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Zachary KincaidView profile

Associate Professor

• Programming Languages
• Formal Methods
• Program Analysis
• +3 more

Zachary Kincaid is an Associate Professor in the Department of Computer Science within the School of Engineering and Applied Science at Princeton University. His research spans programming languages, formal methods, and program analysis, with a focus on making analysis techniques more compositional and robust. He maintains an active research program with numerous publications in top-tier conferences including PLDI, POPL, OOPSLA, and CAV. His research interests include program analysis, logic, and programming languages, with particular emphasis on static analysis, verification, and automated reasoning. His work develops theoretical foundations for program analysis while creating practical tools that can be applied to real-world software systems. He has made significant contributions to termination analysis, invariant generation, and the application of algebraic methods to program verification. Analysis of his recent publications shows a consistent focus on improving the precision and scalability of program analysis techniques. His work frequently bridges theoretical computer science with practical implementation, developing novel algorithms for static analysis while also implementing them in tools like the Duet program analyzer. His research spans multiple subfields including termination analysis, invariant generation, recurrence relations, and algebraic methods for program analysis. Professor Kincaid has advised several PhD students to completion, including Shaowei Zhu (PhD 2024, now a researcher at Amazon) and Charlie Murphy (PhD 2023, now a postdoc at University of Wisconsin–Madison). He currently advises Jake Silverman, Nicolas Koh, and Nikhil Pimpalkhare. He teaches courses at Princeton including COS 320 (Compiling Techniques), COS 516/ELE 516 (Automated Reasoning about Software), and COS 217 (Introduction to Programming Systems). His teaching reflects his research interests, focusing on the theoretical foundations and practical implementation of programming language tools and techniques.

Giannis NikolentzosView profile

Assistant Professor

• Learning on Graphs
• Graph Mining
• Machine Learning
• +1 more

Giannis Nikolentzos is an Assistant Professor at the Department of Informatics and Telecommunications, University of Peloponnese. His research focuses on graph representation learning, machine learning, and artificial intelligence. Prior to this role, he was a postdoctoral researcher at Ecole Polytechnique and completed his PhD in Computer Science at Athens University of Economics and Business, advised by Michalis Vazirgiannis. He holds an MSc in Artificial Intelligence from the University of Southampton and a Diploma in Electrical and Computer Engineering from the University of Patras. His research interests include learning on graphs, graph mining, and machine learning applications in healthcare and social networks. He has contributed to advancements in graph neural networks (GNNs), adversarial attacks on GNNs, and synthetic data generation using variational graph autoencoders. Recent Publications highlight trends in understanding GNN representations, adversarial robustness, and graph reasoning with large language models. His work has been presented at top conferences like NeurIPS, ICML, and ICLR. Nikolentzos has developed the Grakel Python library for graph kernels and contributed to open-source tools like the repository for 'What Do GNNs Actually Learn?' which includes implementation details, datasets (e.g., IMDB-BINARY), and dependencies such as PyTorch and NetworkX.

Michalis K. TitsiasView profile

Lecturer

• Machine Learning
• Computational Statistics
• Bioinformatics
• +1 more

Michalis K. Titsias is a Lecturer in the Department of Informatics at Athens University of Economics and Business, while concurrently serving as a Research Scientist at DeepMind. His research develops machine learning methodologies for Bayesian inference, variational methods, and probabilistic modeling. Education includes: Diploma in Informatics, University of Ioannina MSc in Informatics, University of Ioannina PhD in Computer Science, University of Edinburgh Research spans gradient-based MCMC, variational autoencoders, and Bayesian nonparametrics. Supervised students include Apostolos Adamakos (MSc), Charalambos Tamvakis (BSc), Manolis Christoforou (BSc), Styliani Loukatou (BSc), and Sevastianos Fournaris (MSc). Developed open-source tools for Gaussian processes, variational inference, and Markov chain Monte Carlo methods. Teaching includes undergraduate and postgraduate courses in machine learning, statistics, and artificial intelligence.

