Yang (Gilbert) Ye is an Assistant Professor in the Department of Civil and Environmental Engineering at Northeastern University, joining in January 2025. His research focuses on human-AI/robot teaming, automation in engineering, and assistive technologies, with a particular emphasis on human-centric robotics and sensorimotor processes. He holds a PhD in Civil Engineering from the University of Florida (2024), advised by Dr. Eric Jing Du. **Affiliations**: Member of ASCE, IEEE, and HFES. His work integrates VR/AR, robotics (e.g., drones, exoskeletons), and AI to enhance civil engineering workflows and workforce training. He leads the Ye Lab, actively recruiting PhD students and postdocs with coding experience (Python/C++/C#) and backgrounds in engineering or computer science. **Key Research Themes**: Human-robot interaction, construction automation, exoskeleton training, and delayed feedback mitigation in teleoperation. Over 20 peer-reviewed publications in journals like ASCE JoCEN, IEEE Access, and Advanced Engineering Informatics. **Lab Opportunities**: PhD/postdoc applicants require strong academic records (GPA ≥3.5) and coding skills. Undergrad/master students can apply for thesis/research roles. Funding covers tuition, insurance, and stipends.
Eric Fortune is an Associate Professor in the Department of Biological Sciences at New Jersey Institute of Technology. His research spans neuroethology, electrosensory systems, and computational biology, with a focus on weakly electric fish and sensorimotor integration. Recent Publications highlight work on Neurophysiological adaptations in weakly electric fish Machine learning applications in flu forecasting Behavioral and neural mechanisms of exploration-exploitation trade-offs Grants include multiple NSF-funded projects on collaborative research in active sensing, neuromechanical systems, and social interaction effects on sensory function. Media Coverage features his role in a $5M Amazon rainforest biodiversity contest, where his team counted over 250,000 critters in a square kilometer.
Rishidev Chaudhuri is an Associate Professor at the University of California, Davis in the Department of Neurobiology, Physiology and Behavior within the College of Biological Sciences. His research focuses on computational neuroscience and neural dynamics, employing mathematical models to investigate how neural circuits generate cognitive processes such as memory, perception, and decision-making. His work explores neural dynamics through models of memory systems, attentional mechanisms, and probabilistic inference. Recent publications highlight advances in understanding hippocampal memory scaffolds, parietal-frontal interactions, and neuromorphic computing inspired by brain architecture. Education: BA in Physics (Amherst College), PhD in Applied Mathematics (Yale University) Centers: Center for Neuroscience; affiliated with Applied Mathematics and Neuroscience Graduate Groups Scientific awards and honors are not explicitly mentioned in the provided materials.
Dr. Philipp Allgeuer is a Postdoctoral Research Associate at the Knowledge Technology Research Group within the Department of Informatics at the University of Hamburg. His work focuses on humanoid robotics, bipedal locomotion, and sensor fusion. He holds a PhD from the Autonomous Intelligent Systems Group at the University of Bonn, alongside dual bachelor's degrees in Mechatronic Engineering and Mathematical/Computer Sciences (both with First-Class Honors). His research contributions include the development of the igus Humanoid Open Platform and the NimbRo-OP series of humanoid robots, recognized with awards like the RoboCup HARTING Open Source Award (2016) and the Best Humanoid Award (2018). He has authored influential papers on fused angles for robot balance, tilt phase space representations, and neuro-inspired control architectures. Allgeuer's teams have dominated RoboCup competitions, winning titles in AdultSize and TeenSize leagues multiple times. His open-source software frameworks (e.g., rot_conv_lib , attitude_estimator ) and hardware designs are widely used in robotics research. Recent work explores multimodal human-robot interaction and AI-driven robotic task coordination. He is affiliated with the Knowledge Technology Research Group and contributes to projects like the NICOL humanoid robot, bridging social interaction and reliable manipulation. His research spans from low-level control algorithms to high-level behavior planning systems.
