Dr. Lata Narayanan is a Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal, Canada. Her research spans theoretical and applied aspects of distributed systems, with a focus on algorithms for mobile agents, communication networks, and sensor networks. Department: Computer Science and Software Engineering University: Concordia University Research Interests Lata Narayanan specializes in algorithms for mobile robots and ad hoc networks , with expertise in routing on distributed networks , parallel algorithms , and social network analysis . Her work addresses challenges in sensor network optimization, barrier coverage, and time-energy tradeoffs for evacuation systems. Article Trends Her recent publications (2021-2025) emphasize game theory for network dynamics, cloud resource allocation , and temporal graph exploration . Key themes include strategic diversity, truck-drone delivery logistics, and energy-sharing protocols for mobile agents.
Dr. Haiyan Liu is an Associate Professor of Quantitative Methods, Measurement, and Statistics in the Department of Psychological Sciences at the University of California, Merced, within the School of Social Sciences, Humanities, and Arts. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2018). Her research focuses on advanced statistical modeling of psychological and educational data, including high-dimensional, longitudinal, and social network data. She develops Bayesian methodologies and machine learning techniques to enhance understanding of human behavior, with recent emphasis on structural equation modeling, network dynamics, and nonparametric growth curves. Her work addresses challenges in survey methodology and behavioral data analysis. Dr. Liu’s educational background includes a Ph.D. in Quantitative Psychology from the University of Notre Dame (2018), complementing her current academic role. Her lab, accessible at https://sites.google.com/view/ucmhaiyanliu , supports her research activities. Her research interests span Bayesian SEM, social network analysis, and applications of machine learning to behavioral data, aiming to bridge methodological innovation with practical psychological inquiry. Her recent articles highlight advancements in Bayesian model selection, longitudinal sentiment analysis, and social network mediation. She emphasizes prior specification rigor in Bayesian frameworks and explores nonlinear relationships in social dynamics. Though no awards are explicitly listed, her contributions to statistical methodologies in psychological research reflect significant scholarly impact. Dr. Liu advises students in quantitative methods and has developed software tools like logistic4p for misclassification correction in logistic regression. Her work integrates computational methods with theoretical advancements, positioning her as a key contributor to modern quantitative psychology.
Nicole Wein is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, where she is a member of the Theory of Computation Lab within the Computer Science and Engineering Division. Her research focuses on theoretical computer science, particularly graph algorithms and lower bounds across various domains including distance-estimation, dynamic, parameterized, distributed, and online algorithms. Education: PhD in Computer Science from MIT, advised by Virginia Vassilevska Williams Master's in Computer Science from Stanford University B.S. in Computer Science/Mathematics from Harvey Mudd College Nicole's research centers on theoretical aspects of graph algorithms and computational complexity. She investigates fundamental questions about how algorithms can efficiently handle changing data, extract information from graphs in linear time, and understand the structure of shortest paths, especially in directed graphs. Her work spans multiple algorithmic paradigms including dynamic algorithms that adapt to changing inputs, parameterized approaches for hard problems, and fine-grained complexity that establishes precise relationships between problem difficulty. Analysis of Nicole's recent publications reveals a strong focus on graph algorithms, particularly shortest path problems, spanners, and hardness results. Her work often bridges theoretical insights with practical implications, developing novel techniques for distance estimation, dynamic graph processing, and approximation algorithms. A significant portion of her research examines the structural properties of graphs that enable or constrain efficient computation, with applications across computer science. Nicole actively mentors students at various levels. She currently advises PhD student Jubayer Nirjhor and has worked with undergraduate researchers including Sam Hiken (now a pre-doc at MIT), Michael Wang, and Tony Zhang. Her teaching includes foundational courses like EECS 376: Foundations of Computer Science and specialized courses such as EECS 598: Graph Algorithms. Nicole contributes to the academic community through service as a program committee member for major conferences including SOSA 2025, FOCS 2025, SODA 2025, and others. She co-organized the June 2023 DIMACS workshop on Modern Techniques in Graph Algorithms and previously organized Algorithms Office Hours at MIT to improve communication between theory and applications of algorithms.
