Dr. George Cantwell is an Assistant Professor in the Department of Engineering at the University of Cambridge, affiliated with Cambridge Infectious Diseases. He specializes in computational methods for inference problems, particularly in disease spreading across networks. Education: PhD in Physics from the University of Michigan; postdoctoral fellowship at the Santa Fe Institute His research focuses on network science , complex systems , and statistical inference , with an emphasis on computational approaches. His work spans theoretical and applied domains, including: Message passing algorithms for heterogeneous networks Bias correction in social network analysis (friendship paradox) Statistical inference of network structure from noisy data Modeling judicial voting behavior through network interactions Computational cognitive neuroscience of category learning Recent publications highlight interdisciplinary applications in epidemiology, physics, and cognitive science. He actively mentors students in networks, complex systems, and statistical inference.
Salim El Rouayheb is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. He leads the Coding and Securing Information (CSI) Lab, which focuses on information-theoretic security and privacy in distributed systems. His research spans multiple areas including secure machine learning, private information retrieval, and data synchronization. Dr. El Rouayheb received his Ph.D. in Electrical Engineering from Texas A&M University in 2009. Prior to joining Rutgers, he was an Assistant Professor at the Illinois Institute of Technology (2013-2017), a Research Scholar at Princeton University (2012-2013), and a Postdoctoral Researcher at UC Berkeley (2010-2011). His research interests focus on information-theoretic security in distributed systems, private information retrieval and search, secure machine learning algorithms, and data synchronization in distributed systems. He has made significant contributions to developing frameworks that provide information-theoretic privacy guarantees in various contexts including federated learning, genomic data analysis, and decentralized networks. His work often bridges theoretical foundations with practical applications, particularly in the areas of secure distributed computing and privacy-preserving algorithms. His recent publications demonstrate a strong trend toward applying information-theoretic principles to address privacy and security challenges in machine learning systems, particularly in federated and decentralized settings. Many of his papers explore random walk approaches for decentralized learning, secure matrix multiplication techniques, and privacy mechanisms that can be toggled "on and off" based on correlation patterns in data. His work spans both theoretical contributions in information theory and practical implementations for real-world systems. Dr. El Rouayheb has received several prestigious awards including the NSF CAREER Award (2016), Google Faculty Research Award (2018), and the Rutgers University Walter Tyson Junior Faculty Chair (2019). He has successfully secured multiple research grants including NSF SaTC, NSF CAREER, Google Faculty Research Awards, and Army Research Lab funding. His lab, the Coding and Securing Information (CSI) Lab, currently includes postdoc Xingran Chen, PhD student Zonghong Liu, and undergraduate researchers. The CSI Lab maintains an active research agenda with regular publications in top-tier venues and hosts the Shannon Channel, a series of online talks related to information theory. Dr. El Rouayheb is also involved in organizing workshops on coding theory and information security.
Kevin Crowston is a Distinguished Professor of Information Science at Syracuse University's School of Information Studies (iSchool), where he examines how information technology enables new organizational forms through empirical studies, theoretical modeling, and system design. His work focuses on coordination-intensive processes in virtual settings, with significant contributions to citizen science, data science teamwork, and journalism transformation. Education A.B. in Applied Mathematics (Computer Science), Harvard University, 1984 Ph.D. in Information Technologies, MIT Sloan School of Management, 1991 Research Focus : Crowston investigates coordination mechanisms in human-AI collaboration, particularly through projects like Gravity Spy (combining citizen scientists with machine learning for gravitational wave analysis) and journalism innovation (e.g., ReelFramer for AI-assisted news-to-video translation). His framework addresses how intelligent systems reshape work design, knowledge production, and team dynamics in scientific and media contexts. Publication Trends : Recent articles (2024-2025) reveal three dominant threads: (1) Human-AI co-creation in journalism (deskilling/upskilling dynamics, creative tool adoption), (2) Citizen science evolution with AI (co-learning systems, lexical entrainment), and (3) Socio-technical governance of intelligent machines (control-accountability alignment, project archetypes). These reflect his central inquiry into how technology reconfigures work structures. Scientific Recognition ACM Distinguished Speaker Research Leadership : Crowston currently directs two major NSF initiatives: (1) HCC grant 21-06865 on intelligent support for non-expert information navigation, and (2) FW-HTF grant 21-29047 exploring human-technology collaboration in journalism. He spearheaded a Research Coordination Network establishing socio-technical frameworks for work in the age of intelligent machines, culminating in a special issue of Information, Technology & People . Collaborative Infrastructure : He co-leads the Gravity Spy citizen science ecosystem (integrating LIGO physicists, machine learning systems, and volunteers) and serves as co-editor-in-chief of Information, Technology and People , previously editing ACM Transactions on Social Computing . His MIDST platform research advances stigmergic coordination for data science teams.
