Alexandre Jacquillat is the Maurice F. Strong Career Development Associate Professor and Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. His research focuses on data-driven decision-making with applications in air traffic management, urban mobility, and decarbonization. He holds a PhD in Engineering and MS degrees from MIT and École Polytechnique. Education PhD in Engineering, MIT MS in Technology and Policy, MIT MS in Applied Mathematics, École Polytechnique Research Interests His work develops scalable optimization models for efficient, equitable, and sustainable operations. Key areas include stochastic optimization, large-scale systems design, and machine learning applications in transportation and public policy. Recent projects explore vertiport planning for urban aerial mobility and prescriptive analytics for pandemic response. Awards Harold W. Kuhn Award (2024) INFORMS Harvey Greenberg Research Award (2023) MIT Jamieson Prize for Excellence in Teaching (2023) Multiple INFORMS Best Paper Awards (2015-2023) Named Leading Academic Data Leader by Chief Data Officer Magazine (2021-2022) Teaching & Grants Teaches courses in optimization (15.093, 15.083) and analytics (15.072). His grants support work in robotic warehousing, air traffic scheduling, and disaster response logistics. Advises on transportation analytics for industry and government. Labs/Teams Leads MIT Sloan's operations research group, collaborating with industry partners like McKinsey & Co. and Booz Allen Hamilton on transportation analytics and optimization projects.
Gail E. Kaiser is a Professor of Computer Science and the Director of the Programming Systems Laboratory (PSL) in the Computer Science Department at Columbia University. She has been with Columbia University since 1985, becoming a full Professor in 1998. Prof. Kaiser's research spans software engineering, program analysis, software testing, and software security, with recent focus on addressing challenges in AI/ML systems testing and security. Prof. Kaiser received her PhD in Computer Science from Carnegie Mellon University in 1985 and her ScB in Computer Science and Engineering from MIT in 1979. Her dissertation at CMU was titled "Semantics for Structure Editing Environments" under advisor Nico Habermann, and at MIT she completed "Automatic Extension of an Augmented Transition Network Grammar for Morse Code Conversations" under advisor Al Vezza. Prof. Kaiser's research interests primarily focus on software engineering following a systems building approach, with recent emphasis on static and dynamic program analysis techniques to improve software reliability and security. Since 2005, she has investigated testing "non-testable" programs, particularly in machine learning, data mining, and scientific computing applications where traditional testing oracles are insufficient. She has developed novel techniques and tools for detecting bugs and verifying repairs in complex systems. Concurrently, she has worked on collaboration environments for computational scientists, creating knowledge sharing and domain-aware environments to support scientific workflows. Prof. Kaiser's recent publications demonstrate a strong focus on the intersection of software engineering and artificial intelligence. Her work addresses critical challenges in testing AI systems, code understanding through deep learning, vulnerability detection, and educational tools for computational thinking. There's a clear evolution from traditional software engineering topics toward AI/ML applications, with particular emphasis on metamorphic testing for non-testable systems, code similarity analysis, and educational applications. Prof. Kaiser has received numerous prestigious awards throughout her career: Distinguished Journal Award (10 Years) from 18th IEEE International Conference on Software Testing, Verification and Validation (ICST), April 2025 Best Research Paper Award at 24th IEEE International Conference on Source Code Analysis & Manipulation (SCAM), October 2024 Distinguished Reviewer Awards for ASE 2024 and FSE 2024 ACM SIGSOFT Distinguished Paper Award for "CONCORD: Clone-aware Contrastive Learning for Source Code", July 2023 Best Student Paper Award at ICCE 2021 Multiple ACM SIGSOFT Distinguished Paper Awards dating back to 2014 Presidential Young Investigator in Software Engineering and Software Systems from NSF (1988-1993) Prof. Kaiser has chaired Columbia's doctoral program since 1997 and served on editorial boards including IEEE Internet Computing and as a founding associate editor of ACM Transactions on Software Engineering and Methodology. Her lab has been continuously funded by major agencies including NSF, NIH, DARPA, ONR, NASA, and numerous companies. Current grants include significant NSF funding for secure containers architecture, learning semantics of code for software assurance, and finding semantic security bugs. As Director of the Programming Systems Laboratory (PSL), Prof. Kaiser leads research in software systems, program analysis, and software testing. The lab has developed numerous tools and techniques for software reliability and security, with recent focus on challenges in AI/ML systems. Her work bridges theoretical foundations with practical applications, often resulting in deployable tools that address real-world software engineering challenges.
