Professor Ihab Ilyas is a leading figure in data management and machine learning at the Cheriton School of Computer Science , University of Waterloo , where he holds the Thomson Reuters Research Chair in Data Quality . He is currently on leave from the university, serving as a Distinguished Engineer, Proactive Intelligence at Apple Inc . His research focuses on data cleaning , large-scale data integration , knowledge graphs , and machine learning applications in data quality . He has pioneered systems like HoloClean and Saga , with commercial impacts through co-founded startups Inductiv (acquired by Apple) and Tamr . PhD in Computer Science from Purdue University Co-founder of Inductiv (acquired by Apple) Co-founder of Tamr Research Interests include: Probabilistic and uncertain data management Machine learning for data quality and enrichment Big data systems and information extraction Knowledge graph construction and optimization Scientific Awards and Recognitions include: Fellow of the Royal Society of Canada (2024) C.C. Gotlieb Computer Award (2024) IEEE Fellow (2021) ACM Fellow (2020) Cheriton Faculty Fellowship (2013-2016) Ontario Early Researcher Award
Michael Jong Kim is an Associate Professor at the Sauder School of Business, University of British Columbia, specializing in the Division of Operations and Logistics. His research focuses on dynamic programming, statistical learning, robust optimization, and the exploration vs exploitation trade-offs in sequential decision-making processes. BASc, M.Math, and PhD from the University of Toronto His work spans topics in stochastic optimization, supply chain dynamics, and information dissemination in uncertain environments. Publications highlight contributions to Bayesian inventory control, semi-Markovian system control, and variance regularization in optimization models. Dr. Kim teaches advanced business analytics courses, including Descriptive and Predictive Business Analytics and Advanced Predictive Business Analytics (MBAN) during the 2024-2025 academic year. He can be reached at mike.kim@sauder.ubc.ca or by phone at +1 604.822.8682.
Shipra Agrawal is an Associate Professor at the Department of Industrial Engineering and Operations Research, Columbia University, with affiliations to the Data Science Institute and the Department of Computer Science. Her research bridges optimization and machine learning, focusing on decision-making in uncertain environments. PhD in Computer Science from Stanford University (2011) Researcher at Microsoft Research India (2011–2015) Her work addresses online optimization , reinforcement learning , and game theory , aiming to develop algorithms that balance exploration and exploitation for long-term goals. Applications include internet advertising , revenue management , and resource allocation . Recent publications examine dynamic pricing models, regret bounds in reinforcement learning, and convex knapsack optimization. Her research has been supported by NSF CAREER , Google Faculty Research , and Amazon Research Awards . NSF CAREER Award CMMI-1846792 (2019) Google Faculty Research Award (2017) Amazon Research Award (2017) She has advised PhD students who now hold positions at institutions like Google DeepMind, Amazon, and Facebook. Agrawal serves as an associate editor for Management Science , INFORMS Journal on Optimization , and Journal of Machine Learning Research , and co-chaired major conferences such as COLT 2024 and AISTATS 2025.
