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Shuichi HiraharaView profile

Associate Professor

• Computational Complexity Theory
• Meta-complexity
• Average-case Complexity
• +4 more

Shuichi Hirahara is an Associate Professor at the National Institute of Informatics (NII), Japan, within the Principles of Informatics Research Division. He holds a Ph.D. from the University of Tokyo and has held research positions at the University of Warwick and Nagoya University. His work lies at the intersection of theoretical computer science and cryptography, focusing on foundational questions in computational complexity. Educational Background: Ph.D. in Computer Science, Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo (2016–2019) His research centers on meta-complexity , a field probing the complexity of problems about computational complexity itself, such as the Minimum Circuit Size Problem (MCSP). He investigates average-case complexity , Kolmogorov complexity , and circuit minimization , aiming to understand the limits of efficient computation and to establish secure cryptographic foundations. His work often reveals unexpected hardness results and bridges worst-case and average-case complexity. The recent publications highlight a strong trend toward using meta-complexity to understand cryptographic primitives such as one-way functions and to unify hardness conjectures like the Planted Clique problem. His results have significant implications for proving the security of cryptography based on average-case hardness. Scientific Awards: Young Scientists' Award from MEXT (2024) Yamato Scientific Award (2024) Microsoft Informatics Research Award (2024) Complexity Result of the Year 2022 Funai Research Encouragement Award (2022) Machtey Award (FOCS 2018) Academic Encouragement Award (IEICE, 2019) Hirahara has been actively involved in research funding and academic service. He is the principal investigator of a JSPS Challenging Research (Pioneering) grant on average-time NP-completeness and has participated in several JST and JSPS projects on complexity theory and quantum algorithms. He serves on the program committees of major conferences including STOC, FOCS, and CCC. He is a frequent invited speaker at international workshops and seminars, contributing to the global discourse on computational complexity and cryptography. While no formal advisees are listed, his leadership in research projects suggests an active mentoring role. Labs and Research Teams: He is affiliated with the Informatics Principles Research Division at NII, a leading center for theoretical informatics in Japan. His work is highly collaborative, involving researchers from institutions like JST, University of Tokyo, and international partners.

Francois Le GallView profile

Professor

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

Francois Le Gall is a Professor at Nagoya University's Graduate School of Mathematics, where he leads the Quantum Algorithms and Complexity Group. His research focuses on quantum computation, complexity theory, and distributed algorithms. Research Interests: Quantum algorithms (including Grover's algorithm optimizations and Shor's algorithm generalizations), quantum complexity theory, communication protocols, and distributed computing frameworks. His work bridges theoretical computer science and practical quantum implementations. Publications: Recent articles explore quantum interactive proofs, distributed quantum advantage, and dequantization techniques. A consistent focus is observed on overcoming computational limits in quantum systems and establishing complexity boundaries. Awards: NISTEP Award 2017 ISSAC 2014 Distinguished Paper Award Teaching & Advising: Teaches courses in Linear Algebra, Quantum Computing, and Mathematical Perspectives. Mentors 11+ PhD/Master's students in quantum algorithms and complexity. Leads the QLEAP-AI project since 2020. Group Leadership: Heads a research team developing quantum algorithms for graph problems, matrix operations, and communication complexity, with collaborations across global institutions.

Patrick TotzkeView profile

Researcher

• Theoretical Computer Science
• Formal Verification
• Automata Theory
• +5 more

Patrick Totzke is a theoretical computer scientist at the University of Liverpool's Department of Computer Science. His research focuses on mathematical models of computation, formal verification, and the complexity of strategies in stochastic and temporal games. University of Liverpool (2018–present) Postdoctoral positions: LFCS Edinburgh (2016–2018), University of Warwick (2015), LaBRI Bordeaux (2014) Research Interests include automata theory, game theory, computational logic, and computer-aided verification. Recent work emphasizes history-deterministic systems, timed automata, and decision problems in infinite-state games. Key Publications (2024–2025) explore temporal graph explorability (PSPACE-complete), stochastic nondeterminism resolution (undecidability in NFAs), and bounded-memory strategies in partial-information games. Earlier works address energy-parity objectives (NP∩coNP) and the reachability problem in 2D vector addition systems (NL-complete). Awards Shortlisted for Teacher of the Year (2021) PI on EPSRC-funded project (2021) Co-PI on Royal Society grant (2021) Collaborations include Richard Mayr, Stefan Kiefer, Mahsa Shirmohammadi, and Sougata Bose (postdoc, 2021). He co-chaired Reachability Problems (RP'21) and contributes to open-source tools like alot and LaTeX beamer themes.