• Algorithm Design and Analysis
• Algorithmic Game Theory
• Approximation and Online Algorithms
• +5 more

Dimitrios Fotakis is a Professor at the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA), where he has been serving since February 2009. He also collaborates with the "Archimedes" research unit as an experienced researcher since 2023. His academic journey includes previous positions as a Senior Research Scientist at Yahoo Research (2017-2019), Assistant Professor at the University of the Aegean (2004-2009), and Postdoctoral Researcher at the Max-Planck Institut für Informatik (2001-2003). National Technical University of Athens (2009-present): Professor "Archimedes" Research Unit (2023-present): Collaborating Senior Researcher Yahoo Research (2017-2019): Senior Research Scientist University of the Aegean (2004-2009): Assistant Professor Max-Planck Institut für Informatik (2001-2003): Postdoctoral Researcher Education: He holds a Diploma (1994) and a PhD (1999) from the Department of Computer Engineering and Informatics at the University of Patras, Greece. Dimitrios Fotakis specializes in Theoretical Computer Science with a focus on Algorithmic Game Theory and Approximation Algorithms. His research centers on the algorithmic properties of congestion games, the design of approximate mechanisms without monetary exchanges, and direct algorithms with emphasis on service location problems. His work has produced significant results including optimal algorithms for service location problems, potential functions for generalizations of congestion games, and optimal approximate truth-based mechanisms. With over 120 publications in major international conferences and journals and more than 3,000 citations according to Google Scholar, his research has had substantial impact in the field. His recent publications (2023-2025) demonstrate a continued focus on cutting-edge topics at the intersection of algorithms, game theory, and machine learning. These works span diverse areas including learning-augmented algorithms, graph neural networks, facility location problems, mechanism design, opinion dynamics, and fairness in ranking systems. The breadth of his research shows his ability to bridge theoretical computer science with practical applications in energy systems, social networks, and AI ethics. Teaching: Professor Fotakis teaches courses including Algorithms and Complexity, Discrete Mathematics, Computer Programming, Algorithmic Game Theory, Network Algorithms and Complexity, and Convex Optimization with Applications in Machine Learning. Research Leadership: He has served as Principal Investigator for several significant research projects including BALSAM (Beyond Worst-Case Analysis in Approximation Algorithms and Mechanism Design, 2019-2023), LEADAlgo (Learning-Augmented and Data-Driven Online Algorithms, 2020-2022), and Selfish Resource Allocation through Game Theoretic Models (2009-2021). He has also contributed as a Senior Researcher to multiple THALES projects through the years. Mentorship: Professor Fotakis has supervised numerous PhD students who have gone on to prestigious postdoctoral positions at institutions including MIT, Stanford, Yale, and UT Austin. His academic lineage extends through many successful students now working in top universities and research institutions worldwide.

Panagiotis PatsilinakosView profile

Researcher

• Algorithmic Game Theory
• Mechanism Design
• Learning Augmented Mechanism Design
• +5 more

Panagiotis Patsilinakos is a PSL Junior Research Chair (Post-Doctoral Researcher-Teacher) at LAMSADE, Paris Dauphine-PSL University, where he focuses on learning-augmented algorithmic mechanism design and computational social choice. His work bridges algorithmic game theory, mechanism design, and computational complexity. Education: PhD in Computer Science (2023) from the National Technical University of Athens (NTUA), School of Electrical and Computer Engineering. MEng in Computer Engineering (2016) from the University of Patras, School of Engineering. His PhD thesis explored algorithmic game theory and mechanism design under information scarcity. Research interests include randomized algorithms, approximation algorithms, and the intersection of learning theory with mechanism design. Notable contributions involve online budget-feasible auctions, learning-augmented NP-hard problem solutions, and distortion analysis in voting systems. Teaching includes courses on 'Beyond Worst-Case Analysis' at Paris Dauphine and 'Algorithmic Game Theory' at AUEB/NTUA. Previously served as a teaching assistant for undergraduate/graduate courses at NTUA, including Algorithms and Complexity, and mentored 8 undergraduate theses. Labs/Teams: Member of NTUA's Computation and Reasoning Laboratory. Active contributor to LAMSADE's research initiatives in algorithmic mechanisms and social choice theory.

Andreas GöbelView profile

Professor

• Computational Counting
• Randomness in Computation
• Computational Complexity
• +4 more

Andreas Göbel is a Professor and Chair of Algorithm Engineering at the Hasso Plattner Institute (HPI). His research focuses on computational counting, randomness in computation, computational complexity, graph theory, and stochastic processes. He has contributed extensively to understanding diffusion processes, clique structures in networks, and algorithmic approaches to statistical physics problems. His work bridges theoretical computer science with practical algorithm design. Recent contributions include analyzing SIRS process survival times, clique dynamics in geometric random graphs, and non-linear information diffusion models. He has published widely in top-tier conferences such as IJCAI, AAAI, and SODA. Göbel teaches courses including Probability and Computing, Algorithmix, and Theoretical Foundations of Cryptography. His research group explores both foundational aspects of algorithms and real-world applications, particularly in network science and combinatorial optimization.

