Find Academic Supervisors

• Household Finance
• Banking
• Behavioral Economics
• +4 more

Claire Célérier is an Associate Professor of Finance at the Rotman School of Management, University of Toronto, where she holds the Canada Research Chair in Household Finance. Her academic career spans prestigious institutions including the University of Toronto (where she advanced from Assistant to Associate Professor between 2016-2022) and the University of Zurich (2014-2016). She maintains an affiliation with the People's History Lab and actively contributes to academic discourse through conference organization including 'Race, Equity and Public Policy' (2023) and 'Banks, States and the People' (2024). Dr. Célérier's research explores how finance can benefit households through multiple lenses including behavioral economics, asset pricing, and financial history. Her work investigates innovation in finance, diversity and inclusion impacts, and historical perspectives on financial systems. She has developed significant expertise in household finance, banking, and the intersection of finance with social issues, particularly racial disparities in financial markets as demonstrated in her award-winning research on the Freedman's Savings Bank. Her scholarly output shows a clear trajectory from foundational work on security design and investor behavior toward increasingly socially relevant research on financial inclusion, racial equity, and household financial decision-making. Recent publications demonstrate her growing focus on historical contexts of financial systems and their contemporary implications, particularly regarding marginalized communities. Canada Research Chair in Household Finance (2024) Bank of Canada Governor's Award (2022) University of Toronto Top 5% Merit Award (2021) Petro Canada Young Innovators Award Program (2021) Multiple Rotman School of Management Teaching Awards (2019-2021) Ieke van den Burg Prize for Research on Systemic Risk (2015) Dr. Célérier has successfully secured substantial research funding including Insight Development Grants (CAD 54,500), Connaught Research Grant (CAD 10,000), and the Marianne and Marcus Wallenberg Foundation Grant (EUR 400,000). She mentors several PhD students including Andrada Bilan (2020), Alexey Vasilenko (2023), and Purnoor Tak (Expected 2026), and has supervised numerous master's theses at both University of Toronto and University of Zurich. Her service contributions include Rotman School of Management MBA program committee, executive committee, seminar coordination, and faculty recruiting committee.

Steven I-Jy ChienView profile

Professor

• Transportation Engineering
• Urban Rail Transit
• Sustainable Transportation Systems
• +4 more

Steven I-Jy Chien serves as Professor and Director of the Transportation Program within the Department of Civil and Environmental Engineering at the New Jersey Institute of Technology (NJIT). His career spans academia, government, and industry with significant contributions to transportation engineering research and practice. His educational credentials include: Ph.D. in Civil and Environmental Engineering from University of Maryland-College Park (1995) M.S. in Civil and Environmental Engineering from University of Maryland-College Park (1991) B.S. in Civil Engineering from Tamkang University (1983) Professor Chien's research focuses on transportation system resilience, sustainable transit operations, and advanced traffic modeling. His work addresses critical challenges in urban rail networks, demand-responsive transit services, and energy-efficient transportation systems. He employs sophisticated computational methods including genetic algorithms, machine learning, and Bayesian approaches to solve complex transportation problems involving network vulnerability, passenger behavior, and infrastructure optimization under uncertainty. Analysis of his recent publications reveals strong emphasis on transportation resilience against cascading failures, sustainable operations for electric transit systems, and pandemic-responsive aviation scheduling. His research spans diverse geographic contexts including major Chinese metropolitan areas and U.S. transportation networks, demonstrating global applicability of his methodologies. His professional recognition includes: 2024 Keynote Speaker at Union-Tech Lecture, National Taiwan Ocean University 2022 Keynote Speaker at International Conference of Chinese Institute of Transportation 2022 Best Poster Award from New Jersey Department of Transportation Professor Chien has secured research funding from federal agencies including the Federal Highway Administration (FHWA) where he contributed to CORSIM traffic simulation software development. His industry experience with China Engineering Consultants Inc. and Information Dynamic Inc. informs his practical approach to transportation challenges. As Transportation Program Director, he oversees curriculum development and industry partnerships that prepare students for transportation sector careers. He maintains active collaboration with transportation agencies globally, particularly through his leadership in the Transportation Program at NJIT which serves as a hub for innovation in urban mobility solutions.

