T. Donna Chen is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Virginia. Her research focuses on sustainable transportation systems, transportation economics, travel demand modeling, and crash safety. Ph.D., Civil Engineering, University of Texas at Austin (2015) M.E., Civil Engineering, University of Texas at Arlington (2008) B.S., Civil Engineering, Texas A&M University (2005) Her work explores the impacts of new vehicle technologies on traveler behavior and the environment, with an emphasis on modeling shared autonomous electric vehicle operations, pricing schemes, and life-cycle emissions. She also investigates spatial patterns of electric vehicle adoption and roadway safety for cyclists. Recent publications highlight her contributions to transportation electrification, fleet management, and safety analysis. Key themes include sustainability , autonomous systems , economic modeling , and urban mobility . American Society of Civil Engineers ExCEEd Fellow (2017) NSF IGERT Fellow (2013-2015) FHWA Eisenhower Fellow (2012-2013) Dr. Chen teaches courses on transportation infrastructure design and economics. Her funded projects include research on green vehicle adoption, connected vehicle planning, and dynamic tolling frameworks. She is affiliated with the Transportation Research Board, ASCE TD&I, ITS America, ITE, and Women’s Transportation Seminar.
Karen C. Cheung is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, with cross-appointments in the Faculty of Medicine and the School of Biomedical Engineering where she serves as Director of the Graduate Program. She holds her office in KAIS 3064 and can be reached at (604) 827-4114. Dr. Cheung received her BSc and PhD degrees in Bioengineering from the University of California, Berkeley in 1998 and 2002, respectively. From 2002-2005, she was a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne in Switzerland. She joined UBC in 2006 and has established herself as a leading researcher in biomedical microsystems. Her research spans multiple areas of biomedical engineering with particular focus on lab-on-a-chip systems for cell culture and characterization , inkjet printing for tissue engineering , and implantable neural interfaces . Her work integrates microfluidics, biosensors, and tissue engineering to create platforms that better mimic in vivo conditions for drug screening and disease modeling. Current projects include developing organ-on-a-chip models of the human airway for studying aerosol exposure effects, creating microscale tumor models for cancer research, and advancing silicon photonic biosensors for medical diagnostics. Dr. Cheung leads the Bio-Medical Micro Devices Laboratory at UBC, which houses multiple research teams working on cutting-edge biomedical technologies. Her lab has developed microfluidic platforms capable of precisely controlling oxygen levels around tumor spheroids to study cancer treatment responses under realistic physiological conditions. The lab also works on novel fabrication techniques for microelectrode arrays and tissue clearing protocols for 3D imaging of microtissues. As an educator, Dr. Cheung teaches several specialized courses including ELEC 361 (Molecules to Mechanisms), ELEC 464 (Nanotechnology and Nature), EECE 301 (Topics in Nanotechnology and Microsystems), ELEC 473 (Biological Micro-Electro-Mechanical Systems), and ELEC 521 (Biomedical Microdevices). She has supervised numerous graduate students through their MASc, PhD, and postdoctoral work, with many alumni now holding academic positions or working in the biomedical industry. Her research is supported through multiple funding sources and collaborations with industry partners. Dr. Cheung is affiliated with several research centers including the Airway Centre, Bionics Network, Centre for Blood Research, and the Institute for Computing, Information and Cognitive Systems (ICICS) at UBC.
Prof. Sanjam Garg is an Associate Professor at the University of California, Berkeley in the Computer Science Division under the College of Engineering. His work focuses on cryptography and its applications to security and privacy , with affiliations to the Berkeley Center for Responsible, Decentralized Intelligence (RDI) and the Simons Institute for the Theory of Computing (SITC). Education: Ph.D., Computer Science, University of California, Los Angeles (2013) B.Tech, Computer Science and Engineering, Indian Institute of Technology, Delhi (2008) His research spans cryptography , secure computation , and zero-knowledge proofs , with recent publications on threshold encryption , zkSNARKs , and private set intersection . Trends in his work emphasize blockchain security , obfuscation , and secure multiparty computation . Scientific Honors: ACM Doctoral Dissertation Award (2013) Sloan Research Fellowship (2020) Okawa Research Grant (2016) IACR EUROCRYPT Best Paper Award (2013) FOCS Test of Time Award (2023) Bakar Fellows Spark Award (2023) Prof. Garg has advised researchers like James Bartusek (2024), Akshayaram Srinivasan (2020), and Peihan Miao (2019), who are now at institutions such as Microsoft Research and University of Toronto. He teaches CS 170 on algorithms at Berkeley.
