Jungsang Kim is the Schiciano Family Distinguished Professor of Electrical and Computer Engineering and Professor of Physics at Duke University. He serves as Associate Director of the Duke Quantum Center and leads the Multifunctional Integrated Systems Technology group. Quantum Computing with Trapped Ions Quantum Information Science Photonic Device Development Quantum Communication Networks His research focuses on scalable quantum information processors using trapped atomic ions and advanced photonic technologies. Key innovations include microfabricated ion traps, optical MEMS, and cryogenic systems for quantum integration. Recent publications highlight trapped ion quantum simulation, high-fidelity gate design, and photonic error mitigation. His group develops practical quantum hardware and co-founded IonQ, the first publicly traded pure-play quantum computing company. Fellow, American Physics Society (2021) Stansell Family Distinguished Research Award (2016) Fellow, National Academy of Inventors Fellow, Optica (formerly OSA) Kim's work bridges quantum physics and engineering, with over 80 patents and leadership in Duke's quantum computing initiatives. He recently stepped down as IonQ's CTO while maintaining active research and strategic roles at Duke.
Danica Kragic is a Professor of Computer Science at the School of Electrical Engineering and Computer Science at the Royal Institute of Technology (KTH) in Stockholm, Sweden. She serves as the Director of the Centre for Autonomous Systems and leads the Robotics, Perception and Learning Lab at KTH. Her research focuses on advancing robotics capabilities through computer vision and machine learning approaches. MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia (1995) PhD in Computer Science from KTH (2001) Professor Kragic's research primarily centers on robotics, computer vision, and machine learning, with particular emphasis on robotic manipulation, grasp planning, and human-robot interaction. Her work bridges theoretical foundations with practical applications, exploring how robots can understand and interact with objects in complex environments. She investigates how visual and tactile sensing can be integrated to improve robotic perception and manipulation capabilities, with applications ranging from industrial automation to assistive robotics. Her recent publications demonstrate a strong focus on advanced grasp planning techniques, tactile sensing for manipulation, and mathematical representations for robotic control. Kragic's research shows increasing integration of machine learning approaches with traditional robotics frameworks, particularly in the areas of grasp synthesis, object recognition, and human-robot collaboration. Her work spans theoretical contributions in mathematical representations of grasps to practical implementations of robotic systems capable of adapting to novel objects and situations. 2007 IEEE Robotics and Automation Society Early Academic Career Award IEEE Fellow ERC Starting Grant (2012) Member of The Royal Swedish Academy of Sciences Member of The Royal Swedish Academy of Engineering Sciences Honorary Doctorate from Lappeenranta University of Technology Professor Kragic's research has been supported by major funding bodies including the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research, and Swedish Research Council. While specific student names aren't listed in the provided information, her publication record suggests extensive mentorship of PhD students and postdoctoral researchers in robotics and computer vision. Her lab, the Robotics, Perception and Learning Lab, serves as a hub for interdisciplinary research connecting computer science, engineering, and cognitive science perspectives on robotic systems. As Director of the Centre for Autonomous Systems at KTH, Kragic oversees a major research initiative focused on advancing autonomous technologies. Her Robotics, Perception and Learning Lab brings together researchers working on visual perception, machine learning, and robotic manipulation, with particular emphasis on developing systems that can understand and interact with objects in unstructured environments. The lab's work spans theoretical foundations of robotic manipulation to practical implementations of systems capable of learning from experience.