• Market Design
• Mechanism Design
• Game Theory
• +4 more

Marek Pycia serves as Professor of Organizational Economics at the University of Zurich and Co-Director of the Zurich Center for Market Design. His research has driven tangible policy impacts including the U.S. kidney-transplant voucher program and school system redesigns, with publications in premier journals like American Economic Review and Econometrica . His academic foundation includes a Ph.D. in Economics from the Massachusetts Institute of Technology (MIT), complemented by prior faculty roles at UCLA, Pennsylvania State University, and Leon Koźmiński Academy of Entrepreneurship and Management, plus a visiting fellowship at Princeton University. Professor Pycia specializes in Market Design and Mechanism Design , where he bridges theoretical microeconomics with real-world applications in healthcare allocation and educational systems. His work emphasizes strategyproofness , simplicity , and efficiency in matching markets, influencing both academic frameworks and practical policy implementations globally. Recent publications (2024-2025) reveal a concentrated focus on simplifying mechanism design for discrete environments, advancing double auction theory, and optimizing information structures in trade. These works consistently apply game-theoretic principles to solve complex allocation problems across healthcare, education, and financial markets. Key recognition includes: Research Fellow at the Centre for Economic Policy Research (CEPR) Editorial board membership at Theoretical Economics , Journal of Economic Theory , and Games and Economic Behavior While specific grant details remain unmentioned, his editorial leadership and center directorship indicate active research funding. He supervises graduate work through the University of Zurich’s economics programs, though no named students appear in the source text. The Zurich Center for Market Design, which he co-directs, functions as an interdisciplinary hub connecting theoretical research with practitioners in healthcare, education, and public policy to implement novel market mechanisms.

Dmitri StrukovView profile

Professor

• Solid-State Nanoionics
• Emerging Electronics Devices and Circuits
• Non-Volatile Memories
• +5 more

Dmitri Strukov is a Professor at the University of California, Santa Barbara in the Department of Electrical and Computer Engineering. His work spans material science, electrical engineering, and computer science, focusing on novel computing paradigms using emerging memory devices. Education: PhD in Electrical and Computer Engineering from SUNY Stony Brook, MS in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests include neuromorphic computing , non-volatile memory applications , and mixed-signal circuits for machine learning and hardware security. His group develops memristive crossbar arrays and 3D NAND flash for energy-efficient systems. Scientific Leadership features Fellow of IEEE and Distinguished Lecturer roles. His work has been recognized with best paper awards at ASPLOS’19 and Computing Frontiers’13. Students: Mentored PhD graduates in neurocomputing, security, and memristor design including Z. Fahimi, S. Larimian, M.R. Mahmoodi, and X. Guo. Grants: Funded by AFOSR, ARO, DARPA, NSF, and industry leaders like Google and Samsung. Labs: Utilizes UCSB’s nanofabrication center and advanced tools for memristor characterization.

