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Dániel MarxView profile

Professor

• Parameterized Algorithms
• Computational Complexity
• Algorithmic Graph Theory
• +7 more

Dániel Marx is a tenured Professor at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. He leads research in the area of Algorithmic Foundations and Cryptography, with a focus on parameterized algorithms and computational complexity. He has held previous positions at the Max Planck Institute for Informatics and the Institute for Computer Science and Control of the Hungarian Academy of Sciences (MTA SZTAKI). His work is highly theoretical, aiming to understand the precise complexity of algorithmic problems, especially on bounded-treewidth graphs and in parameterized settings. PhD: Budapest University of Technology and Economics, 2005 Postdoc: Tel Aviv University, Humboldt University Berlin Senior Research Fellow: MTA SZTAKI, 2012–2019 Senior Researcher: Max Planck Institute for Informatics, 2019–2020 Faculty: CISPA Helmholtz Center, 2020–present His research interests lie at the intersection of theoretical computer science and discrete mathematics. He is particularly known for his work on parameterized algorithms , fine-grained complexity , and lower bounds . His group investigates algorithmic graph theory problems, including domination, independence, homomorphism, and clustering, especially under structural constraints like bounded treewidth. He has pioneered techniques in dynamic programming, kernelization, and hardness proofs based on the Exponential Time Hypothesis. The recent publications of Dániel Marx reveal a consistent focus on establishing tight complexity bounds for fundamental algorithmic problems. His work spans from exact and parameterized algorithms to approximation and counting problems. A recurring theme is the analysis of problems on bounded-treewidth graphs, where he explores the boundary between tractable and intractable cases. He also contributes to network design (e.g., Steiner problems), clustering, and subgraph counting, often providing complete classifications of complexity based on parameters. European Research Council Starting Grant European Research Council Consolidator Grant Humboldt Research Fellowship for Experienced Researchers Dániel Marx has advised numerous researchers and collaborated widely across institutions. His research is supported by prestigious grants, including ERC grants that funded his group at MTA SZTAKI. He actively leads a research group at CISPA focused on parameterized algorithms and complexity. While specific PhD students are not listed in the provided text, his extensive co-authorship network indicates a strong mentoring and collaborative presence. His work has significant implications for the foundations of computer science and algorithm design. He leads the research group on Parameterized Algorithms and Complexity at CISPA, continuing his long-standing focus on theoretical algorithm design and analysis. His team works on foundational problems in graph algorithms and complexity theory, aiming to develop new algorithmic techniques and understand the limits of efficient computation.

Lars BirkedalView profile

Professor

• Logic and Semantics of Programming Languages
• Type Theories
• Program Logics
• +6 more

Lars Birkedal is a Professor of Computer Science at Aarhus University, Denmark, where he serves as Head of the Logic and Semantics Group. He previously held positions at the IT University of Copenhagen until 2012 and served as Head of Department of Computer Science at Aarhus from 2014 to 2017. His educational background includes a Ph.D. in Computer Science from Carnegie Mellon University, USA, completed in December 1999. Birkedal's primary research interests focus on the logic and semantics of programming languages and type theories. His current work centers on program logics for reasoning about distributed, concurrent, higher-order, and imperative programs; cyber-security; and type theories with guarded recursion. He has made significant contributions to separation logic, particularly through the development of the Iris framework, which earned him the 2023 Alonzo Church Award. His research bridges theoretical foundations with practical applications in program verification and security. His recent publications demonstrate a strong trend toward mechanized verification of complex systems, with particular emphasis on concurrent and distributed programming. The research spans multiple subfields including separation logic variants, capability systems, information flow security, and formal verification of low-level systems. A notable pattern is the integration of logical frameworks with practical verification tools, often implemented in proof assistants like Coq. His scientific achievements have been recognized with numerous prestigious awards: Fellow of the ACM ERC Advanced Grant from the European Research Council (2023) 2023 Alonzo Church Award for Outstanding Contributions to Logic and Computation Villum Investigator grant from the Villum Foundation (2019) Danish Minister of Research Elite Research Award (2015) Sapere Aude Advanced Grant from the Danish National Science Research Council (2013) ACM SIGPLAN Milner Award (2013) Birkedal has advised numerous PhD students and postdocs, including Lau Skorstengaard, Kristoffer Just Andersen, Morten Krogh-Jespersen, and Robbert Krebbers. His research has been supported by significant grants including an ERC Advanced Grant (2023), a Villum Investigator grant (2019), and a Sapere Aude Advanced Grant (2013). He has served in editorial roles including Editor-in-Chief of Logical Methods in Computer Science (2014-2020) and Chairman of the board, and as PC Chair for POPL 2020. He leads the Logic and Semantics Group at Aarhus University and has been instrumental in several major research projects including the CHORDS project (Compositional Higher-Order Reasoning about Distributed Systems) and the Center for Basic Research in Program Verification (CPV). His earlier ModuRes project (Modular Reasoning about Concurrent Higher-Order Imperative Programs) laid important groundwork for verification of modern programming language features.