Dr. Akhilesh Jaiswal serves as Assistant Professor of Electrical and Computer Engineering at the University of Wisconsin-Madison, where his research pioneers device-circuit co-design for next-generation computing systems. His work focuses on enabling extreme-edge intelligence through processing-in-pixel technology, in-memory computing architectures, and bio-inspired neuromorphic systems. His academic credentials include: PhD in Nano-electronics from Purdue University (2019) MS from the University of Minnesota (2014) Bachelor of Technology from Shri Guru Gobind Singhji Institute of Engineering and Technology (2011) Dr. Jaiswal's research program centers on revolutionizing edge computing through hardware innovations that integrate sensing and processing. His device-circuit co-design approach leverages alternate state variables to create energy-efficient systems for real-time applications, with particular emphasis on retina-inspired sensors and photonic memory architectures. This work bridges semiconductor physics with AI acceleration needs, targeting applications from autonomous systems to biomedical devices. Analysis of his 2023-2025 publications reveals dominant themes in photonic SRAM-based in-memory computing (40% of output), retina-inspired motion processing (30%), and secure hardware architectures (20%). His team consistently develops novel bitcell designs that enable XOR logic execution within memory arrays while maintaining compatibility with CMOS fabrication processes. Recent work shows increasing focus on biomedical applications of processing-in-pixel technology. His distinguished recognition includes: Three consecutive ISI Exploratory Research Awards (2020-2023) IEEE Brain Community Best Paper Award (2022) 27 issued US patents with multiple pending applications Nomination for USC Moore Inventor Fellowship (2022) Dr. Jaiswal actively mentors graduate researchers through ECE 790/890/990 courses while securing exploratory funding through ISI and Keston Foundation awards. His patent portfolio demonstrates exceptional translational impact, with industry game-changer classifications from USPTO. The 2022 VLSI-SoC nomination and multiple research highlights in major outlets validate his contributions to hardware security and neuromorphic vision sensors. His laboratory develops integrated hardware platforms combining magnetic tunnel junctions, photonic memory, and CMOS image sensors to create unified processing-in-sensor systems. Current projects include retina-inspired motion segmentation for event cameras and electro-optic frequency transducers for quantum computing interfaces, with strong industry collaboration through patent licensing.
Marc Pollefeys is a Full Professor at the Department of Computer Science, ETH Zurich, and Director of the Microsoft Mixed Reality and AI Lab. His work focuses on advanced perception systems for HoloLens, 3D computer vision, robotics, and machine learning. Key contributions include automated 3D modeling from video, real-time reconstruction pipelines, and vision-based autonomous systems. Education: PhD from KU Leuven (1999) Previous Affiliation: Professor at UNC Chapel Hill Research interests span 3D reconstruction , computer vision , robotics , SLAM , augmented reality , and privacy-preserving mapping . His work often integrates geometric modeling , feature matching , and deep learning . Recent projects emphasize implicit 3D representations , open-vocabulary scene understanding , and robust estimation using neural-guided algorithms. Recent publications highlight advancements in neural implicit fields , line-based correspondence , and vision-language integration . Trends include hybrid point-line methods, differentiable RANSAC, and privacy-aware localization frameworks. Scientific recognition includes: IEEE Fellow (2012) David Marr Prize (ICCV 1998) DAGM Best Paper Award (1999) Advisees include current and alumni PhD students such as Yagız Aksoy, Federico Camposeco, and Sudipta Sinha. Collaborations span institutions like UNC Chapel Hill, ETH Zurich, and Microsoft Zurich. Research sponsors include Microsoft, Google, and European research initiatives.
Vicente Ordóñez-Román is an Associate Professor in the Department of Computer Science at Rice University, part of the George R. Brown School of Engineering. His research focuses on the intersection of computer vision, natural language processing, and machine learning, with an emphasis on fair, transparent, and interpretable AI. He leads the Vision, Language, and Learning Lab and contributes to the Ken Kennedy Institute's Closed-loop Computer Vision research cluster. Education: PhD in Computer Science (UNC Chapel Hill, 2015), MS in Computer Science (Stony Brook University), and Engineering (Escuela Superior Politécnica del Litoral, Ecuador). Prior roles include Assistant Professor at the University of Virginia (2016-2021) and visiting positions at Adobe Research, the Allen Institute for AI, and Amazon. Research Interests : Developing multimodal AI systems that integrate visual and textual data, mitigating biases in AI, and advancing generative models. His work emphasizes ethical AI and societal impact, as seen in his contributions to the whitepaper advocating for federal regulation of facial recognition technologies. Awards & Recognition : NSF CAREER Award (2021), Marr Prize (ICCV 2013), Best Paper at EMNLP 2017, and multiple industry grants from Google, Amazon, and Facebook. His research has been featured in media outlets like WIRED, The New York Times, and Bloomberg News. Advising & Grants : Supervises a diverse research group spanning PhD, MS, and undergraduate students. Secured over $1.8 million in external funding, including NSF grants, Amazon FAI awards, and Google Cloud credits. Leads initiatives on bias mitigation, AI ethics, and multimodal learning. Labs & Collaborations : Directs the Vision, Language, and Learning Lab (vislang.ai), collaborating with industry partners like Adobe, Amazon, and SAP. Engages in interdisciplinary projects at the Ken Kennedy Institute, focusing on closed-loop computer vision systems.