Dr. Timothy H. Murphy is a Professor in the Department of Psychiatry at the University of British Columbia's Faculty of Medicine. He is also an Associate Member of the School for Biomedical Engineering and a Member of the Djavad Mowafaghian Centre for Brain Health. Dr. Murphy leads the Dynamic Brain Circuits in Health and Disease initiative and the Division of Neuroscience and Translational Psychiatry at UBC. Dr. Murphy received his Ph.D. from Johns Hopkins University in 1989 and his B.Sc. from Saint Mary's College Maryland in 1984. His research focuses on understanding brain circuit structure-function relationships in relation to stroke recovery, psychiatric disorders, and neurological diseases. He specializes in mesoscale imaging techniques to study cortical activity patterns and develop automated approaches for brain imaging and stimulation. His laboratory develops innovative tools including open-source hardware for automated mouse brain imaging, synthetic data generation for behavioral analysis, and chronic recording systems that enable simultaneous mesoscale cortical imaging with subcortical or peripheral nerve activity monitoring. Research from the Murphy Lab has significantly advanced our understanding of how brain circuits reorganize after stroke and in models of psychiatric disorders. Dr. Murphy's recent publications reveal trends in mesoscale cortical imaging, development of synthetic data for behavioral analysis, and exploration of circuit-level changes in neurological and psychiatric disease models. His work bridges basic neuroscience with potential clinical applications for stroke recovery and mental health treatments. Dr. Murphy has mentored numerous students and postdoctoral fellows who have gone on to successful careers in neuroscience and related fields. His laboratory has received funding to support innovative approaches to understanding brain circuit function and recovery mechanisms. The Murphy Lab maintains strong collaborative ties across UBC and develops open-source tools that are widely adopted by the neuroscience community. Their work on automated home-cage imaging systems, synthetic behavioral data generation, and chronic recording technologies represents significant methodological advances in the field.
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 .
Jonathan Cannon is an Assistant Professor in the Department of Psychology, Neuroscience & Behaviour at McMaster University's Faculty of Science. His research focuses on timing and rhythm in perception and action, with particular interest in timing-related neural dynamics in the basal ganglia, cerebellum, and supplementary motor area. His work combines mathematical modeling with experimental approaches to understand the neural basis of rhythm perception and production. Dr. Cannon's research interests span timing and rhythm perception , neural dynamics , dynamical systems theory , Bayesian cognition , neural oscillations , and autism research . His approach centers on formulating and simulating neurophysiological and cognitive models, drawing on dynamical systems theory and Bayesian cognitive frameworks. His work incorporates psychophysics, EEG experiments, and collaborations with experimentalists to investigate how the brain processes rhythmic information. Analysis of his recent publications reveals a strong focus on the intersection of rhythm perception, motor control, and autism spectrum disorder. His work demonstrates how beat perception co-opts motor neurophysiology, with particular attention to predictive processes in rhythmic cognition. His research shows reduced precision of motor and perceptual rhythmic timing in autistic adults, while also finding intact sequence learning abilities in certain contexts. Dr. Cannon teaches advanced courses including Machine Learning Methods for Brain Modelling and Neural Data Analysis (PSYCH 734), Computational Models in Neuroscience (NEUROSCI 3MN3), and Neuroscience Seminars. His teaching reflects his interdisciplinary approach that bridges mathematics, neuroscience, and cognitive science. Beyond his academic work, Dr. Cannon is an active musician who performs on violin and guitar, particularly in klezmer and folk music contexts. He has also demonstrated entrepreneurial spirit through founding Flying Leap Games and developing the storytelling game 'Wing It,' which successfully crowdfunded and reached numerous retailers.
Stéphane Doncieux is a University Professor in Computer Science at Sorbonne University, where he is affiliated with the Institute of Intelligent Systems and Robotics (ISIR), a joint research laboratory with CNRS. Since January 2024, he has served as Director of ISIR, following a term as Deputy Director from 2019 to 2023. He leads the ASIMOV research team and is based at the Pierre and Marie Curie Campus in Paris. His primary research interests lie in cognitive and developmental robotics, with a strong focus on open-ended learning, evolutionary algorithms, and adaptive systems. He investigates how robots can autonomously learn diverse skills through mechanisms such as novelty search, quality-diversity optimization, and intrinsic motivation. His work bridges theoretical foundations in artificial life and practical applications in robotic manipulation, perception, and control. The recent publications highlight a consistent trend in advancing robotic learning under sparse rewards and in open-ended environments. Key themes include quality-diversity optimization for grasping, state representation learning, sim-to-real transfer, and the development of behavioral repertoires. These works are published in high-impact journals such as IEEE Transactions on Robotics, Evolutionary Computation, and Frontiers in Robotics and AI. Coordinator, DREAM FET H2020 project (2015–2018) Principal Investigator, ANR projects on Creative Adaptation by Evolution, Learning Movement Skills, and Grasping with Multimodal Feedback Involved in European initiatives including VeriDREAM and HumanE-AI-Net He has supervised numerous PhD and Master’s students, including Leni Le Goff, Giuseppe Paolo, Alban Laflaquière, and Achkan Salehi, often in collaboration with leading researchers like Olivier Sigaud and Jean-Baptiste Mouret. He teaches computer science and robotics at both undergraduate and graduate levels at Sorbonne University. Doncieux has been instrumental in shaping research directions in evolutionary and developmental robotics, notably through his leadership in the IEEE Task Force on Evo-Devo-Robotics and his editorial contributions. His lab, ASIMOV, fosters interdisciplinary research integrating computer science, neuroscience, and engineering to create more autonomous and intelligent robotic systems.