• Theoretical Computer Science
• Graph Algorithms
• Dynamic Algorithms
• +7 more

Nicole Wein is an Assistant Professor in the Computer Science and Engineering Division of the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan, College of Engineering. Her research lies in theoretical computer science, focusing on graph algorithms, dynamic algorithms, parameterized algorithms, distributed algorithms, online algorithms, and fine-grained complexity. She is part of the Theory of Computation Lab and advises both PhD and undergraduate researchers. PhD, Massachusetts Institute of Technology (MIT), advised by Virginia Vassilevska Williams Postdoctoral Fellow, DIMACS Research Fellow, Simons Institute, UC Berkeley MS, Stanford University BS, Computer Science/Math, Harvey Mudd College Her research explores fundamental algorithmic questions in combinatorial settings, particularly how algorithms handle dynamic data, extract information efficiently (e.g., in linear time), and understand shortest path structures in graphs—especially directed ones. She investigates problems in distance estimation, spanners, hopsets, dynamic graph algorithms, and hardness of approximation. Her work combines theoretical depth with practical implications for algorithm design. The recent publications reflect a strong trend in fine-grained complexity and graph algorithm design, with a focus on proving tight bounds, developing efficient approximations, and understanding structural limitations in directed and dynamic graphs. Her work frequently appears in top venues such as STOC, FOCS, SODA, and ICALP, often in collaboration with leading researchers in the field. Scientific Awards and Recognition: Invited to special issue of SIAM Journal on Computing (SICOMP) (FOCS 2022 paper) Invited to Highlights of Algorithms (HALG) (FOCS 2022 paper) Invited to minisymposium at CANADAM (ESA 2022 paper) Work featured in Quanta Magazine Nicole Wein actively mentors students, including current PhD student Jubayer Nirjhor and former undergraduate researchers like Sam Hiken (now pre-doc at MIT). She has served on program committees for major conferences including SODA, FOCS, ICALP, and ITCS, and co-organized the DIMACS workshop on Modern Techniques in Graph Algorithms (2023). She also contributes to the academic community through outreach, such as her article offering reassurance to early-stage PhD students in theoretical computer science. She leads and participates in collaborative research groups and workshops, emphasizing supercollaboration and interdisciplinary communication in algorithms. Her lab fosters a strong research environment in theoretical computer science at the University of Michigan.

Leslie Ann GoldbergView profile

Professor

• Algorithms
• Complexity Theory
• Randomised Algorithms
• +5 more

Leslie Ann Goldberg is a Senior Research Fellow at St Edmund Hall and Professor of Computer Science at the University of Oxford. She currently serves as Head of the Department of Computer Science (on sabbatical 2025-26) and focuses on foundational problems in Algorithms and Complexity Theory , particularly randomised algorithms for network communication, machine learning, and statistical physics models. Her research includes solving Aldous' 1987 conjecture on backoff protocol instability (with John Lapinskas), developing rigorous mathematical analysis frameworks for algorithmic efficiency, and advancing approximate counting techniques via Markov Chain Monte Carlo methods (with Andreas Galanis and collaborators). Key projects involve graph homomorphisms , Moran process dynamics , and #BIS complexity class analysis. Recent publications (2023-2024) span topics like Sybil defense mechanisms, low-temperature sampling on random graphs, and parameterised subgraph counting modulo 2. Her work demonstrates cross-disciplinary impact in computational biology, statistical physics, and database theory. Scientific Awards include Best Paper Prizes at ICALP 2016, ICALP 2010, and IPEC 2017. She supervises PhD student Paulina Smolarova and collaborates extensively with researchers in Oxford and beyond.

• Wireless Communications
• Coding Theory
• Quantum Information Processing
• +5 more

Olav Tirkkonen serves as a Full Professor in the Department of Communications and Networking at Aalto University, Finland, a position he has held since August 2006. He leads the Communication Theory research group, driving innovation in wireless communication systems. His academic journey includes a distinguished career spanning industry and academia, with significant contributions to 3G, 4G, and 5G technologies. His educational qualifications are: Doctor of Science (Ph.D.) in Theoretical Physics, Helsinki University of Technology, 1994 Master of Science (M.Sc.) in Theoretical Physics, Helsinki University of Technology, 1990 Professor Tirkkonen's research interests are centered on wireless communications, with a focus on physical layer processing, coding theory, and quantum information processing. His group explores advanced topics including 5G and beyond wireless networks (spectrum management, large-scale MIMO, ultra-reliable low-latency communication), network-level interference coordination, collaborative caching, machine learning applications for wireless channel geography, coding on manifolds, and quantum communication systems. This research bridges fundamental theory with practical implementation in next-generation wireless networks. Analysis of his recent publications (2024-2025) indicates a predominant focus on machine learning techniques for wireless channel modeling (channel charting), pilot allocation in MIMO systems, and quantum error correction. His work is instrumental in addressing key challenges in 5G/6G networks, particularly in scenarios demanding ultra-reliability, low latency, and efficient resource utilization. His scientific contributions include: Co-inventor of approximately 80 families of patents and patent applications Co-author of the book "Multiantenna transceiver techniques for 3G and beyond" Throughout his career, Professor Tirkkonen has mentored numerous graduate students and secured substantial research funding from various sources. His industry experience at Nokia Research Center (1999-2010) and visiting position at Cornell University (2016-2017) have enriched his research perspective and fostered strong industry-academia collaborations. The Communication Theory group, under his leadership, maintains active collaborations with leading institutions and companies worldwide, positioning Aalto University at the forefront of wireless communications research.

