Xiaozhe Wang is an Associate Professor in the Department of Electrical and Computer Engineering at McGill University, holding the Canada Research Chair (Tier II) in Resilient and Stable Zero-Emission Electric Power Grids and the Rubin & So Foundation Faculty Scholar. He joined McGill in 2016 after a postdoctoral fellowship at MIT under Prof. Konstantin Turitsyn. He earned his Ph.D. from Cornell University (2015), with a minor in Applied Mathematics, and holds degrees from Zhejiang University (B.S., 2010) and Cornell (M.Eng., 2011). His research focuses on resilient power grids, data-driven methodologies, and cybersecurity in energy systems. Key areas include electric vehicle integration, stability assessment, and control strategies for renewable energy systems. He develops advanced techniques for uncertainty quantification, wide-area monitoring, and adversarial attack detection. Notable achievements include pioneering work on polynomial chaos expansion for probabilistic assessment and sparse identification for nonlinear dynamics. His articles explore topics like microgrid control, false data injection attacks, and decentralized energy trading. Awards: Canada Research Chair (Tier II), Rubin & So Foundation Scholar Grants/Projects: Focus on resilience, cybersecurity, and renewable integration funded via NSERC, Mitacs, and industry partnerships. He advises students through fellowships like Mitacs Elevate and Banting Postdoctoral Fellowships. His lab emphasizes interdisciplinary approaches to modern grid challenges, including lab experiments and field trials.
Professor Diana Inkpen is a faculty member at the School of Electrical Engineering and Computer Science, University of Ottawa. She holds a Ph.D. from the University of Toronto's Department of Computer Science and degrees from the Technical University of Cluj-Napoca, Romania (M.Sc. and B.Eng.). Her research focuses on computational linguistics, natural language processing (NLP), and artificial intelligence, with specialties in natural language understanding/generation, lexical semantics, and semantic web agents. Education: Ph.D., Computer Science, University of Toronto M.Sc., Computer Science and Engineering, Technical University of Cluj-Napoca B.Eng., Computer Science and Engineering, Technical University of Cluj-Napoca Affiliations: Director, NLP Lab Editor-in-Chief, Computational Intelligence journal Associate Editor, Natural Language Engineering journal Her research has led to roles such as program co-chair for AI 2012 and leadership in organizing international workshops. She has received funding from NSERC, SSHRC, and OCE, and was honored with a Visiting Professor title at the University of Wolverhampton, UK. Her teaching spans courses like Information Retrieval, Natural Language Processing, and Prolog programming. Professor Inkpen's work bridges NLP innovations with societal challenges, including mental health surveillance via social media analysis and bias mitigation in AI systems. She actively contributes to interdisciplinary projects, such as detecting hate speech and legal text entailment, while maintaining a global research network through collaborations and international conference involvement.
Shinji Watanabe is an Associate Professor at Carnegie Mellon University's Language Technologies Institute and a Courtesy Professor in the Electrical and Computer Engineering department. He holds a Ph.D. (Dr. Eng.) from Waseda University, Japan, and has held research roles at NTT Communication Science Laboratories, Mitsubishi Electric Research Laboratories (MERL), and Johns Hopkins University. His research focuses on automatic speech recognition, speech enhancement, and machine learning for speech processing. Watanabe has published over 300 peer-reviewed papers and received the Best Paper Award at IEEE ASRU 2019. His work emphasizes robust speech processing in challenging environments, multilingual models, and neural audio codecs. He leads the ESPnet toolkit development for end-to-end speech processing systems and contributes to technical committees like IEEE SLTC and APSIPA SLA. Recent research trends include streaming speech systems, universal speech enhancement (URGENT challenges), and fusion of discrete speech units with self-supervised representations. He explores scalable speech foundation models through benchmarks like ML-SUPERB 2.0 and investigates cross-modal audio-visual processing in challenges like MISP 2025. Education : B.S., M.S., Ph.D. (Waseda University) Affiliations : CMU Language Technologies Institute, CMU ECE, Former roles at MERL and Johns Hopkins Key Projects : ESPnet, OpenWhisper-Style Models, URGENT Challenge Frameworks
Adam Finkelstein is a Professor in the Department of Computer Science at Princeton University, where he has been a faculty member since 1997. He holds a PhD and Master's in Computer Science from the University of Washington and a dual degree in Physics and Computer Science from Swarthmore College. Finkelstein is renowned for his interdisciplinary work at the intersection of computer graphics, audio processing, and machine learning, and he co-organized the Art of Science exhibition at Princeton. Education: PhD, Computer Science, University of Washington MS, Computer Science, University of Washington BA, Physics and Computer Science, Swarthmore College His research spans audio processing (e.g., speech enhancement, voice conversion, audio metrics), computer graphics (e.g., line drawing algorithms, stylized rendering, image manipulation), and machine learning (e.g., self-supervised learning, differentiable programming). His work often bridges technical and creative domains, exemplified by collaborations at Pixar and Adobe Creative Technologies Lab. Recent publications highlight advancements in audio super-resolution and voice conversion using deep learning frameworks, as well as stylized line rendering for animated 3D models. His contributions to perceptual audio metrics and shader optimization further underscore his impact on human-centric computational systems. Scientific Awards: NSF CAREER Award Alfred P. Sloan Fellowship Fellow of the Association for Computing Machinery (ACM) Finkelstein has secured foundational grants for his research and actively mentors students, though no specific advisees are listed. He also explores collaborative tools for internet music performance, reflecting his broader interest in distributed systems and user interfaces.
Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.
LEE Wee Sun is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he previously served as Head of Department, Vice Dean of Undergraduate Studies, and Vice Dean of Research. His academic journey began with a B.Eng. in Computer Systems Engineering from the University of Queensland (1992) and a Ph.D. from the Australian National University (1996), followed by research roles at the Australian Defence Force Academy and MIT. Education: Ph.D., Australian National University, Canberra, Australia (1996) B.Eng. in Computer Systems Engineering, University of Queensland, Brisbane, Australia (1992) Research Focus: Professor Lee pioneers work in Machine Learning , Planning Under Uncertainty , and Approximate Inference , with emphasis on integrating AI subfields for holistic reasoning. His current projects include "Learning to Decompose for Reasoning and Planning" (enhancing LLMs via self-supervised problem decomposition) and "Learning to Reason with Visual-Linguistic Inputs" (unifying vision, language, and reasoning in single architectures). Publication Trends: Recent work (2023-2025) centers on bridging LLMs with classical AI techniques, featuring breakthroughs in uncertainty quantification, multi-task optimization, and graph-based reasoning. Key themes include sparsity-aware vehicle routing, epistemic uncertainty for reliable LLMs, and differentiable neural solvers for combinatorial problems. Awards: IJCAI-JAIR Best Paper Prize (2022) RSS Test of Time Award (2021) RoboCup Best Paper Award (IROS 2015) HRATC 1st Place (2015) IPPC POMDP Track 1st Place (2011, 2014) UAI Google Best Student Paper (2014) Semeval-1 1st/2nd Place (2007) J.G. Crawford Prize (ANU 1996) Leadership & Service: As steering committee chair for ACML and area chair for NeurIPS/ICML/AAAI/IJCAI, Professor Lee shapes global AI discourse. His administrative roles at NUS and collaborations with MIT/Singapore-MIT Alliance demonstrate commitment to advancing AI education and research infrastructure. While student advisees aren't listed, his leadership positions imply extensive mentoring. Research Ecosystem: His work drives NUS's AI initiatives including Knowledge@Computing projects on reasoning frontiers. Current efforts focus on making AI systems robust through uncertainty-aware planning and multi-modal integration, with applications in robotics, verification systems, and combinatorial optimization.
Aishwarya Ganesan is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign. Her research focuses on distributed systems, storage systems, and fault tolerance mechanisms, with emphasis on high-performance computing and datacenter infrastructure. She leads projects addressing challenges in replicated storage, consensus protocols, and system resilience. Her work explores fault tolerance in disaggregated datacenters, log abstractions for low-latency applications, and novel replication strategies for modern storage systems. She has developed frameworks like LazyLog and IONIA to improve system efficiency and reliability. Her research also extends to automatic reliability testing for cluster management controllers and analyzing distributed storage vulnerabilities. Key contributions include demonstrating how redundancy alone does not guarantee fault tolerance, and proposing consistency-aware durability mechanisms for storage systems. She received the NSF CAREER Award in 2024 for her research on storage-aware fault tolerance. Her work spans 21 peer-reviewed publications, with notable contributions in conferences like SOSP, EuroSys, and FAST. Current projects investigate fault tolerance in emerging memory technologies and system recovery protocols for consensus-based storage.
Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.