Dr. Scott Hayes is an Associate Professor in the Department of Psychology at The Ohio State University, specializing in Clinical Psychology and Cognitive Neuroscience. He directs the Buckeye Brain Aging Lab (B-BAL) and collaborates with the OSU Center for Brain Health and Performance. His research focuses on neuroimaging techniques to study the effects of physical activity, fitness, and aging on brain structure and function, particularly in memory-related disorders. Education: PhD in Clinical Psychology (Neuropsychology) from the University of Arizona (2006), MA (2002), and BA (1998) with dual honors in Biology and Psychology from Skidmore College. He completed postdoctoral fellowships at Duke University and VA Boston Healthcare System. Research interests include cognitive neuroscience of memory, neural correlates of aging, and applying advanced MRI methods to clinical populations. His work highlights how cardiorespiratory fitness impacts brain health and cognitive decline. Awards include the 2024 Distinguished Teaching Award, Spivack Emerging Leader (2017), and multiple NIH-funded grants. He has authored over 50 peer-reviewed articles, focusing on topics like Alzheimer’s biomarkers, PTSD-related cognitive deficits, and exercise interventions. Labs/Teams: Director of B-BAL, affiliated with OSU Jameson Crane Center for Sports Medicine Institute. Current projects explore brain fitness interventions and resilience in aging populations.
Fulvio Domini is a Professor in the Department of Cognitive, Linguistic and Psychological Sciences at Brown University. He joined Brown in 1999 after completing his MSc in Electrical Engineering and PhD in Experimental Psychology at the University of Trieste, Italy. His research focuses on how the human visual system processes 3D information to enable interaction with the environment, combining computational modeling with behavioral experiments. Key areas include perception-action coupling, depth cue integration, and visuomotor adaptation. Education: Masters in Electrical Engineering, University of Trieste, Italy PhD in Experimental Psychology, University of Trieste, Italy Research Interests: 3D vision and depth perception Integration of stereo and motion cues Perception-action link in grasping movements Computational modeling of visual processing Funded Research: Multiple NSF grants including BCS #1827550 ($523,550, 2018) Investigates temporal integration of visual cues and affine shape representations Teaching: Courses include Computational Vision, Perception, and immersive reality simulations. Recent courses: CLPS 1591 (Vision for Action/Perception), CLPS 0540 (Simulating Reality). Lab: Active research on visuomotor control and perception mechanisms, with a focus on dynamic environments and adaptive systems.
Rob van Beers is an Assistant Professor at the Faculty of Behavioural and Movement Sciences at Vrije Universiteit Amsterdam, with affiliations to Neurocontrol, IBBA, and AMS - Sports. His research focuses on human motor control, spatial perception, and computational modeling using Bayesian approaches to understand sensory-motor integration under uncertainty. He holds ancillary roles as a Researcher at Radboud University (Nijmegen) since 2015 and serves on the Editorial Board of the Journal of Neurophysiology since 2015. His work contributes to UN Sustainable Development Goals related to health and well-being. Key research interests include motor learning dynamics, sensorimotor adaptation, and the neural basis of spatial orientation. Recent studies explore Alzheimer’s impacts on motor adaptation and Bayesian inference in vestibular path integration. Teaching responsibilities include courses on linear systems dynamics, physical measurement techniques, and motor systems regulation. His work spans 42 peer-reviewed articles, with datasets published on platforms like Dryad and Zenodo.
Robert S. Allison is a Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. His research focuses on human perceptual responses in virtual environments, stereoscopic vision, and eye movement analysis. He is affiliated with the York Centre for Vision Research, Sensorium (Digital Arts & Technology), and the Centre for Innovation in Computing at Lassonde. His research interests include depth perception in natural and virtual environments, human-computer interface design for VR, machine vision applications, and the measurement of human motion. He has supervised multiple graduate students and contributed to over 260 publications. His work spans topics like cybersickness mitigation, display lag effects, and perceptual adaptation in VR. Key grants include NSERC-funded projects on perception in virtual environments and collaborations with institutions like the Australian Research Council. His teaching includes courses on human perception in human-computer interaction and digital logic design. Recent articles highlight advancements in understanding motion perception, VR-induced sickness, and multisensory integration. He collaborates widely, with affiliations including the VISTA program and York's Connected Minds initiative.
Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology
Courtney N. Reed is a Lecturer in Digital Technologies at Loughborough University London, where she joined in November 2023. She maintains a dual role as a visiting research fellow at the Max Planck Institute for Informatics. Her academic journey includes a BMus in Electronic Production and Design from Berklee College of Music (2016), followed by an MSc (2018) and PhD (2023) in Computer Science from Queen Mary University of London. Prior to her current position, she completed postdoctoral research at both the Max Planck Institute for Informatics and King's College London. Bachelor of Music: Electronic Production and Design, Berklee College of Music (2016) Master of Science: Computer Science, Queen Mary University of London (2018) Doctor of Philosophy: Computer Science, Queen Mary University of London (2023) Dr. Reed's research explores the entangled relationships between humans, bodies, instruments, and technology in music interaction, with particular focus on vocal electromyography (VoxEMG) and the vocalist-voice relationship. Her work incorporates feminist and post-human theories to examine sociopolitical contexts within arts technology, aiming to design for creativity while acknowledging individual, messy bodies in artistic practice. She has developed an open-source platform for vocal electromyography to investigate how biosignal feedback changes understanding and perception of the body in vocal performance. Her interdisciplinary approach bridges music technology, human-computer interaction, and embodied interaction studies. Analysis of Dr. Reed's recent publications (2023-2025) reveals a strong thematic focus on embodied interaction in music technology, with particular emphasis on vocal performance, biosignal feedback, and the philosophical underpinnings of digital instrument design. Her work consistently integrates theoretical frameworks like Karen Barad's agential realism with practical applications in digital musical instruments. Key trends include the exploration of ambiguity in data representation, the sociocultural dimensions of timbre in instrument design, and the development of novel methodologies for understanding embodied musical experiences through micro-phenomenology and ethnographic approaches. ACM SIGCHI Outstanding Dissertation Award (2024) for her thesis 'Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback' Best Newcomer Award at Loughborough University London's Community Awards Celebration (2024) Dr. Reed actively contributes to the academic community through conference organization and leadership roles. She serves as Member-at-Large on the NIME Board, previously chaired papers for NIME 2024, and co-organized the IBM SkillsBuild Sprint at Loughborough London. She has also chaired sessions at the ACM TEI Conference and co-chaired the Student Design Competition. Her collaborative work spans multiple institutions and includes significant contributions to interdisciplinary projects that bridge music, technology, and human experience. She has been instrumental in developing the senSInt research group and the RaveNET wearable network project. Dr. Reed leads the senSInt research group which focuses on sensorimotor interaction in music and performance contexts. The group develops innovative technologies including the VoxEMG platform for vocal electromyography, the Bones anti-corset for vocal performance, and the RaveNET network of wearable biosensing nodes. These projects explore the intersection of biosignals, embodied interaction, and musical expression, creating novel frameworks for understanding how technology mediates human creativity and performance. The group frequently collaborates with musicians, technologists, and theorists to develop and test these systems in real-world performance contexts.
Silvia Arber holds a joint appointment as Full Professor for Neurobiology/Cell Biology at the Biozentrum, University of Basel, and serves as Senior Group Leader at the Friedrich Miescher Institute (FMI) in Basel, Switzerland. Her laboratory investigates the organization, function, and development of neuronal circuits controlling motor behavior, with a particular focus on how these circuits enable precise movement control. Arber obtained her PhD in 1996 from the Friedrich Miescher Institute under Pico Caroni, followed by postdoctoral training with Thomas Jessell at Columbia University (1996-2000), where she studied transcription factors in spinal cord neuronal differentiation. Her educational background includes Biology II studies at the Biozentrum of the University of Basel with graduation in Cell Biology (1987), a diploma thesis at the FMI (1990), and graduate work at the FMI (1992). Her research program centers on elucidating how neuronal circuits orchestrate accurate motor behavior in response to sensory cues and voluntary movement initiation. Using mouse as a model system, her laboratory employs multi-faceted approaches including advanced mouse genetics, viral technologies for transsynaptic circuit tracing, optogenetics and pharmacogenetics for functional manipulation, quantitative behavioral analysis, electrophysiology, and gene expression profiling. Her work has revealed precise synaptic interactions within dedicated motor circuit modules throughout the nervous system and how these impact function, with implications for understanding diseases causing motor deficits and spinal cord injury. Analysis of Arber's publication record shows a consistent focus on motor circuit organization, with particular emphasis on transcriptional control mechanisms, circuit connectivity mapping, and the relationship between developmental processes and functional circuit organization. Her work bridges molecular, cellular, and systems neuroscience, providing fundamental insights into how the nervous system controls movement. The Brain Prize (2022) Elected to the National Academy of Sciences of the United States (2020) Physiological Society Annual Review Prize Lecture (2019) Pradel Research Award (2018) W. Alden Spencer Award (2018) Louis-Jeantet Prize for Medicine (2017) ERC Advanced Grant (2010-2015) EMBO Member (2005) EMBO Young Investigator Award (2001) While specific students are not listed in the provided materials, Arber's laboratory has received significant research funding including an ERC Advanced Grant (2010-2015) and multiple prestigious awards supporting her research program. Her laboratory at the Biozentrum (Room 11.038) collaborates closely with the Friedrich Miescher Institute, where she serves as Senior Group Leader. The research group employs cutting-edge technologies for neural circuit analysis and has contributed fundamental insights into motor circuit organization, with implications for understanding and potentially treating movement disorders and spinal cord injuries.
