TCP, congestion control algorithms, and network security protocols. His research often bridges theoretical foundations with practical implementation, ensuring real-world applicability of his innovations. His recent publications demonstrate a continued focus on cutting-edge networking challenges, particularly in low-latency routing, datacenter networks, and network security. The trend shows increasing attention to space-based networking, in-switch processing, and novel approaches to congestion control. His work consistently addresses the fundamental tension between theoretical network design and practical deployment constraints. Scientific Awards: IEEE Internet Award (2012) Usenix NSDI Best Paper Award (2011) ACM SIGCOMM Test of Time Award (2011) Roger Needham Award (2007) Professor Handley has served on numerous program committees including ACM SIGCOMM and Usenix NSDI. His work has influenced both academic research and industry standards, with contributions to IETF RFCs including Multipath TCP and TCP encryption. He has mentored numerous students and researchers who have gone on to make significant contributions in the networking field. His research group at UCL focuses on next-generation network architectures, with particular expertise in low-latency routing, datacenter networks, and network security. The group maintains strong collaborations with industry partners to ensure practical relevance of their research.

Aravindan VijayaraghavanView profile

Associate Professor

• Theoretical Computer Science
• Machine Learning Theory
• Data Science
• +7 more

Aravindan Vijayaraghavan is an Associate Professor in the Department of Computer Science at Northwestern University (affiliated with McCormick School of Engineering). He also holds courtesy appointments in the Industrial Engineering and Management Sciences (IEMS) department. Research interests include theoretical computer science , machine learning algorithms , quantum information , and combinatorial optimization under non-worst-case paradigms. He leads IDEAL (Institute for Data, Economics, Algorithms and Learning) as Site Director at Northwestern and former Institute Director (2023-24). Academic Background : PhD in Computer Science from Princeton University (advisor: Moses Charikar ) Bachelor's Degree in Computer Science from Indian Institute of Technology Madras Postdoctoral work at Courant Institute (NYU) and Carnegie Mellon University via Simons Collaboration grants Research Contributions : Developed smoothed analysis frameworks for random matrices with dependent entries Created sum-of-squares certificates for anti-concentration beyond Gaussian distributions Advanced quantum entanglement certification algorithms for subspaces Improved weak-to-strong generalization theory with data distribution expansion properties Designed error-tolerant e-discovery protocols for legal document classification Scientific Recognition : NSF CAREER Award NSF AITF Award (CCF-1637585, CCF-2154100) Google Research Scholar Program grant Amazon Research Awards program support Simons Postdoctoral Fellowship Academic Leadership : General Chair for FOCS 2024 Co-organizer of Junior Theory Workshop and Northwestern QTW series Active in program committees for COLT , ICML , NeurIPS , and STOC conferences Teaching Portfolio : CS262: Mathematical Foundations of CS (Continuous Mathematics for Computer Science) CS212: Mathematical Foundations of Computer Science (multiple offerings since 2015) CS496: Graduate Algorithms (since 2016) CS396/496: Quantum Computation & Information (co-taught with S. Rao) CS497: Machine Learning Theory (Spring 2025 offering)