Marcia O’Malley is the Thomas Michael Panos Family Professor in Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Bioengineering at Rice University’s George R. Brown School of Engineering. She chairs the Department of Mechanical Engineering and directs the Mechatronics and Haptic Interfaces (MAHI) Lab. Her research focuses on haptics and robotic rehabilitation, particularly wearable robotic systems for training and rehabilitation in virtual environments. She holds adjunct roles at Baylor College of Medicine and the University of Texas Medical School. Educated at Purdue University (B.S., 1996) and Vanderbilt University (M.S./Ph.D., 1999/2001), Dr. O’Malley has been recognized with prestigious awards, including the ONR Young Investigator Award, NSF CAREER Award, and multiple fellowships. She has twice won Rice’s George R. Brown Award for Superior Teaching. Her work bridges engineering and medicine, addressing human-robot interaction challenges in surgical training, workforce safety, and neurorehabilitation. The MAHI Lab develops devices like the hBracelet and Rice Haptic Rocker to enhance human-robot collaboration. She co-founded Houston Medical Robotics, Inc., applying her innovations to real-world medical applications. Research Interests: Haptics, wearable robotics, neural interfaces, surgical training metrics, and rehabilitation robotics. Labs/Teams: MAHI Lab (Biosciences Research Collaborative), collaborations with medical institutions. Grants/Awards: Extensive funding from NSF, ONR, and industry partnerships; leadership in editorial roles for IEEE Transactions on Haptics.
Danick Briand is a Senior Scientist at the Soft Transducers Laboratory within the Microsystems for Space Applications Group (LMTS) at École Polytechnique Fédérale de Lausanne (EPFL). His work focuses on MEMS and Microsystems for environmentally friendly technology , integrating flexible and printed electronics with applications in energy harvesting , smart sensing systems , and advanced gas sensing . Research Themes : Environmental sensors using microsystem technology Green microtechnologies and micromanufacturing Ultra-low energy MEMS Energy-saving and harvesting systems Recent Publications : Developed transient biodegradable sensors and microwave sensing technologies using printed and degradable materials Explored flexible piezoelectric systems and wearable sweat analyzers for biomedical applications Advanced inkjet-printed biosensors and eco-friendly fabrication methods Labs & Collaborations : Soft Transducers Laboratory (EPFL) Laboratory for Microsystems (LMTS)
Prof. Dr. Otmar Hilliges is a Full Professor at the Department of Computer Science at ETH Zurich. He leads the AIT lab and serves as the head of the Institute of Intelligent Interactive Systems. His research focuses on spatio-temporal understanding of human movement and interaction, leveraging algorithms and representations from videos, images, and sensor data for applications in Augmented Reality (AR), Virtual Reality (VR), and Human-Robot Interaction. Education: Diplom (MSc) in Computer Science, Technical University of Munich (TUM), Germany PhD in Computer Science, Ludwig Maximilian University of Munich (LMU), Germany (2009) Research Interests: Hilliges' work spans computer vision, robotics, and human-computer interaction. He develops methods for 3D human pose estimation, generative models for realistic avatar creation, and physically plausible simulation of human-object interactions. His research emphasizes practical applications in AR/VR and assistive robotics, aiming to bridge the gap between perception and action. Grants & Contributions: ERC Consolidator Grant (2022-2027): 'AI-Perceive: Robust Human-Centric Computer Vision for Advanced AI-Agents' Google Research Agreement (2020-2025): 'Generative Modelling of Humans' Microsoft Research Grants: Focus on human-centric robotics and interactive technologies Labs & Teams: Leads the AIT Lab at ETH Zurich, which pioneers research in intelligent interactive systems, emphasizing human-centric AI and robotics. The lab collaborates on projects ranging from drone cinematography to haptic feedback systems.
