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Lirong XiaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +9 more

Lirong Xia is a Professor of Computer Science at Rutgers University - New Brunswick and Deputy Director of DIMACS (Center for Discrete Mathematics and Theoretical Computer Science). He holds a Ph.D. in Computer Science from Duke University, an M.A. in Economics from Duke, and a B.E. in Computer Science and Technology from Tsinghua University. His research focuses on the intersection of artificial intelligence, machine learning, and social choice theory, addressing challenges in voting systems, fair division, privacy, and multi-agent systems. Key research areas include algorithmic fairness, computational social choice, and mechanism design. Recent work explores equitable voting rules, privacy-preserving mechanisms, and strategic behavior analysis. Notable publications include advancements in computational social choice and privacy in voting systems. Xia has been recognized with prestigious awards such as the NSF CAREER Award and IEEE’s “AI’s 10 to Watch.” Education: Ph.D. Computer Science, Duke University (2011) M.A. Economics, Duke University (2010) B.E. Computer Science and Technology, Tsinghua University (2004) Awards: NSF CAREER Award Simons-Berkeley Research Fellowship 2018 Rensselaer James M. Tien’66 Early Career Award IEEE Intelligent Systems “AI’s 10 to Watch” Advising: Supervised over 30 students, including PhDs and master’s candidates in AI, algorithms, and social choice theory.

• Machine Learning
• Data Mining
• Bioinformatics
• +11 more

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

Ruth CharneyView profile

Professor

• Geometric Group Theory
• CAT(0) Spaces
• Hyperbolic Groups
• +4 more

Ruth Charney holds the Theodore and Evelyn Berenson Professorship in Mathematics at Brandeis University. Her research specializes in geometric group theory, particularly CAT(0) spaces, hyperbolic groups, Artin groups, and automorphism groups. She earned her Ph.D. from Princeton University (1977) after undergraduate studies at Brandeis. She previously held positions at Ohio State University, Yale University, and UC Berkeley. Her foundational work explores geometric structures of groups, boundaries of metric spaces, and random group properties. Recent publications focus on outer space constructions for Artin groups, Morse boundary classifications, and acylindrical hyperbolicity in infinite-type groups. Honors include presidencies of the American Mathematical Society (2021-2023) and Association for Women in Mathematics (2013-2015), Polya Lectureship, and multiple fellowships. She has supervised 17 Ph.D. students and secured continuous NSF funding since 2011 for projects on automorphism groups and geometric structures. She organized major conferences including Artin groups workshops at MSRI and Oberwolfach, and serves on advisory boards for CRM Montreal, MSRI, and multiple NSF panels. Her outreach includes lectures at international conferences and programs advancing women in mathematics.

Anthony QuasView profile

Professor

• Ergodic Theory
• Dynamical Systems
• Symbolic Dynamics
• +7 more

Anthony Quas is a Professor in the Department of Mathematics and Statistics at the University of Victoria, Canada. He holds a PhD from the University of Warwick and has held academic positions at institutions including the University of Memphis and King's College, Cambridge. His research focuses on ergodic theory and dynamical systems, with contributions to multiplicative ergodic theory, Lyapunov exponents, and symbolic dynamics. Education: B.A. in Mathematics (First Class), Cambridge University (1986–1989) Certificate of Advanced Study in Mathematics (Distinction), Cambridge University (1989–1990) PhD in Mathematics, University of Warwick (1990–1994) Research Interests: Ergodic theory and its connections to stochastic processes Lyapunov exponents and operator cocycles Symbolic dynamics and shift spaces Applications to percolation theory and information theory Publications and Grants: Over 70 peer-reviewed articles, including work on multiplicative ergodic theorems and metastable systems Recipient of NSERC and NSF grants, and former Canada Research Chair (2005–2014) Editor for Dynamical Systems: An International Journal Awards and Recognition: Tyson Medal (1990) Cambridge Smith Prize (1992) Organizer of international workshops and conferences (e.g., Banff International Research Station) Teaching and Service: Supervised multiple PhD and Master’s students Contributed to outreach initiatives like Pi in the Sky magazine Served on NSERC Grant Selection Committees and editorial boards

