Zijian Shao is a Postdoctoral Research Associate at Princeton University's School of Engineering and Applied Science, affiliated with the Department of Electrical Engineering. His work focuses on advanced antenna design, electromagnetic modeling, and machine learning applications in RF/mmWave systems for 5G/6G telecommunications. Advisor: Kaushik Sengupta Email: zs9193@princeton.edu Office: Engineering Quadrangle Atrium Shao's research explores the intersection of machine learning and electromagnetic design , particularly for next-generation wireless communication. He specializes in antenna miniaturization , MIMO decoupling , and metasurface-enabled beamforming , with applications in sub-terahertz circuits and integrated sensing systems. His recent publications demonstrate expertise in deep learning-assisted inverse design of multi-port RF systems and spoof surface plasmon polariton-based antenna optimization . While no formal awards are listed, his work contributes to advancing compact, high-efficiency antenna arrays for 5G/6G networks.
Professor Christian F. Doeller is a leading cognitive neuroscientist serving as Director of the Department of Psychology at the Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) in Leipzig and Vice President of the Max Planck Society (since 2023). His roles include honorary professorships at the University of Leipzig (2019) and TU Dresden (Cognitive Neuroscience of Learning and Memory). He holds a PhD in Psychology from Saarland University (2005) and has held positions at institutions such as UCL (London), Radboud University (Nijmegen), and NTNU (Trondheim). His research focuses on spatial navigation, memory systems, and cognitive mapping in the human brain, leveraging neuroimaging (fMRI, EEG) and computational modeling. Key areas include hippocampal/entorhinal cortical function, grid cells, and the neural basis of spatial and conceptual representations. Recent work explores non-Euclidean spatial cognition, value-based decision making using grid-like maps, and hormonal influences on navigation. His lab combines experimental psychology, neuroimaging, and theoretical neuroscience to understand how brains build predictive models of environments and concepts. Publications emphasize cognitive maps, neural representations of space/value, and memory formation mechanisms. Over 100 journal articles span high-impact journals like Nature Neuroscience , Neuron , and Current Biology . His work bridges basic research and translational applications in neurodegenerative disorders and spatial cognition deficits.
Michael Levine is the Anthony B. Evnin '62 Professor in Genomics and Professor of Molecular Biology at Princeton University, where he also serves as Director of the Lewis-Sigler Institute for Integrative Genomics. He joined Princeton in 2015 after a distinguished career at UC Berkeley, where he was Professor of Genetics and held leadership roles in genetics and genomics programs. His research focuses on how noncoding regions of the genome regulate gene expression in space and time during development. His lab has pioneered studies in Drosophila and the protovertebrate Ciona intestinalis , uncovering fundamental mechanisms such as enhancer function, transcriptional bursting, short-range repression, and long-range enhancer-promoter interactions. His work has also revealed evolutionary insights into the origins of vertebrate innovations like the neural crest and neurogenic placodes. Levine's recent publications demonstrate a strong emphasis on quantitative and live-imaging approaches to dissect gene regulation dynamics. His work integrates experimental embryology with computational modeling, especially using deep learning to predict transcriptional outcomes. Themes across his recent articles include biomolecular condensates, chromatin architecture, and the physical principles underlying enhancer function. Elected to the National Academy of Sciences (1998) Molecular Biology Award, National Academy of Sciences (1996) Wilbur Cross Medal, Yale University (2009) EG Conklin Medal, Society of Development Biology (2015) Dr. Levine has trained numerous researchers and co-authored studies with emerging scientists, indicating active mentoring and grant-funded research. His leadership roles at major institutes and sustained publication record reflect a robust, well-supported research program. He has also contributed to national genomics initiatives, including service at the DOE Joint Genome Institute. His lab operates at the intersection of molecular biology, genomics, and quantitative developmental biology, utilizing model organisms and cutting-edge imaging and computational tools to unravel the logic of gene regulatory networks.
