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Lydia Kavraki
Lydia Kavraki
Professor
Rice University
Lydia E. Kavraki is the Noah Harding Professor of Computer Science, professor of Bioengineering, professor of Electrical and Computer Engineering, and professor of Mechanical Engineering at Rice University. She is the Director of the Ken Kennedy Institute at Rice.
Kavraki received her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University working with Professor Jean-Claude Latombe.
Kavraki's research interests span robotics, AI, and biomedicine. In robotics and AI, she is interested in enabling robots to work with people and in support of people. Her research develops the underlying methodologies for achieving this goal: algorithms for motion planning for high-dimensional systems with kinematic and dynamic constraints, integrated frameworks for reasoning under sensing and control uncertainty, novel methods for learning and for using experiences, and ways to instruct robots at a high level and collaborate with them. Kavraki’s lab is inspired by a variety of applications: from robots that will assist people in their homes, to robots that assist in surgeries, to robots that would build space habitats. In biomedicine she develops computational methods and tools to model protein structure and function, understand biomolecular interactions, aid the process of medicinal drug discovery, analyze the molecular machinery of the cell, and help integrate biological and biomedical data for improving human health. Her work has applications, among others, in personalized immunotherapy. Kavraki’s research blends her extensive interdisciplinary background in computer science, artificial intelligence, machine learning, bioengineering and biomedical sciences promoting the convergence of these disciplines.
Mike Davies
Mike Davies
Director
Intel's Neuromorphic Computing Lab
Mike Davies is Director of Intel’s Neuromorphic Computing Lab. Since joining Intel Labs in 2014, Mike has researched neuromorphic prototype architectures, algorithms, software, and systems. His group is responsible for Intel’s Loihi series of research chips. Previously, as a founding employee of Fulcrum Microsystems and its director of silicon engineering, Mike pioneered high performance asynchronous design methodologies as applied to several generations of industry-leading Ethernet switch products. He joined Intel in 2011 by Intel’s acquisition of Fulcrum.
Daniel Gandhi
Daniel Gandhi
Software Engineering Manager, Compute R&D
Boston Dynamics
Daniel Gandhi leads Boston Dynamics' R&D team focused on integrating high-performance compute and communication capabilities into robots. With a career spanning robotics and automation in diverse settings—from early-stage startups to Fortune 500 companies, and from basic research to large-scale production—Daniel has a proven track record of successfully bringing cutting-edge technologies to market. He holds a BS in Physics, Computer Science, and Mathematics from Brandeis University and an MS in Electrical Engineering from Stanford.
Ludovic Righetti
Ludovic Righetti
Associate Professor
New York University (NYU)
Ludovic Righetti is jointly appointed in the Electrical and Computer Engineering Department and in the Mechanical and Aerospace Engineering Department at the Tandon School of Engineering of New York University. He is also holder of an International Chair at the Artificial and Natural Intelligence Toulouse Institute. He co-created and co-directs the Center for Robotics and Embodied Intelligence. He is also a member of the Center for Responsible AI, Center for Urban Science and Progress and Center for Advanced Technology in Telecommunications.
He leads the Machines in Motion Laboratory, where his research focuses on the planning and control of movements for autonomous robots, with a special emphasis on legged locomotion and manipulation. He is more broadly interested in questions at the intersection of decision-making, automatic control, optimization, applied dynamical systems and machine learning and their application to physical systems. He also cares about the societal implications of his work and more broadly he is interested in use of technology that can empower people, improve quality of life and help create more just, open and equal societies.
He studied at the Ecole Polytechnique Fédérale de Lausanne (Switzerland) where he received an engineering diploma in Computer Science (eq. M.Sc.) and a Doctorate in Science under the supervision of Professor Auke Ijspeert. He was a postdoctoral fellow at the Computational Learning and Motor Control Lab with Professor Stefan Schaal (University of Southern California) and a independent research group leader at the Max-Planck Institute for Intelligent Systems in Tübingen, Germany. He moved to New York University in September 2017.
He has received several awards, most notably the 2010 Georges Giralt PhD Award given by the European Robotics Research Network (EURON) for the best robotics thesis in Europe, the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Best Paper Award, the 2016 IEEE Robotics and Automation Society Early Career Award, the 2016 Heinz Maier-Leibnitz Prize from the German Research Foundation and NYU's 2024 Jacobs Excellence in Education Innovation Award.
Balakumar Sundaralingam
Balakumar Sundaralingam
Senior Research Scientist
NVIDIA
I am a Senior Research Scientist at NVIDIA. My research interests are in enabling robots to navigate and interact in unstructured environments. This includes inventing new techniques for robots, combining perception, machine learning, numerical optimization, and control theory. I also focus on efficient and practical implementation of invented techniques in modern robotic systems.
An example of my work is cuRobo, which formulates motion planning as a trajectory optimization problem and uses parallel compute on GPU to solve in 30ms. This work also leverages nvblox for collision avoidance from depth cameras. This research has been integrated into MoveIt as a planner plugin, available at Isaac ROS cuMotion.
Some of my other research efforts explored the application of sampling-based optimization for reactive manipulator control. We developed a framework (STORM) that enables optimization over non-differentiable cost terms leveraging MPPI, a sampling based optimization technique. Our framework has enabled robots to reactively avoid obstacles (CoRL 2021), move around humans to grasp objects (ICRA 2022), and also correct its behavior based on human language feedback (RSS 2022).