Dr. Xinan Zhang is an Associate Professor in the School of Engineering at The University of Western Australia (UWA), specializing in Electrical, Electronic, and Computer Engineering. He holds a BEng from Fudan University (2008) and a PhD from Nanyang Technological University (2014). Before joining UWA in 2019, he held roles as a Lecturer and Research Fellow in Singapore and Australia. His research focuses on power electronics, electrical machine drives, and renewable energy, with over 60 top-tier publications. He is the Portfolio Lead for Industry Engagement in UWA's School of Engineering and co-leads the Power and Clean Energy (PACE) research group. Education: BEng in Electrical Engineering, Fudan University (2004–2008) PhD in Electrical Engineering, Nanyang Technological University (2010–2014) Research Interests: Dr. Zhang’s work spans power electronics, renewable energy systems, energy storage, and smart grid technologies. He emphasizes practical applications, such as battery management systems for vanadium redox flow batteries and adaptive control strategies for microgrids. His contributions address challenges in energy efficiency, grid stability, and sustainable power solutions. Articles & Trends: Recent publications focus on advanced control algorithms for inverters, battery modeling, and renewable energy integration. His work combines data-driven methods with traditional control theory to enhance system efficiency and reliability. Notable areas include DC microgrid control, vanadium redox flow battery optimization, and model predictive control for power electronics. Awards: Listed in Stanford University’s Top 2% Scientists (2020–2022) Grants & Collaborations: He leads or co-leads projects funded by the Australian government and industry partners, including the GenX Betavoltaic Battery Pilot Manufacturing Process and Mine Electrification . These projects aim to advance clean energy technologies and industrial applications. Labs & Teams: As co-lead of the PACE group, he fosters interdisciplinary collaboration to tackle global energy challenges, aligning with UN Sustainable Development Goals for affordable and clean energy (SDG 7).
Massachusetts Institute of TechnologyUnited States
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
Rutgers, The State University of New JerseyUnited States
Kostas Bekris is a Professor in the Department of Computer Science at Rutgers University, specializing in Robotics and Artificial Intelligence. His research focuses on motion planning, autonomous manipulation, and robot control, with notable contributions to tensegrity robotics, perception-driven systems, and large-scale package handling. He leads a team conducting groundbreaking work in robotics, supported by grants from NSF, NASA, and industry collaborators like ExxonMobil. His group emphasizes interdisciplinary approaches, combining machine learning, topological methods, and differentiable physics modeling to advance robot capabilities in complex environments. Education details are not explicitly stated in the provided texts, but his academic career has included significant mentorship of PhD students and postdoctoral researchers. Key projects involve vision-driven manipulation pipelines, obstacle detection systems (PROBE), and resilient robot designs inspired by biological structures. He has been recognized for his work through prestigious awards including the NASA Early Career Grant and multiple NSF grants, as well as team achievements in robotics competitions like the Amazon Picking Challenge. Research interests span robotics subfields such as: Autonomous manipulation in cluttered environments Learning-based control for dynamic systems Topological data analysis for motion reasoning Tensegrity and soft robotics architectures Sim-to-real transfer in robotic tasks His team's work has produced open-source software tools and datasets, advancing benchmarks in manipulation and perception. Recent articles emphasize scalable solutions for industrial automation and robust navigation strategies in unstructured settings. Scientific achievements include: Development of PROBE for proprioceptive obstacle detection Advances in differentiable physics engines for tensegrity systems NSF-funded projects on robotic rearrangement and modular morphologies Advising contributions span over a decade, with current advisees focusing on topics like non-prehensile manipulation and large-scale storage optimization. Collaborations with industry (e.g., ExxonMobil) and academic partners (Yale University) reflect his commitment to applied robotics research. Labs and teams under his leadership include the Rutgers CS Robotics Group, contributing to projects like the ARIAC challenge platform and packing/industrial automation systems. Future work targets improved robot resilience in disaster scenarios and enhanced human-robot collaboration paradigms.
Lam James is a Chair Professor of Control Engineering at the University of Hong Kong (HKU), affiliated with the Faculty of Engineering. He holds a BSc (1st Hons.) in Mechanical Engineering from the University of Manchester, and MPhil/PhD degrees from the University of Cambridge. His academic journey includes roles as a Croucher Fellow, Lecturer at the University of Melbourne, and faculty member at City University of Hong Kong before joining HKU in 1993. Professor Lam serves as Editor-in-Chief for four international journals including IET Control Theory and Applications and Journal of The Franklin Institute . His research focuses on networked control systems, vibration control, control theory, and multi-agent systems. With 600+ peer-reviewed publications, he maintains an H-index of 102 (Web of Science) and 108 (Scopus), recognized as a Highly Cited Researcher in multiple fields. Education: BSc Manchester (Mechanical Engineering), MPhil/PhD Cambridge Editorial Roles: 20+ journals, including leadership roles since 2012 His awards include Foreign Membership of Academia Europaea (2024), National Academy of Artificial Intelligence membership (2025), and two State Natural Science Awards (2015, 2019). He is a Fellow of multiple institutions including IEEE, IMechE, and HKIE.