Christos TzamosView profile

Associate Professor

• Computer Science
• Machine Learning
• Statistical Learning
• +3 more

Christos Tzamos is an Associate Professor in the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens, and a researcher at Archimedes AI. He received his PhD from MIT and BS from National Technical University of Athens. His research bridges computer science, machine learning, statistics, and algorithmic economics. Research Focus: Machine learning theory, algorithmic mechanism design, optimization, and statistical methods with applications to economics. Current projects include active learning, adaptive information acquisition, and robust algorithm design. Awards: NSF CAREER Award (2022), NeurIPS Outstanding Paper Award (2019), George Sprowls Award for best CS PhD thesis at MIT (2017), multiple programming competition medals. Student Advising: Currently advising 4 PhD students and 1 postdoc, with 3 graduated PhD students now at Georgia Tech, Yale, and UT Austin.

Georgios AmanatidisView profile

Assistant Professor

• Algorithmic Game Theory
• Computational Social Choice
• Fair Division
• +3 more

Georgios Amanatidis is an Assistant Professor at the Department of Informatics, Athens University of Economics and Business, and Collaborating Senior Researcher at the Archimedes Research Unit. His research specializes in algorithmic problems at the intersection of discrete mathematics, computer science, and economics, with particular focus on fair division, algorithmic mechanism design, and graph sampling. Research Areas: Algorithmic game theory, computational social choice, fair resource allocation, approximation algorithms, submodular optimization, and mechanism design without payments. Current Projects: ARCHIMEDES project on game theory and multi-agent learning; Algorithmic Fair Division in Dynamic, Socially Constrained Environments (NWO Veni Grant); Combinatorial Reverse Auctions for crowdsourcing markets (COMRADE HFRI Grant).

Stratis SkoulakisView profile

Assistant Professor

• Algorithmic Game Theory
• Machine Learning
• Optimization
• +1 more

Stratis Skoulakis is an Assistant Professor in Computer Science at Aarhus University, specializing in mathematical foundations of game theory, machine learning, and optimization. His research examines collective behavior of algorithms in economic systems like auctions and traffic networks. Recipient of the 2025 Villum Young Investigator Award (9 million DKK). Research Focus: Analyzes interactions between machine learning systems in economic contexts, including convergence properties of game-theoretic dynamics and optimization in non-convex environments. Education: PhD in Algorithmic Game Theory from National Technical University of Athens.

Ioannis TollisView profile

Professor

• Graph Analytics
• Network Visualization
• Data Science
• +2 more

Dr. Ioannis G. Tollis is Professor of Computer Science at the University of Crete, specializing in graph visualization and algorithms. His research spans graph drawing, network visualization, biomedical data modeling, and information visualization. Awarded IEEE's Young Engineer of the Year (1993) and Outstanding Service Award (1993), he has authored 8 books and 180+ publications with over 8,500 citations (h-index 40). Research focuses on algorithms for graph layouts, including dominance drawings, planar embeddings, and hierarchical visualizations. Applications extend to biomedical networks and large-scale data analytics. Previously head of FORTH's Biomedical Informatics Lab and VP at Tom Sawyer Software, he founded the Journal of Graph Algorithms and Applications.

Karampetakis NicholasView profile

Professor

• Control Systems
• Matrix Theory
• Polynomial Methods
• +3 more

Nicholas Karampetakis is Professor in the School of Mathematics at Aristotle University of Thessaloniki, specializing in algebraic methods for control systems. His research develops polynomial matrix techniques for system analysis, control design, and symbolic computation. Key research areas include: Generalized matrix inverses and decomposition Control system discretization Structural invariants of linear systems Symbolic algorithms for polynomial matrices Recent works (2021-2023) address stabilization of singular systems, polynomial matrix equivalences, and discretization of time-delay systems. Teaches advanced mathematics and supervises graduate research in systems theory.

