Deva Kannan Ramanan is a Professor at the Robotics Institute of Carnegie Mellon University , focusing on computer vision , machine learning , and human-centered robotics . His work bridges neurorobotics and visual perception , with applications in autonomous driving and 4D reconstruction . Research Topics Computer Vision 3-D Vision and Recognition Visual Servoing Neurorobotics Human-Centered Robotics Graphics & Creative Tools His recent publications in CVPR , ICRA , and ICCV emphasize 4D human reconstruction , neural rendering , and vision-language models for autonomous systems. He serves as General Chair of CVPR 2027 and Program Chair of CVPR 2018 , with IARPA funding for aerial-ground rendering (2023-2027). Current students include PhD candidates Sally Chen, Kangle Deng, and Zhiqiu Lin, while past advisees like Arun Vasudevan and Olga Russakovsky now hold positions at Amazon and Meta respectively.
Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.
Amir Zamir is a tenure-track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology Lausanne (EPFL) in the School of Computer & Communication Sciences. Previously, he worked at UC Berkeley, Stanford, and UCF with prominent researchers including Silvio Savarese, Jitendra Malik, Mubarak Shah, Rahul Sukthankar, and Leonidas Guibas. He currently leads the Visual Intelligence & Learning Lab at EPFL and serves as chief scientist of Duranta, having previously been the CVML chief scientist of Aurora Solar (a Forbes AI 50 company valued at $4B in 2022) from 2015 to 2022. Dr. Zamir's research spans computer vision, machine learning, and artificial intelligence, with a focus on developing general multi-modal/multi-task vision systems that operate as active agents in the real world. His work emphasizes slow science principles, seeking fundamental understanding over quick publications. Key research areas include embodied vision, multimodal foundation models, computational imaging, and vision-language integration. His notable projects include 4M, Taskonomy, Gibson Environment, Omnidata, and MultiMAE, which have significantly influenced the field of computer vision and embodied AI. Zamir has made substantial contributions to the computer vision community through numerous high-impact publications and leadership roles. His work demonstrates a consistent focus on creating vision systems that go beyond narrow and passive methods toward more general, active, and embodied approaches. The trajectory of his research shows increasing sophistication in handling multiple modalities and tasks within unified frameworks, culminating in recent work on multimodal foundation models that can handle diverse vision tasks. Dr. Zamir has received numerous prestigious awards including the Young Researcher Award 2022 from ECCV, the PAMI Mark Everingham Prize 2022, SIGGRAPH 2022 Best Paper Award for CLIPasso, CVPR 2018 Best Paper Award for Taskonomy, and CVPR 2016 Best Student Paper Award. He is also an ELLIS Faculty Scholar and received the NVIDIA Pioneering Research Award in 2018 for the Gibson Environment. As an advisor, Dr. Zamir has mentored numerous PhD students including Roman Bachmann, Andrei Atanov, Rishubh Singh, Jason Toskov, Kunal Pratap Singh, Zhitong Gao, Mingqiao Ye, and Muhammad Uzair Khattak. His former PhD students include Oguzhan Kar (now at Apple), Alexander Sasha Sax (co-advised with Jitendra Malik, now at Meta FAIR), and Teresa Yeo (now at MIT-Singapore Alliance). Dr. Zamir teaches several advanced courses including CS-503 Visual Intelligence, CS-500 AI Product Management, COM-304 Intelligent Systems, and ENG-615 Topics in Autonomous Robotics. Dr. Zamir leads the Visual Intelligence & Learning Lab at EPFL, which focuses on developing fundamental methods for visual intelligence that can operate effectively in real-world environments. The lab takes an interdisciplinary approach combining computer vision, machine learning, robotics, and cognitive science to create systems that can perceive, understand, and interact with the world. Current research directions include multimodal foundation models, computational imaging, embodied vision, and personalization of generative models.