• Algorithms
• Complexity Theory
• Theoretical Computer Science
• +5 more

Jesper Nederlof is an Associate Professor in the Algorithms and Complexity group at the Department of Information and Computing Sciences, Faculty of Science, Utrecht University. His research focuses on designing efficient algorithms for computationally hard problems, particularly in the areas of parameterized complexity, graph algorithms, and NP-complete problems. He received his M.Sc. in Applied Computing Science from Utrecht University in 2008 and his Ph.D. from the University of Bergen in 2011 with the thesis 'Space and Time Efficient Structural Improvements of Dynamic Programming Algorithms' under supervision of Pinar Heggernes. Nederlof's research interests span multiple areas of theoretical computer science, with a particular focus on designing algorithms for NP-complete problems with small exponential worst-case run time. His work extends to algorithmic game theory, information theory, representation theory, approximation algorithms, and online algorithms. He has made significant contributions to parameterized complexity, particularly in developing algorithms parameterized by structural graph parameters like treewidth and cutwidth. His publication record shows a consistent output of high-quality research in top theoretical computer science venues. His recent work demonstrates trends toward tighter bounds for exponential-time algorithms, improved space complexity, and connections between different complexity hypotheses like ETH. Many papers focus on structural parameters of graphs to develop more efficient algorithms for fundamental problems like Hamiltonian cycle, Steiner tree, and subset sum. EATCS-IPEC Nerode Prize (2023) WG best paper award (2020) Nederlof has been involved in teaching courses on algorithms, (non)-linear optimization, graph theory, (vector) calculus, modeling, and management and product development. His research has been supported by various grants including an NWO open competition project during his postdoctoral period and an EU ERC Starting Grant for the project 'Finding Cracks in the wall of NP-Completeness' (2020-2025). As a member of the Algorithms and Complexity group at Utrecht University, Nederlof collaborates with researchers working on foundational aspects of computing, contributing to the group's reputation in theoretical computer science research.

Anders Alnor MathiasenView profile

Researcher

• Static Program Analysis
• Computational Complexity
• Software Engineering
• +2 more

Anders Alnor Mathiasen is a Researcher at Aarhus University , focusing on theoretical aspects of static program analysis and computational complexity. He has co-authored influential work on Andersen's Pointer Analysis (APA), establishing tight complexity bounds and parallelizability limits. Key Research Areas : Static program analysis, algorithm complexity, formal verification, and parallel computing. Academic Contributions : His 2021 POPL paper formalized the cubic bottleneck of APA, providing optimal algorithms under sparsity conditions. Collaborations : Worked with Andreas Pavlogiannis on theoretical advancements in program analysis.