Christiane Fellbaum serves as Lecturer with Rank of Professor in Princeton University's Program in Linguistics and Department of Computer Science, where she has been a senior research scholar since returning in 1987 after postdoctoral work at the University of Paris. Her foundational contributions to computational linguistics include co-developing WordNet and co-founding the Global WordNet Association. Her educational background features: Ph.D. in Linguistics, Princeton University (1980) Postdoctoral Fellowship, University of Paris Fellbaum's research integrates theoretical linguistics with computational applications, specializing in lexical semantics, corpus analysis, and semantic network construction. Her work bridges computational linguistics and lexicography through projects like WordNet and Medical WordNet, with recent emphasis on multilingual resources, bias analysis in embeddings, and African language technology development. She examines semantic phenomena including idioms, verb alternations, and emotion scales through both corpus linguistics and formal ontological frameworks. Analysis of her publication trajectory reveals sustained innovation in lexical resource development since the 2000s, evolving from foundational WordNet studies to contemporary work on large language model adaptation and social bias mitigation. Current research demonstrates increasing interdisciplinary collaboration across NLP, cognitive science, and social justice applications. Her scientific recognition includes: Wolfgang Paul Prize from the German Humboldt Foundation (2001) Antonio Zampolli Prize (2006) Fellbaum has secured continuous research funding from the U.S. National Science Foundation, European Union Seventh Framework, Frank Moss Foundation, and Tim Gill Foundation. She actively mentors junior researchers through Princeton's Independent Work seminars and hosts the North American Computational Linguistics Olympiad (NACLO), while leading major international collaborations including the KYOTO and SIERA European projects. As director of the WordNet project and permanent fellow at the Berlin-Brandenburg Academy of Sciences, she maintains leadership in global lexical resource initiatives through the Princeton Language and Intelligence initiative and Natural and Artificial Minds research group.
Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.
Soroosh Shafiee is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell University since July 2023. Before joining Cornell, he held postdoctoral positions at the Tepper School of Business (Carnegie Mellon University) and the Automatic Control Laboratory (ETH Zurich). He earned a B.Sc. and M.Sc. in Electrical Engineering from the University of Tehran and a Ph.D. in Operations Research from École Polytechnique Fédérale de Lausanne (EPFL). His research focuses on optimization under uncertainty, robust optimization, optimal transport, and their applications in machine learning and finance. Specific interests include designing algorithms for data-driven optimization, analyzing statistical and computational complexity, and exploring nonconvex optimization structures. Swiss National Science Foundation Early PostDoc Mobility Fellowship (2020) PhD Thesis Distinction Award, EPFL (2020) His work bridges theoretical foundations with practical applications, contributing to areas such as distributionally robust optimization, Wasserstein-based methods, and scalable algorithm development. His research has been published in top venues like Journal of Machine Learning Research and Operations Research .
Yves Wautelet serves as an Associate Professor at the Faculty of Economics and Business at KU Leuven, where he conducts research in conceptual modeling, business process management, and digital transformation. His work addresses critical challenges at the intersection of information systems engineering and business strategy, with particular focus on sustainability-driven modeling approaches and IT governance frameworks. His primary research interests include: Conceptual modeling methodologies and frameworks Business process management and information systems design Digital transformation strategies and implementation IT governance and business-IT alignment Sustainability-driven modeling for circular economy Agile software development practices and methods Requirements engineering with user stories Wautelet's recent publication record demonstrates significant scholarly productivity with numerous 2024-2025 publications spanning conceptual modeling frameworks for sustainability (Circulise), tools for identifying ambiguity in user stories (AmbiTRUS), and approaches to align strategic and operational agility. His work bridges theoretical foundations with practical applications across diverse domains including healthcare, circular economy, software development, and organizational transformation. His research consistently applies model-driven approaches to solve complex real-world problems, often integrating sustainability considerations into information systems engineering. As a promotor and co-promotor, Wautelet currently supervises multiple doctoral research projects including: Automatic generation of conceptual models from textual descriptions (2024-2028) Sustainability-Driven Modeling Assistant for Twin Transition in Vietnam (2024-2028) Home Care Business Process Management using Distributed Ledger Technologies (2024-2028) Teaching Modeling Skills in BPMN formalism (2021-2025) His research is conducted through the Information Systems Engineering Research Group (LIRIS) at KU Leuven's Brussels campus, where he contributes to advancing model-driven approaches for addressing contemporary business and technological challenges.