Sewoong Oh is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he has been faculty since 2019. His research focuses on the foundations of machine learning with particular emphasis on differential privacy, secure and robust machine learning, and federated learning. Prior to joining UW, he was at the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign from 2012-2019. He is affiliated with multiple NSF AI Institutes including ACTION (Agent-based Cyber Threat Intelligence and Operation), AI-EDGE (Future Edge Networks and Distributed Intelligence), and IFML (Foundations of Machine Learning). Oh received his PhD in Electrical Engineering from Stanford University in 2011 under Andrea Montanari, followed by postdoctoral work at MIT's Laboratory for Information and Decision Systems under Devavrat Shah. His educational background spans top institutions in theoretical computer science and electrical engineering. His research spans the critical intersection of machine learning security, privacy, and robustness. Oh's work addresses fundamental challenges in making AI systems reliable and trustworthy, with particular focus on defending against backdoor attacks, developing privacy-preserving algorithms, and creating efficient tokenization methods for language models. His recent SuperBPE work demonstrates how moving beyond traditional subword tokenization can significantly improve language model efficiency and performance. His research consistently bridges theoretical foundations with practical implementations, as evidenced by numerous open-source repositories containing production-ready code. Oh's publications reveal a consistent trajectory toward addressing security and privacy concerns in increasingly complex machine learning systems, with recent work focusing on language model tokenization, data-centric AI, and federated learning frameworks. His research shows strong interdisciplinary connections between theoretical computer science, statistics, and practical machine learning systems. ACM SIGMETRICS best paper award (2015) NSF CAREER award (2016) ACM SIGMETRICS rising star award (2017) GOOGLE Faculty Research Awards (2017, 2020) 2024 ICML Best Paper Award (for work by advisee Jon Hayase) Professor Oh maintains an active research group with numerous PhD students, postdocs, and undergraduate researchers. His students have secured prestigious positions at leading tech companies including Amazon, Google, and Snap, as well as academic appointments at institutions like University of Wisconsin-Madison and Shanghai Jiao Tong University. He has received substantial research funding through his participation in multiple NSF AI Institutes and industry research awards from Google. His lab maintains several active GitHub repositories implementing cutting-edge research in backdoor defense, robust statistics, and privacy-preserving machine learning.
Vineeth N Balasubramanian is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad, with affiliate faculty status in the Department of Artificial Intelligence. His research focuses on the intersection of deep learning, machine learning, and computer vision, emphasizing explainability, robustness, and real-world applications. He leads Lab 1055, which investigates problems such as Explainable and robust AI/ML systems Lifelong learning in evolving environments Multimodal vision-language models Applications in agriculture, autonomous navigation, and human behavior analysis His recent work includes causal reasoning in transformers, vision-language model capabilities, and drone-based object detection. Funded by organizations like Google, Microsoft, Intel, and DST, he has received multiple awards including the World's Top 2% Scientists (2022-23), INSA/INAE Fellowships, and Best Paper recognitions. Lab 1055 collaborates with institutions like CMU, UBC, and Monash University, contributing to cutting-edge advancements in AI.
Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines
Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu
Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.
Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research focuses on machine learning and computer vision with applications in 3D scene understanding, generative modeling, and multi-agent systems. Education: Diploma in Electrical Engineering and Information Technology, Technical University of Munich (TUM) PhD in Computer Science, ETH Zurich Postdoctoral Fellow, University of Toronto Research Interests: Structured prediction in deep learning Generative adversarial networks and stability Multi-modal vision-language models 3D scene reconstruction from single images Embodied agent collaboration Semantic segmentation with temporal coherence Recent Publications: Highlight trends in neural rendering, video object segmentation, and reinforcement learning with applications to 3D modeling and multi-agent systems. Notable innovations include SAIL-VOS dataset for amodal segmentation and NeRFDeformer for single-view scene transformation. Scientific Awards: NSF CAREER Award, 3M and Amazon research awards, multiple student recognition awards, ETH Zurich PhD medal, and best paper at Intelligent Tutoring Systems 2014. Teaching: Offers graduate courses in Pattern Recognition (ECE 544) and Machine Learning (CS 446/ECE 449). Previously taught at University of Toronto and ETH Zurich. Labs & Collaborations: Leads research at Coordinated Science Laboratory (UIUC) with collaborations across University of Toronto, ETH Zurich, and industry partners like Samsung SAIT and Amazon.