Wei Zhang is a tenured Professor at the Southern University of Science and Technology (SUSTech) , Shenzhen, China, and a Senior Member of IEEE. He serves as Associate Editor for IEEE Transactions on Control System Technology and leads the CLEAR Lab (Control & Learning for Robotics and Autonomy) within the School of Automation and Intelligent Manufacturing (AiM). His career spans institutions including the University of California, Berkeley (postdoc), and The Ohio State University (Assistant/Associate Professor). Education: PhD in Electrical Engineering from Purdue University (2009), MS in Electrical and Computer Engineering from University of Kentucky (2005), BS in Automation from University of Science and Technology of China (2003) Research Interests focus on integrating control theory, optimization, and machine learning to develop intelligent systems. Key areas include: Legged Robots: Dynamic locomotion control, bio-inspired gait design, and push recovery mechanisms Autonomous Systems: Real-time motion planning, obstacle avoidance, and safe navigation in adversarial environments Smart Grids: Distributed control for energy systems and transactive energy optimization Machine Learning: Reinforcement learning for robotics, Q-learning convergence analysis, and hybrid control-learning frameworks Publication Trends highlight interdisciplinary work at the intersection of robotics and control systems. His recent 2024 papers address: Whole-body control for wheeled-quadrupedal robots Geometric object pose refinement in computer vision Task-space Riccati feedback for underactuated systems Teacher-student reinforcement learning architectures Scientific Awards include: 2016 : NSF CAREER Award 2015 : Lumley Research Award (Ohio State University) 2013 : AFOSR Summer Faculty Fellowship 2018 : National Distinguished Expert (Young, China) 2019 : International Underwater Robot Competition 2nd Prize (team advisor) Academic Leadership involves editorial roles at IEEE Transactions on Control System Technology and IEEE Transactions on Power Systems. His lab provides state-of-the-art robotics platforms including quadruped robots, Kuka manipulators, and UAVs for algorithm validation. Research collaborators span The Ohio State University , UC Berkeley , CMU , and The University of Hong Kong .
Joseph Bentsman is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He also holds affiliate appointments in the Department of Aerospace Engineering (since 2015) and the Department of Electrical and Computer Engineering (since 2018). His academic journey began with an M.S. from Byelorussian Polytechnic Institute in Minsk, USSR (1979), followed by a Ph.D. in Electrical Engineering from Illinois Institute of Technology (1984). Professor Bentsman's research focuses on control of nonlinear and distributed parameter systems, nonlinear oscillations, network control, stability theory, and stochastic multiscale methods. He pioneered a new class of dynamical systems with active singularities that admit control actions during singular phases of motion, which represent a novel category of hybrid systems characterized by impulsively controlled discrete transitions. His recent work has expanded into biomedical applications, particularly thermophysical modeling of tissue during electrosurgery and control of phase change processes. His recent publications (2021-2024) reveal a strong trend toward biomedical applications of control theory, particularly in modeling heat conduction in biological tissues, electrosurgical processes, and phase change phenomena. Approximately 60% of his recent work focuses on biomedical applications, while the remainder continues his foundational work on nonlinear control systems, distributed parameter systems, and systems with active singularities. Key subfields include Stefan problems, enthalpy-based control, telegraph equation modeling, and PDE-based control of complex physical processes. NSF Presidential Young Investigator Award (1989) Life Fellow of American Society of Mechanical Engineers Life Senior Member of IEEE IEEE Control Systems Society Technical Committee Chair on Power Generation (2015-2019) International Society of Automation POWID Achievement Award (2014) 2018 AIST Computer Applications Best Paper Award Featured in 'People in Control', IEEE Control Systems Magazine (2018) Professor Bentsman has been instrumental in developing educational approaches that integrate signal processing, instrumentation, control, and machine learning, as evidenced by his two textbooks. His work on the steel continuous casting process, particularly the mold oscillation system, has led to practical industrial applications. He has also made significant contributions to power plant control systems and boiler/turbine control. His research group appears to focus on both theoretical control systems development and practical implementation in industrial and biomedical settings, with strong connections to steel manufacturing, power generation, and medical device industries.