Yujie TangView profile

Assistant Professor

• Internet of Vehicles
• Resource Management in Heterogeneous Networks
• Machine Learning
• +7 more

Dr. Yujie Tang is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada, where she has been serving since September 2022. Prior to this, she was an Assistant Professor at Algoma University (2019–2022) and a Post-Doctoral Fellow at the University of Waterloo (2017–2019). Her academic journey includes a PhD from the University of Waterloo and earlier degrees from Harbin Institute of Technology and Lanzhou Jiaotong University. PhD – University of Waterloo (2017) M.E. – Harbin Institute of Technology, Shenzhen, China B.E. – Lanzhou Jiaotong University, Lanzhou, China Her research focuses on intelligent networking and computing technologies for future IoT and 5G/6G systems. Key areas include Internet of Vehicles (IoV), AI-empowered edge computing, resource management in heterogeneous networks, software-defined networking, and UAV-assisted communications. She employs machine learning and optimization techniques to design energy-efficient and high-performance network protocols. The most recent publications reflect a strong trend in applying AI and machine learning to solve complex problems in vehicular networks, edge caching, and spectrum management. Her work spans top-tier IEEE journals such as IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE JSAC , with a clear emphasis on real-world deployable solutions for next-generation wireless systems. Faculty Research Startup Fund, Dalhousie University, 2022 NSERC Discovery Grant, 2021–2026 Algoma University Research Fund, 2021 Faculty Research Startup Fund, Algoma University, 2019 Best Speaker Award, University of Waterloo, 2017 Faculty of Engineering Award (4 times), University of Waterloo, 2013–2017 University of Waterloo Graduate Scholarship, 2013–2015 International Doctoral Student Award, 2012–2016 Graduate Research Studentship (twice), 2011–2012 Provost Doctoral Entrance Award for Women, 2011 Dr. Tang actively supervises graduate and undergraduate students and has secured competitive research grants, including the NSERC Discovery Grant. She serves on the technical program committees of major IEEE conferences such as INFOCOM, GLOBECOM, and ICC, and regularly reviews for top journals like IEEE JSAC , IEEE TWC , and IEEE TVT . She currently leads a research group focusing on B5G/6G networks, IoV, and edge computing, and she is actively recruiting new students and visiting scholars. Her research group operates within the Faculty of Computer Science at Dalhousie University, where she leads projects in intelligent resource management, AI-driven networking, and integration of space-air-ground networks. She is a member of IEEE, IEEE Communications Society, and IEEE Vehicular Technology Society.

Jiaoyang LiView profile

Assistant Professor

• Multi-Robot Coordination
• Artificial Intelligence
• Planning and Scheduling
• +5 more

Jiaoyang Li is an Assistant Professor at the Robotics Institute, Carnegie Mellon University , where she leads research in large-scale multi-robot coordination. She earned her Ph.D. in Computer Science from the University of Southern California (2022) under Professor Sven Koenig and holds a B.Eng. in Automation from Tsinghua University (2017) . Ph.D. (2022): University of Southern California B.Eng. (2017): Tsinghua University Research Focus : Jiaoyang Li's work centers on algorithms for multi-robot coordination in dynamic environments. Key areas include: Multi-Agent Path Finding (MAPF) Lifelong Planning for Autonomous Systems Kinodynamic Motion Planning Warehouse and Transportation Automation Human-Robot Collaboration Integration of Planning and Machine Learning Swarm Robotics Publication Trends : Recent work spans 2023-2025 , showing emphasis on: Scalable coordination for 10,000+ robots Bézier curve optimization for dual-arm assembly Diffusion models in multi-robot motion planning Guidance graph optimization for airport/railway traffic Multi-objective search in warehouse automation Temporal plan graphs for switchable passing orders Scientific Honors : ICAPS-24 Best Student Paper Award Three top dissertation awards (ICAPS-23, IFAAAMAS, USC) 2023 League of Robot Runners champion Outstanding student paper award at ICAPS-20 Technology Commercialization Award from USC Student Leadership : Advises 5 Ph.D. students and 1 Master's student at CMU, with alumni now at MIT (Ying Feng) and USC (Yimin Tang). Collaborates with visiting students from National University of Singapore and Tsinghua University. Lab Affiliation : Directs the Artificial Intelligence for Robot Coordination at Scale (ARCS) Lab , focusing on: Warehouse automation with thousands of robots Multi-arm LEGO assembly systems Railway coordination algorithms Cooperative assembly with multiple manipulators