Sami Yli-Karjanmaa is a Docent at the Faculty of Theology , University of Helsinki , specializing in early Jewish and Christian literature. His research focuses on themes such as reincarnation, soteriology, and virtue ethics in Alexandrian theology, with a particular emphasis on figures like Philo of Alexandria and Clement of Alexandria. Key Projects : Supervised the Doctoral Programme in Theology and Religious Studies (2018–2020), led the project Clement of Alexandria's Position on Reincarnation (2016–2019), and contributed to collaborative research on sacred texts and traditions. Scientific Awards : Recipient of the University of Helsinki Teaching Technology Award (2003) and the Quality Award for E-Learning Development (2005). His recent publications explore reincarnation in Alexandrian thought, individualism in Philo’s philosophy, and salvific cycles in early Christian theology. Yli-Karjanmaa has also organized academic events, served as an expert member of The Studia Philonica Annual (since 2018), and contributed to media discussions on theology and reincarnation.
Tom A.E. Oomen is a Full Professor in the Department of Control Systems Technology at the Eindhoven University of Technology (TU/e). He holds affiliations with the Mechanical Engineering School and the EAISI High Tech Systems institute. His research focuses on data-driven control, motion control of mechatronic systems, and system identification, with applications in industries like automotive, medical, and energy systems. Academically, he earned his MSc and PhD from TU/e, and held visiting positions at KTH (Sweden) and the University of Newcastle (Australia). He has received notable grants (NWO Veni/Vidi) and awards, including IEEE and Mechatronics Paper Prize recognition. Oomen is a Senior Member of the IEEE and serves as an Associate Editor for IFAC Mechatronics and IEEE Control Systems Letters. His work bridges fundamental research and industry collaboration, emphasizing advanced motion control and learning algorithms. Projects include PROACTHIS (projection-based control) and ML4CONTROL (AI-driven motion control). He has supervised 99+ works and contributed to over 500 research outputs.
Richard E. Mayer is a Distinguished Professor of Psychology at the University of California, Santa Barbara, affiliated with the College of Letters & Science. His work bridges cognition, instruction, and technology to advance understanding of how people learn and how to design effective educational interventions. Research focuses on multimedia learning , computer-supported learning environments , and computer games for learning Current projects examine immersive virtual reality, generative learning strategies, and social cues in online instruction Recipient of top honors: Thorndike Award, Scribner Award, and APA’s Distinguished Contribution Award Principal Investigator on grants from the Office of Naval Research , Institute of Education Sciences , and National Science Foundation He leads the Mayer Lab for Research on Learning and Instruction, which emphasizes learning for transfer —ensuring learners apply knowledge in new contexts. His research has established 12 instructional design principles for online learning environments.
Gary Pielak is a Kenan Distinguished Professor of Chemistry, Biochemistry, and Biophysics at the University of North Carolina at Chapel Hill, with a joint appointment in the School of Medicine. His research focuses on high-resolution protein NMR studies in living cells and the biophysics of tardigrade desiccation-tolerance proteins, bridging structural biology and molecular biophysics. Education: BS in Chemistry from Bradley University (1977), PhD in Biochemistry from Washington State University (1983), Postdoc at the University of British Columbia (1983-1986), Postdoc at Oxford University (1986-1988). Research Interests center on understanding protein structure, stability, and function in physiologically relevant environments. Key areas include: In-Cell NMR: Quantifying protein behavior in living cells using advanced NMR techniques. Macromolecular Crowding: Studying synthetic polymers and proteins as crowding agents to mimic cellular environments. Tardigrade Biology: Exploring desiccation-tolerance mechanisms in intrinsically disordered proteins from water bears. Recent Publications highlight interdisciplinary trends, combining AI-driven stability prediction, solid-state NMR for dry protein analysis, and molecular glass/gel applications for preservation. His Scientific Awards include: NIH Pioneer Award DuPont and Morrow Young Faculty Awards Multiple UNC Mentorship Awards Mentorship is a cornerstone, with a focus on training graduate students and advancing NMR methodologies. His group employs Research Methods : 19F, 1H, 15N, and 13C NMR Circular Dichroism and Calorimetry Protein Expression in E. coli
Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science at Temple University’s Fox School of Business. He serves as the Founding Director of the Center for Business Analytics and Disruptive Technologies and has a secondary appointment in Information Systems. His roles include Ph.D. Program Concentration Director for Operations and Supply Chain Management. With over 240 publications, he ranks #1 globally in Information Systems Research publishing and holds a robotics patent. His research focuses on AI, blockchain, healthcare analytics, and cybersecurity, with notable contributions to digital transformation and operations management. Awards include the INFORMS ISS Distinguished Fellow and POMS Fellow. He advises major journals like Production and Operations Management and organizes conferences like POMS 2018. Media mentions span NYT, WSJ, and CBS. Education: University of Washington faculty Texas A&M University faculty Research Interests: Dr. Kumar explores AI applications, blockchain in fintech, healthcare system optimization, and supply chain analytics. He investigates cybersecurity’s operational impacts and data mining strategies. His work bridges theory and practice, addressing challenges in digital transformation and platform economies. Publications Trends: Recent work emphasizes digital platforms (e.g., blockchain adoption, telemedicine impact), organizational behavior (real-time feedback systems), and healthcare operations (personalized treatment algorithms). He frequently collaborates internationally, with studies appearing in Management Science, Information Systems Research, and Decision Sciences. Awards & Recognition: Changjiang Scholars Chair (China) ISB Visiting Professorship POMS Executive Editor Grants & Labs: Secured NIH grants for health analytics projects. Leads Temple’s analytics center fostering industry partnerships. Active in editorial roles (e.g., Production and Operations Management Journal Deputy Editor). Teaching: Offers courses in data science, optimization methods, and innovation entrepreneurship at graduate/undergraduate levels.
Christopher Kanan is a tenured Associate Professor of Computer Science at the University of Rochester, leading the AI Initiative within the Hajim School of Engineering & Applied Sciences. He holds secondary appointments in Brain and Cognitive Sciences, the Goergen Institute for Data Science and AI (GIDS-AI), and the Center for Visual Science. His research focuses on deep learning systems for artificial general intelligence (AGI), including continual learning, medical computer vision, and visual question answering. Previously, he was an Associate Professor at RIT’s Carlson Center for Imaging Science and a leader at Paige.AI, contributing to the FDA-cleared Paige Prostate system. Kanan earned his PhD from UC San Diego, completed postdoctoral work at Caltech, and worked at NASA JPL. Education: PhD in Computer Science, UC San Diego MS in Computer Science, University of Southern California Bachelor’s in Philosophy and Computer Science, Oklahoma State University Research Interests: Kanan’s work spans foundational AI capabilities like continual learning, medical imaging (pathology and radiology), multi-modal reasoning, and cognitive science-inspired models. His lab develops bias-robust AI systems and applies deep learning to healthcare and fusion research. Articles Trends: His recent work emphasizes out-of-distribution generalization, foundation models in pathology, and stability in continual learning. Key themes include AI applications in healthcare, model robustness, and neuroscience-inspired algorithms. Awards: NSF CAREER Award Senior Member, AAAI and IEEE DoE and NSF grants totaling $5M+ DARPA/ARL awards Advising & Grants: Mentored over 10 PhD students, including Robik Shrestha and Usman Mahmood. Secured grants for AI in nuclear fusion and medical imaging. Led RIT’s Center for Human-aware AI (CHAI) as Associate Director. Labs & Teams: Heads the University of Rochester AI Initiative, collaborates with Paige.AI, and leads teams advancing AI in pathology and robotics. His lab’s KLab (klab.cis.rit.edu) focuses on vision and learning systems.