Shreyas Sen is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University, jointly affiliated with the School of Biomedical Engineering. He directs the Center for Internet of Bodies (C-IoB) and leads the SPARC Lab, focusing on circuits/systems for secure, energy-efficient body-centric communication and biomedical applications. Education: B.E. (Jadavpur University, 2006), M.S. & Ph.D. (Georgia Tech, 2009-2011). His research spans Human Body Communication (HBC), hardware security, IoT/IoB systems, and low-power sensing. Notable innovations include Electro-Quasistatic HBC (EQS-HBC) technology, which enables covert body-wire communication with 100x lower energy than conventional methods. Research Themes: Secure communication for medical wearables Physical layer security in IoT Ultra-low-power biosensors Side-channel attack mitigation in cryptographic ICs Publications: Over 200 papers in top venues (ISSCC, DAC, CICC, HOST) with focus on HBC systems, electromagnetic security, and biomedical circuits. Recent work explores EQS-HBC privacy properties (Nature Scientific Reports, 2019) and 350x current-domain signature attenuation for AES security (ISSCC 2020). Awards: NSF CAREER Award (2020) AFOSR Young Investigator (2016) MIT TR35 India (2018) 9 Best Paper Awards Intel Outstanding Researcher (2020) He advises over 20 students and leads industry collaborations through Ixana Inc. (founded 2020), commercializing EQS-HBC technology. Current efforts include EQS-based authentication systems and radiation dosimetry wearables.
Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.
Tushar Krishna is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology, with a courtesy appointment in the School of Computer Science. He earned his PhD in Electrical Engineering and Computer Science from MIT in 2014, an MSE in Electrical Engineering from Princeton University in 2009, and a B.Tech in Electrical Engineering from IIT Delhi in 2007. His research spans computer architecture, interconnection networks, networks-on-chip (NoC), and AI/ML accelerator systems, with a focus on optimizing data movement in modern computing platforms. His work is funded by NSF, DARPA, IARPA, SRC, Department of Energy, Intel, Google, Meta, Qualcomm, and TSMC. His papers have been cited over 17,000 times, with three receiving IEEE Micro's Top Picks recognition, one earning an honorable mention, and four winning best paper awards. Dr. Krishna leads the Synergy Lab at Georgia Tech and has developed several influential tools including ASTRA-sim for distributed AI/ML training, MAESTRO and SCALE-sim for accelerator design space exploration, and Garnet2.0 for NoC simulation. His recent work focuses on large language model acceleration, distributed training systems, and neuro-symbolic AI architectures. He has received numerous teaching and research awards including induction into the HPCA Hall of Fame (2022), the Class of 1940 Teaching Effectiveness Award (2018), and the Roger P. Webb Outstanding Mid-career Faculty Award (2024). HPCA Hall of Fame Inductee (2022) Roger P. Webb Outstanding Mid-career Faculty Award (2024) Richard M. Bass/Eta Kappa Nu Outstanding Junior Teacher Award (2023) Roger P. Webb Outstanding Junior Faculty Award (2021) Class of 1940 Course Survey Teaching Effectiveness Award (2018) Dr. Krishna currently serves as Associate Director for the Center for Research into Novel Computing Hierarchies (CRNCH) and co-chair of the Chakra Execution Traces and Benchmarks Working Group. He has held the ON Semiconductor (Endowed) Junior Professorship at Georgia Tech (2019-2021) and has been a visiting professor at MIT EECS, Harvard University CS, and a researcher at Intel's VSSAD group.