I received my Ph.D. in Computing (Robotics) from the University of Utah under the supervision of Prof. Tucker Hermans. My Ph.D. research focused on planning and tactile perception for in-hand manipulation leveraging trajectory optimization, learned models, and structured inference. In the past, I have worked on mobile robots, including mapping with LIDAR and reactive collision avoidance. I have also built robotic systems ranging from mobile robots to dexterous manipulation systems.
Hyesoon Kim
Hyesoon Kim
Professor
Georgia Institute of Technology
Dr. Hyesoon Kim received her Ph.D. degree in electrical and computer engineering from the University of Texas at Austin. Her research interests include high-performance energy-efficient computer architectures, programmer-compiler-architecture interaction, low-power high-performance embedded processors, and compiler and hardware support for dynamic optimizations, virtual machines, and binary instrumentation.
Elizabeth Farrell Helbling
Elizabeth Farrell Helbling
Assistant Professor
Cornell University
Countless science fiction works have set our expectations for small, mobile, autonomous robots for use in a broad range of applications. The ability to move through highly dynamic and complex environments can expand capabilities in search and rescue operations and safety inspection tasks. These robots can also form a diverse collective to provide more flexibility than a multifunctional robot. Advances in multi-scale manufacturing and the proliferation of small electronic devices have paved the way to realizing this vision with centimeter-scale robots. However, there remain significant challenges in making these highly-articulated mechanical devices fully autonomous due to the severe mass and power constraints. Helbling’s research looks at the integration of the control system, sensors, and power electronics within the strict weight and power constraints of these vehicles, taking a holistic approach to navigating the inherent tradeoffs in each component in terms of their size, mass, power, and computation requirements.
Farrell did her postdoctoral work at Harvard University and the Wyss Institute for Biologically Inspired Engineering with Professor Wood. She completed her graduate work with Professor Wood in 2019, where she focused on the systems-level design of the Harvard RoboBee, an insect-scale flapping-wing robot, and HAMR, a bio-inspired crawling robot. Her work on the first autonomous flight of a centimeter-scale vehicle was recently featured on the cover of Nature, and can be seen at the Boston Museum of Science, World Economic Forum, London Science Museum, and the Smithsonian, as well as in the popular press (The New York Times, PBS NewsHour, Science Friday, and the BBC). She received her bachelor’s degree in engineering sciences at Smith College.
Christopher Batten
Christopher Batten
Professor
Cornell University
I am a Professor of Electrical and Computer Engineering and a graduate field member of Computer Science at Cornell University. My research group is part of the Computer Systems Laboratory, and we largely work at the intersection of computer architecture, electronic design automation, and digital VLSI including projects on parallel programming frameworks, programmable accelerator design, interconnection networks, productive VLSI chip design methodologies, and architectures for future emerging technologies. Building prototype systems is an integral part of my research, as this is one of the best ways to validate assumptions, gain intuition about physical design issues, and provide platforms for future software research.
My research has been recognized with several awards including the ACM/IEEE MICRO Hall of Fame, a Cornell Engineering Research Excellence Award, an AFOSR Young Investigator Program award, an Intel Early Career Faculty Honor Program award, an NSF CAREER award, a DARPA Young Faculty Award, an IEEE Micro Top Picks selection, and MLCAD Best Artifact Award. My teaching has been recognized with the Ruth and Joel Spira Award for Excellence in Teaching, a Kenneth A. Goldman '71 Teaching Award, two Michael Tien '72 Excellence in Teaching Awards, and a James M. and Marsha D. McCormick Award for Outstanding Advising of First-Year Engineering Students.
Sabrina M. Neuman
Sabrina M. Neuman
Assistant Professor
Boston University
Sabrina M. Neuman is an Assistant Professor of Computer Science at Boston University. Her research interests are in computer architecture design informed by explicit application-level and domain-specific insights. She is particularly focused on robotics applications because of their heavy computational demands and potential to improve the well-being of individuals in society. She received her S.B., M.Eng., and Ph.D. from MIT, and she was a postdoctoral NSF Computing Innovation Fellow at Harvard University. She is a 2021 EECS Rising Star, and her work on robotics acceleration has received Honorable Mention in IEEE Micro Top Picks 2022 and IEEE Micro Top Picks 2023. She holds the 2023-2026 Boston University Innovation Career Development Professorship.
Brian Plancher
Brian Plancher
Assistant Professor
Barnard College, Columbia University
I am an Assistant Professor of Computer Science at Barnard College, Columbia University where I lead the Accessible and Accelerated Robotics Lab (A²R Lab). I hold affiliate positions in the Department of Computer Science and Electrical Engineering at the Fu Foundation School of Engineering and Applied Science, Columbia University. I am also a co-chair for the Tiny Machine Learning Open Education Initiative (TinyMLedu) and an associate co-chair for the IEEE-RAS TC on Model Based Optimization for Robotics.
My research is focused on optimizing robotic systems at all scales by developing, optimizing, implementing, and evaluating next-generation algorithms and edge computational systems, through algorithm-hardware-software co-design (e.g., MPCGPU, GRiD, TinyMPC). As such, my research is at the intersection of Robotics and Computer Architecture, Embedded Systems, Numerical Optimization, and Machine Learning.
I also want to promote a responsible, sustianable, and accessible future for robotics and edge computing, including the development of new interdisciplinary, project-based, open-access courses that lower the barriers to entry for cutting-edge topics like robotics, parallel programming, and embedded machine learning (e.g., Global TinyML Education, Parallel Optimization for Robotics).
I enjoy spending my free time with my family and skiing in the winters.
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