Dr. Rebecca Pratt is a tenured Professor in the Department of Foundational Medical Studies at Oakland University William Beaumont School of Medicine (OUWB), where she has been a faculty member since January 2018. She previously held professorial roles at Michigan State University College of Osteopathic Medicine (MSUCOM), where she taught anatomy, embryology, neuroanatomy, physiology, and histology, and served as Associate Professor and Director of Histology at the West Virginia School of Osteopathic Medicine. She also held appointments at Grand Valley State University and completed postdoctoral training at Purdue University. Ph.D., Cell Biology and Oncology, Purdue University B.S., Zoology and Botany/Plant Pathology, Michigan State University Dr. Pratt's research focuses on the fascial system and its role in whole-body health, including fascial continuity, muscle attachment, somatic pain transmission, and biochemical communication. She integrates radiology into anatomy education and advocates for evidence-based medical curricula. Her work bridges clinical anatomy, histology, embryology, and physiology with modern educational practices. Her recent publications reflect a strong emphasis on fascial anatomy, medical education innovation, and the integration of imaging in teaching. Themes include plastination, generational learning trends, and fascia’s role in women's health and athletic performance, published in journals like Clinical Anatomy and Anatomical Sciences Education , as well as in Women and Men’s Health and NIKE magazines. Scientific awards and honors include: Basmajian Award (American Association of Anatomy) Keith and Marion Moore Award (AAA) Five consecutive Golden Apple Teaching Awards at MSUCOM Golden Apple Award at OUWB Dr. Pratt has served in major leadership roles, including President of the International Fascia Research Society, Board Member of the American Association of Anatomy (AAA) and the American Association of Clinical Anatomy (AACA), and Chair of multiple AAA committees. She is a Visiting Anatomy Professor at Weill Cornell and St. George’s University School of Medicine, and faculty advisor for the Docapellas at OUWB. She has been an invited speaker internationally and contributed to high-impact projects like the BodyWorlds Fascial Net Plastination Project. She actively serves on OUWB’s Admissions and Student Promotion and Retention Committees. Dr. Pratt leads and organizes major international initiatives, including the Women's Clinical Health Summit in Rio de Janeiro (2024) and the Fascia Research Congress in New Orleans (2025), fostering global collaboration in fascial science. Her lab and research team focus on fascial anatomy and medical education, working closely with institutions in Italy (University of Padova) and Germany (BodyWorlds project).
Vikramaditya G. Yadav is an Associate Professor at the University of British Columbia (UBC) in the Department of Chemical and Biological Engineering, Faculty of Applied Science. He directs the Master of Engineering Leadership (MEL) Program in Sustainable Process Engineering and leads the BioFoundry research group. Education: B.A.Sc., University of Waterloo (2007) Ph.D., Massachusetts Institute of Technology (2013) Postdoctoral Associate, Harvard University (2014) His research spans sustainable chemical manufacturing, metabolic engineering, and biotechnology. Key areas include: Designing biosynthetic enzymes for biomass valorization Developing bioremediation strategies for industrial water quality Creating innovative drug delivery systems and tissue engineering solutions Advancing synthetic biology for pharmaceutical and bioenergy applications His recent work focuses on ocular drug delivery, cannabinoid biosynthesis in E. coli, lignin-based nanoparticles for cancer therapy, and computational analysis of plant secondary metabolites. Collaborations with start-ups, industry, and medical labs drive innovation in Canada's bioeconomy. Professional Leadership: Chair, Biotechnology Division of the Chemical Institute of Canada Associate Editor, The Canadian Journal of Chemical Engineering He is affiliated with UBC's BioProducts Institute and contributes to project-based learning pedagogy.