Lane A. Hemaspaandra (formerly Hemachandra) is a Professor at the Department of Computer Science, University of Rochester, New York. His academic career spans over three decades, with research focusing on computational complexity theory (especially structural complexity) and computational social choice theory . He holds a Ph.D. in Computer Science from Cornell University (1987) and has been recognized with prestigious awards such as the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation and NSF Presidential Young Investigator (1989–1995). Education: B.S. in Computer Science and Mathematics & Physics, Yale University (1981) M.S. in Computer Science, Stanford University (1982) M.S. in Computer Science, Cornell University (1984) Ph.D. in Computer Science, Cornell University (1987) Hemaspaandra's research bridges theoretical computer science with political science and economics , particularly analyzing the computational complexity of election systems. His work includes foundational studies on Carroll/Dodgson voting , control complexity , and manipulative attacks in single-peaked societies. He has pioneered the use of complexity as a shield against election manipulation and control. The 15 most recent articles (2021–2024) span topics like backbone opacity , electoral control dichotomies , iterative constant-setting for complexity , and online bribery in sequential elections . These works often intersect with parameterized complexity , multi-agent systems , and game-theoretic models . Scientific Awards: AAAI Senior Member (2020–...) ACM Distinguished Scientist (2007–...) Alexander von Humboldt Foundation Renewed Research Stay (2018–2019) SIGACT Distinguished Service Prize (2013) Edward Peck Curtis Award for Undergraduate Teaching (2012) Hertz Foundation Fellowship (1982–1987) He has advised 15 Ph.D. students and postdocs, including prominent researchers like Prof. Piotr Faliszewski (AGH University) and Dr. Curtis Menton (Google). His NSF-funded projects explore complexity-theoretic approaches to election systems, and he has collaborated with institutions in Germany, Japan, and Poland.
Professor Iwona M. Jasiuk is a multi-disciplinary academic affiliated with the University of Illinois, holding professorships in Mechanical Science and Engineering, Biomedical and Translational Sciences, Bioengineering, Aerospace Engineering, and other departments. She is also affiliated with the National Center for Supercomputing Applications (NCSA), Beckman Institute for Advanced Science and Technology, and the Carl R. Woese Institute for Genomic Biology. Her research focuses on composite materials, bio-inspired structures, additive manufacturing, and computational mechanics, with a strong emphasis on integrating artificial intelligence into materials science. Her work spans topics such as material characterization, metamaterials design, and radiation effects on materials. Notable research areas include thin-ply composites, lattice structures derived from geometric principles, and the mechanical properties of bio-inspired systems like equine hoof walls. She has pioneered the use of deep learning networks for predicting material behavior in complex systems. Professor Jasiuk has received prestigious awards, including the ASME Fellow, SES Fellow, and Vebleo Scientist Award. Her research is supported by collaborations across engineering, biology, and computational fields, leveraging advanced facilities like NCSA for high-performance computing.
Professor Daniel Oron is affiliated with the University of Sydney, where he joined in 2004 after completing his PhD in Operations Research at the Hebrew University of Jerusalem. His research focuses on Combinatorial Optimization, particularly Scheduling Theory, addressing challenges like batch scheduling with setups, customer delivery models, and scheduling under deteriorating conditions. He teaches courses such as Quantitative Business Analysis, Management Science, and Business Analytics Honours. His editorial role includes serving on the board of the Journal of Industrial & Management Optimization . Recent research contributions span multi-agent scheduling, energy recharging in scheduling, and coupled task optimization. He advises two current PhD students: Johnson (Two-agent scheduling problems) and Renjie Yu (Multi-agent scheduling with parallel batching). Publications highlight advancements in scheduling algorithms, resource allocation, and optimization under constraints. Notable works include minimizing late jobs with step-learning models and analyzing parameterized complexity in single-machine scheduling.
Jonathan Pila is a Reader in Mathematical Logic at the University of Oxford's Mathematical Institute, with a focus on model theory and number theory. He is affiliated with the Mathematical Logic and Number Theory research groups. BScHons (University of Melbourne, 1984) PhD (Stanford University, 1988) His research explores intersections of mathematical logic with number theory, particularly via o-minimality, addressing problems like the Andre-Oort conjecture, Zilber-Pink conjecture, and Ax-Schanuel theorems in algebraic and Diophantine geometry. Recent work includes advancements on functional transcendence, canonical heights in Shimura varieties, and uniform parameterization techniques with applications to Diophantine problems. Leverhulme Trust Research Fellowship (2008-2010) Clay Research Award (2011) LMS Senior Whitehead Prize (2011) ASL Karp Prize (2013) Elected FRS (2015) Rolf Schock Prize (2022) Frontiers of Science Award (2023)
Venkatesan Guruswami is a Chancellor's Professor in the Department of Electrical Engineering and Computer Sciences and Professor in the Department of Mathematics at the University of California, Berkeley. He previously served as faculty at Carnegie Mellon University for 13 years and held a Miller Research Fellowship at UC Berkeley. His research focuses on Theoretical Computer Science , particularly in Error-Correcting Codes , Approximation Algorithms , Quantum Computing , and Hardness of Approximation . Guruswami has made groundbreaking contributions to list decoding and quantum code constructions, with works featured in Science Magazine and the Journal of the ACM (where he serves as Editor-in-Chief). Education : B.Tech (1997, IIT Madras), Ph.D. (2001, MIT), Miller Research Fellowship (2001-02, UC Berkeley) Research Areas : Theory of error-correcting codes, approximation algorithms, pseudorandomness, probabilistically checkable proofs, and quantum coding theory Guruswami's recent work explores quantum LDPC codes , parameterized inapproximability , and stream decodable codes . He has received prestigious awards including the NSF CAREER award , David and Lucile Packard Fellowship , and Sloan Research Fellowship . His advising spans a wide range of students and postdocs, with notable contributions to coding theory and computational complexity .