Dr. Yuzhang Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering. Previously, he held an Assistant Professor position at the University of Massachusetts Lowell (2018–2023). He earned his Ph.D. from Northeastern University and B.Eng./M.S. from Tsinghua University. His research focuses on smart grids, renewable energy systems, cyber-physical resilience, and machine learning applications. He leads editorial roles for IEEE Transactions on Power Systems and chairs IEEE PES Task Forces on standard test cases and distribution system operations. Dr. Lin’s research has been funded by NSF, DOE, ONR, and others. He is a recipient of the NSF CAREER Award and Northeastern’s Graduate Student Outstanding Research Award. His work emphasizes data-driven solutions for grid resilience, including state estimation, cyber-physical defense, and distributed energy integration. The Lin Group actively seeks PhD candidates interested in advancing smart grid technologies. Education: Ph.D. (Northeastern University), B.Eng./M.S. (Tsinghua University) Grants: NSF, DOE OE/EECE/CESER, ONR, NYSERDA, MassCEC Service Roles: IEEE PES Task Force Co-chair (Standard Test Cases), Secretary (Distribution System Operations Subcommittee) Publications span top journals/conferences, focusing on state estimation, inverter-based resource control, and machine learning for grid systems. His lab develops cutting-edge tools for power system resilience and renewable energy integration.
University of California, Los AngelesUnited States
Christopher S. Tang is a UCLA Distinguished Professor and Edward W. Carter Chair in Business Administration at the Anderson School of Management , where he researches global supply chain management with a focus on social innovation in developing countries . He also serves as Senior Associate Dean for Global Initiatives and Faculty Director of the Center for Global Management . Education: Ph.D. in Management Science (1985, Yale University) M.Phil. in Administrative Science (1983, Yale University) M.A. in Statistics (1983, Yale University) B.Sc. in Mathematics (First Class Honors, 1981, King’s College, University of London) His research explores the intersection of corporate responsibility and supply chain innovation , addressing topics like microfinancing , mobile platforms for developing economies , direct agricultural procurement , and disaster response logistics . He emphasizes visibility, integrity, and agility in uncertain environments. Recent work highlights AI adoption benefits for supply chains , strategies to reduce forced labor risks , and policy impacts on ride-sharing platforms . His research bridges operations management and social justice , advocating for environmental stewardship alongside business growth. Scientific Awards: Salzberg Medallion (2017) Lifetime Fellow, INFORMS (2011) Responsible Research in Management Award (2017) Teaching Excellence Award (multiple years, UCLA-NUS) Dean’s Excellent Service Award (2014) As an influential adviser and consultant , Tang has worked with Amazon, HP, IBM, Nestlé, GKN , and Accenture . He has taught at Stanford University, UC Berkeley, Hong Kong University of Science and Technology , and served as visiting professor at Cambridge University and the Institute of Advanced Study at HKUST .
