Sean Andersson is a Professor in Mechanical Engineering and Systems Engineering at the College of Engineering, Boston University, and serves as Director of the BU Robotics Lab. His research bridges systems and control theory with applications in nanotechnology , atomic force microscopy , and robotics . His work in nanobioscience focuses on single molecule tracking and high-speed imaging in atomic force and fluorescence microscopy, leveraging control theory to enhance imaging capabilities. In robotics, he develops stochastic control methods for autonomous systems operating in complex environments, emphasizing multi-agent systems , sparsely sampled data , and symbolic control frameworks . Recent publications highlight trends in receding horizon control , persistent monitoring , neural style transfer for imaging , and stochastic policy optimization . The Andersson Lab also explores compressive sensing and optimal control for sensor networks and nanoscale fluid dynamics.
Professor Kenneth Ruud is a leading expert in theoretical and computational chemistry at UiT The Arctic University of Norway. He serves as Director General of the Norwegian Defence Research Establishment and leads the Hylleraas Centre for Quantum Molecular Sciences. His research focuses on relativistic quantum chemistry, developing advanced ab initio methods for molecular property calculations, and integrating QM/MM and continuum solvent models. Education: PhD from University of Oslo (1998, supervised by Trygve Helgaker) Postdoc: University of San Diego with Peter Taylor (1998-2000) His work spans relativistic effects in molecular properties, vibronic coupling, and X-ray spectroscopy. He contributes to software development through programs like Dalton, Dirac, ReSpect, and OpenRSP. Recent publications highlight applications in spin-vibronic dynamics, heavy metal L/M-edge XAS, and topological materials. Key scientific contributions include relativistic DFT for nuclear spin-rotation constants, polarizable embedding models for vibrational spectra, and quantum dynamics frameworks. Awards recognize his impact through the Dirac Medal (2008) and multiple academy memberships. Elected to Norwegian Academy of Science and Letters Fellow of American Association for the Advancement of Science (AAAS) Foreign member of Finnish Academy of Science and Letters He actively participates in open science initiatives and serves on boards including Norges Forskningsråd and CAROS center for subsea robotics. Current projects involve quantum molecular science in extreme environments and computational protocol development.
Hasti Seifi is an Affiliated Associate Professor at the Department of Computer Science (DIKU), University of Copenhagen, specializing in Human-Centred Computing. Her research focuses on haptics, augmented reality, and human-robot interaction. Institution: University of Copenhagen Department: Department of Computer Science Section: Human-Centred Computing Research interests include: Designing innovative haptic feedback systems Exploring tactile experiences in augmented reality Developing human-robot interaction frameworks Creating generative models for haptic design Investigating social touch technologies Advancing mid-air and ultrasound haptic interfaces Recent publications demonstrate a strong focus on: Generative haptic modeling for AR/XR systems Human-robot interaction dynamics Text input in extended reality environments Visual-haptic multisensory integration Ultrasound mid-air haptic design tools Social touch technologies
Jeffrey L. Krichmar is a Professor in the Department of Cognitive Sciences and Department of Computer Science at the University of California, Irvine. His academic journey includes a B.S. in Computer Science from the University of Massachusetts Amherst (1983), an M.S. in Computer Science from The George Washington University (1991), and a Ph.D. in Computational Sciences and Informatics from George Mason University (1997). Prior to UCI, he served as Assistant Professor at George Mason University (1997-1999) and Senior Fellow at The Neurosciences Institute (1999-2007). University of California, Irvine (2007-present) George Mason University (1997-1999) The Neurosciences Institute (1999-2007) His research focuses on neurorobotics , exploring how embodied cognition and biologically plausible neural models can enhance robotic systems. Key areas include spiking neural networks , neuromodulation , path planning , and interactive tactile robots for therapeutic applications. His work bridges neuroscience , robotics , and cognitive science , with applications in autonomous vehicles , neuroprosthetics , and AI explainability . Recent publications emphasize spiking neural networks for navigation , neuromodulated attention , and neuromorphic hardware integration. The development of CARLsim, a GPU-accelerated spiking neural network simulator now in version 6.0, represents a major technical contribution. His team's work on socially assistive robots like CARL-SJR targets therapeutic applications for autism and ADHD. Scientific Awards IJCNN 2020 Best Paper Award Finalist for Best Student Paper at IJCNN 2018 Best Paper Award at IEEE IJCNN 2009 Grants include National Science Foundation funding for neural models of decision-making (2009). His lab (Cognitive Anteater Robotics Laboratory) develops systems that use large-scale brain simulations for autonomous behavior , with applications in adaptive robotics , sensorimotor learning , and neuroethology . Current projects explore neuromodulatory influences on attention systems and cognitive flexibility .
Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he directs the UW Robot Learning Laboratory. He also serves as a Principal Research Scientist in the Seattle Robotics Lab at NVIDIA Research and co-chairs the IEEE Robotics and Automation Society Technical Committee on Robot Learning. Dr. Boots received his Ph.D. from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, where he was a member of the Sense, Learn, Act (SELECT) Lab co-directed by Carlos Guestrin and his advisor Geoff Gordon. Prior to joining the University of Washington faculty, he was an Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech, and before that, he completed a post-doc in the Robotics and State Estimation Lab directed by Dieter Fox at the University of Washington. Professor Boots' research focuses on the intersection of machine learning, artificial intelligence, and robotics, with particular emphasis on developing theory and systems that tightly integrate perception, learning, and control. His work spans computer vision, state estimation, localization and mapping, high-speed navigation, motion planning, and robotic manipulation. His group develops algorithms drawing from deep learning and neural networks, nonparametric statistics, graphical models, nonconvex optimization, quantum physics, online learning, reinforcement learning, and optimal control. The research demonstrates a strong theoretical foundation while maintaining practical relevance to real-world robotic systems. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with robotics, particularly in model predictive control, motion planning, and learning-based approaches to robot control. His work shows increasing focus on developing theoretically grounded methods that can handle the complex, nonlinear dynamics of real-world robotic systems while maintaining computational efficiency. The publications span top venues including ICRA, CoRL, IROS, and NeurIPS, demonstrating broad impact across multiple subfields of robotics and AI. Finalist for Best Systems Paper at Conference on Robot Learning (CoRL-2021) Multiple papers selected for oral presentations at top robotics conferences Work recognized for theoretical contributions and practical applications in robot learning As director of the UW Robot Learning Laboratory, Boots leads a vibrant research group focused on fundamental and applied research in robot learning. The lab maintains strong collaborations with NVIDIA Research and has produced numerous high-impact publications that bridge theory and practice. Professor Boots teaches courses in autonomous robotics, machine learning, and reinforcement learning, contributing to both undergraduate and graduate education at the University of Washington.