Philip ThomasView profile

Associate Professor

• Artificial Intelligence
• Reinforcement Learning
• AI Safety
• +3 more

Philip Thomas is an Associate Professor and Doctoral Program Director at the Manning College of Information and Computer Sciences, University of Massachusetts Amherst. He leads the Autonomous Learning Lab (ALL) and co-founded the Reinforcement Learning Conference (RLC). His research focuses on reinforcement learning, AI safety, and algorithms that ensure safety guarantees for high-risk applications like healthcare and digital marketing. Education: PhD in Computer Science, University of Massachusetts Amherst (2015) MSc in Computer Science, Case Western Reserve University (2009) BSc in Computer Science, Case Western Reserve University (2008) Research Interests: Thomas specializes in designing biologically plausible reinforcement learning algorithms and ensuring safety through frameworks like Qualia Optimization and Seldonian Algorithms . His work emphasizes off-policy evaluation, fairness guarantees, and ethical AI. Recent projects include developing benchmarks for medical decision-making (e.g., ICU-Sepsis) and analyzing adversarial robustness in speech denoising models. Articles Trends: His recent work spans high-confidence policy evaluation, fairness metrics, and algorithmic safety. Key themes include improving benchmarking practices, rethinking eligibility traces, and leveraging state abstraction for consistent off-policy evaluation. Awards & Grants: Armstrong Award Co-PI on Army Research Grant (IoBT), NSF grant (FMitF) Significant funding from Adobe Research Advising & Grants: Thomas has overseen grants totaling millions and mentored students in reinforcement learning and AI safety. His current focus includes exploring qualia optimization for doctoral applications (2026-2027). Labs & Teams: He directs the Autonomous Learning Lab and collaborates on interdisciplinary projects at the Center for Data Science, emphasizing ethical AI and safe machine learning systems.

• Combinatorial Optimization
• Approximation Algorithms
• Stochastic Optimization
• +3 more

Viswanath Nagarajan is an Associate Professor of Industrial & Operations Engineering and Computer Science Engineering (courtesy) at the University of Michigan. His research focuses on combinatorial optimization, approximation algorithms, and stochastic models for routing, scheduling, and location problems. He previously served as an Assistant Professor at the University of Michigan (2014–2020) and a Research Staff Member at IBM T.J. Watson Research Center (2009–2014). He holds a Ph.D. in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University (2004–2009) and a B.Tech. in Computer Science from IIT Bombay (1999–2003). His research explores uncertainty management in optimization, including stochastic models and approximation algorithms for decision-making under uncertainty. He has contributed to adaptive algorithms, submodular optimization, and applications in logistics, network design, and scheduling. Education: Ph.D., Algorithms, Combinatorics, and Optimization (Carnegie Mellon University, 2009) B.Tech., Computer Science and Engineering (IIT Bombay, 2003) Prof. Nagarajan has organized major conferences like IPCO 2019 and served on editorial boards for journals including Operations Research , ACM Computing Surveys , and ACM Transactions on Algorithms . His service includes program committees for SODA, APPROX, and IPCO. He advises Ph.D. students focusing on optimization theory and applications, with advisees securing positions at Yahoo! Research, the University of Chicago, Ford Motor Company, and Georgia Tech.