Alina Vdovina is a Professor of Mathematics at The City College of New York (CCNY) and a member of the doctoral faculty at the CUNY Graduate Center. Her office is located at North Academic Center 8/201, with an alternative listing showing MR 333, and she can be reached at (212) 650-5161 or via email at avdovina@ccny.cuny.edu. Dr. Vdovina's research focuses on Geometric Group Theory and its interactions with Dynamical systems, K-theory of C*-algebras, and Knot Theory. Her work bridges multiple mathematical disciplines, exploring the connections between algebraic structures, geometric representations, and topological properties. She has made significant contributions to the understanding of higher-rank graphs, cube complexes, and their applications in operator algebras. Her recent publications demonstrate a consistent focus on the interplay between geometric structures and algebraic properties, with particular attention to how group-theoretic concepts manifest in topological and combinatorial settings. Her work spans theoretical developments in group theory, applications to operator algebras, and connections to discrete mathematics. Dr. Vdovina is actively involved in mentoring students, with recent successes including Kadar He moving to the PhD program at CUNY's Graduate Center, Nicholas Videen transitioning from musician to CCNY mathematics graduate, and Joshua Bourne-Inniss advancing from community college to CCNY mathematics graduate degree. Her research presentations include "Groups acting on buildings and their subgroups" at GAGTA 2025 and "C*-algebras coming from buildings and their K-theory" at the Conference on Analytic Group Theory in Austin, Texas.
Stefan Woltran is a Full Professor in the Databases and Artificial Intelligence department at TU Wien. He serves as Vice Dean of Academic Affairs for the Informatics Master program and leads the Research Unit for Databases and Artificial Intelligence. His research focuses on logic-based AI, including Propositional Logic, Nonmonotonic Reasoning, Argumentation frameworks, Knowledge Representation, and Logic Programming. He coordinates the Double-Degree Program Logic and Computation. His research projects include analyzing formal properties of logic-based AI approaches, complexity analysis, and developing algorithms via logic and dynamic programming. Notable projects include the HYPAR and REVEAL-AI initiatives exploring abstract argumentation and AI problem-solving. He has contributed to over 150 publications since 2001, focusing on argumentation frameworks, computational complexity, and formal methods. Woltran teaches courses such as Abstract Argumentation, Formal Methods in Computer Science, and Theoretical Computer Science. His work integrates theoretical advancements with practical solver development, such as the ASPARTIX system for argumentation tasks. He actively participates in international conferences and competitions in computational argumentation, emphasizing the application of formal methods to real-world problems.
Bianca Viray is a Professor in the Department of Mathematics at the University of Washington. Her research focuses on arithmetic geometry, number theory, and algebraic geometry, particularly exploring rational points on varieties and Brauer-Manin obstructions. She actively organizes the UW Number Theory Seminar and collaborates on projects addressing the distribution of mathematical potential and inclusive education. Viray's work bridges theoretical advancements with pedagogical contributions, including workshops on effective mathematical communication and resources for academic success. Her research interests emphasize the intersection of arithmetic and algebraic geometry, with a focus on quadratic points, conic bundles, and the persistence of obstructions in rationality questions. She has contributed to foundational studies on Brauer groups, del Pezzo surfaces, and the arithmetic of curves, often involving collaborations on computational algebraic geometry tools like Magma and Sage. Viray's articles span topics like quartic del Pezzo surfaces, parameterized points on curves, and number fields generated by linear systems. She advocates for equitable mathematics education through initiatives rooted in Federico Ardila’s axioms, emphasizing dignity and accessibility in academic environments.
Martin Grohe is a Professor at the School of Logic and Theory of Discrete Systems , part of the Department of Computer Science at RWTH Aachen University . His research spans Algorithms and Complexity , Logic , Database Theory , Graph Theory , and Machine Learning , with a focus on integrating logical frameworks into computational models. His recent work explores graph neural networks , Weisfeiler-Leman algorithms , and parameterized complexity , as seen in publications on isomorphism testing , database repairing , and probabilistic query evaluation . While no specific scientific awards are mentioned, his contributions to graph theory and machine learning are widely recognized through numerous peer-reviewed publications.