Sebastian Schmidt is a researcher at the Technical University of Munich (TUM) within the Department of Informatics - I26 (Data Analytics and Machine Learning) under Prof. Dr. Stephan Günnemann. His work is situated in the TUM School of Computation, Information and Technology, where he contributes to cutting-edge research in machine learning applications. His research focuses on active learning methodologies with particular emphasis on out-of-distribution detection and uncertainty estimation in open-world perception systems. His work bridges theoretical machine learning with practical applications in computer vision and robotics, as evidenced by publications in top-tier conferences like CVPR, ICRA, and IROS. His recent publications demonstrate a consistent research trajectory in developing active learning frameworks that address real-world challenges in perception systems. The publications show increasing sophistication from temporal stream processing to multi-agent robotic applications and safety-constrained sensor fusion. As a member of the Data Analytics and Machine Learning group, he collaborates extensively with colleagues including Leo Schwinn, Stephan Günnemann, and others across multiple publications. His research has practical implications for autonomous systems requiring reliable perception under uncertainty.
Caterina Urban is a Research Scientist (Chargé de Recherche) at INRIA and École Normale Supérieure (ENS) in Paris, France. She is a member of the INRIA research team ANTIQUE (ANalyse StaTIQUE), where she focuses on formal methods and static analysis. Prior to her current position, she was a postdoctoral researcher at the Chair of Programming Methodology, led by Peter Müller at ETH Zurich. Dr. Urban holds a PhD in Computer Science (2015) from École Normale Supérieure, Paris, where she worked under the joint supervision of Radhia Cousot and Antoine Miné. She also earned a Master's degree (2011) and Bachelor's degree (2009) in Computer Science, both with full marks and honors (summa cum laude) from the Università degli Studi di Udine, Italy. Her research interests span the whole spectrum of formal methods with a focus on developing rigorous methods and tools to enhance the reliability of computer software, particularly data science applications. Her main area of expertise is static analysis based on abstract interpretation. Dr. Urban is currently engaged in several research projects including Lyra (focusing on data science software), Libra (fairness certification for neural networks), and SAIF (addressing safety concerns in machine learning-based systems). Dr. Urban's recent publications demonstrate her expertise in applying abstract interpretation to diverse areas including machine learning, data science, program verification, and security. Her work bridges theoretical foundations with practical applications, particularly in ensuring the reliability and trustworthiness of increasingly critical data science and machine learning systems. She has received recognition for her work through invitations to serve on program committees for major conferences including OOPSLA 2026, PLDI 2026, and CAV 2026. She is also the general chair of iFM 2025 in Paris. Dr. Urban actively mentors the next generation of researchers, supervising PhD students and postdoctoral researchers. She teaches courses on abstract interpretation and its applications at the Master Parisien de Recherche en Informatique (MPRI) and various international summer schools. She has developed several open-source software tools including Lyra (a static analyzer for data science applications), Libra (for fairness certification of neural networks), and Typpete (SMT-based static type inference for Python).
