Dr. Damien Charrieras is an Associate Professor at the School of Creative Media , City University of Hong Kong, where he serves as Program Leader for the Master of Art in Creative Media and deputy program leader for the PhD in Creative Media. His work bridges cultural studies, new media theory, and the intersection of financial technologies with artistic practices through the ACIM Laboratory for Interventions in Speculative Finance . Doctoral research on digital artists' trajectories in Montreal FRQSC Postdoctoral Fellowship at McGill University Research spans game engine design , datafication of creativity , blockchain in art , and post-anthropocentric creative processes . With over HK$3.14M in grants as Principal Investigator, he explores situated digital creativity in urban contexts, critical worldbuilding pedagogy , and the mediation of technical systems in cultural production. His publications in Cities , Organized Sound , and Human Relations analyze the reconfiguration of artistic practices through software infrastructures and automation paradigms. Scientific awards include: FRQSC Postdoctoral Fellowship
David Bindel is an Associate Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences, College of Engineering, and Cornell Ann S. Bowers College of Computing and Information Science. He earned his Ph.D. in Mathematics from the University of California, Berkeley in 2006. His research focuses on applied numerical linear algebra, eigenvalue problems, and their applications in plasma physics, network analysis, and nonlinear systems. He develops methods for analyzing complex systems, including magnetic confinement in stellarators, stability of MHD systems, and community detection in networks. His work bridges theoretical foundations with practical computational tools, such as formal verification of linear algebra algorithms and scalable Gaussian process models. Bindel’s research explores the interplay between structure and computation, leveraging eigenvalue analysis to address challenges in computer vision, opinion dynamics, and engineering design. He has contributed to advancements in numerical methods for large-scale systems, including iterative solvers, spectral approximation techniques, and stochastic optimization. His interdisciplinary approach spans applied mathematics, computer science, and physics, with applications in fusion energy, machine learning, and network science. Recent work highlights include high-order expansions for magnetic confinement, adaptive filtering for dynamical systems, and Bayesian optimization strategies. His publications emphasize rigorous analysis alongside computational scalability, addressing both theoretical and practical aspects of modern scientific computing. Despite no explicitly listed awards, his contributions reflect significant impact in his fields.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University, directing the Interactive Perception and Robot Learning Lab. Previously, she was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems (2012-2017). She holds a PhD from KTH Royal Institute of Technology (Stockholm) and degrees from Chalmers University and TU Dresden. Her research focuses on perception, learning, and real-time multi-modal methods for autonomous robotic manipulation and grasping, aiming to bridge principles of human sensorimotor coordination with robotic implementation. Education: PhD in Robotics (KTH), MSc in Art & Technology (Chalmers), Diploma in Computer Science (TU Dresden) Research interests include developing goal-directed, real-time robotic systems capable of meaningful feedback for execution and learning. Key areas are dexterous manipulation, imitation learning, and cross-embodiment policy transfer. Notable contributions include the TidyBot platform and work on force-aware surgical robotics. Awards include the 2019 IEEE ICRA Best Paper Award, 2019 IEEE RA Early Career Award, and 2020 RSS Early Career Award. Her lab explores intersections of robotics, ML, and computer vision. Advising: Actively mentoring students/postdocs in manipulation, perception, and learning. Grants and collaborations span NSF, Stanford AI Lab, and industry partnerships. Future work emphasizes robust real-world deployment and human-robot collaboration. Labs/Teams: Leads the Interactive Perception and Robot Learning Lab, contributing to Stanford’s AI ecosystem. Previously managed the MPI AMD group, fostering interdisciplinary research in autonomous systems.
Lisa Walcott is an Associate Professor of Art at Hope College, where she has been a faculty member since 2018. She teaches sculpture courses, the capstone course for art majors, and a First-Year Seminar on artistic processes. Her studio practice focuses on kinetic sculptures and installations that explore themes of home, the everyday, and the feminine experience. MFA in Sculpture from Cranbrook Academy of Art (2010) B.A. from Trinity Christian College (2005) Walcott’s research intersects sculpture, installation art, and kinetic design, often recontextualizing domestic objects to investigate themes of interdependence, repair, and the unseen forces in everyday life. Her work frequently involves mechanical components and materials like wax, motors, and household items to create immersive, motion-driven installations. Her recent artworks, such as Making a Mountain (2024) and A Soft Punch to the Gut (2024), exemplify her focus on transforming utilitarian structures into emotive, kinetic pieces. These works, alongside digital collages like The Way I Figure It (2023), reveal a trajectory toward blending physical materials with conceptual frameworks to explore domestic ingenuity and tension. Walcott actively mentors students through faculty research collaborations and founded the Hope College Art Club. She exhibits nationally at institutions like the Eli and Edythe Broad Art Museum and The Sculpture Center, with solo exhibitions at venues including Manifest Gallery (Cincinnati) and the Urban Institute for Contemporary Art. Her studio practice integrates material investigations and workshops to refine techniques, which she translates into both pedagogy and art. Notable solo exhibitions include Swarm (2013) at the Broad Art Museum and Tight Spots (2021) at Saugatuck Center for the Arts.