• Geometry
• Algebra
• Discrete Mathematics
• +7 more

Milagros Izquierdo is a Professor in the Department of Mathematics at Linköping University , Sweden. She is affiliated with the research division of Algebra, Geometry and Discrete Mathematics (ALGD) , which conducts research in algebra, geometry, topology, combinatorics, and graph theory. Institution: Linköping University (LiU) Department: Department of Mathematics (MAI) Research Division: Algebra, Geometry and Discrete Mathematics (ALGD) Position: Professor Her research centers on the geometry and topology of Riemann surfaces, particularly focusing on automorphism groups, moduli spaces, Schottky groups, and equisymmetric strata. She investigates algebraic and geometric structures arising from group actions on surfaces, with connections to complex analysis and discrete mathematics. The recent publications (2021–2024) demonstrate a consistent focus on symmetry in Riemann surfaces, classification of automorphism groups, and stratification of moduli spaces. Her work lies at the intersection of algebra, geometry, and topology, often involving prime genus surfaces and cyclic or equisymmetric group actions. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: There is no public information available in the provided texts regarding students advised, research grants, or funding. However, her active publication record suggests involvement in research projects and likely supervision of doctoral students within the Department of Mathematics. Labs and Research Teams: She is a key member of the Algebra, Geometry and Discrete Mathematics research group at Linköping University, which comprises around twenty academics and doctoral students. This group fosters collaborative research in pure mathematics, particularly in areas aligned with her expertise.

Jan SnellmanView profile

Associate Professor

• Algebraic Combinatorics
• Commutative Algebra
• Representation Theory
• +7 more

Jan Snellman is an Associate Professor (Docent) in the Department of Mathematics at Linköping University, affiliated with the Algebra, Geometry and Discrete Mathematics (ALGD) division. He holds a permanent academic position and is actively involved in teaching and research. PhD, Stockholm University, 1998 Postdoctoral positions at École Polytechnique (France) and University of Wales, Bangor (UK) Permanent faculty at Linköping University since October 2005 His research lies at the intersection of algebra and combinatorics, with a focus on algebraic combinatorics, commutative algebra, representation theory, and arithmetical functions. He has made significant contributions to the study of Gröbner bases, combinatorial structures in algebra, and convolutions on arithmetical functions, including the ternary convolution. His work often bridges abstract algebra with enumerative and discrete methods. The most recent publications and preprints (2024–2025) reflect ongoing research in poset probability, convolutions, blocking ideals, and generating functions, indicating sustained scholarly activity. Earlier works from 2003–2007 explore saturated chains, Laplacians on multicomplexes, and unitary convolutions, showing a long-standing interest in combinatorial algebra and number-theoretic functions. The research consistently engages with algebraic structures derived from combinatorial objects. Jan Snellman has supervised multiple graduate students, including PhD candidates Jonna Gill, Mikael Hansson, and Vincent Umutabazi. He has also guided numerous Master’s and candidate thesis projects in areas such as concave partitions, phylogenetic analysis, and Gröbner basis algorithms. His supervision spans both theoretical algebra and applied combinatorics. He has not received any explicitly mentioned scientific awards in the provided text. Jan Snellman is a member of the Algebra, Geometry and Discrete Mathematics (ALGD) research group at Linköping University, which focuses on algebra, geometry, topology, combinatorics, and graph theory. The group includes about twenty researchers and doctoral students, fostering a collaborative environment for advanced mathematical research.