Michalis VazirgiannisView profile

Professor

• Machine Learning
• Deep Learning
• Graph Analysis
• +11 more

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique in France, where he leads the Data Science and Mining group (DaSciM). He holds a degree in Physics and a PhD in Informatics from Athens University (Greece), and a Master's degree in AI from Heriot Watt University, Edinburgh (UK). His academic career spans multiple prestigious institutions including Fraunhofer and Max Planck MPI in Germany, INRIA/FUTURS in Paris, AUEB in Greece, Telecom-Paristech, ENS in France, Tsinghua and Jiaotong Shanghai in China, and Deusto University in Spain. Professor Vazirgiannis's research focuses on machine and deep learning methods for graph analysis, including community detection, graph clustering, node embeddings, and influence maximization. His work in text mining encompasses Graph of Words, word embeddings with applications to web advertising and marketing, event detection, and summarization. He has active collaborations with industrial partners in analytics and machine learning for large-scale data repositories across various application domains such as recommendations, meeting summarization, influence metrics for scientific and social networks, and predictive maintenance. His recent publications demonstrate a strong emphasis on Graph Neural Networks, multilingual NLP (particularly for French and Arabic), and applications of deep learning to diverse domains including social networks, legal text, and biomedical data. There's a clear trajectory toward developing more efficient, explainable, and specialized models that address real-world challenges in data analysis. ERCIM fellowship Marie Curie EU fellowship Tencent "Rhino-Bird International Academic Expert Award" (2017) Best Paper Award at IJCAI 2018 Best Paper Award at CIKM 2013 Professor Vazirgiannis has supervised 29 completed PhD theses and has attracted significant R&D funding from national and international sources, including research agencies and industrial partners such as Google, Airbus, Huawei, Deezer, BNP, and LVMH. He leads or has led several academic research chairs including DIGITEO (2013-15), ANR/HELAS (2020-25), and AXA (2015-2018). The DaSciM research group, which he leads at École Polytechnique, has extensive experience in real-world R&D projects involving large-scale data mining. The team maintains active collaborations with major industrial partners including AIRBUS, Google, BNP, Tencent, and Tradelab, working on cutting-edge machine learning projects. The group has co-organized major conferences such as ECML PKDD 2011 and ECML/PKDD 2017 and participates in the senior organization of AI and data mining events like AAAI and IJCAI.

Supartha PodderView profile

Assistant Professor

• Quantum Computing
• Complexity Theory
• Quantum Cryptography
• +5 more

Supartha Podder is an Assistant Professor in the Department of Computer Science at Stony Brook University, located in Stony Brook, NY. He holds a PhD from the Centre for Quantum Technologies at the National University of Singapore and master's degrees from École Normale Supérieure de Cachan (now ENS Paris Saclay) and the Chennai Mathematical Institute. Before joining Stony Brook, he was a postdoctoral researcher at the University of Ottawa under Anne Broadbent and at the University of Texas at Austin under Scott Aaronson. His research focuses on quantum and classical complexity theory, particularly exploring scenarios where quantum computation surpasses classical methods. Key interests include quantum cryptography, quantum algorithms, and the intersection of complexity theory with cryptographic systems. He also investigates foundational topics like analysis of Boolean functions and communication complexity. Podder teaches graduate and undergraduate courses in theoretical computer science, including the Theory of Computation (CSE 540, CSE 350) and advanced topics in Quantum Computing and Applications (CSE 550). He actively mentors a research group with PhD candidates and undergraduates, focusing on quantum computing and related areas. His service roles include program committee memberships for TQC 2024 and QCNC 2025, NSF panel participation, and session chair roles at ITCS 2025. He leads the AALO charity initiative with undergraduates to support underprivileged students in India. His research has led to notable contributions in quantum algorithms, communication complexity, and cryptographic protocols, with recent work appearing in venues like STOC, FOCS, and SICOMP.

Hendrik Jan HoogeboomView profile

Associate Professor

• Theoretical Computer Science
• Formal Languages
• Algorithms
• +13 more

Hendrik Jan Hoogeboom is an Associate Professor in the Department of Computer Science at Leiden University, affiliated with the Leiden Institute of Advanced Computer Science (LIACS) and the Theory research cluster. Research Interests: His work lies at the intersection of theoretical computer science and computational biology. He specializes in formal languages , algorithms , logic & automata (including tree-walking automata), and infinitary languages . A major theme is modeling natural computation, such as gene assembly in ciliates using graph polynomials and delta-matroids, and molecular computing paradigms like reaction systems and spiking neural P systems. Publication Trends: His recent publications (2017–2024) show a sustained focus on reaction systems and graph polynomials. He has developed formal models to analyze DNA rearrangements, explored the algebraic structure of gene assembly, and investigated the computational power of various natural computing models, often using combinatorial and algebraic tools from graph theory. Scientific Recognition: While specific awards are not listed, his extensive publication record in premier journals like Theoretical Computer Science , European Journal of Combinatorics , and Fundamenta Informaticae signifies high recognition in his field. Teaching and Mentorship: He has supervised several PhD students, including Robert Brijder, Jun Wang, and Rudy van Vliet. He teaches core courses such as Data Structures , Algorithms , Automata Theory , and Complexity , and has led seminars on topics like combinatorial game theory and SAT solvers. Laboratories and Teams: He is a key member of the Theory research cluster at LIACS, focusing on fundamental aspects of computer science. His work is deeply collaborative, especially with colleagues like Robert Brijder and W.A. Kosters.