Ayse Coskun is a Professor in the Electrical and Computer Engineering Department at Boston University's College of Engineering. She serves as Director of the Center for Information and Systems Engineering (CISE) and as interim Associate Dean for Research and Faculty Development. Her research focuses on the intersection of computer systems, energy efficiency, and AI. Dr. Coskun received her PhD from the University of California, San Diego in 2009. Prior to joining academia, she worked at Sun Microsystems (now Oracle). Her research spans energy-efficient computing, cloud computing, high performance computing, computer architecture, and embedded systems, with recent work focusing on AI's impact on data center energy demands. Her publication record shows consistent innovation across multiple domains, with recent work emphasizing AI applications for improving cloud security (through frameworks like DeltaSherlock and Praxi) and transforming data centers into grid-responsive assets (Emerald AI project). Her research bridges theoretical advances with practical applications, resulting in tools adopted by industry partners including IBM. IBM Faculty Award (2020) Ernest S. Kuh Early Career Award (2017) NSF CAREER Award (2012-2017) Multiple best paper and artifact awards at top conferences As an educator, Dr. Coskun teaches courses including EC327 Introduction to Software Engineering, EC535 Introduction to Embedded Systems, and EC713 Advanced Computing Systems and Architecture. She has advised numerous PhD students including Mert Toslali, Anthony Byrne, and Burak Aksar. Her lab maintains strong industry partnerships with IBM, Intel, AMD, and Oracle, and collaborates with academic institutions worldwide including Brown University, MIT, EPFL, and CEA-Tech in France. Dr. Coskun leads the Coskun Lab, which secured a $500K grant from Sandia National Labs for AI-based analytics in high performance computing systems, demonstrating the practical impact of her research on critical computing infrastructure.
Professor Tim Dodwell holds a personal chair in Machine Learning at the University of Exeter, spanning the Department of Mechanical Engineering and the Institute of Data Science and AI. He leads the Data Centric Engineering Group and serves as co-founder and CTO of digiLab, a deep tech startup. His prestigious appointments include a 5-year Turing AI Fellowship from the Alan Turing Institute and the Romberg Visiting Professorship at Heidelberg University in Scientific Computing. His academic foundation includes a 1st class BSc in Mathematics from the University of Bath (2004-2008) and a PhD in Applied Mathematics from the Bath Institute of Complex Systems (2009-2012), where he researched variational models for complex materials under Professors Giles Hunt and Mark Peletier. Dodwell's research pioneers the intersection of applied mathematics, probabilistic machine learning, and high-performance computing, with signature contributions to Multilevel Methods in Bayesian Inverse Problems , Generative Hybrid Modelling , and Machine Learning in Safety Critical Engineering . His work bridges theoretical data science with industrial applications across nuclear fusion, aerospace materials, air traffic control, nuclear decommissioning, water treatment, and urban solar energy systems. His major recognitions include: Turing AI Fellowship (2019-2024) Romberg Visiting Professorship at Heidelberg University Visiting Professorship at MIT Prize Fellowship in Engineering Mathematics (2013-2015) Pro Vice Chancellors Fellowship (2015-2018) Through competitive fellowships and digiLab initiatives, Dodwell secures funding for uncertainty quantification research while driving real-world impact in sustainability sectors. His dual academic-industry roles enable rapid translation of theoretical advances into engineering solutions, particularly through digiLab's twinLab platform which delivers 60,000x acceleration in simulation workflows. He directs the Data Centric Engineering Group at Exeter and co-founded digiLab's multidisciplinary team comprising AI specialists, domain experts, and educators. The organization operates through three synergistic pillars: developing AI solutions for critical infrastructure, building the twinLab platform for industrial ML deployment, and running an ML academy for practitioner training through datacamps, internships, and specialized courses.