Dr. Joanna Deaton Bertram is an Assistant Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering. She concurrently holds an Assistant Professor appointment in Surgery, underscoring her interdisciplinary commitment to advancing medical robotics. Dr. Bertram leads a research laboratory devoted to the design, modeling, and control of robotic systems for surgical and interventional applications, working closely with Duke’s clinical and engineering communities. Education Ph.D. in Robotics, Georgia Institute of Technology, 2024 M.S. in Mechanical Engineering, Georgia Institute of Technology, 2024 B.S. in Biomedical Engineering, Georgia Institute of Technology, 2018 Research Interests Dr. Bertram’s research program is centered on medical robotics , with particular emphasis on continuum robotics and image-guided interventions . Her work integrates novel mechanical design with advanced control algorithms and smart materials to create robotic systems capable of navigating complex anatomical pathways. A hallmark of her approach is the incorporation of real-time fiber-optic shape and force sensing (using Fiber Bragg Grating technology) to provide surgeons with unprecedented feedback during procedures. Application domains include steerable needles for brachytherapy , robotic guidewires for endovascular surgery , and pediatric neuroendoscopy . Publication Themes Across more than fifteen peer-reviewed articles, Dr. Bertram has systematically advanced the state of the art in surgical robotics , fiber-optic sensing , and robotic system modeling . Her 2024 tutorial on Nitinol and Tungsten tendon attachment techniques provides practical guidance for building highly articulated continuum robots, while her 2023 series on the COAST guidewire robot demonstrates model-based design and simultaneous shape/force sensing for large-deflection medical devices. Earlier work explored 3D-printed patient-specific robotic tools and carbon-nanotube flexible sensors, illustrating a trajectory from fundamental sensor research to full robotic system integration. Scientific Recognition & Collaboration Although no major external awards are explicitly listed, Dr. Bertram’s publications in top-tier venues such as IEEE Robotics and Automation Letters , IEEE Transactions on Medical Robotics and Bionics , and IEEE/ASME Transactions on Mechatronics attest to strong peer recognition. She actively invites motivated graduate students, post-docs, and research staff to join her lab, fostering an open and interdisciplinary environment. Advising & Grants Dr. Bertram’s lab is presently recruiting trainees at all levels. While specific funded grants are not enumerated, her dual departmental appointments and extensive publication record suggest active federal or foundation support. Prospective students and collaborators are encouraged to contact her directly at joanna.d.bertram@duke.edu . Laboratory & Teams Dr. Bertram directs a laboratory within Duke University’s Pratt School of Engineering that collaborates closely with clinicians in the School of Medicine. The group focuses on rapid prototyping of medical devices, in-vitro and ex-vivo validation, and translation of robotic technologies to the operating room.
Gianmarco Pinton is an Associate Professor in the Department of Biomedical Engineering at the University of North Carolina at Chapel Hill. His research focuses on nonlinear ultrasound and mechanical wave propagation, with applications to medical imaging and therapy. He specializes in traumatic brain injury, shear shock waves, and ultrasound therapy. Ph.D., M.S., and B.S.E. in Biomedical Engineering/Physics from Duke University His lab develops physics and simulation tools for nonlinear wave propagation, aiming to create advanced diagnostic ultrasound methods. Key areas include traumatic brain injury, transcranial imaging, and therapeutic ultrasound. His recent work explores super-resolution imaging, brain motor circuits, and Alzheimer's disease vascular mapping using ultrasound. Article trends highlight innovations in transcranial ultrasound, super-resolution techniques, lung imaging, and neuromodulation. His publications address image degradation, contrast agents, and shear wave dynamics in neurological contexts.
Dr. Ilias Gerostathopoulos is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, affiliated with the Network Institute and the Department of Information Management & Software Engineering. He specializes in self-adaptive systems, cyber-physical systems, and machine learning operations (MLOps). His work focuses on software architectures for autonomous systems, decision-making under uncertainty, and experiment-driven adaptation frameworks. He teaches courses such as 'Fundamentals of Adaptive Software' and 'Information Management', emphasizing practical applications of adaptive systems and data-driven decision-making. Gerostathopoulos has been awarded the Best Presentation Award (2021) for contributions to evaluating self-adaptive systems. His research addresses challenges in industrial self-adaptation, MLOps architectures, and robotics. Key research themes include: Architecture-based self-adaptation in robotics and CPS MLOps frameworks and systematic analysis of AI systems Uncertainty management in autonomous systems Experiment-driven learning and tool development Notable contributions include the ExpEngine tool for workflow optimization and the ReBeT framework for robotic systems. His work bridges theoretical software engineering with practical industrial implementations.