Yukiko Gotoh is a Professor at the Department of Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, The University of Tokyo. She serves as the Deputy Director and Principal Investigator at the International Research Center for Neurointelligence (IRCN). Her research focuses on understanding the mechanisms that regulate neural stem/progenitor cell fate during embryonic brain development and in the adult brain. Dr. Gotoh's research interests include: Genetic and epigenetic regulation of neural stem/progenitor cell fate Neuronal maturation processes Genesis and maintenance of adult neural stem cells Relevance of neural stem/progenitor cell dysregulation in neurodevelopmental disorders such as autism spectrum disorders Investigation of mechanisms regulating neural stem-progenitor cell fate during neocortical development Genetic and epigenetic regulation of neuronal activation Analysis of Dr. Gotoh's recent publications reveals a strong focus on neural stem cell biology, epigenetic regulation, and neurodevelopmental disorders. Her work demonstrates how chromatin modifiers like Polycomb group proteins and HMGA proteins regulate neural stem cell fate decisions during brain development. A significant portion of her research explores the embryonic origins of adult neural stem cells and how dysregulation of these processes contributes to conditions like autism spectrum disorders and schizophrenia. Her laboratory also investigates the basic mechanisms of cellular responses to viral infection in the brain and their relevance to neurodevelopmental disorders. Dr. Gotoh has made significant contributions to understanding: The role of Polycomb group proteins in neural development How chromatin modifiers regulate neurogenic potential Cell cycle regulation in neural stem cells The PDK1-Akt pathway in neuronal migration Layer-specific heterogeneity of astrocytes Mechanisms underlying schizophrenia-related abnormalities Dr. Gotoh's laboratory conducts research on multiple fronts related to neural development and stem cell biology. Her team investigates: Mechanisms regulating neural stem-progenitor cell fate during neocortical development Genetic and epigenetic regulation of neuronal activation The embryonic origin of adult neural stem cells Dysregulation of neural stem-progenitor cell and neuronal fate in neurodevelopmental disorders Innate immune responses in the brain
Amy Vaughan Van Hecke serves as Assistant Chair and Professor in Marquette University's Department of Psychology, leading research on autism spectrum disorder (ASD) and social development across the lifespan using neuroimaging and psychophysiological methods. She directs community initiatives to improve autism services access for underserved Milwaukee populations through the Next Step Clinic. Her academic foundation includes: B.A. in Psychology from Smith College Ph.D. in Developmental Psychology from the University of Miami Dr. Van Hecke's research centers on brain activity, heart rate regulation, and social behavior in individuals with and without ASD, utilizing high-density EEG and MRI to examine neural responses to interventions like PEERS ® . Current projects investigate pandemic impacts on social interaction in autistic adults and neural mechanisms of social isolation remediation. Her work bridges laboratory neuroscience with community-based clinical applications. Publication analysis reveals consistent focus on intervention efficacy (particularly PEERS ® ), neural plasticity measurement, and comorbid conditions across developmental stages. Recent work emphasizes gender-specific outcomes, family impacts, and pandemic-related mental health, demonstrating methodological diversity from EEG asymmetry to community-based participatory research. Her distinguished scientific recognition includes: Kirschstein National Research Service Award (NRSA) from the National Institute of Mental Health Dr. Van Hecke mentors students through her Marquette Autism Project lab and the Next Step Clinic training program while securing major grants from Marquette University, Johnson Controls Foundation, and the Greater Milwaukee Funders’ Collaborative. She teaches undergraduate/graduate courses in developmental psychology and statistics. Advising: Clinical psychology graduate mentorship (excluding 2025 intake); undergraduate research supervision Grants: $500k+ secured for Next Step Clinic serving underserved Milwaukee children She co-directs the Marquette Interdisciplinary Autism Initiative and the Next Step Clinic, which employs a Family Navigation model in Milwaukee's Metcalfe Park neighborhood to provide autism screening, diagnosis, and therapy for children aged 15 months-10 years facing systemic barriers to care.
Ian Greenhouse serves as an Assistant Professor in the Department of Human Physiology within the College of Arts and Sciences at the University of Oregon. He directs the Action Control Laboratory, where he investigates the neurophysiological mechanisms underlying human movement initiation and cancellation using multimodal approaches including electrophysiology, neuroimaging, and brain stimulation. Education: Undergraduate degree in Psychology from Tufts University Ph.D. from the University of California, San Diego Postdoctoral training at the University of California, Berkeley Research Focus: Dr. Greenhouse's work centers on action control neurophysiology , specifically examining motor inhibition processes during response stopping and preparation. His lab employs electromyography (EMG) , transcranial magnetic stimulation (TMS) , and magnetic resonance spectroscopy (MRS) to probe corticospinal excitability in healthy and clinical populations. Key investigations include neural computations for action preparation, biomarkers of stopping failure, and relationships between motor performance and brain chemistry (e.g., GABA). Publication Trends: Analysis of Dr. Greenhouse's 2022-2025 publications reveals intensified focus on subcomponents of response inhibition (pause vs. cancel processes) and neurochemical modulation of motor control. His work increasingly integrates menstrual cycle effects on GABA with action stopping metrics, while maintaining core investigations of corticospinal dynamics during unimanual/bimanual preparation. Recent studies show growing clinical applications in stroke rehabilitation. Scientific Awards: No awards were documented in the source materials. Advising and Research: As laboratory director, Dr. Greenhouse mentors students in the Action Control Laboratory's research program. Although specific grants aren't detailed, his high-output publication record spanning neuroimaging, electrophysiology, and clinical applications suggests sustained external funding for equipment-intensive neuroscience research. Laboratory Operations: The Action Control Laboratory (https://actioncontrollab.uoregon.edu) operates from Gerlinger Hall (Room 348), utilizing TMS-EMG integration, MRS, and behavioral paradigms to study action control. Current projects examine preparatory inhibition in stroke recovery, interhemispheric dynamics during movement preparation, and individual differences in stopping processes using the stop-signal task framework.