Jakub KlikowskiView profile

Assistant Professor

• Machine Learning
• Data Stream Classification
• Imbalanced Data
• +12 more

Jakub Klikowski is an Assistant Professor in the Department of Computer Systems and Networks at the Faculty of Electronics, Wrocław University of Science and Technology. He has been employed since October 2018 and earned his PhD in engineering and technical sciences in March 2022 for his dissertation on ensemble methods for imbalanced data stream classification. He is actively involved in research and teaching, contributing to multiple research teams including the Machine Learning Team and Advanced Data Analysis Methods Team. Research Interests: Classification of imbalanced and streaming data Ensemble learning and one-class classification Natural language processing (NLP) Concept drift detection and adaptive learning Graph neural networks and spam/disinformation detection Multi-criteria optimization and evolutionary algorithms His research is primarily conducted within the IDSTREAM and GEOM projects, focusing on developing robust classifiers for challenging data environments. His recent work emphasizes hybrid preprocessing, ensemble weighting, and drift detection mechanisms. Publication Trends: His publications from 2019 to 2022 reveal a consistent focus on improving classification performance in imbalanced data streams using ensemble techniques, preprocessing strategies, and drift detection. Key themes include weighted bagging, Hellinger distance, centroid analysis, and genetic optimization, all applied to real-world decision-making tasks. Scientific Awards: Rector of Wrocław University of Science and Technology Award (2020) Distinction in the Secundus program for young scientists (2023) Honorable Mention for doctoral dissertation (2022) Advising and Grants: Dr. Klikowski supervises diploma theses on topics such as NLP, text summarization, hate speech detection, and graph neural networks. He is involved in research projects including IDSTREAM and GEOM, which support his work in data stream classification and optimization. He collaborates with leading researchers like Prof. Michał Woźniak and Prof. Robert Burduk. Labs and Teams: He is an active member of the Machine Learning Team, Advanced Data Analysis Methods Team, and other research groups at the Department of Computer Systems and Networks. These teams foster interdisciplinary research in AI, data science, and network optimization.

• Science Communication
• Climate Change Communication
• Cognitive Psychology
• +5 more

Jordan Harold is a Lecturer in Psychology at the School of Psychology, University of East Anglia (UEA) , with research focused on improving science communication, particularly in the context of climate change. He is also a member of the Tyndall Centre for Climate Change Research and ClimateUEA , contributing to interdisciplinary efforts in climate science and policy. His work bridges cognitive psychology and public decision-making, aiming to make scientific evidence more accessible and usable. Education: BSc in Psychology (First Class Honours), University of Warwick MSc in Science (with Distinction), Open University PhD, University of East Anglia – focused on cognition of data visuals and climate science communication at the science-policy interface His research investigates how individuals interpret scientific data, especially climate-related information, and how communication strategies can be optimized. He employs experimental methods, eye-tracking, surveys, and interviews. Key projects include co-designing IPCC report visuals and developing guidelines for communicating climatic uncertainties. His work supports policymakers and the public in making informed, evidence-based decisions. His recent publications center on data visualization in IPCC reports, assessing visual complexity, and improving accessibility. These works reflect a strong trend toward interdisciplinary science communication, cognitive psychology, and climate policy interface research. Scientific Contributions: Contributor and drafting author to IPCC Special Reports (Global Warming of 1.5°C, Climate Change and Land) Collaboration with InfoDesignLab and Future Climate for Africa programme Development of practical guidelines for climate data communication He has advised on communication strategies for major climate assessments and has been featured in media such as Carbon Brief. Jordan Harold has not supervised any named students in the provided texts and has not received any explicitly mentioned awards. He is actively involved in research but no lab or team leadership is specified beyond collaborative projects.

Tyler GenaoView profile

Assistant Professor

• Number Theory
• Arithmetic Geometry
• Algebraic Geometry

Tyler Genao is a Zassenhaus Assistant Professor (postdoc) at the Ohio State University. He completed his PhD in 2023 at the University of Georgia under advisor Pete L. Clark. His research focuses on Number Theory and Arithmetic Geometry, with emphasis on elliptic curves, modular forms, and algebraic structures. His work explores topics like torsion bounds, isogeny classes, and modular curves. Recent research trends include studying torsion subgroups of elliptic curves over number fields, properties of Shimura curves, and computational approaches to non-unitary partitions. His articles often intersect algebraic geometry and number theory, addressing questions in arithmetic dynamics and Galois representations. No awards or grants are explicitly mentioned in the provided text.

Thomas SchindlerView profile

Assistant Professor

• Logic
• Metaphysics
• Epistemology
• +7 more

Thomas Schindler is an Assistant Professor at the University of Amsterdam , where he leads the ERC Starting Grant project "Generalisation into Sentence and Predicate Positions" (2023-2028). His academic journey includes Marie Curie Fellowship (2018-2020), Research Associate at University of Bristol (2020-2022), and Junior Research Fellow at Clare College, University of Cambridge (2015-2018). He earned his PhD summa cum laude from LMU Munich (2015) under Hannes Leitgeb. His research intersects logic, metaphysics, epistemology, philosophy of language, and philosophy of mathematics , with a focus on deflationism, abstract objects, semantic paradoxes, and type-free theories . Recent publications analyze the logical function of property talk, Gödelian solutions to paradoxes, and minimalist accounts of numbers. His work demonstrates that disquotational truth can simulate higher-order logic while addressing generalization problems. Scientific awards include ERC Starting Grant , Marie Curie Fellowship , DFG funding , and multiple scholarships from DAAD, Tilburg University, and LMU Munich. He actively contributes to academic communities through editorial work ( Dialectica 2020-2022), refereeing for journals like Mind and Journal of Philosophical Logic , and organizing workshops on abstract objects and paradoxes.