Christopher Umans is a Professor of Computer Science at the California Institute of Technology, where he serves as the William M. Coughran Jr. Leadership Chair and Executive Officer for the Department of Computing and Mathematical Sciences. He joined Caltech in 2002 as an Assistant Professor, became Associate Professor in 2008, and was promoted to Professor in 2010. He has held multiple leadership positions including Division Deputy Chair (2018-2020) and Executive Officer since 2020. His educational background includes a B.A. from Williams College (1996) and a Ph.D. in Computer Science from the University of California, Berkeley (2000), where he was advised by Christos Papadimitriou. After completing his Ph.D., he was a postdoc at Microsoft Research from 2000-2002 before joining Caltech. Professor Umans's research focuses on theoretical computer science, particularly computational complexity with an algebraic flavor. His work spans derandomization, explicit combinatorial constructions, algebraic algorithms, coding theory, and hardness of approximation. He has made significant contributions to understanding the complexity of fundamental problems like matrix multiplication through group-theoretic approaches and developing fast algorithms for generalized discrete Fourier transforms over finite groups. His recent publications reveal a strong emphasis on algebraic methods in computation, with particular focus on matrix multiplication algorithms, generalized DFTs, and polynomial factorization. His work consistently bridges theoretical computer science with deep mathematical concepts from group theory, representation theory, and algebraic combinatorics, demonstrating how these mathematical structures can yield more efficient computational methods. His scientific recognition includes the prestigious Simons Investigator in Computer Science award and the Northrop Grumman Prize for Excellence in Teaching at Caltech. Professor Umans has advised students including Chloe Ching-Yun Hsu, who received the Henry Ford II Scholar Award. His research has been supported by multiple NSF grants including 'AF: Small: Group Theory and Representation Theory in Matrix Multiplication and Generalized DFTs' and 'AF: Small: Algorithms for Matrix Multiplication, Polynomial Factorization and Generalized Fourier Transform.' He serves on numerous program committees including FOCS, STOC, and CCC, and is Vice-Chair of SIGACT (2021-24). He is an active member of Caltech's Theory Group within the Computing and Mathematical Sciences department, contributing to its research direction and mentoring junior researchers. His work often involves collaborations with researchers across institutions, as evidenced by his extensive publication record with co-authors from various universities.
Professor Damien Woods is a faculty member at Maynooth University's Faculty of Science & Engineering, specifically affiliated with the Department of Computer Science and the Hamilton Institute. He leads groundbreaking research in DNA computing, molecular programming, and optical computing, focusing on self-assembly, algorithmic design, and computational complexity. ERC Consolidator Grant: 'Computationally Active DNA Nanostructures' SFI ERC Support Award EIC Pathfinder Challenge Grant: 'DISCO - DNA Infrastructure for Storage and Computation' His research projects explore programmable DNA storage, molecular robotics, and robust self-assembly systems. Recent publications span diverse topics like algorithmic DNA tile assembly, thermodynamic stability, and computational universality in nanosystems. Awards include ERC and SFI grants, with a focus on bridging theoretical computer science and experimental molecular biology. Scientific Contributions include: 2022: 'Turning Machines' - Molecular Robotics 2019: 'Diverse Molecular Algorithms' in Nature 2017: 'A Cargo-Sorting DNA Robot' in Science
Dr. Sasha Rubin is a Senior Lecturer and leader of the Computational Logic for AI (LOGIC-AI) group at the School of Computer Science, The University of Sydney. He holds a PhD in Mathematics and Computer Science from the University of Auckland and previously worked at the University of Naples Federico II. His research focuses on logic foundations of AI, including synthesis, planning, formal methods, and multi-agent systems. He teaches courses like Models of Computation and supervises students in topics like probabilistic systems and reinforcement learning. Research Interests: Mathematical Logic, Formal Verification, Temporal Logic Synthesis, Automated Reasoning, and Multi-Agent Systems. He has published extensively in top venues like IJCAI, AAAI, and ACM Transactions. His work includes verification of agent navigation, strategy logic, and planning under uncertain environments. Awards: Recognized as an Australian Research Field Leader in Theoretical Computer Science (2020). He serves on editorial boards for JAIR and conferences like KR, and organizes events such as the Australasian Association for Logic Conference (2024). Supervision and Grants: Current students include Ethan HIRSCHOWITZ and Kunal OSTWAL. Past supervision spans MPhil/PhD projects on probabilistic systems, ML classifier fairness, and symbolic automata. His grants include studies on logic and robots in anonymous graphs. Professional Activities: Member of EATCS, ACM, and mentor for the Sydney Summer Innovation Programme. He leads the LOGIC-AI lab and collaborates internationally, notably with Giuseppe De Giacomo at Sapienza University of Rome.