Sam Emaminejad is an Associate Professor in the Department of Electrical and Computer Engineering at the Henry Samueli School of Engineering and Applied Science, University of California Los Angeles (UCLA). His research focuses on developing advanced wearable bioelectronic systems for continuous, noninvasive health monitoring and personalized therapeutics. Key Research Areas: Biomarker detection via flexible sensors Microfluidic and ferrobotic systems Stress and drug level monitoring Biodegradable and breathable wearable materials Recent Trends: Analysis of sweat and interstitial fluids using microneedles, aerogel skins, and programmable microfluidics. Machine learning integration for physiological evaluation is prominent. Awards & Collaborations: While specific awards aren't listed, he collaborates with major UCLA Health and Engineering faculty, including Ali Khademhosseini and Dino Di Carlo, on projects funded by NIH T32 grants and institutional fellowship programs. Grants & Labs: Leads projects in NIH-funded wearable sensor research, including the development of autonomous systems for cystic fibrosis and glucose monitoring. His lab explores ferrobotic swarms and hydrogel-based interfaces for clinical and consumer applications.
Wenhao Ding is a Research Scientist at NVIDIA's Autonomous Vehicle Group, focusing on enhancing the safety and robustness of physical autonomous systems, particularly autonomous vehicles. His research integrates multi-modal large language models, reinforcement learning, and causal discovery to improve model reasoning capabilities. He holds a Ph.D. from Tsinghua University's Department of Electronic Engineering, with a thesis on 'Generative AI for Critical Digital Twins.' Key research interests include safety-critical scenario generation, causal representation learning, and offline reinforcement learning. His work emphasizes closed-loop simulation for autonomous systems and has led to contributions like the SafeBench benchmarking platform and the RealGen scenario generation framework. He has received the 2022 Qualcomm Innovation Fellowship. Notable collaborations include projects with Prof. Marco Pavone at Stanford and internships at Amazon Lab126 (Astro team) and Bosch Center for AI. He actively reviews for top conferences (ICML, NeurIPS, CVPR) and journals (IEEE T-ITS, RA-L). His recent focus on privacy risks in robotics and causal-aware driving models underscores his commitment to trustworthy AI systems. He organizes conferences like the 2024 IEEE International Automated Vehicle Validation Conference and co-hosted the Secure and Safe Autonomous Driving (SSAD) Workshop at CVPR 2023. His interdisciplinary work bridges theory and practice, addressing critical challenges in autonomous systems' safety and generalization.
Guodong Shi is Associate Professor at the University of Sydney's Australian Centre for Robotics, heading the Centre for Robotics and Intelligent Systems. His research develops theoretical frameworks for multi-agent coordination, distributed optimization, and networked control systems. Current projects investigate collective decision-making under information constraints, privacy-preserving optimization, and game-theoretic formulations for social and robotic networks. His group develops algorithms for distributed solution of linear equations, Boolean networks, and equilibrium seeking. Doctoral supervision includes projects on acrobatic legged robots, reinforcement learning for robotic stability, and safe control under dynamic environments. Laboratory capabilities support theoretical and experimental validation. Research has applications in autonomous swarm robotics, smart grid optimization, and social network analysis. Teaching includes graduate courses on networked systems and optimization.
Dr. Craig R. Forest is a Professor at the Georgia Institute of Technology's Woodruff School of Mechanical Engineering, specializing in bioMEMS, neuroengineering, and high-throughput instrumentation. He leads the Precision Biosystems Laboratory, focusing on developing robotic tools for neuroscience and genomics. His research bridges mechanical engineering with biological systems, creating innovations like the PatcherBot for automated electrophysiology. Forest earned his Ph.D. (2007) and M.S. (2003) from MIT and B.S. (2001) from Georgia Tech. He has been recognized with awards including the 2013 Georgia Tech Class of 1940 W. Roane Beard Outstanding Teacher Award and Engineer of the Year (2013). His work emphasizes interdisciplinary collaboration, particularly through initiatives like CREATE-X and the Invention Studio, fostering student entrepreneurship and maker culture. Key contributions include ultra-high-throughput genomics tools, microfluidic systems, and acoustic reporter genes for medical imaging. Forest’s lab explores emerging fields like intracellular robotics in neuroscience and molecular communication networks, with applications in drug discovery and personalized medicine. Scientific awards highlight his impact in education and engineering innovation. His grants and collaborations span academic and industrial partnerships, advancing both theoretical and applied research in bioengineering and nanotechnology.