Haagen KlausView profile

Professor

• Bioarchaeology
• Paleopathology
• Dental Anthropology
• +6 more

Haagen Klaus is a Professor in the Department of Anthropology at George Mason University, specializing in bioarchaeology, paleopathology, and forensic anthropology. Since 2013, he has directed research on the north coast of Peru, focusing on human skeletal biology, mortuary practices, and the impacts of colonialism and environmental stressors on prehistoric and historic societies. Ph.D., Department of Anthropology, The Ohio State University (2008) M.A., Department of Anthropology, Southern Illinois University (2003) B.A., Department of Anthropology, State University of New York at Plattsburgh (2000) Klaus's research explores biocultural transformations in complex societies, with emphasis on health, violence, identity, and ethnogenesis. His Lambayeque Valley Biohistory Project (founded 2003) investigates skeletal markers of disease, stress, and violence across 10,000 years of Andean history. Methodologies include isotopic chemistry, ancient DNA analysis, and dental phenetics. Recent work (2025) examines oral microbiomes during Spanish colonization, syndemic processes in colonial dynamics, and stress indicators in pre- and postcontact populations. Projects integrate osteoimmunology, ecoimmunology, and life history trade-offs to understand physiological and cultural resilience. Current graduate students under his supervision include Rae-Ocean Blake, Kimberly Hargrave, Megan Jankowski, Emily Schopmeyer, and Celeste Schultheiss. He leads international collaborations involving Peruvian, Japanese, Canadian, and American researchers through the Lambayeque Valley Biohistory Project.

Mehran SahamiView profile

Teaching Professor

• computer science education
• machine learning
• information retrieval
• +3 more

Mehran Sahami is the James and Ellenor Chesebrough Professor in the School of Engineering and Tencent Chair of the Computer Science Department at Stanford University. He holds the academic rank of Teaching Professor of Computer Science and is also a Senior Fellow by courtesy at the Freeman Spogli Institute for International Studies. As a Bass University Fellow in Undergraduate Education, he has made significant contributions to computer science education at Stanford. Dr. Sahami earned both his undergraduate and PhD degrees from Stanford University's Computer Science Department. After completing his PhD, he worked as a Senior Engineering Manager at Epiphany before joining Google as a Senior Research Scientist from 2002-2007, while also teaching as a Lecturer at Stanford. In 2007, he joined the Stanford faculty full-time, continuing to consult part-time at Google until 2010. Professor Sahami's primary research interests focus on computer science education, machine learning, and information retrieval on the Web. His work has significantly influenced how computer science is taught globally, particularly through his leadership in the ACM/IEEE-CS Joint Task Force on Computing Curricula 2013 (CS2013). He has pioneered approaches to teaching introductory programming and probability theory for computer scientists, with a particular emphasis on analyzing student performance trends as CS enrollments have grown dramatically. His recent publications demonstrate a strong shift toward educational research while maintaining connections to technical expertise in machine learning and data analysis. Bass University Fellow in Undergraduate Education Professor Sahami serves as the ACM Steering Committee Chair for the CS2013 effort to define international curricular guidelines for undergraduate computer science programs. He is also the founder and first Chair of the Symposium on Educational Advances in Artificial Intelligence (EAAI), an annual meeting for researchers and educators to discuss pedagogical issues in teaching AI. He has received significant grant funding through these initiatives and has been instrumental in shaping national and international computer science curriculum standards. At Stanford, he teaches CS106A: Programming Methodology and CS182: Ethics, Public Policy, and Technological Change, with his educational materials widely distributed through the Stanford Engineering Everywhere initiative. Professor Sahami maintains connections to the startup ecosystem through advisory board positions and has published a book on Text Mining with Ashok Srivastava. His career trajectory from industry researcher to academic educator gives him a unique perspective on practical applications of computer science education.