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.
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.
Zakia Hammal is an Assistant Research Professor with dual appointments at Carnegie Mellon University, holding positions in the Robotics Institute within the School of Computer Science and the Department of Biomedical Engineering in the College of Engineering. Her work bridges computer science, machine learning, artificial intelligence, and social/behavioral psychology to advance computational models for human behavior analysis. Dr. Hammal's educational background includes a PhD in Computer Science, a Master of Artificial Intelligence and Algorithmic with specialization in Image Processing, and an Engineer's degree in Computer Science with specialization in Computer Systems. Her academic journey has positioned her at the intersection of technical expertise and healthcare applications. Her research focuses on multimodal human behavior modeling in social interaction, with particular emphasis on health informatics and affective computing (Emotion AI). Dr. Hammal's work has pioneered computational models for multimodal assessment of psychiatric disorders, including depression severity evaluation, automatic pain intensity measurement, assessment of expressiveness in children with facial abnormalities, analysis of non-verbal communication in mother-infant interaction, and identification of behavioral markers in autism spectrum disorder. Her approach integrates computer vision, machine learning, and behavioral psychology to create systems that can objectively measure human behaviors that are often subjective in clinical settings. Analysis of her recent publications reveals a consistent trajectory toward more sophisticated multimodal approaches to healthcare challenges, particularly in pain assessment and mental health diagnostics. Her work increasingly emphasizes interpretable AI models that can translate complex behavioral patterns into clinically meaningful insights, with growing attention to applications for vulnerable populations including infants, elderly patients, and those with craniofacial abnormalities or autism spectrum disorder. Women in AI Awards North America 2023 – AI Researcher of the Year Award Outstanding Reviewer Award at FG 2015 Best Paper award at ACII 2015 Outstanding Paper award at ICMI 2012 Dr. Hammal has secured significant research funding, primarily from the U.S. National Institutes of Health, including an R01 grant for developing a Multimodal Behavioral AI platform for pain assessment and management, and additional grants for automatic pain assessment in older adults with dementia. Her leadership extends to mentoring through her involvement in organizing workshops and conferences that train the next generation of researchers in affective computing and health informatics. As an active leader in her field, Dr. Hammal serves as ACM ICMI Steering Board Committee Member, Associate Editor for IEEE Transactions on Affective Computing and IEEE Transactions on Multimedia, and has organized numerous influential workshops including the International Workshop on Automated Assessment of Pain and Face and Gesture Analysis for Health Informatics. She is set to serve as Program Chair for FG 2025, ACII 2025, and ICMI 2026, demonstrating her growing influence in shaping the future direction of research in multimodal interaction and affective computing.
Yimin D. Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at Temple University's College of Engineering, where he leads the Advanced Signal Processing (ASP) Lab. His research spans statistical signal processing, array processing, radar, wireless communications, and convex optimization. Research Interests: Dr. Zhang's work focuses on cutting-edge signal processing techniques including compressive sensing, sparse arrays, time-frequency analysis, and robust beamforming. These are applied to radar systems, satellite navigation, assisted living, and wireless networks. His research addresses core challenges in target localization, direction-of-arrival estimation, and spectrum-efficient joint radar-communication systems. The recent publications highlight a strong trend in exploiting sparsity, virtual arrays, and deep learning to enhance resolution and robustness in radar and communication systems. Themes include multi-frequency processing, low-rank matrix recovery, and optimized OFDM waveforms for dual-function systems. Scientific Awards: 2016 IET Radar, Sonar and Navigation Premium Award 2017 IEEE Aerospace and Electronic Systems Society Harry Rowe Mimno Award 2018 IEEE Signal Processing Society Young Author Best Paper Award (coauthor) Advising and Grants: Dr. Zhang has served as Principal or Co-Principal Investigator on over $6 million in research funding from the NSF, AFRL, ONR, and DARPA. While student advisees are not listed, his leadership of the ASP Lab suggests active mentorship of graduate researchers. He contributes extensively to the academic community as an associate editor for IEEE Transactions on Signal Processing and editor for Signal Processing journal, and serves on key IEEE technical committees. Labs and Teams: He directs the Advanced Signal Processing (ASP) Lab at Temple University, which focuses on developing novel algorithms for real-world applications in radar, communications, and navigation. Previously, he led the Wireless Communications and Positioning Lab and the RFID Lab at Villanova University.