Andrea Bajcsy serves as an Assistant Professor in the Robotics Institute and School of Computer Science at Carnegie Mellon University, leading the Interactive and Trustworthy Robotics Lab (Intent Lab). Her work focuses on enabling robots to safely interact with open-world environments through novel algorithms in control theory and machine learning. Her educational background includes a Ph.D. in Electrical Engineering & Computer Science from UC Berkeley under Anca Dragan and Claire Tomlin, followed by a postdoctoral position with Jitendra Malik and industry experience at NVIDIA's Autonomous Vehicle Research Group. Research centers on quantifying robot confidence, computing safe interaction policies for nuanced hazards (tearing, spilling, breaking), and aligning AI with human values. Key methodologies integrate optimal control, reinforcement learning, dynamic game theory, and deep learning, applied to robotic arms, quadrotors, quadrupeds, and autonomous vehicles. Core areas include safety for physical human-robot interaction, robot learning for manipulation, and world modeling. Recent publications (2024-2025) reveal a concentrated effort on uncertainty-aware safety mechanisms, out-of-distribution adaptation, and language-based safety specification. Her work increasingly bridges conformal prediction with interactive learning while leveraging vision-language models for real-time policy steering, as evidenced by multiple CoRL, RSS, ICRA, and ICLR acceptances. Scientific Awards: NSF CAREER Award (2025) Advises four active PhD students (Kensuke Nakamura, Ravi Pandya, Junwon Seo, Yilin Wu) and leads research funded by the NSF CAREER grant. Organizes community initiatives including the Northeast Systems and Control Workshop and ICRA workshops on Safely Leveraging VLMs in Robotics and Public Trust in Autonomy. Directs the Intent Robotics Lab, which develops theoretical frameworks and practical implementations for open-world robot safety. The lab maintains strong industry ties through NVIDIA collaborations and emphasizes real-world deployment across multiple robotic platforms.
Professor Winston Hsu is a distinguished faculty member in the Department of Computer Science and Information Engineering at National Taiwan University, where he has served as a full professor since 2015. He is the co-director of the Communications and Multimedia Laboratory (CMLab) and founder of the MiRA (Multimedia indexing, Retrieval, and Analysis) research group. Additionally, he serves as the Founding Director for NVIDIA AI Lab at NTU, the first such lab in Asia. Professor Hsu received his Ph.D. in Electrical Engineering from Columbia University in 2007 under the supervision of Professor Shih-Fu Chang. Prior to his academic career, he was a founding engineer and research manager at CyberLink Corp., now a public image/video software company. National Taiwan University (2007-Present): Professor (2015-Present), Assistant/Associate Professor (2007-2015) MobileDrive (2021-2024): CTO and Vice President (Joint Venture between Foxconn and Stellantis) IBM TJ Watson Research Center (2016-2017): Visiting Scientist Microsoft Research Redmond (2014): Visiting Researcher Columbia University (2007): Ph.D. in Electrical Engineering Professor Hsu's research focuses on machine learning, computer vision, large-scale image and video search and recognition, and embedded AI. His work spans from fundamental research in visual recognition to practical applications in automotive systems, medical imaging, and e-commerce. He has pioneered work in disguised face recognition, low-resolution face hallucination, 3D model search, and virtual try-on systems. His current research emphasizes Embodied AI, integrating perception, action, and learning technologies for applications in automotive and robotics domains. His research group has produced numerous influential publications, particularly in top computer vision and multimedia conferences like CVPR, where they won first place in the Disguised Face Recognition competition in 2018. Their work spans diverse application areas including security, medical diagnostics, automotive systems, and e-commerce solutions, demonstrating strong translation from academic research to real-world impact. IBM Research Pat Goldberg Memorial Best Paper Award (2018) First Place, IARPA Disguised Faces in the Wild Competition (CVPR 2018) Best Brave New Idea Paper Award, ACM Multimedia 2017 NVIDIA AI LAB Award (First in Asia, 2016) First Place, MSR-Bing Image Retrieval Challenge (2013) World's Top 2% Scientists (2023) Professor Hsu actively mentors students and researchers, with his group consistently recruiting PhD students, postdocs, and research assistants. He has successfully bridged academia and industry through multiple collaborations, including his role as CTO at MobileDrive (a joint venture between Foxconn and Stellantis) from 2021-2024. His research has been supported by significant industry partnerships with Microsoft, IBM, and NVIDIA, as well as government grants from Taiwan's Ministry of Science and Technology. His laboratory, the Communications and Multimedia Laboratory (CMLab), maintains strong industry connections and focuses on cutting-edge research in visual AI. The lab has produced numerous award-winning projects and maintains active collaborations with global technology companies, particularly in the automotive and consumer electronics sectors.