Joydeep Biswas is an Associate Professor in the Computer Science Department at the University of Texas at Austin, where he serves as the Director of the Autonomous Mobile Robotics Laboratory (AMRL). He is also affiliated with Texas Robotics, the UT Machine Learning Laboratory, and UT Good Systems. Previously, he was an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. Dr. Biswas earned his PhD in Robotics from Carnegie Mellon University in 2014 and his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008. His educational background has provided him with a strong foundation in both theoretical and applied aspects of robotics and artificial intelligence. Dr. Biswas's research focuses on enabling long-term autonomy for mobile robots operating in human environments. His work spans robot perception, motion planning, control systems, and AI, with the ultimate goal of creating self-sufficient autonomous mobile robots that can perform tasks accurately and robustly in real-world settings. He is particularly interested in perception, planning, and failure recovery for autonomous mobile robots, which supports his vision of having autonomous service mobile robots deployed at campus-to-city scale, both indoors and outdoors, performing assistive tasks over deployments spanning years. His IJCAI 2019 Early Career Spotlight talk summarizes much of his research to date and ongoing interests. His recent research has shown a strong trend toward social navigation, human-robot interaction, and the application of machine learning techniques to robotics problems. There's a clear progression from fundamental robotics research toward more complex, real-world applications that require robots to understand and navigate human social spaces effectively. His work increasingly integrates large language models and other advanced AI techniques with traditional robotics approaches, as evidenced by his recent publications on topics like preference-conditioned navigation, social navigation benchmarks, and instruction-following navigation systems. Dr. Biswas has received numerous prestigious awards including the NSF CAREER Award (2021), J.P. Morgan Faculty Research Award (2019), Amazon Research Award (2019), and a grant from Northrop Grumman Mission Systems (2018). These awards recognize his innovative contributions to the field of robotics and autonomous systems. As a dedicated educator and mentor, Dr. Biswas actively supervises PhD and master's students, with his PhD student Sadegh Rabiee winning the student poster award at the Northrop Grumman University Symposium 2019. He has secured significant grant funding from the National Science Foundation for projects including 'Introspective Perception and Planning for Long-Term Autonomy' and 'Interactive Synthesis and Repair For Robot Programs,' demonstrating his ability to secure competitive research funding and his commitment to advancing the field. Dr. Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL), which serves as a hub for interdisciplinary research in mobile robotics. The lab has developed notable resources such as the UT Campus Object Dataset (CODA) for 3D perception research and SOCIALGYM, a framework for benchmarking social robot navigation. His team regularly deploys robots on the UT Austin campus and in urban environments to test and refine their approaches in realistic settings, bridging the gap between simulation and real-world application.
Kevin M. Lynch is a Professor of Mechanical Engineering at Northwestern University's McCormick School of Engineering, where he also serves as Director of the Center for Robotics and Biosystems. His research spans multiple domains of robotics, with particular expertise in dynamics, motion planning, and feedback control of mechanical systems. Dr. Lynch received his Ph.D. in Robotics from Carnegie Mellon University in 1996, with a thesis on "Nonprehensile Robotic Manipulation: Controllability and Planning" under advisor Prof. Matthew T. Mason. He earned his B.S.E. with honors in Electrical Engineering from Princeton University in 1989. His research interests focus on robotics, particularly dynamics, motion planning, and feedback control of mechanical systems. He investigates mechanics, planning, and control of robotic manipulation (juggling, throwing, pushing, rolling, vibration, etc.) and locomotion. His work also explores self-organizing systems, particularly decentralized control of mobile sensor networks and swarm robotics, underactuated dynamic systems, and physical human-robot interaction with industrial applications. Recent research has expanded into bio-inspired active electrosense, underwater robotics, control and optimization for robot swarms, swarm shape control, and functional electric stimulation. Dr. Lynch's recent publications demonstrate a strong trend toward rehabilitation robotics and human-robot interaction, particularly in lower-limb exoskeletons for gait training and rehabilitation. His work bridges fundamental robotics research with practical applications in healthcare, showing increasing integration of swarm robotics principles with human-centered design. The publications also reveal continued strong contributions to fundamental robotics theory, particularly in swarm formation control and manipulation dynamics. George Saridis Leadership Award in Robotics and Automation (2022) Harashima Award for Innovative Technologies (2017) IEEE Fellow (2010) Charles Deering McCormick Professor of Teaching Excellence (2007-10) Society of Automotive Engineers Ralph R. Teetor Educational Award (2007) Early Career Award in Robotics and Automation (2001) McCormick School of Engineering and Applied Science Teacher Of The Year Award (1998-1999) NSF Career Award (1998) As Director of the Center for Robotics and Biosystems, Dr. Lynch oversees significant research grants and initiatives in robotics. He has made substantial contributions to robotics education through his "Modern Robotics" book and associated Coursera specialization, which has reached thousands of students worldwide. His professional service includes serving as Editor-in-Chief of IEEE Transactions on Robotics, where he oversaw a 90% increase in submissions during his tenure. Dr. Lynch leads research groups focusing on swarm robotics and rehabilitation robotics, with particular emphasis on the Center for Robotics and Biosystems at Northwestern University. His teams integrate expertise from mechanical engineering, electrical engineering, computer science, and rehabilitation medicine to develop innovative robotic systems for both industrial applications and healthcare solutions.