Johan Gustav Bellika is a Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway, based in Tromsø. His work bridges clinical medicine with health informatics, focusing on practical applications that improve healthcare delivery and patient outcomes. Current Position: Professor, Department of Clinical Medicine Institution: UiT The Arctic University of Norway Location: Tromsø, Norway Contact: johan.gustav.bellika@uit.no Professor Bellika's research spans several critical areas in modern healthcare. His primary focus is on health informatics with particular emphasis on medical data privacy, electronic health records, and the application of artificial intelligence in clinical settings. He has made significant contributions to understanding chronic pain management through technology, primary care research networks, and patient-centered care models. His work often involves large population studies such as the Tromsø Study, examining how digital health tools impact healthcare utilization and patient outcomes. His research demonstrates a consistent thread connecting technological innovation with practical clinical applications. Bellika has been instrumental in developing privacy-preserving architectures for healthcare data analysis, which enable researchers to gain insights from sensitive health information without compromising patient privacy. His work on federated learning frameworks represents cutting-edge approaches to analyzing medical data while maintaining strict privacy controls. Health Informatics and Medical Data Privacy Chronic Pain Management through Technology Primary Care Research Networks (PraksisNett) Electronic Health Records and Clinical Decision Support Patient-Centered Digital Health Solutions Federated Learning Applications in Healthcare Professor Bellika's publication record shows a strong trend toward interdisciplinary research that combines clinical medicine, computer science, and public health. His recent work increasingly focuses on the intersection of artificial intelligence and healthcare, particularly how machine learning can be applied to chronic conditions while maintaining rigorous privacy standards. The geographical scope of his research extends across Norway with particular emphasis on Northern Norwegian populations, providing valuable insights into healthcare delivery in Arctic and remote regions. His collaborative approach is evident through numerous co-authored publications with researchers across multiple institutions and disciplines. While specific awards aren't detailed in the available information, his extensive publication record in reputable journals suggests recognition within his field. Professor Bellika has been actively involved in several major research initiatives including the Tromsø Study and PraksisNett, Norway's nationwide practice-based research network. His work on privacy-preserving architectures for healthcare data has significant implications for how medical research can be conducted while respecting patient confidentiality. He appears to be particularly focused on translating research findings into practical tools for clinicians, as evidenced by his work on audit and feedback systems for antibiotic prescribing.
Daniel E. Ho holds multiple prestigious positions at Stanford University: William Benjamin Scott and Luna M. Scott Professor of Law Professor of Political Science Professor of Computer Science (by courtesy) Senior Fellow, Stanford Institute for Economic Policy Research Senior Fellow, Stanford Institute for Human-Centered Artificial Intelligence Faculty Fellow, Center for Advanced Study in the Behavioral Sciences He serves on the National Artificial Intelligence Advisory Commission (NAIAC), as Senior Advisor on Responsible AI at the U.S. Department of Labor, and as a Public Member of the Administrative Conference of the United States (ACUS). Ho earned his J.D. from Yale Law School and Ph.D. from Harvard University, completing a clerkship with Judge Stephen F. Williams on the U.S. Court of Appeals for the District of Columbia Circuit. His research bridges artificial intelligence, law, and public policy with emphasis on: Regulatory governance frameworks for AI systems Fairness and bias mitigation in algorithmic decision-making Environmental enforcement using satellite imagery and computer vision Methods for estimating racial disparities without direct demographic data Legal AI reliability and statutory research systems Analysis of his 2025 publications reveals a consistent focus on practical AI governance tools addressing real-world regulatory challenges. Key themes include developing benchmarks for legal applications, mitigating hallucination in legal AI tools, and creating systems for statutory research. His work demonstrates strong integration of technical AI methods with policy implementation, particularly in environmental enforcement and fairness assessment. As Director of the Regulation, Evaluation, and Governance Lab (RegLab), Ho leads interdisciplinary research partnerships with government agencies. While specific grant details aren't provided, RegLab's operational model indicates substantial research funding for policy-relevant AI projects. No student advisees are mentioned in available materials. Ho's leadership extends to national advisory roles where he shapes federal AI policy through evidence-based recommendations, particularly regarding environmental protection and civil rights enforcement mechanisms.
Dr. Tarek Sayed is a Professor at the University of British Columbia's Department of Civil Engineering within the Faculty of Applied Science. He specializes in Transportation Engineering, focusing on road safety analysis, traffic operations, and Intelligent Transportation Systems (ITS). He serves as the Director of the Bureau of Intelligent Transportation Systems and Freight Security (BITSAFS-Engineering) and Editor of the Canadian Journal of Civil Engineering . His research addresses three core areas: improving road safety evaluation techniques, enhancing safety through traffic operations and highway design analysis, and advancing ITS technologies. Notable contributions include frameworks for safety-audits of infrastructure projects like the Sea to Sky Highway and methodologies for evaluating transit signal priority systems in Vancouver. He has authored/co-authored over 250 publications and supervised 60 graduate students. Key Roles: Professor, BITSAFS Director, Journal Editor Awards: Tier 1 Canada Research Chair, Wilbur Smith Award, Sandford Fleming Award, Prince Michael International Road Safety Award Consulting: Global projects in traffic safety and ITS for agencies like ICBC, FHWA, and Ashghal His research integrates Bayesian statistical methods, extreme value theory, and machine learning to model traffic conflicts, pedestrian behavior, and safety interventions. Current projects include real-time safety optimization using autonomous vehicle data and analyzing shared space interactions between cyclists and pedestrians. Dr. Sayed chairs national/international committees, including the U.S. Transportation Research Board’s safety data committee. His work bridges academia and practice, influencing policy and infrastructure investment decisions worldwide.