Laszlo BabaiView profile

Professor

• Theoretical Computer Science
• Combinatorics
• Graph Isomorphism
• +3 more

László Babai is a Professor in the Department of Computer Science at the University of Chicago. He is renowned for his groundbreaking contributions to theoretical computer science, combinatorics, and algorithm design. Key research areas: Graph Isomorphism, Computational Complexity, Group Theory, and Combinatorics. His 2017 work on the quasipolynomial-time Graph Isomorphism algorithm remains a landmark achievement. He has advised numerous doctoral students in both Computer Science and Mathematics. His academic leadership extends to founding initiatives like the free journal Theory of Computing and educational programs such as Budapest Semesters in Mathematics .

Edoardo SerraView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +5 more

Edoardo Serra is an Associate Professor in the Department of Computer Science at Boise State University (BSU), a role he has held since July 2021. He previously served as an Assistant Professor at BSU from 2015 to 2021 and holds a joint appointment as a Senior Researcher at Pacific Northwest National Laboratory (PNNL) since June 2021. Since January 2023, he has co-directed the Computing Ph.D. Program at BSU and serves as General Chair of the 2024 ACM CIKM Conference. His academic journey includes a Ph.D. in Computer Science Engineering from the University of Calabria, Italy (2012), followed by postdoctoral positions at the University of Calabria and the University of Maryland. He also served as a Visiting Researcher at UCLA (2010–2011). His research focuses on AI/ML applications in cybersecurity, graph representation learning, generative AI, and robust AI systems. Notable projects include: NSF-funded cybersecurity curriculum integration Department of Defense-funded analysis of terrorist networks Idaho Department of Commerce precision agriculture initiatives Key research areas include graph neural networks, adversarial robustness, and ML-driven security solutions. His work has been recognized with awards such as Best Application Paper (2021) and Best Paper Award (2018). He actively contributes to professional service roles, including program chairs and editorial boards. Current projects emphasize AI ethics, generative models, and scalable graph algorithms. He advises on applied AI consulting for industry and government, focusing on model interpretability and cybersecurity implications.

Shivanjali KhareView profile

Assistant Professor

• Internet of Things (IoT) Data Security
• Hybrid Cryptography
• Wireless Sensor Technologies
• +3 more

Dr. Shivanjali Khare is an Assistant Professor in the Computer Science department at the University of New Haven, affiliated with the Tagliatela College of Engineering. She leads the SARI (Security and ARtificial Intelligence) Research Lab and teaches courses in Artificial Intelligence and Data Mining. Her research focuses on enhancing cybersecurity through hybrid cryptography, IoT data security, machine learning, and wireless sensor technologies to empower user protection in the digital age. Education: Ph.D. and M.S. in Computer Science from the University of Louisiana at Lafayette (2021 and 2016, respectively). Research Interests: IoT Data Security Hybrid Cryptography Systems Wireless Sensor Networks Big Data Sharing Mechanisms AI-Driven Cybersecurity Solutions Publications highlight contributions in IoT security protocols, cryptographic algorithm optimization, and machine learning applications. Notable work includes energy-efficient secure IoT drone systems (ESIoD) and ensemble learning for anomaly detection in smart homes. Recent outreach includes conducting cybersecurity seminars for senior citizens through the New Haven Free Public Library, demonstrating her commitment to societal impact and public education.

• Operator Algebra
• C*-algebras
• K-theory
• +3 more

Søren Eilers is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on Operator Algebras , particularly the classification of C*-algebras related to discrete and low-dimensional structures. He is a member of the FNU network 'Automorphisms and Invariants for Operator Algebras' and advocates for experimental mathematics using computational methods in pure mathematics. Education: MS in Mathematics and Computer Science, University of Copenhagen (1993) PhD in Mathematics, University of Copenhagen (1995) Research Interests: Operator Algebras K-theory Symbolic Dynamics Discrete Mathematics Experimental Mathematics Recent Publications (2016-2024) demonstrate expertise in graph C*-algebras , symbolic dynamics , and computational approaches to pure mathematics, with key collaborations in Denmark, Japan, Canada, and the U.S. Scientific Leadership: President, Danish Mathematical Society (2006-2008) Principal Investigator, Villum Fonden (2012-2016) Main Organizer, Mittag-Leffler Institute Program (2016) Advisory Roles: Supervised 28 master's theses and mentored 9 PhD students/postdocs (2003-2022) across institutions in Denmark, Canada, Japan, and the U.S.