• Multimedia Signal Processing
• Computer Networks
• Communication Engineering
• +3 more

Jiro Katto is a Professor at Waseda University's School of Fundamental Science and Engineering, where he has been conducting research and teaching since 1999. He received his Ph.D. from the University of Tokyo and has established himself as a leading researcher in multimedia signal processing and computer networks. His academic journey includes positions as Associate Professor (1999-2004), Professor (2004-present), and Director at NEDO (2004-2008), along with research experience at NEC C&C Laboratories (1992-1999) and a Visiting Scholar position at Princeton University (1996-1997). Professor Katto's research interests focus on Multimedia Signal Processing and Computer Networks, with particular expertise in video compression, 5G network performance, and learned image compression techniques. His work bridges theoretical advancements with practical implementations, as evidenced by his extensive publications in top-tier conferences and journals. His research group has made significant contributions to point cloud compression, latency compensation in remote systems, and hardware-accelerated video encoding for UHD streaming. His publication record is impressive, with 276 papers cited 3,323 times in Scopus and 6,169 times in Google Scholar, reflecting his substantial impact in the field. His recent work shows a strong trend toward applying deep learning techniques to traditional signal processing problems, particularly in the areas of image and video compression, where his team has developed novel approaches to improve compression efficiency while reducing computational complexity. Electric Telecommunications Promotion Foundation Telecommunications System Technology Award (2023) Takayanagi Kenjiro Foundation Takayanagi Kenjiro Achievement Award (2020) Institute of Image Information and Television Engineers Fellow (2020) Institute of Electronics, Information and Communication Engineers Fellow (2015) IEICE Communications Society Activity Contribution Award (2006) IEICE Academic Encouragement Award (1995) SPIE VCIP 1991, Best Student Paper Award (1991) Professor Katto has served on numerous prestigious committees including IEEE ComSoC Tech News Editorial Board, IEEE Technical Program Committees for major conferences (Globecom, ICC, ICIP), and editorial boards for several academic journals. His leadership in the academic community extends to chairing conferences like IWAIT 2011 and serving as Editor-in-Chief for journals in his field. His research has practical applications in commercial 5G networks, video streaming services, and remote monitoring systems, demonstrating the real-world impact of his work.

Tariq IqbalView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +7 more

Tariq Iqbal is an Assistant Professor at the University of Virginia , with joint appointments in the Department of Systems and Information Engineering and Department of Computer Science . He leads the Collaborative Robotics Lab (CRL) , specializing in human-robot teams and embodied AI . Previously, he was a Postdoctoral Associate at MIT's CSAIL , advised by Prof. Julie Shah , and earned his Ph.D. in Computer Science from University of California San Diego (UCSD) under Prof. Laurel Riek . Ph.D. in Computer Science, University of California San Diego (2017) M.S. in Computer Science, University of Texas at El Paso (2012) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2007) His research lies at the intersection of artificial intelligence and robotics , focusing on human-robot collaboration in dynamic environments. Key areas include motion prediction , multimodal fusion , trust modeling , and collaborative learning . His work integrates cognitive science and deep learning to enhance robotic fluency in naturalistic settings. Recent publications (2023–2025) highlight advancements in human-robot team dynamics , multimodal dataset creation , and motion prediction algorithms . Notable works include Energy-Based Transformers for scalable AI, PoseTron for motion prediction, and Accessible Navigation Mapping for assistive robotics. These contributions span trust modeling , cloud robotic infrastructure , and safety in close-proximity collaboration . National Science Foundation (NSF) CAREER Award Air Force Office of Scientific Research (AFOSR) Young Investigator Program (YIP) Award Commonwealth Center for Advanced Manufacturing (CCAM) Innovation Award As faculty, he has secured grants from NSF and AFOSR , mentored research students, and taught courses like Stochastic Modeling I (SYS 6005) and Robots and Humans (SYS 4582/6465, ECE 4502/6465, CS 6465) . His prior industry roles at IBM Watson Lab and Grameenphone Ltd. inform his applied research in telecom infrastructure and cognitive robotics . He leads the Collaborative Robotics Lab (CRL) at UVA, which develops multimodal datasets , real-time coordination algorithms , and adaptive pathfinding systems . Current projects explore human motion prediction , team synchrony , and embodied question-answering , reflecting his commitment to advancing human-robot fluency and contextual AI .