Yao Yang is an Assistant Professor in the Department of Chemistry and Chemical Biology at Cornell University's College of Arts and Sciences. His research focuses on developing multimodal operando electron microscopy and synchrotron X-ray methods to probe electrochemical dynamics at solid-liquid interfaces for energy materials. PhD, Cornell University (2021) Miller Postdoctoral Fellow, UC Berkeley (2021-2024) Research interests span fundamental electrochemistry and energy material interfaces, particularly CO2 reduction, clean H2 production, and rechargeable batteries. The Yang group specializes in operando electrochemical liquid-cell scanning transmission electron microscopy (EC-STEM) and correlative synchrotron X-ray methods at Cornell Center for Materials Research (CCMR) and Cornell High Energy Synchrotron Source (CHESS). Recent publications highlight atomic-scale imaging of catalyst dynamics, Tafel slope analysis, and epitaxial growth techniques for enhanced electrocatalysts. Articles demonstrate interdisciplinary approaches combining electrochemistry, nanoscience, and advanced characterization. Scientific Awards: 2025 ACS Materials and Interfaces Outstanding Presentations by Young Investigators Award 2024 Journal of Materials Research Distinguished Invited Speaker Miller Postdoctoral Fellowship (2021-2024) 2023 Best Early Career Presentation at MRS Spring 2022 ACS AC/DC Rising Stars in Analytical Chemistry Contact: yaoyang@cornell.edu
Martin Z. Bazant is the E. G. Roos (1944) Professor of Chemical Engineering and Professor of Mathematics at the Massachusetts Institute of Technology (MIT), holding the Digital Learning Officer role in the Department of Chemical Engineering. His research focuses on mathematical modeling of electrochemical systems, transport phenomena, and applied mathematics, with significant contributions to battery technology and electrochemical energy storage. He is affiliated with MIT’s Department of Mathematics and the MIT Energy Initiative (MITEI), leading initiatives like the Center for Battery Sustainability and D3BATT. Education: Ph.D. from Harvard University (1997), M.S. and B.S. from the University of Arizona (1993, 1992). His work bridges theory and application, addressing challenges in lithium-ion batteries, solid-state systems, and electrolyte dynamics. Notable achievements include pioneering studies on coupled ion-electron transfer mechanisms and phase separation in battery materials. He is an elected member of the National Academy of Engineering (2025) and a Fellow of the Electrochemical Society (2023). As an educator, he develops MOOCs on transport phenomena and contributes to digital learning initiatives. His research group explores advanced battery diagnostics, machine learning for materials science, and environmental applications of electrochemical processes. Key collaborations include startups like Lithios, Inc., and leadership roles in professional societies such as the International Electrokinetics Society.
Seth Lewis Gilbert is a Professor and Head of the Department of Computer Science at the National University of Singapore (NUS), within the School of Computing . He holds the Dean's Chair Associate Professor title and focuses on algorithms for large-scale distributed systems , emphasizing scalability and fault-tolerance . His work spans wireless networks , contention resolution , dynamic networks , and blockchain protocols . Ph.D. in Computer Science, MIT (2007) M.S. in Computer Science, MIT (2003) B.S. in Electrical Engineering & Mathematics, Yale University (1999) His research explores trusted coordination in systems with untrusted and unreliable participants, addressing challenges like Byzantine agreement , load balancing , and contention resolution . He has pioneered formal frameworks for accountability in distributed protocols and developed novel algorithms for asynchronous task allocation . Recent publications include work on consensus protocols , leader election , and contention resolution , reflecting his focus on dynamic network environments . His research has earned recognition at venues like DISC, ICDCS, and CCS. Scientific Awards & Honors : Young Researcher Award (NUS, 2014) Faculty Teaching Excellence Award (2013/14, 2014/15, 2015/16) School of Computing Teaching Excellence Honour Roll (2016-2021) Best Paper Awards at DISC (2023, 2022), ICDCS (2022), IPDPS (2022) Best Student Paper Award at DISC (2022) He serves on the Steering Committee for DISC as Treasurer and has chaired program committees for DISC, OPODIS, and SPAA. His work bridges foundational algorithmic theory with practical applications in blockchains and dynamic networked systems .