• Computer Science
• Environmental Science
• Engineering
• +1 more

Chengcheng Xu is an active academic researcher focusing on computer science, mathematics, and environmental engineering domains. His recent publications highlight collaborative work with Jun Chen, Tianfeng Wang, Man Chen, and Zhisong Pan on advanced machine learning techniques applied to SAR image analysis and graph representation learning. Key research areas include: Graph contrastive learning frameworks (GRAIL) Class-aware graph Siamese networks Gradient-prior guided semi-supervised segmentation Synthetic Aperture Radar (SAR) ship detection His work appears in journals like IEEE Sensors Journal , Information Processing & Management , and Neurocomputing , emphasizing efficient annotation strategies and representation learning advancements.

Hualou LiangView profile

Professor

• Neuroengineering
• Machine Learning
• Cognitive Neuroscience
• +4 more

Hualou Liang is a Professor of Biomedical Engineering at Drexel University's School of Biomedical Engineering, Science & Health Systems. He is also affiliated with the Computational Neuroscience Initiative at the University of Pennsylvania. His office is located in Monell 103 at Drexel University. Dr. Liang earned his PhD in Physics from the Chinese Academy of Sciences in China. His educational background in physics provides a strong foundation for his interdisciplinary research at the intersection of engineering, neuroscience, and data science. Dr. Liang is known for pioneering the application of causal functional connectivity measures, particularly Granger causality, to the analysis of complex, high-dimensional neuroscience data. His research spans biomedical data analysis, machine learning, cognitive and computational neuroscience. His lab explores neural mechanisms of visual perception during various cognitive tasks using computational approaches. Current research focuses include: development of software package (www.brain-smart.org) for analyzing brain circuits; neuronal oscillations and attentional control; development of advanced signal processing methods for nonstationary, multivariate neurobiological data (spikes, field potentials, EEG/MEG, fMRI etc); and algorithm development for brain-computer interface or brain-machine Interface. Dr. Liang's recent publications demonstrate a strong trend toward applying large language models and advanced machine learning techniques to biomedical problems, particularly in dementia prediction, drug analysis, and neural data interpretation. His work bridges traditional neuroscience with cutting-edge artificial intelligence approaches. Elected Fellow of the American Institute for Medical Biological Engineering (AIMBE) 1st and 2nd Place at the INTERSPEECH 2024 TAUKADIAL Challenge for Dementia Prediction Using LLMs Recipient of FDA Grant Extension for Machine Language Learning Based Research on Heterogeneous Treatment Effect Models 2020-2021 Pennsylvania CURE Grant Awardee 2016 Commonwealth Universal Research Enhancement (CURE) Program Grant Awardee Dr. Liang has successfully mentored students like Felix Agbavor, who has co-authored multiple publications and won awards with him. His research is supported by significant grants including NIH funding and FDA extensions. The Liang Lab is actively recruiting postdocs in biomedical deep learning and systems/computational neuroscience, indicating ongoing research momentum and funding. Dr. Liang has collaborated extensively with researchers across Drexel University and with colleagues at the University of Pennsylvania. The Liang Lab is a vibrant research group focused on problems at the intersection of machine learning and neuroscience. The lab collaborates with other research groups, including Vikas Bhandawat's lab, on projects studying neural control of movement. The lab has developed innovative approaches using data science to advance understanding of brain function, discover causal structures from observations, and apply deep learning and natural language processing to biomedical challenges. Recent notable work includes being the first to report using GPT-3 for early diagnosis of dementia, which was featured on the GPT-3 Wikipedia page.

• Network Security
• Cyber-Physical Systems Security
• BGP Routing Security
• +6 more