Alex Wong is an Assistant Professor of Computer Science at Yale University, specializing in computer vision, robotics, and medical imaging. His research focuses on sensor fusion, unsupervised learning, 3D vision, robust perception under adverse conditions, and medical image analysis. He holds degrees from the University of California, Los Angeles (UCLA), including a B.S., M.S., and Ph.D. in Computer Science. Wong’s work bridges theoretical advances with practical applications, particularly in depth estimation, autonomous systems, and medical diagnostics. He has received prestigious awards such as the NeurIPS Outstanding Student Paper Award (2011) and the ICRA Best Paper Award in Robot Vision (2019). His research often addresses challenges in unstructured environments, emphasizing robustness and adaptability. Recent projects include developing novel frameworks for unsupervised depth completion, adversarial robustness in vision systems, and multimodal fusion techniques. His contributions span conferences like CVPR, ICCV, and ICRA, with a strong focus on advancing AI for real-world applications in healthcare and robotics. Education: B.S., Computer Science, UCLA M.S., Computer Science, UCLA Ph.D., Computer Science, UCLA Awards: NeurIPS Outstanding Student Paper Award (2011) ICRA Best Paper Award in Robot Vision (2019) His lab at Yale Engineering focuses on AI-driven solutions for perception challenges, collaborating across disciplines to advance medical imaging and autonomous systems. Current efforts explore generative models, continual learning, and vision-language integration for robust scene understanding.
Priyanka Raina is an Assistant Professor of Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. She leads the Stanford Accelerate research group, focusing on domain-specific hardware architectures and agile hardware-software co-design. Her work emphasizes high-performance, energy-efficient accelerators for emerging technologies. Education: B.Tech. from IIT Delhi (2011), M.S. and Ph.D. from MIT (2013/2018). Postdoctoral experience includes NVIDIA Research (2018) and Amazon Visiting Academic (2023–present). Awards include the Sloan Research Fellowship (2024), NSF CAREER Award (2023), and Terman Faculty Fellowship (2018). Research Interests: Domain-specific architectures, near-memory computing, design productivity, and machine learning acceleration. Her group develops frameworks like AHA (Agile Hardware) for efficient accelerator design and compilers. Awards: Over 10 major awards, including best paper recognitions at VLSI, MICRO, and JSSC. Active roles as Associate Editor for IEEE JSSC and Program Chair for IEEE Hot Chips (2020). Advising & Teaching: Supervises multiple PhD and MS students in hardware design, CGRAs, and ML accelerators. Teaches VLSI design courses (EE271/272/372) and oversees independent studies in embedded systems and chip design. Labs & Collaborations: Affiliated with Stanford PORTAL Center, AHA Center, and SystemX Alliance. Projects include MINOTAUR (edge AI accelerator), Amber (CGRA-based SoC), and EMBER (RRAM macros).
Angjoo Kanazawa is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She leads the Kanazawa AI Research (KAIR) lab under the Berkeley Artificial Intelligence Research (BAIR) umbrella and serves on the advisory board of Wonder Dynamics. Her research focuses on the intersection of computer vision, computer graphics, and machine learning, with a particular emphasis on 4D reconstruction of dynamic scenes, neural radiance fields (NeRF), and systems that model human-environment interactions from 2D visual data. Education : Ph.D., Computer Science (2017), University of Maryland, College Park BA, Mathematics and Computer Science (2012), New York University (NYU) Her work aims to build systems that can capture, perceive, and understand complex 3D/4D worlds from photographs and videos, enabling applications in scene reconstruction, motion analysis, and generative modeling. She has pioneered techniques for scaling NeRFs across GPUs (NeRF-XL), developing open-source tools like nerfstudio and gsplat. Her recent publications focus on topics like self-occluded avatar recovery (SOAR), decentralized diffusion models, and 4D reconstruction of articulated objects for robotics. Kanazawa's research has been recognized with prestigious awards including the IEEE CS TCPAMI Young Researcher Award (2024) , Sloan Research Fellowship (2023) , and Google Faculty Research Award (2021) . Her lab has trained numerous students who now hold positions at leading institutions and companies like Anthropic, Meta Reality Labs, and Luma AI. Key Scientific Awards : IEEE CS TCPAMI Young Researcher Award (2024) Sloan Research Fellow (2023) Hellman Fellow (2022) Bakar Fellows Spark Award (2022) Google Faculty Research Award (2021) Her KAIR lab collaborates extensively with industry partners and academic institutions, including the Max Planck Institute and Google Research. She has served as an advisor for PhD students and postdocs who now lead teams at UC Berkeley, MIT, Stanford, and Luma AI, while her teaching includes graduate courses like CS 280A (Computer Vision) and CS 294-173 (Learning for 3D Vision).