Zhiyong Huang is an Associate Professor at the National University of Singapore (NUS) School of Computing. He holds multiple leadership roles, including Deputy Director of the NUS Business Analytics Centre, Director of the Computing Translational Research & Development (C-TReND) Centre, and Assistant Dean (Industry Relations). He is also a Senior Principal Investigator at the NUS Chongqing Research Institute. Education: PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), MEng and BEng in Computer Engineering from Tsinghua University. Leadership: Senior Member of ACM and IEEE, Pioneer Member of ACM SIGGRAPH, and Chair of the Singapore ACM SIGGRAPH Chapter. Research Interests: His work spans Data Analytics, Machine Learning, Computer Vision, Human-Robot Interaction, and Computer Graphics. Key projects include the NUS Digital Twin initiative and secure data analytics pipelines. Article Trends: Recent publications focus on time series generation, cryptocurrency benchmarks, medical image registration, and phishing detection. These works integrate machine learning, computer vision, and multimodal systems. Scientific Awards: Finalist, World Technology Summit & Awards (Entertainment, 2010) Bronze, National Science and Technology Progress Award (1992) Tsinghua 12.9 Distinguished Young Teacher Award (1989) Grants & Service: Extensive involvement in Singapore's IT Standards Committee, review panels for EDB SIIRD projects, and editorial roles. He has served as PC co-chair, local chair, and reviewer for numerous conferences and journals.
Jiwon Yun is an Associate Professor in the Department of Linguistics at Stony Brook University. She holds a Ph.D. in Linguistics from Cornell University (2013) and a B.S.E. in Computer Science & Engineering from Seoul National University (cum laude with honors). Her research focuses on speech prosody, syntax-semantics interfaces, and computational modeling of sentence processing, with special attention to East Asian languages like Korean, Mandarin, and Japanese. Dr. Yun teaches undergraduate and graduate courses in computational linguistics, semantics, pragmatics, and experimental phonetics. Her work bridges theoretical linguistics and experimental methods, examining how prosody interacts with syntactic and semantic structures. Recent publications investigate prosodic disambiguation of syntactic ambiguities, computational models of relative clause processing, and intonation patterns in Korean and Mandarin. She has developed tools for automated speech corpus analysis and maintains a Hangul-Yale Romanization Converter for linguistic research. Her research has been published in journals like Humanities and Social Sciences Communications , Linguistic Inquiry , and Journal of East Asian Linguistics . Teaching responsibilities include courses such as LIN 335 (Computational Linguistics) and LIN 627 (Computational Semantics).
Yvonne Rogers is a Professor of Interaction Design and Director of the UCL Interaction Centre (UCLIC), with a joint appointment as Deputy Head of the Department of Computer Science at University College London. She joined UCL in 2011 after holding professorships at the Open University (2006-2011) and Indiana University (2003-2006). Her pioneering work spans ubiquitous computing, interaction design, and human-computer interaction, with a current focus on human-centered AI. She co-authored the definitive 'Interaction Design' textbook (6 editions, 300,000+ copies) and serves as CTO of LetThink.com. Research interests center on designing technologies that enhance human cognition and daily activities: Ubiquitous Computing: Augmenting learning/work activities through pervasive technologies Human-AI Interaction: Developing AI systems that enhance human decision-making Health Technologies: Creating VR therapies and ADHD support systems Community Resilience: Designing tools for environmental monitoring and sustainable practices Publication analysis reveals three dominant trends: (1) VR applications for mental health (emotion regulation, ADHD support), (2) AI systems for cognitive augmentation, and (3) community-centered tools for healthcare/environmental challenges. Recent work demonstrates strong emphasis on vulnerable populations including children with ADHD, older adults, and postoperative patients. Major scientific recognition includes: International Member, National Academy of Engineering (2024) ACM SIGCHI Lifetime Research Award (2022) Royal Society Robin Milner Medal (2022) Royal Society Fellowship (2022) Microsoft Outstanding Collaborator Award MRC Suffrage and Science Award (2020) Triple Fellow status (ACM, BCS, CHI Academy) Advises PhD students including Leon Reicherts, Sheena Visram, and Tu Duong. Secured major grants including EPSRC Dream Fellowship on ageing/computing and led the Intel Collaborative Research Institute on Sustainable Connected Cities (2012-2018). Directs UCLIC research center and previously established the Pervasive Interaction Lab at Open University.
Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.
Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.
Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.
Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.