Elchanan MosselView profile

Professor

• Probability Theory
• Combinatorics
• Machine Learning
• +4 more

Elchanan Mossel is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), with a joint appointment at the Institute for Data, Systems, and Society (IDSS). His research focuses on probability, combinatorics, statistical inference, and their applications to computer science and social choice theory. He earned his B.Sc. from the Open University of Israel, and both his M.Sc. (1997) and Ph.D. (2000) in Mathematics from the Hebrew University of Jerusalem. Before joining MIT in 2016, he was a faculty member at UC Berkeley and held visiting positions at the Weizmann Institute and the Wharton School of the University of Pennsylvania. Mossel's work bridges theoretical foundations and applied problems, including computational complexity, randomized algorithms, Markov random fields, and evolutionary biology. He has made significant contributions to the theory of social choice, game theory, and the mathematical underpinnings of machine learning. Notable achievements include resolving the Majority Is Stablest conjecture and advancing phylogenetic reconstruction methods. His awards include the Sloan Fellowship and Miller Fellowship. He has mentored numerous graduate students, including Sebastien Roch, Allan Sly, and Miklos Racz, and has held editorial roles in journals like the Electronic Journal of Probability . Mossel's teaching spans topics from introductory probability to advanced courses on social networks and game theory.

Paul FranzonView profile

Professor

• Quantum Information Science
• Machine Learning & AI Hardware
• 3D Integration
• +4 more

Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)

• Operator Algebras
• K-theory
• Symbolic Dynamics
• +4 more

Søren Eilers is a Professor at the Department of Mathematical Sciences, University of Copenhagen, affiliated with both the QA (Analysis & Quantum) and AG (Algebra & Geometry) sections. He holds editorial roles at Journal of Mathematical Analysis and Applications and contributed to Springer's Operator Algebra and Dynamics proceedings. Education : M.S. (1993), PhD (1995) in Mathematics from the University of Copenhagen. Positions : Assistant Professor (1996–1999), Associate Professor (1999–2008), Professor (2008–present). Research Interests : Focus on operator algebras, particularly C*-algebras associated with discrete structures. Active in symbolic dynamics, K-theory, and experimental mathematics. His work bridges algebraic structures and dynamical systems, with notable contributions to the classification of graph C*-algebras and the LEGO counting problem. Recent efforts emphasize computer-aided methods in mathematical research. Key Contributions : Pioneered geometric classification of graph C*-algebras, explored flow equivalence of shift spaces, and authored Introduction to Experimental Mathematics (2017). His research has been supported by grants such as the Villum Fonden's Experimental Mathematics initiative (2012–2016). Teaching & Mentorship : Supervised 28 PhD theses and numerous projects in analysis, discrete mathematics, and experimental mathematics. Taught courses in functional analysis, dynamical systems, and experimental methods. Scientific Leadership : Organized major programs like the 2016 Mittag-Leffler Institute's classification of operator algebras. Served as President of the Danish Mathematical Society (2006–2008) and led NordForsk's Operator Algebras and Dynamics network (2009–2012). International Collaboration : Extensive visiting appointments at institutions like MSRI (Berkeley), Fields Institute (Toronto), and Institut Mittag-Leffler (Stockholm). Maintains global research networks in operator algebras and dynamics.

Andrea SimonettoView profile

Research Professor

• Optimization
• Control Theory
• Quantum Computing
• +5 more

Andrea Simonetto is a Research Professor at the Applied Mathematics Unit (UMA) , ENSTA Paris, Institut Polytechnique de Paris. His work spans optimization, control theory, and learning algorithms for large-scale and streaming data , with applications in smart grids, intelligent transportation, personalized health, and quantum computing. Current research focuses on online algorithms for time-varying optimization , personalized optimization for cyber-physical systems , and variational quantum algorithms . Past contributions include theoretical and algorithmic advances in convex/non-convex optimization, distributed optimization (robotic networks, smart grids), and signal processing for sparse reconstructions and parallel computing in particle filtering. Key application domains include renewable energy integration , quantum state preparation , and human-in-the-loop control systems . His research is published in journals like ACM Transactions on Quantum Computing , IEEE Control Systems Letters , and Automatica .