Adriana Schulz is an Assistant Professor in the Department of Computer Science & Engineering at the University of Washington's College of Engineering. She leads a research group focused on computational design, computer-aided design (CAD), and digital fabrication. Her work bridges computer science with practical applications in manufacturing, robotics, and sustainable design. Dr. Schulz received her Ph.D. in Computer Science from MIT in 2018 under the supervision of Professor Wojciech Matusik. Prior to her doctoral studies, she earned a Master's degree in Mathematics from IMPA (Instituto Nacional de Matemática Pura e Aplicada) in Rio de Janeiro, where she worked with Professor Luiz Velho, and a Bachelor's degree in Electronics Engineering from UFRJ (Federal University of Rio de Janeiro). Her research interests center around computational tools that enhance design and manufacturing processes. She develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches. Her work spans multiple domains including robotics, textiles, electronics, and architecture, with a strong emphasis on creating practical tools that designers and engineers can use in real-world applications. She explores how machine learning, particularly neurosymbolic approaches, can improve design workflows and enable new capabilities in computational design systems. Analysis of her recent publications reveals a strong trend toward more intelligent and user-centered design tools. Her research increasingly integrates machine learning with traditional CAD systems to create more intuitive interfaces, supports sustainable design practices with computational tools, and develops novel fabrication techniques that push the boundaries of what's possible with digital manufacturing. She has made significant contributions to zero-waste fashion design, immersion cooling for high-performance computing, and CAD program understanding through novel representation learning techniques. Innovators Under 35 - MIT Technology Review Bolsa Aluno Nota 10 from FAPERJ Engineer 20000 award Dr. Schulz actively mentors several PhD students and postdoctoral researchers, including Haisen Zhao, Ben Jones, Yuxuan Mei, and others, often in collaboration with colleagues across different departments. Her research has attracted significant media attention, with coverage in major outlets including MIT News, BBC, IEEE Spectrum, Wired, and TechCrunch. Her work on Interactive Robogami was noted as the most read article in the International Journal of Robotics Research in its publication year. She leads a vibrant research group at the University of Washington that focuses on computational design systems, with particular emphasis on creating tools that bridge the gap between digital design and physical fabrication. Her team develops novel algorithms for CAD systems, computational fabrication techniques, and sustainable design approaches that have practical applications across multiple industries.
Ming-Syan Chen is a distinguished academic holding dual roles as a Distinguished Research Fellow and Director of the Research Center for Information Technology Innovation (CITI) at Academia Sinica, Taiwan, and a Distinguished Professor jointly appointed across multiple departments at National Taiwan University (NTU), including Electrical Engineering (EE), Computer Science and Information Engineering (CSIE), and the Graduate Institute of Communication Engineering (GICE). His career spans academia and industry, with prior roles as a research staff member at IBM Watson Research Center and leadership positions in Taiwan's technology sector. Education: He earned a B.S. in Electrical Engineering from National Taiwan University, followed by M.S. and Ph.D. degrees in Computer, Information, and Control Engineering from the University of Michigan, Ann Arbor. Research Interests: Chen's work focuses on databases, data mining, machine learning, multimedia networking, and cloud computing. He has authored over 350 papers and holds numerous patents, contributing to foundational advancements in query processing, data management, and networked systems. Award Highlights: Recipient of ACM and IEEE Fellowships, National Chair Professorship (lifetime honor), Teco Award, Pan Wen Yuan Distinguished Research Award, and IBM's Outstanding Innovation Award. His contributions span research, teaching, and technology commercialization. Leadership & Service: Former Dean of NTU's College of Electrical Engineering and Computer Science, CEO of Taiwan's Networked Communication Program, and Editor-in-Chief of the International Journal of Electrical Engineering. He has chaired international conferences and served on editorial boards of journals like IEEE TKDE and VLDB. Labs & Teams: Leads the Network Database Laboratory and collaborates on national initiatives in information and communication technologies. His research groups focus on data science, distributed systems, and social network analysis.