Dr. Arnab Bhattacharya is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur since December 2020. He previously served as Associate Professor (2014–2020) and Assistant Professor (2007–2014) at IIT Kanpur. Education: PhD in Computer Science (2007), University of California, Santa Barbara MS in Computer Science (2007), University of California, Santa Barbara Bachelor of Computer Science and Engineering (2001), Jadavpur University Research Focus spans Databases , Data Mining , Information Retrieval , and Artificial Intelligence . His work emphasizes graph analytics , skyline queries , probabilistic data , and knowledge graph management . Article Trends reveal expertise in graph neural networks , trajectory-aware systems , statistical significance in databases , and legal/medical data mining . His methodologies often integrate chi-square statistics , approximate indexing , and provenance tracking . Scientific Awards: IBM Faculty Research Award Yahoo! Faculty Research and Engagement Program Award Best Paper at COMAD 2011 Best Student Paper at COMAD 2010 Top-Five Student Paper at ICDM 2005 ICDM Student Travel Award sponsored by IBM Contact: Office RM 409, Department of Computer Science and Engineering, IIT Kanpur Email: arnabb@iitk.ac.in | Phone: +91-512-259-7650
University of Illinois Urbana-ChampaignUnited States
Mei-Po Kwan is a distinguished academic holding multiple roles at The Chinese University of Hong Kong (CUHK). She serves as the Head of Chung Chi College, Choh-Ming Li Professor of Geography and Resource Management, Director of the Institute of Space and Earth Information Science, and Professor at The Jockey Club School of Public Health and Primary Care. Her research focuses on geospatial health, urban mobility, environmental health, and innovative GIS methods, emphasizing individual-level environmental exposure and social disparities. Education: Ph.D. Geography, University of California, Santa Barbara (1994) M.A. Urban Planning, University of California, Los Angeles (1989) B.SoSci. Geography, The Chinese University of Hong Kong (1985) Research Interests: Dr. Kwan’s work bridges health, transport, and environmental geography, leveraging cutting-edge GIS and mixed-methods approaches. Key themes include: Uncertain Geographic Context Problem (UGCoP) and Neighborhood Effect Averaging Problem (NEAP) GIS-based analysis of greenspace, air/noise pollution, and mobility Geospatial AI and real-time sensing technologies Health disparities in urban environments Grants & Awards: She has secured over $68 million in research funding, including recent grants from the Hong Kong Research Grants Council and Innovation and Technology Commission. Notable awards include Fellowships from the American Association of Geographers, Royal Geographical Society, and multiple Highly Cited Researcher recognitions (2019, 2021). Professional Service: Editor of Annals of the American Association of American Geographers , founder of the International Geospatial Health Research Network, and member of prestigious editorial boards and advisory councils. Labs & Teams: Leads the Institute of Space and Earth Information Science and collaborates on projects like the 15-minute city framework, carbon monitoring systems, and geospatial health analytics.
Swiss Federal Institute of Technology in LausanneSwitzerland
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.
Professor Holly Thorpe is a Professor of Sociology in Te Huataki Waiora / School of Health at the University of Waikato, specializing in Sport and Human Movement. She serves as Associate Dean Research in the Division of Health PVC Office and is an internationally recognized sociologist of sport, physical culture and gender whose work has earned prestigious fellowships and awards including Fulbright, Leverhulme, and Royal Society honors. Her educational background includes a PhD from the University of Waikato and Papa Reo Level 1 certification from Te Wananga o Aotearoa, reflecting both academic excellence and cultural engagement with Māori language and knowledge systems. Professor Thorpe's research centers on equity and inclusion in sporting cultures, with particular attention to women's health and wellbeing, sport and gender, and the impact of social media on athletic experiences. She employs critical and feminist theoretical frameworks with expertise in qualitative methods to examine how sport, physical activity, and health intersect in rapidly changing social landscapes. Her work spans gender studies, feminist methodologies, sports science, sociology of health, and digital media studies within sporting contexts, with a strong commitment to community-engaged research. Her extensive publication record reveals consistent thematic focus on contemporary issues including menstruation in sport, digital technologies and athlete experiences, pandemic impacts on wellbeing, and the gendered dimensions of mega-sport events. Her work frequently employs intersectional analysis and collaborates across disciplines to address complex questions about embodiment, identity, and social change in sporting cultures, particularly as they relate to women and marginalized communities. Professor Thorpe's significant scholarly recognition includes: Royal Society Early Career Research Excellence Award for Social Sciences (2018) Fellow of the North American Society for the Sociology of Sport (2018) Royal Society James Cook Fellowship for a two-year project focused on women's wellbeing through and beyond pandemic (2021) She actively supervises students and has secured substantial research funding including the Te Punaha Matatini CORE Research Institute project 'Youth Wellbeing in Uncertain Times: The Voices of Rangatahi from Flood-Effected Tairāwhiti Gisborne' (2023-2025). Professor Thorpe collaborates extensively with international and national sports organizations including the International Olympic Committee, High Performance New Zealand, Sport New Zealand, Skateistan, and Voice in Sport to ensure her research has real-world impact on policy and practice. She serves as Series co-editor for 'New Femininities in Digital, Physical and Sporting Cultures' and maintains active media engagement through The Conversation and other outlets. Professor Thorpe leads several significant research initiatives including 'Reimagining Fields of Play' (2023) and co-organized the 'Gender, Health and Wellbeing Symposium' (2022), demonstrating her commitment to creating spaces for critical dialogue about sport, gender, and health while advancing culturally responsive methodologies that center Indigenous and diverse perspectives.