Joy Arulraj is an Associate Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research focuses on data systems, machine learning, and database systems, with a particular emphasis on video analytics and adaptive query processing. He leads the Data Systems and Analytics Group and is developing the EVA AI-Relational Data System. Dr. Arulraj's research interests span data systems, machine learning, database systems, video analytics, and adaptive query processing. His work centers on developing systems that efficiently process complex queries, particularly for video analytics and machine learning workloads. He has made significant contributions to GPU database systems, non-volatile memory database management, and adaptive query processing techniques. His research often bridges the gap between theoretical database principles and practical implementations for modern hardware architectures. His recent publications show a strong trend toward video analytics systems, adaptive query processing for machine learning workloads, and GPU-accelerated database systems. The EVA system represents a major focus of his recent work, providing end-to-end exploratory video analytics capabilities. His research also addresses fundamental database concepts like buffer management, query optimization, and storage management, adapting these principles for modern hardware and application requirements. Dr. Arulraj has advised numerous graduate students including Pramod Chunduri, Gaurav Tarkok Kakkar, Jiashen Cao, and Sayan Sinha. His graduated students have gone on to work at companies like ServiceNow, Meta Research, and the Korean Army. He actively teaches database system courses at Georgia Tech, including Database System Implementation (CS 4420/6422) and Advanced Database System Implementation (CS 4423/6423), where students build database systems from scratch using C++ and the BuzzDB framework. He maintains an active research program with consistent publication output across top database and systems conferences. His work spans from theoretical database principles to practical system implementations, with a recent emphasis on video analytics, machine learning integration with database systems, and leveraging modern hardware like GPUs and non-volatile memory for database applications.
Paolo Gardoni is the Alfredo H. Ang Family Professor and an Excellence Faculty Scholar in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, with additional professorial appointments in Industrial & Enterprise Systems Engineering and Biomedical & Translational Sciences. He also serves as Director of the MAE Center and Editor-in-Chief of Reliability Engineering & System Safety. Education Ph.D. in Civil Engineering, University of California, Berkeley (2002) M.A. in Statistics, University of California, Berkeley (2001) M.Eng. in Structural Engineering, University of Tokyo (1997) Laurea (BS+MS equivalent) in Structural Engineering, Politecnico di Milano (1997) Research Interests Gardoni’s scholarship integrates probabilistic methods with large-scale infrastructure systems to advance reliability, risk, and life-cycle analysis. His work quantifies the performance of deteriorating systems under natural and anthropogenic hazards, models societal impacts of disasters, and develops decision frameworks for sustainable and resilient infrastructure. He also examines ethical, social, and legal dimensions of risk, and investigates optimal strategies for hazard mitigation, disaster recovery, and climate adaptation. Across more than 250 refereed journal papers, he has advanced sub-fields ranging from probabilistic mechanics and earthquake engineering to catastrophe bond pricing and engineering ethics, leveraging tools such as stochastic differential equations, Bayesian networks, and physics-informed machine learning. Awards & Honors Alfredo Ang Award on Risk Analysis and Management of Civil Infrastructure (ASCE, 2021) Best Paper Awards in ASCE Journal of Sustainable Water in the Built Environment (2019) and Geotechnical Research (2018 Telford Premium Prize) Fellowships and named professorships: Alfredo H. Ang Family Professor, Excellence Faculty Scholar, and courtesy or honorary professorships at Tsinghua, IIT Guwahati, Tongji, Jianghan, and Loughborough universities. Research Leadership & Funding Gardoni has secured over $58 million in research funding from NSF, DHS, NIST, USAID, Qatar National Research Fund, and other agencies. He directs the MAE Center—formerly an NSF Engineering Research Center—focused on multi-hazard engineering approaches, and is Editor-in-Chief of Reliability Engineering & System Safety (Elsevier, IF 9.4). He founded and formerly led the journal Sustainable and Resilient Infrastructure (Taylor & Francis) and serves on editorial boards of nine additional journals. Advising & Mentorship He has graduated 27 PhD and 35 Master’s students, many of whom now hold faculty positions worldwide. His group maintains an active pipeline of doctoral and post-doctoral researchers working on resilience analytics, infrastructure monitoring, and risk-informed decision-making. Laboratories & Collaborations He leads the MAE Center and is affiliated with the Critical Infrastructure Resilience Institute (CIRI) and the Biomedical and Translational Sciences group. International collaborations span the UK (Loughborough), India (IIT Guwahati), and China (Tsinghua, Tongji, Jianghan), fostering cross-disciplinary research in reliability and resilience engineering.