Vangelis MarkakisView profile

Associate Professor

• Theoretical Computer Science
• Game Theory
• Mechanism Design
• +3 more

Vangelis Markakis is an Associate Professor at the Department of Informatics, Athens University of Economics and Business (AUEB), where he has been a faculty member since 2009. He holds a PhD in Theoretical Computer Science from Georgia Institute of Technology (2005), an M.Sc. in Computer Science (Georgia Tech 2004), and a Diploma in Electrical and Computer Engineering from NTUA (2000). His academic career includes postdoctoral research at the University of Toronto and CWI (Amsterdam). Research Interests: Theoretical computer science, algorithm design, game theory, mechanism design, social networks, combinatorial optimization Awards: Recipient of the 2025 ACM SIGecom Test of Time Award for foundational work on fair allocations (2004) and the 2003 ACM EC Best Student Paper Award His work bridges algorithmic game theory, fair division, and computational economics, with notable contributions to approximation algorithms for Nash equilibria and auction mechanisms. He has supervised numerous PhD and MSc students, including prominent researchers now at institutions like University of Essex, NTU Singapore, and Yale. Scientific activities include extensive program committee memberships (ACM EC, AAAI, AAMAS, SAGT, WWW) and organizing conferences like ISCO 2012 and ACAC 2009. His teaching portfolio covers game theory, data structures, and advanced algorithms.

Panagiotis RondogiannisView profile

Professor

• Logic Programming
• Nonmonotonic Reasoning
• Functional Programming
• +3 more

Panagiotis Rondogiannis is a Professor in the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens. He holds degrees from the University of Patras (B.Sc., 1989) and the University of Victoria, Canada (M.Sc. 1991, Ph.D. 1994). His research focuses on Logic Programming, Nonmonotonic Reasoning, Functional/Dataflow Programming, and Semantics of Programming Languages, with a particular emphasis on higher-order extensions and fixpoint theory. His work spans foundational aspects of logic programming semantics, including stable model semantics, categorical fixpoint theory, and non-monotonic reasoning frameworks. Key contributions include advancements in higher-order Datalog, lexicographic preference modeling, and the application of game semantics to logic programs. He has published extensively in top venues like Theory and Practice of Logic Programming (TPLP), ACM Transactions on Computational Logic (TOCL), and International Conference on Logic Programming (ICLP). Rondogiannis has collaborated with leading researchers such as Angelos Charalambidis and William W. Wadge, exploring topics like bilattice-based fixpoint theory and the implementation of recursive dataflow graphs in TensorFlow. His research bridges theoretical foundations with practical applications in areas like knowledge representation and programming language design.

• Electromagnetic Scattering
• Chiral Media Analysis
• Numerical Linear Algebra
• +3 more

Roupa Paraskevi is an External Lecturer at the Department of Mathematics, National and Kapodistrian University of Athens. Her research focuses on advanced topics in applied mathematics, including electromagnetic scattering in chiral media, numerical linear algebra, and statistical modeling. She specializes in inverse problems, matrix function estimation, and high-dimensional data analysis, with applications in engineering and physics. Her work bridges theoretical developments and computational methods, addressing challenges such as far field pattern analysis, layered obstacle scattering, and penalized least squares optimization. Key methodologies include the method of fundamental solutions (MFS), exponential moment matching (e-MoM), and generalized cross-validation techniques. Roupa Paraskevi has published extensively on electromagnetic wave interactions with chiral materials, two-dimensional scattering phenomena, and efficient numerical approaches for matrix computations. Her publications demonstrate expertise in both forward and inverse scattering problems, with contributions to both theoretical frameworks and practical algorithm development. While no specific student advisees or scientific awards are mentioned in the provided text, her research portfolio reflects sustained contributions to computational electromagnetics and numerical analysis. Current research directions include advancing scattering theory for complex media and improving statistical methodologies for high-dimensional problems.

Dimitris AchlioptasView profile

Professor

• Theoretical Computer Science
• Algorithms
• Randomized Algorithms
• +5 more

Dimitris Achlioptas is a Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on theoretical computer science, algorithms, and probabilistic methods. He has contributed extensively to areas such as satisfiability problems, random constraint satisfaction, combinatorial optimization, and phase transitions in random structures. His work bridges algorithm design with probabilistic analysis, often leveraging tools from discrete mathematics and statistical physics. Key research themes include the Lovász Local Lemma, random k-SAT, and the interplay between computational complexity and statistical properties of problems. Notable contributions address algorithmic barriers from phase transitions, solution space geometry of random CSPs, and efficient sampling techniques for combinatorial structures. His recent work explores applications of probabilistic methods in machine learning and optimization, including scalable algorithms for high-dimensional problems.