Dustin Tingley is a Professor of Government at Harvard University and holds a joint appointment at the Harvard Kennedy School of Public Policy . He serves as Interim Vice Provost for Advances in Learning and directs the Data Science and Technology Group and the Harvard Initiative on Learning and Teaching . He earned a PhD in Politics from Princeton and a BA in political science and math from the University of Rochester. Key Roles : Deputy Vice Provost (past), Chair of Harvard's Standing Committee on Climate Education Research Focus : Climate change politics, data science, causal inference, and international political economy His recent work explores the political economy of climate transitions , public opinion on carbon policies , and machine learning applications in social sciences . He co-founded ABLConnect , a repository for active learning pedagogy, and organized conferences on causal mechanisms , teaching with AI , and equitable classrooms . Awards : Gladys M. Kammerer Award (2015) for co-authored book Sailing the Water’s Edge Notable Publications : Uncertain Futures: How to Unlock the Climate Impasse (2023, with Alex Gazmararian) The Political Economy of the Clean Energy Transition (2025)
Jesse Hoey is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo and leader of the Computational Health Informatics Lab (CHIL). He serves as a Faculty Affiliate at the Vector Institute and is Editor-in-Chief of the IEEE Transactions on Affective Computing. His research spans affective computing, health informatics, and socially assistive robotics, with a particular focus on developing technologies for elderly care and cognitive assistive applications. Hoey's research interests center around affective intelligence, Bayesian affect control theory (BayesACT), and decision-theoretic planning in uncertain domains. His work integrates social psychology with artificial intelligence to create emotionally aware systems that can interact naturally with humans, particularly those with cognitive impairments such as Alzheimer's disease. He has developed models for social interaction, emotion recognition, and uncertainty management in human-robot collaboration. His recent publications demonstrate a strong trend toward medical applications of AI, particularly in ultrasound analysis and healthcare technology. Many of his papers focus on self-supervised learning techniques for medical imaging and the application of affective computing principles to assistive technologies for dementia care. His work bridges theoretical AI with practical healthcare applications, showing increasing emphasis on real-world implementation. Editor-in-Chief of IEEE Transactions on Affective Computing Hoey has supervised numerous PhD and Master's students through the Computational Health Informatics Lab, with research spanning socially assistive robotics, affective computing, and health informatics. His lab has received funding for projects related to AI for dementia care, smart home technologies, and emotion-aware systems. The CHIL lab collaborates with healthcare institutions including the Toronto Rehabilitation Institute. The Computational Health Informatics Lab (CHIL) focuses on developing intelligent systems that understand and respond to human emotions and social contexts. Current projects include emotionally aligned social robots for dementia care, self-supervised learning for medical ultrasound, and models of social organization as uncertainty management. The lab combines theoretical work in Bayesian modeling with practical applications in healthcare technology.