Randy Freeman is a Professor of Electrical and Computer Engineering at Northwestern University's McCormick School of Engineering. He joined the university in 1996 after earning his Ph.D. from the University of California, Santa Barbara. His research focuses on nonlinear control theory, robust control, multi-agent systems, and distributed control systems. Freeman has been recognized with the NSF CAREER Award (1997) and has held editorial roles in prominent journals like the IEEE Transactions on Automatic Control. Education: Ph.D., Electrical Engineering, University of California, Santa Barbara (1996) M.S., Electrical Engineering, University of Illinois at Urbana-Champaign B.S., Electrical Engineering, Cornell University His research explores advanced control strategies for complex systems, including nonlinear feedback systems, distributed averaging, and multi-agent coordination. Key contributions include work on self-healing swarm control, distributed environmental monitoring, and privacy-preserving consensus algorithms. His publications span journals like IEEE Transactions on Robotics and IEEE Control Systems Letters . Scientific Awards: NSF CAREER Award (1997) Advising and Grants: Freeman has contributed to collaborative robotics projects and sensor network research, supported by grants from NSF and other agencies. His work bridges theoretical control systems with practical applications like robotics and environmental monitoring. Labs and Teams: Affiliated with the Master of Science in Robotics Program and collaborates on multi-agent systems and distributed control initiatives.
Ming Cao is a Full Professor at the University of Groningen (Netherlands), holding positions in the Department of Discrete Technology and Production Automation, the Engineering and Technology Institute Groningen, and serving as Chair of the Jantina Tammes School of Digital Society, Technology and AI. His academic roles include Director of the Jantina Tammes School and membership in prestigious organizations such as the International Federation of Automatic Control (IFAC) and the European Commission’s DG CNECT. Cao’s research focuses on multi-agent systems, autonomous robotics, complex networks, and cooperative control, with applications in robotics, epidemic modeling, and biomimetic sensors. Education: PostDoc in Mechanical Engineering from Princeton University (2008), PhD in Electrical Engineering from Yale University (2007). Research Interests: Multi-agent systems, distributed decision-making, cooperative control, robotic teams, seal whisker-inspired flow sensing, and privacy-preserving control systems. Recent Trends in Articles: Recent work emphasizes co-evolutionary dynamics in social-technical systems, privacy in control systems, and biomimetic robotics. Key topics include feedback mechanisms in cooperation, hypergraph-based epidemic models, and seal whisker mechanics for underwater sensing. Awards: European Control Award (2016), Manfred Thoma Medal (2017), ERC Grant (2012). Grants: Vidi Grant from NWO (2015) for agent coordination research. Labs/Teams: Jan C. Willems Center for Systems and Control, Research Center for Data Science and Systems Complexity (DSSC). Active in editorial roles for journals like Artificial Life and Robotics and the SIAM Journal on Control and Optimization .
Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, holding joint appointments at the Courant Institute of Mathematical Science and the Center for Data Science. He currently serves as the Interim Director of the Center for Data Science. His academic career spans over a decade at NYU, where he has taught probability and statistics to data-science students. Dr. Fernandez-Granda's research focuses on designing and analyzing data-science methodology, with current emphasis on machine learning applications in medicine, climate science, and scientific imaging. His work bridges theoretical foundations with practical applications across multiple domains. His notable contributions include: Development of the COBRA (COnfidence-Based chaRacterization of Anomalies) score for automatic assessment of impairment and disease severity Applications of machine learning to improve climate projections through the M2LInES project Research on magnetic resonance fingerprinting for quantitative tissue parameter estimation Development of AI systems for medical diagnostics including Alzheimer's detection and breast cancer diagnosis Dr. Fernandez-Granda is the author of the book "Probability and Statistics for Data Science," published by Cambridge University Press. The book serves as a comprehensive guide to the two pillars of data science, featuring real-world datasets and addressing fundamental challenges like overfitting, the curse of dimensionality, and causal inference. His research has been supported by grants from the National Science Foundation (Division of Mathematical Sciences, grants 1616340 and 2009752) and the Alzheimer's Association (grant AARG-NTF-21-848627). Dr. Fernandez-Granda is actively involved in several collaborative projects: M2LInES project: An international collaboration focused on improving climate projections using machine learning to capture unaccounted physical processes at the air-sea-ice interface Math and Data group: Exploring the intersection of mathematical theory and data science applications