Yan Gu is an Associate Professor of Mechanical Engineering at Purdue University, located in West Lafayette, Indiana. He is affiliated with the School of Mechanical Engineering within the College of Engineering. His research focuses on legged locomotion, humanoid and quadrupedal robots, wearable robotics, hybrid dynamical systems, control systems, state estimation, and dynamics. He leads the TRACE Lab and has been recognized with prestigious awards including the NSF CAREER Award (2021) and multiple teaching accolades. Gu holds a Ph.D. from Purdue University (2017) and a B.S. from Zhejiang University, China (2011). His work emphasizes robust control strategies for legged robots in dynamic environments, including adaptive ankle torque control, time-varying foot-placement algorithms, and state estimation techniques for non-inertial surfaces. His recent publications explore multimodal datasets in animal-robot interaction and the stabilization of quadrupedal locomotion on accelerating platforms. His research has been supported through grants such as the NSF CAREER Award, and his contributions span both theoretical advancements in hybrid control systems and practical applications in wearable robotics and exoskeleton design. Gu’s TRACE Lab serves as a hub for innovative robotics research, addressing challenges in robot-environment interaction and dynamic stability.
Nina Balcan is the Cadence Design Systems Professor of Computer Science at Carnegie Mellon University's School of Computer Science, with affiliations in both the Machine Learning Department (MLD) and Computer Science Department (CSD). She maintains her office in Gates Hillman Center (GHC) 8205 and is a prominent figure in theoretical machine learning and algorithmic game theory. Her research spans multiple critical areas in computer science, with a strong focus on the theoretical foundations of machine learning, algorithm design and analysis, and computational approaches to game theory and economics. Balcan has made significant contributions to developing principled algorithms for deep learning, learning with limited supervision, representation learning, and life-long learning. Her work uniquely bridges theoretical computer science with practical applications, particularly in the analysis of complex objects and processes, including algorithmic learning and multi-agent systems. Analysis of her recent publications reveals a strong trend toward data-driven algorithm design, with particular emphasis on learning to optimize combinatorial algorithms, revenue-maximizing mechanisms, and robust learning frameworks. Her work consistently demonstrates how theoretical guarantees can inform practical algorithm development across diverse domains from optimization to economics. Major Awards and Honors: ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award (awarded to the outstanding young computer professional of the year) Winner of Outstanding Student Paper Award at UAI 2024 Winner of Exemplary Artificial Intelligence Track Paper Award at ACM EC 2019 Runner Up Best Paper Award at COLT 2012 Professor Balcan has served as Program Committee Co-chair for major conferences including NeurIPS 2020, ICML 2016, and COLT 2014, demonstrating her leadership in the machine learning community. Her teaching portfolio at CMU includes foundational courses such as 10-701 Machine Learning, 10-315 Machine Learning, and 10-715 Advanced Introduction to Machine Learning, where she has mentored numerous students in both theoretical and applied aspects of the field. Her research group focuses on developing theoretically sound yet practically applicable machine learning algorithms, with particular attention to algorithm configuration, data-driven optimization, and learning in strategic environments. Current projects involve learning to improve combinatorial algorithms, designing revenue-maximizing mechanisms, and developing robust learning frameworks that can operate effectively in challenging environments.
AI Xin is a Lecturer in the School of Computing at the National University of Singapore (NUS), specializing in Artificial Intelligence and Data Science. She teaches courses such as machine learning, deep learning, and data mining, including advanced modules like CS4225 and CS5425. Education: Ph.D. in Electrical and Computer Engineering from NUS; B.Eng. from Xidian University, China. Her research spans Game Theoretical Modelling , Optimization Methods , Algorithm Design , and Wireless Networks . She has contributed to multi-agent systems, algorithmic game theory, and wireless community networks, focusing on robust and distributed solutions. Her recent publications highlight trends in game theory for wireless networks , distributed coverage algorithms , and optimization for network efficiency , with a strong emphasis on theoretical and practical applications in AI and networking. Scientific Awards: Teaching Excellence Award (NUS, 2024). She has taught courses on Big Data Systems for Data Science and Computational Thinking , bridging academic rigor with industry relevance through her prior experience in risk management, supply chain, and sales at BHP Billiton Marketing Asia.