Phuong H. Nguyen is an Associate Professor at the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He leads the digital Power & Energy Systems (digi-PES) lab, focusing on smart energy systems, distributed control, and IoT integration for future energy grids. His research addresses energy transition challenges, including microgrid optimization, flexibility markets, and data-driven grid management. Nguyen holds a PhD from TU/e (2010) and has held visiting researcher positions at Clemson University. His work contributes to UN SDG goals related to affordable and clean energy. Education: Bachelor's in Electrical Engineering, Hanoi University of Science and Technology (2002) Master's in Electrical Engineering, Asian Institute of Technology (2004) PhD in Electrical Engineering, TU/e (2010) Key Research Areas: Smart Grids and Cyber-Physical Systems Distributed Energy Resource Management Local Energy Markets and Flexibility Provision IoT and Big Data in Energy Systems Awards & Recognition: Best Paper Award SEST 2020 Projects & Grants: REACT-D: Reactive Power Management (Project Manager) Al-driven Applications to Smarten Power System Operation (Project Lead) UNIversity Campus as a Self-Regulated Network (Co-Manager) Labs & Teams: The digi-PES lab develops cyber-physical tools for energy transition, including microgrid simulations, flexibility markets, and grid resilience strategies.
Anocha Aribarg is a Professor of Marketing and Area Chair of Marketing at the Ross School of Business, University of Michigan, with additional faculty affiliation at the Center for Southeast Asian Studies (CSEAS). Her interdisciplinary research bridges psychological theory, consumer behavior, and advanced statistical modeling to address complex marketing challenges. Education: PhD in Marketing, University of Wisconsin, 2004 MBA, University of Wisconsin-Milwaukee, 1998 BS in Statistics, Chulalongkorn University, Thailand, 1994 Research Focus: Prof. Aribarg investigates cognitive processes in consumer decision-making, specializing in individual/joint choice dynamics, product search behaviors, and responses to marketing stimuli. Her methodology integrates Bayesian econometrics with physiological measures (eye tracking, skin conductance) and multi-method experimental designs to uncover hidden decision mechanisms. Publication Trends: Her 14 recent publications (2009-2024) reveal consistent innovation in choice modeling, with increasing emphasis on attention dynamics (2020), moral psychology in aesthetics (2022), and high-frequency service data integration (2023). Work appears predominantly in Marketing Science Journal of Marketing Research, and Psychological Science, demonstrating methodological rigor across consumer behavior, advertising, and service contexts. Academic Service: She serves as Associate Editor for Marketing Science, Journal of Marketing, and Journal of Marketing Research, while contributing to the Journal of Retailing editorial board. Teaching: Prof. Aribarg delivers graduate-level courses in Marketing Research and Analytics and Predictive Analytics at Ross, emphasizing data-driven decision frameworks.
Dr. Kai Gong is an Assistant Professor of Civil and Environmental Engineering at Rice University, with affiliations at the Rice Advanced Materials Institute and Ken Kennedy Institute. His research focuses on sustainable infrastructure materials, environmental sustainability, and materials science. He holds a Ph.D. in Civil & Environmental Engineering and Materials Science from Princeton University, an MEngSci from Monash University (Australia), and dual B.S. degrees from Monash University and Central South University (China). Research Interests: Development of durable, sustainable infrastructure materials Waste encapsulation and conversion to value-added products Carbon mineralization and utilization Advanced characterization techniques (synchrotron/neutron scattering) Data-driven modeling and atomistic simulations Notable Awards: 2023 Le Chatelier Medal (Cement and Concrete Research) 2024 Giatec Award for Best Paper in Sustainability Walbridge Fund Graduate Award (2019) His work integrates computational methods (e.g., molecular dynamics) with experimental techniques to address decarbonization challenges in infrastructure. The Gong Research Group actively seeks motivated researchers for opportunities in sustainable materials innovation.