Mark D. Gross is a Professor of Computer Science and Director of the ATLAS Institute at the University of Colorado Boulder. His research focuses on modular robotics, tangible interaction design, digital fabrication, and computational design. He co-founded Modular Robotics and Blank Slate Systems, leveraging his expertise in educational technology and maker culture. Education: PhD in Design Theory & Methods from MIT. Academic history includes roles at Carnegie Mellon University and University of Washington Seattle. His work spans architecture, robotics, and human-computer interaction. Research Interests: Modular robotics and computationally enhanced construction kits Tangible interaction and e-textiles Shape-changing interfaces and swarm robotics Sketch-based interaction and digital fabrication Publications highlight advancements in collaborative AR systems, shape-changing interfaces (e.g., LiftTiles, ShapeBots), and accessible technologies like FluxMarker. His work bridges computational tools and creative design processes. Advising: Guided students such as Ryo Suzuki (PhD '20). Entrepreneurial ventures include companies spun off from research. Labs/Teams: Leads projects at the ATLAS Institute, emphasizing interdisciplinary collaboration in robotics, design, and human-centered computing.
Overview Dr. Catherine Prentice is a Professor of Marketing and Head of the Marketing Discipline at the University of Southern Queensland, Australia. She also serves as Director of the Asia Pacific Association for Gambling Studies (APAGS) and holds editorial roles across multiple top-tier journals including Journal of Business Research and International Journal of Contemporary Hospitality Management . Her research focuses on artificial intelligence, emotional intelligence, service innovation, consumer behavior, and gambling studies, with significant contributions to the tourism and hospitality sectors. Education MBA, Lincoln University (2001) PhD in Marketing, Victoria University (2009) Research Interests Dr. Prentice’s work bridges theoretical and applied research, emphasizing the practical implications of her findings. Key areas include: - The role of AI in enhancing customer engagement and loyalty - Emotional intelligence in service encounters and leadership - Consumer psychology in tourism and hospitality contexts - Strategic marketing in the gambling and gaming industries - Sustainability and innovation in service delivery Publications & Impact Her over 100 peer-reviewed articles span journals like Journal of Hospitality & Tourism Research and Tourism Management , with a focus on empirical investigations of service quality, customer loyalty, and organizational performance. Recent work explores the intersection of AI and human service interactions. Leadership & Service In addition to her academic roles, Dr. Prentice contributes to industry through: - Editorial board memberships across 12+ Q1 journals - Leadership in professional associations like NAGS and ANZMAC - Consultancy in business strategy and marketing for corporations and governments
Igor Mezić is a Professor in the Department of Mechanical Engineering at the University of California, Santa Barbara. His research focuses on operator-theoretic analysis of nonlinear dynamical systems, with applications in microfluidics, nanotechnology, and biotechnology. He holds affiliations with the Institute for Collaborative Biotechnologies and the California Nanoscience Institute. Education: MS in Mechanical Engineering, University of Rijeka, Croatia PhD in Mechanical Engineering, California Institute of Technology Research Interests: Mixing and separation in fluids, particle dynamics at micro/nano scales, Atomic Force Microscope (AFM) dynamics, and complex systems theory. His work bridges physical phenomena with mathematical modeling for device design and improvement. Awards: Early Career Award (NSF) Alfred P. Sloan Fellowship George S. Axelby Award (IEEE) United Technologies Special Achievement Prize J.D. Crawford Prize (SIAM) Advising & Grants: Supported by NSF Early Career Award. Active in mentoring through interdisciplinary research groups focusing on fluid dynamics and nanotechnology. Labs & Teams: Leads research through the Mezić Group (mgroup.me.ucsb.edu), collaborating on projects spanning microfluidic systems, biotechnology applications, and multiscale dynamics.