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Minyi Huang is a Professor in the School of Mathematics and Statistics at Carleton University. His research focuses on Mean Field Stochastic Control, Stochastic Algorithms in Multi-Agent Systems, and Wireless Networks. He holds a Ph.D. from McGill University (2003) and has held postdoctoral positions at the University of Melbourne and the Australian National University. Dr. Huang is a Fellow of IEEE and a Member of SIAM. Education: Ph.D. in Electrical and Computer Engineering, McGill University (2003) M.Sc. in Systems and Control, Chinese Academy of Sciences (Beijing) B.Sc. in Mathematics, Shandong University (Jinan, China) Research Interests: Huang's work centers on stochastic control, mean field games, and multi-agent systems. His contributions include theoretical advancements in mean field social optimization, graphon-based control frameworks, and applications in wireless networks and economic models. He has organized workshops on Mathematical Cybernetics and Stochastic Processes, fostering interdisciplinary collaboration. Scientific Awards: Fellow of the IEEE Advising & Grants: Huang has advised numerous graduate students on topics in stochastic control and mean field theory. His grants include funding for international PhD students through Carleton's initiatives. He collaborates on projects involving mean field models for production output and social dynamics. Labs/Teams: Associated with the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS), contributing to collaborative research in control theory and applied mathematics.
Smita Ghosh is an Assistant Professor in the Department of Mathematics and Computer Science at Santa Clara University, part of the College of Arts and Sciences. Her research focuses on social network analysis, algorithms for information diffusion, and applications in cybersecurity, disaster management, and machine learning. She holds a B.Tech. from the West Bengal University of Technology, India, and an M.S. and Ph.D. from the University of Texas, Dallas. Her work addresses challenges in rumor containment, clickbait detection, and optimizing network models for social media content analysis. Recent publications include studies on hypergraph-based solutions for rumor blocking and stochastic models for emergency response in social networks. She also explores cross-modal topic modeling for enhancing content detection algorithms. Notable contributions include developing data-driven strategies for identifying hate speech spreaders and improving wildfire severity predictions using environmental features. Her research bridges theoretical computer science with real-world applications in public health, education, and disaster management. Her academic contributions include organizing conference proceedings like the 18th International Conference on Algorithmic Aspects in Information and Management (AAIM 2024). She actively contributes to educational initiatives such as the Classroute project, creating multilingual educational content for Punjabi and Urdu speakers.
LING Chun Kai is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on multiagent systems, computational game theory, and machine learning applications in adversarial real-world domains like cybersecurity and logistics. Educational background includes a PhD in Computer Science (2017-2023) from Carnegie Mellon University and a First Class BEng in Computer Engineering (2015) from NUS. Previously, he was a Postdoctoral Research Scientist at Columbia University. Current research interests span computational game theory, machine learning for multi-agent systems, equilibrium characterization in imperfect information settings, and applications in network security, logistics, and recreational games. Key methodological contributions include scalable algorithms for game solving, differentiable game solvers, and copula-based statistical modeling. Recent publications focus on attacker-defender graph games, language negotiation agents, and modeling games with incomplete information. Collaborations include researchers from Columbia University, Carnegie Mellon, and institutions working on GameSec, AAAI, Neurips, and ICML venues. Scientific Awards: IJCAI 2018 Distinguished Paper Award GameSec 2023 Best Paper Award GameSec 2024 Best Paper Award Singapore Teaching and Academic Research Talent Scheme (2024) Teaching includes courses on AI Planning and Decision Making (CS4246, CS5446) and Advanced Topics in Artificial Intelligence (CS6208).
Frank Christian Stephan is a Professor at the National University of Singapore (NUS), with joint appointments in the Department of Mathematics (primary) and School of Computing (secondary). His research spans mathematical logic, theoretical computer science, and computational complexity. Research Interests: Recursion theory and Kolmogorov complexity Inductive inference and learning theory Automata theory and automatic structures Parity games and algorithmic randomness Selected Publications include works on quasipolynomial time algorithms for parity games (STOC 2017 Best Paper) and semi-automatic structures. His scientific awards include the STOC 2017 Best Paper Award and the EATCS-IPEC Nerode Prize 2021. He teaches courses such as Computational Complexity (AY 2023/2024 Sem 2, AY 2024/2025 Sem 2), Advanced Automata Theory (multiple editions), and Mathematical Logic (undergraduate). He co-organizes the Logic Seminar at NUS.