Gabi Dreo Rodosek is a distinguished researcher and faculty member at Bundeswehr University Munich , Germany. With a prolific publication record spanning over three decades, she has contributed extensively to the fields of network security, cyber-physical systems, BGP routing security, and software-defined networking (SDN). Research Interests: Network Security & Intrusion Detection BGP Routing Security & RPKI Software-Defined Networking (SDN) Malware Analysis & Adversarial Machine Learning Mobile Ad-hoc Networks (MANETs) Content Delivery Networks & IP Geolocation Her recent work focuses on advanced BGP security mechanisms, including path plausibility algorithms and RPKI deployment strategies. She has also explored adversarial techniques in machine learning, particularly in the context of malware detection and evasion. Her research in SDN-based defenses includes novel frameworks for DDoS mitigation and anomaly detection in 5G networks. Scientific Contributions: Over 159 peer-reviewed publications (1991–2025) Key contributions to BGP security, including RPKI validation and route leak mitigation Pioneering work in adversarial machine learning for cybersecurity Development of SDN-based intrusion detection and threat hunting frameworks Collaborations & Advising: She has mentored and collaborated with numerous researchers, including Nils Rodday, Peter Hillmann, Florian Steuber, and Raphael Labaca Castro. Her work often involves interdisciplinary teams tackling challenges in network security, AI-driven defense mechanisms, and resilient communication systems. Labs & Teams: While specific lab affiliations are not detailed in the provided text, her extensive collaboration network and affiliation with Bundeswehr University Munich suggest involvement in leading cybersecurity and networking research initiatives.

• Geometric Topology
• Low-Dimensional Topology
• Geometric Group Theory
• +2 more

Funda Gultepe is a Visiting Assistant Professor in the Department of Mathematics and Statistics at the University of Toledo's College of Natural Sciences and Mathematics. Her research focuses on geometric topology, geometric group theory, and low-dimensional manifold theory. Primary Affiliation: Department of Mathematics and Statistics, University of Toledo Academic Rank: Visiting Assistant Professor Research Interests: Dr. Gultepe's work centers on hyperbolic geometry, free group automorphisms, and topological structures in 3-manifolds. Key areas include: Cannon-Thurston maps for hyperbolic groups Curve complex and arc graph metrics Dehn twisting in connected sums of manifolds Normal surface theory in free product decompositions Mapping class groups and geometric flows Train track representations of automorphisms Publication Trends: Her mathematical contributions span formal logic, geometric topology, and group theory. Recent works examine hyperbolic geometry's role in free group automorphisms, while earlier studies focus on topological decompositions and Dehn twisting mechanisms.

Nitesh ChawlaView profile

Professor

• Artificial Intelligence
• Data Science
• Network Science
• +3 more

Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame, where he serves as the founding Director of the Lucy Family Institute for Data and Society. His research focuses on artificial intelligence, data science, and network science, with a mission to advance technology for the common good through interdisciplinary collaboration. Education: Ph.D. in Computer Science and Engineering, University of South Florida, 2002 M.S. in Computer Science, University of South Florida, 2000 B.E. in Computer Science and Engineering, University of Poona, 1997 Research Focus: Chawla pioneers work at the intersection of AI and societal impact, specializing in machine learning fairness, graph neural networks, federated learning, and large language model applications. His lab addresses real-world challenges in healthcare, education, and social equity through innovative data-driven methodologies. Publication Trends: Recent work (2023-2025) demonstrates a strong emphasis on foundation models for graphs, ethical AI deployment, and human-centered applications. Key themes include LLM robustness, federated learning architectures, bias mitigation, and educational AI, reflecting a consistent focus on scalability and societal relevance. Awards & Honors: Fellow, American Association for the Advancement of Science (AAAS) AAAI Fellow (2024) ACM Fellow IEEE Fellow (2022) Leadership: Directs the Lucy Family Institute for Data and Society, an interdisciplinary hub developing data science solutions for global challenges. The institute fosters collaborations across academia, industry, and communities to translate research into measurable social impact.

Przemysław GordinowiczView profile

Assistant Professor

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +2 more

Dr. Przemysław Gordinowicz serves as an Assistant Professor at Lodz University of Technology within the Department of Applications of Contemporary Mathematical Analysis. Based in room 161 with contact details (+48) 42 631-36-33 and pgordin@p.lodz.pl, he maintains an active research profile in discrete mathematics. His research centers on Graph Theory and Combinatorics, specializing in graph coloring extendability problems for planar and outerplanar graphs, pursuit-evasion games (cops and robbers, localization games), and polynomial method applications. He also investigates random graph models, discrete Laplacian problems, and extremal graph theory, employing combinatorial and algebraic techniques to solve complex structural problems. Analysis of his 15 most recent publications (2015-2025) reveals consistent focus on three interconnected themes: (1) polynomial method applications to list coloring extendability, (2) theoretical advances in pursuit-evasion games across diverse graph classes, and (3) firefighter problem variants including surviving rate calculations. His work bridges theoretical combinatorics with algorithmic applications, frequently appearing in top-tier journals like Journal of Graph Theory and Electronic Journal of Combinatorics. He has supervised PhD student Paweł Twardowski, whose 2023 dissertation addressed graph polynomial applications to list coloring extendability in planar graphs. No information about research grants or collaborative teams is available in the provided materials.