Adam Teodor PolakView profile

Assistant Professor

• Algorithms
• Fine-Grained Complexity
• Learning-Augmented Algorithms
• +3 more

Adam Teodor Polak serves as an Assistant Professor in the Department of Computing Sciences at Bocconi University, where his research centers on theoretical algorithms with dual emphases on fine-grained complexity and learning-augmented algorithms. His work investigates fundamental questions about computational hardness while developing prediction-enhanced algorithms that maintain worst-case guarantees. Polak earned his PhD from Jagiellonian University in 2019 under Paweł Idziak, including a research visit at MIT with Virginia Vassilevska Williams. He subsequently held postdoctoral positions at the Max Planck Institute for Informatics and EPFL before joining Bocconi. His research program addresses why computational problems resist efficient solutions and how imperfect predictions can robustly improve algorithmic performance. This manifests in two interconnected streams: establishing conditional lower bounds for problems like 3SUM and Orthogonal Vectors, and designing learning-augmented frameworks for dynamic graph problems, caching, and optimization that blend theoretical rigor with practical machine learning insights. Recent publications reveal accelerating momentum in algorithms with predictions, with over half of his 2023-2025 output appearing in top ML venues (ICML, NeurIPS, ICLR) alongside traditional theory conferences (STOC, SODA). This cross-pollination demonstrates how worst-case theoretical guarantees can coexist with data-driven performance gains across graph algorithms, scheduling, and combinatorial optimization. Scientific recognition includes: Best Paper Award at ESA 2024 for knapsack algorithm breakthroughs Bronze Medal at ACM ICPC World Finals (2011) 2nd Place in PACE 2018 Challenge for Steiner tree algorithms Polak actively shapes the field through program committee service (ESA, ICALP, SOSA) and community building, notably co-organizing the 2022 Workshop on Algorithms with Predictions (ALPS) and decade-long high-school algorithmics workshops. His industry collaborations with Teroplan and Google demonstrate real-world impact in route planning and distributed systems. Current teaching includes graduate Algorithms courses at Bocconi, while his experimental work on GPU-accelerated graph algorithms and medical computer vision continues to bridge theoretical insights with practical implementation challenges.

• Machine Learning
• Computer Vision
• Distance Metric Learning
• +4 more

Xiaochen Yang is an Assistant Professor in the Department of Artificial Intelligence at the School of Computer Science and Control Engineering, University of Chinese Academy of Sciences. With a PhD from University College London completed in 2020, Dr. Yang has established a prolific research career spanning machine learning, computer vision, and computational mathematics. Their work bridges theoretical foundations with practical applications across multiple domains. Dr. Yang's research interests focus on advancing machine learning methodologies, particularly in distance metric learning, few-shot learning, and multimodal systems. Their work spans both theoretical aspects of numerical methods for stochastic differential equations and practical applications in computer vision, hyperspectral imaging, and security of large language models. This interdisciplinary approach has led to significant contributions in multiple fields. Analysis of Dr. Yang's recent publications reveals a strong trajectory in developing novel machine learning architectures, with particular emphasis on few-shot learning techniques, multimodal fusion approaches, and applications of diffusion models. Their work demonstrates increasing sophistication in handling complex data modalities while maintaining mathematical rigor, particularly evident in their publications from 2023-2025. Dr. Yang has established productive collaborations across institutions, as evidenced by their extensive co-author network spanning Chinese academic institutions and international partners. Their research has been published in top-tier venues including IEEE Transactions, Pattern Recognition, NeurIPS, and CVPR, reflecting the high impact of their contributions to the field.

• theoretical aspects of cryptography
• computational assumptions
• theoretical aspects of machine learning
• +6 more

Mohammad Mahmoody is an Associate Professor in the Computer Science Department at the University of Virginia (UVA), where he conducts theoretical research at the intersection of cryptography and machine learning. His research focuses on understanding fundamental limitations in cryptography and machine learning through the lens of lower bounds, impossibility results, and security barriers. Key areas include cryptographic protocols, adversarial robustness in learning systems, and computational complexity. His recent publications show a strong trend toward bridging cryptographic techniques with machine learning security, particularly in understanding data poisoning attacks, robust learning under adversarial conditions, and the computational foundations of security guarantees. His work spans both theoretical computer science venues (CRYPTO, EUROCRYPT, TCC) and machine learning conferences (NeurIPS, ICML). He actively advises graduate students and postdocs, with a track record of mentoring PhD candidates through completion of their degrees. His service to the academic community includes extensive program committee work for major conferences in both cryptography and machine learning. Dr. Mahmoody teaches advanced courses including Theory of Computation, Algorithms, and Cryptography at both undergraduate and graduate levels, demonstrating his commitment to educating the next generation of theoretical computer scientists.