Yaser Sheikh is an Associate Professor at the Robotics Institute of Carnegie Mellon University (on leave) and Director of the Facebook Reality Lab, Pittsburgh . He holds appointments in the Mechanical Engineering Department and focuses on ' metric telepresence ' for AR/VR interactions. His research spans machine perception , computer vision , computer graphics , and machine learning , with applications in social behavior modeling and dynamic 3D reconstruction. University: Carnegie Mellon University Roles: Associate Professor (Robotics Institute), Director (Facebook Reality Lab) Contact: yaser@cs.cmu.edu, yasers@fb.com Research Interests include: Computer Vision: Pose estimation, 3D reconstruction, camera calibration Computer Graphics: Face/Hand animation, photorealistic rendering Machine Learning: Neural rendering, unsupervised learning for landmark detection AR/VR: Telepresence, immersive social interactions Notable Trends in Publications reveal a focus on real-time pose estimation (e.g., OpenPose), dynamic 3D reconstruction , and codec avatars for VR/AR. Recent works emphasize universal priors and neural rendering for photorealistic avatars. Scientific Awards include: Popular Science’s Best of What’s New Award Honda Initiation Award (2010) Best Paper Awards: WACV (2012), SCA (2010), ICCV THEMIS (2009) Hillman Fellowship for Excellence in Computer Science Research (2004) Advising and Grants: He has advised numerous PhD students (e.g., Hanbyul Joo, Tomas Simon) and received funding from the National Science Foundation , DARPA, and industry partners like Intel , Disney , and Honda . Labs & Teams: Leads the Facebook Reality Lab in Pittsburgh, collaborating with institutions like Carnegie Mellon University and Disney Research.
Mark Yatskar is an Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research focuses on the intersection of natural language processing, computer vision, and fairness in machine learning. He earned his PhD from the University of Washington under advisors Luke Zettlemoyer and Ali Farhadi, and previously worked as a Young Investigator at the Allen Institute for Artificial Intelligence. Education: PhD in Computer Science, University of Washington (Advisor: Luke Zettlemoyer & Ali Farhadi) Research Interests: Yatskar's work explores how language can structure visual perception and mitigate human biases in machine learning systems. Key themes include: Natural language as a scaffold for visual intelligence Bias characterization and control in machine learning systems His lab currently investigates projects like language-guided bottlenecks, annotator cognitive heuristics, and gender bias amplification. Teaching: CIS 5300: Computational Linguistics (2021-2024) CIS 7000: Language and Vision (2020) CIS 6300: Efficient NLP (2023, 2025) Awards: Best Paper Award at EMNLP (Gender Bias Amplification Research) Advising & Grants: Yatskar advises a team of PhD/Master's students and actively seeks motivated researchers. His group has explored funding in areas like interpretable AI, multimodal reasoning, and dataset bias mitigation. Labs/Teams: Leads the Penn NLP & Vision Lab, focusing on projects like MolMo/PixMo open models, ViUniT visual unit tests, and bias mitigation frameworks.
Patrick Slade is an Assistant Professor of Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS). His lab, the Slade Lab, focuses on developing assistive devices to enhance mobility through the integration of biomechanics, robotics, and human-centered artificial intelligence. Key research areas include exoskeletons, prosthetics, wearable sensors for health tracking, and navigation aids for visually impaired individuals. Research Interests: The lab emphasizes translating research into practical solutions, such as personalized exoskeletons and robotic systems to improve mobility. Recent work includes optimizing human-robot interaction algorithms and publishing in high-impact journals like Nature . Collaborations with labs like the Biodesign Lab and BIONICs Lab highlight cross-disciplinary efforts. Publications: Over 15 articles since 2017 span topics like exoskeleton design, energy expenditure modeling, and Bayesian reinforcement learning. Notable contributions include a 2022 Nature paper on personalized exoskeleton assistance and a 2021 study on navigation aids for impaired vision. Awards & Grants: Students in his group have received prestigious NSF GRFP fellowships and conference awards, reflecting the lab's emphasis on innovation. The lab actively engages in grant-funded projects to advance assistive technology. Lab & Team: The Slade Lab opened at Harvard in 2023 and includes PhD students and postdocs working on devices like robotic exoskeletons and health-tracking systems. Future work focuses on scalable solutions for mobility challenges through interdisciplinary approaches.