Clare Harris serves as Professor of Visual Anthropology and Head of the School of Anthropology and Museum Ethnography at the University of Oxford, concurrently holding curatorship of Asian Collections at the Pitt Rivers Museum and a Fellowship at Magdalen College. Her academic leadership spans institutional administration, museum curation, and doctoral supervision within Oxford's anthropological ecosystem. Her educational foundation includes a BA from the University of Cambridge and MA/doctorate from SOAS, University of London. Early career roles comprised Lecturer in Anthropology of Art at the University of East Anglia (1994-1998) before her Oxford appointment. Harris pioneers research at the intersection of visual anthropology, Tibetan studies, and museum decolonization. Her work critically examines Himalayan visual culture, colonial/post-colonial photography, and museum representation politics, evolving toward digital repatriation and community-centered curation. She integrates anthropological fieldwork (conducted in Tibetan diaspora communities since the 1990s) with art historical analysis and archival research. Her 13 recent publications (2004-2022) reveal a progressive trajectory from Tibetan art history toward urgent museum decolonization debates, with consistent focus on photographic practices and cross-cultural representation. Key thematic developments include the shift from historical analysis to active digital repatriation projects and increased engagement with contemporary artists. Harris's accolades include: Fellowship of the British Academy (2019) E. Gene Smith Prize for The Museum on the Roof of the World (2014) Visual Anthropology Prize for In the Image of Tibet (2000) She currently supervises five DPhil students while examining over 25 completed doctorates globally. Research funding spans British Academy personal grants, Leverhulme Trust support, and major collaborative projects including AHRC, Wenner-Gren Foundation, and EU grants—most notably the current AHRC-funded Decolonising the Museum: Digital Repatriation of the Gaidinliu Collection . At the Pitt Rivers Museum, Harris leads high-impact curatorial initiatives including the Abramović collaboration ( Gates and Portals ), Nyema Droma's Performing Tibetan Identities , and community projects like My Tibet Museum , establishing innovative frameworks for museum-public engagement.
Frank L. Hammond III serves as Assistant Professor at Georgia Tech's Woodruff School of Mechanical Engineering since April 2015, directing the Adaptation Robotic Manipulation (ARM) Laboratory. A Carnegie Mellon PhD graduate, he previously held postdoctoral positions at MIT and Harvard as a Ford Fellow. His interdisciplinary work bridges mechanical engineering, biomedical applications, and computational design. Education Ph.D. in Mechanical Engineering, Carnegie Mellon University M.S. in Mechanical Engineering, University of Pennsylvania M.S. in Electrical Engineering, University of Pennsylvania B.S. in Electrical Engineering & Biomedical Engineering, Drexel University Hammond's research pioneers adaptive robotic manipulation (ARM) systems that operate in unstructured human environments through bioinspired computational design. His lab develops xenomorphic (non-biomorphic) robots using soft pneumatic actuation, flexible electronics, and machine learning to achieve biological-level versatility. Key application domains include wearable human augmentation devices , haptic-enabled surgical teleoperation , and autonomous soft platforms for medical and industrial use. The ARM methodology integrates empirical biomechanics characterization with simulation-driven optimization and rapid prototyping. Analysis of his 15 most recent publications (2023-2025) reveals three dominant trends: (1) Medical rehabilitation breakthroughs through intention-driven exoskeletons with soft bioelectronics, (2) Novel locomotion strategies for soft robots in complex environments (sand, water, cluttered spaces), and (3) Advanced haptic feedback systems leveraging multimodal sensory substitution for proprioceptive restoration. These works consistently bridge biomechanics, control theory, and human factors. Awards Ford Postdoctoral Research Fellowship at Harvard School of Engineering Hammond actively mentors graduate researchers including PhD candidates Lucas Tiziani (soft actuators) and Bangyuan Liu (earthworm robotics), and Master's student Alex Hart (pediatric haptics). His lab secures research funding for projects like tunable mechanical interfaces for neuropathy treatment and cognition-focused wearable devices, with strong industry and clinical partnerships evident in co-authored medical device publications. The ARM Lab maintains robust collaborations across Georgia Tech's robotics, neuroscience, and biomedical engineering communities. The Adaptation Robotic Manipulation Laboratory operates from Whitaker Building Room 4102, housing specialized facilities for soft robot fabrication (3D printing, shape deposition manufacturing) and biomechanics testing. Current projects include pediatric haptic feedback displays, biomimetic swimming robots, and kirigami-skinned earthworm robots for subsurface locomotion. The lab emphasizes translational research with multiple pending medical device patents and active participation in K-12 STEM outreach programs.