• Cryptography
• Quantum Computing
• Lattice-based Cryptography
• +3 more

Zvika Brakerski is a Professor at the Department of Computer Science and Applied Mathematics of the Weizmann Institute of Science. He is an active researcher in theoretical computer science with a focus on cryptography and quantum computing. His work bridges foundational aspects of computer science with practical cryptographic applications. His educational background includes: Ph.D. from Weizmann Institute of Science (2011), advised by Shafi Goldwasser M.Sc. from Faculty of Engineering of Tel-Aviv University (2002), advised by Boaz Patt-Shamir B.Sc. joint degree from Faculty of Engineering and School of Computer Science of Tel-Aviv University (2001) Brakerski's research primarily focuses on the foundations of computer science, with particular emphasis on cryptography and quantum computing. His work explores the intersection of these fields, developing new cryptographic primitives that are secure against quantum adversaries while also leveraging quantum phenomena for cryptographic purposes. He has made significant contributions to lattice-based cryptography, fully homomorphic encryption, and quantum cryptography, establishing new connections between computational complexity and cryptographic security. His recent publications demonstrate a consistent focus on advancing the theoretical foundations of post-quantum cryptography while exploring novel applications of quantum computing to cryptographic problems. There's a clear trend toward establishing connections between seemingly disparate areas such as quantum information theory, computational complexity, and traditional cryptographic constructions. His work often introduces new frameworks that unify previously separate concepts, creating bridges between theoretical computer science and practical cryptographic applications. Brakerski has mentored students and collaborated with numerous researchers in the field, contributing to the advancement of cryptographic theory and quantum computing. His work has been supported by various research grants, though specific details are not provided in the source material.

• Machine Learning
• Artificial Intelligence
• Medical Imaging
• +5 more

Giovanni Montana is a Professor of Data Science at the University of Warwick, with joint appointments in the Departments of Statistics and Warwick Manufacturing Group (WMG). His research focuses on statistical and machine learning methods, particularly in sequential decision-making, reinforcement learning, and multi-agent systems. He has held academic roles at Imperial College London (2005–2013) and King’s College London (2013–2018), advancing interdisciplinary research in bioinformatics, neuroimaging, and healthcare. Supported by grants from EPSRC, MRC, and Wellcome Trust, his work bridges theoretical advancements and real-world applications. Education: PhD in Computational Statistics (University of Warwick), postdoctoral research at the University of Chicago, and industry experience at Bristol-Myers Squibb. His career spans statistical genetics, medical imaging, and AI-driven healthcare solutions. Research interests include deep reinforcement learning, multimodal biomedical data integration, and AI ethics. Notable achievements include developing biomarker discovery algorithms and AI models for chest X-ray analysis, showcased in media such as BBC and MIT Technology Review. Awards include Chartered Statistician and UKRI Turing AI Acceleration Fellowship. Labs/Teams: Leads the Data Science Research Group at Warwick, fostering collaborative projects in AI, robotics, and healthcare. Active in grant review panels for major funding bodies like EPSRC and the Royal Society.

• Computer Vision
• Deep Learning
• 3D Scene Analysis
• +3 more

Amir Atapour-Abarghouei is an Assistant Professor in the Department of Computer Science at Durham University, affiliated with the VIViD research group. His research focuses on computer vision, deep learning, and 3D scene understanding. His primary investigations include depth estimation, semantic segmentation, and neural architecture search. Recent projects explore monocular depth estimation using synthetic data with domain adaptation through image style transfer. Dr. Atapour-Abarghouei supervises multiple PhD students and has developed significant open-source implementations for depth perception algorithms. His technical work employs PyTorch and addresses complex computer vision challenges in robotic navigation and autonomous systems.

Julieanna Powell-TurnerView profile

Professor

• Environmental Sustainability
• Climate Change Resilience
• Resource Security
• +2 more

Julieanna Powell-Turner is Professor of Environmental Organisational Sustainability and Associate Dean for Research and Innovation at the University of Chester's Faculty of Science, Business and Enterprise. She leads the Centre for Research into Environmental and Sustainable Transitions (CREST), driving interdisciplinary work on climate resilience, resource security, and social cohesion aligned with UN SDGs. With 20+ years in sustainability research, she has secured significant UKRI funding and supervised doctoral projects in decarbonization and green skills. Her recent publications focus on climate action governance (2025), corporate sustainability barriers (2025), and semantic access controls for healthcare systems (2022-2024). Powell-Turner oversees institutional research frameworks including REF/KEF and manages postgraduate programs, emphasizing participatory action research and cross-sector partnerships.

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.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +6 more

Shwai He is a PhD student and Affiliate Assistant Professor at the University of Maryland, advised by Ang Li. Their research focuses on advancing AI systems through innovative approaches in large language models (LLMs), fairness in machine learning, and efficient neural network architectures. Key areas include multi-agent systems, causal modeling for bias mitigation, and optimization techniques for mixture-of-experts models. Research interests span artificial intelligence, machine learning, and natural language processing with emphasis on practical applications like healthcare diagnostics and social pairing systems. Notable work includes developing GNWT-based multi-agent digital twins for social platforms and improving LLM transparency through token analysis. Publications highlight contributions to counterfactual fairness, dynamic-depth transformers, and parameter-efficient methods. Current efforts explore computational efficiency in vision-language models and bio-inspired antibody prediction systems. No scientific awards have been mentioned. Advising and grants: Currently a PhD student under Ang Li's supervision. Research involves collaborations across computer science and bioinformatics domains.

• Theoretical Computer Science
• Parameterized Complexity
• Graph Algorithms
• +3 more

Brage Isak Bakkane Keiserås is a Doctoral Research Fellow in the Department of Informatics at the University of Oslo, Faculty of Mathematics and Natural Sciences. His research is centered on theoretical aspects of computer science, particularly algorithmic graph theory and parameterized complexity. His research interests include: Theoretical Computer Science Parameterized Complexity Graph Algorithms Discrete Mathematics Algorithm Design Computational Complexity The available publication indicates a strong focus on hardness results for domination problems under structural graph parameters, particularly using Mim-Width as a complexity measure. This aligns with current trends in fine-grained complexity and structural parameterizations in algorithm design. He is actively involved in the research project Leveraging Energy-Aware Programming (LEAP) , which suggests an interest in broader programming technology applications, possibly connecting theoretical foundations with energy-efficient computing paradigms. There are no scientific awards listed in the provided text. There is no information available about student advising or research grants beyond project affiliation. No lab or team name is explicitly mentioned, though his work is likely conducted within the Programming Technology research group at UiO.

• Algorithms on strings
• Small-space algorithms
• Data structures
• +4 more

Tatiana Starikovskaya is an Assistant Professor in the Talgo team at the Computer Science Department of École normale supérieure (ENS Paris), France. Her work focuses on the design and analysis of algorithms for string processing, space-efficient computation, and complexity theory, with applications in bioinformatics and data security. Education : PhD in Mathematics (2013) and MSc in Mathematics (2009) from Lomonosov Moscow State University; MSc in Data Science (2009) from Moscow Institute of Physics and Technology and Yandex School of Data Analysis. Her research explores the intersection of algorithm theory and practical applications, particularly in streaming models , probabilistic text indexing , and clustering of sequence data . Key themes include handling wildcards , k-mismatch problems , and edit distance under space constraints. Current projects include PARSe (ANR-20-CE48-0001) , aiming to develop ultra-efficient algorithms for noisy string data, and AlgoriDAM (ANR-19-CE48-0016) , focusing on algorithmic theory for modern data models. She co-supervises PhD students Gabriel Bathie and T. El Ghazi, with collaborations spanning institutions like University of Wrocław and IRISA. She has served on program committees for major conferences (STACS, ESA, ICALP, CPM) and organized the CPM Summer School (2023) and New Horizons of Stringology (2024) workshop at Cirm. Teaching roles include courses at ENS Paris (Algorithmique, Programming for Non-CS Students) and Higher School of Economics (Moscow).