Sunil K. Agrawal is a Professor of Mechanical Engineering and Professor of Rehabilitation and Regenerative Medicine at Columbia University, where he directs a highly interdisciplinary rehabilitation robotics program bridging the School of Engineering and Applied Sciences and the College of Physicians and Surgeons. His research focuses on developing robotic systems to restore and enhance human mobility for individuals with neurological disorders, pediatric conditions, and age-related decline. Education: PhD in Mechanical Engineering, Stanford University, 1990 MS in Mechanical Engineering, Ohio State University, 1986 BS in Mechanical Engineering, Indian Institute of Technology (IIT) Kanpur, 1984 Research Focus: Dr. Agrawal’s work centers on rehabilitation robotics , where he integrates dynamic systems, control theory, and optimization to create robotic devices that assist in gait training, balance improvement, and functional movement restoration. His studies span stroke, Parkinson’s disease, cerebral palsy, vestibular disorders, and spinal cord injury, using devices like the Tethered Pelvic Assist Device (TPAD) and robotic exoskeletons. Scientific Honors: Machine Design Award, ASME (2016) Robotics and Mechanisms Award, ASME (2016) Fellow, American Institute of Medical and Biological Engineering (AIMBE) (2016) Fellow, American Society of Mechanical Engineers (ASME) (2004) Alexander von Humboldt Foundation U.S. Senior Scientist Award (2007) Friedrich Wilhelm Bessel Research Award (2002) Presidential Faculty Fellow Award, The White House (1994) Research Collaborations & Funding: Dr. Agrawal collaborates with faculty across Neurology, Rehabilitation Medicine, Pediatric Orthopedics, Otolaryngology, Geriatrics, and Psychiatry. His work is supported by the National Science Foundation, National Institutes of Health, and the Spinal Cord Injury Research Board. Laboratory & Outreach: He leads an active research group focused on translational robotic systems, with ongoing clinical trials for stroke, Parkinson’s, cerebral palsy, and elderly fall prevention. His lab develops novel robotic braces, exoskeletons, and VR-integrated training platforms.
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.
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.
Professor Line Roald is a faculty member in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. Her research focuses on power system optimization, renewable energy integration, grid resilience, and wildfire risk mitigation using stochastic optimization and data-driven methods. Education : PhD (2016), MS (2012), BS (2009) from ETH Zurich Key Research Areas : Power Systems Optimization, Renewable Energy Integration, Wildfire Risk Mitigation, Stochastic Programming, Grid Decarbonization Her work addresses critical challenges in sustainable energy systems, including balancing grid efficiency and risk, optimizing electrolyzer scheduling for flexibility, and predicting cascading blackout severity using graph neural networks. She has developed frameworks for carbon intensity comparison and wildfire risk assessment in power systems. Scientific Awards : 2024 Inclusion, Equity and Diversity in Engineering Award 2024 Vilas Faculty Early Career Investigator Award 2023 IEEE Power Tech Best Student Paper Award 2021 NSF CAREER Award 2019 MTLE Fellow Professor Roald mentors graduate students and teaches courses including Introduction to Optimization and On-Line Control of Power Systems . Her publications highlight innovative approaches to grid security, carbon-efficient energy markets, and climate resilience in infrastructure systems.
Xiaoming Li is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Delaware , focusing on compiler optimization, GPU computing, and hardware-software interaction. His work bridges machine learning with code generation to enhance program efficiency. B.S. and M.E. from Nanjing University (1998, 2001) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2006) Research interests include: Compiler optimizations for static and dynamic code transformation Machine learning-driven code generation techniques FFT algorithms for sparse and hybrid systems Non-traditional compilers for SAT solvers and virtual machines GPU acceleration for large-scale computational problems His publications span 15 years , emphasizing: FFT optimization across GPU/CPU architectures Compiler techniques for heterogeneous systems Adaptive scheduling and error resilience Integration of empirical and model-driven approaches Notable awards: NSF CAREER Award (2008) Best Paper Award at ADAPT Workshop (2013) Advising highlights: Current students: Ryan Taylor, Sha Li, Shuo Chen, Yuanfang Chen, Chao Yang, Chaoyu Chen Graduates: Liang Gu (FFT Libraries), Jakob Siegel (GPGPU Frameworks), Murat Bolat (Context-Aware Compilation)
Stefan Krastanov is an Assistant Professor at the University of Massachusetts Amherst, focusing on quantum hardware design, control, and optimization across multiple layers of quantum computing and networking technologies. His work bridges physical hardware descriptions with logical circuit compilation, emphasizing resilience in noisy quantum systems. Research Interests include Quantum Hardware Design, Entanglement-Based Networking, Quantum Error Correction, and Modeling Software for Quantum Systems. His primary lab is the Quantum Information Lab , with affiliations to the Advanced Classical and Quantum Information Research Lab. Recent work trends highlight advancements in quantum repeater networks, error-corrected compilation, and photonic neural networks. His publications span topics like non-Markovian dynamics simulation, NP-hard optimization in quantum dot arrays, and scalable spin quantum memory control. Labs and Teams: Quantum Information Lab (leading experimental/theoretical work) and collaborations through the Advanced Classical and Quantum Information Research Lab.
Robert W. Levenson is a Professor of Psychology at the University of California, Berkeley, and Professor of the Graduate School. He directs the Berkeley Psychophysiology Laboratory and the Institute of Personality and Social Research. His work focuses on emotion, psychophysiology, and affective neuroscience, particularly in aging and neurodegenerative disorders. He has held roles such as Director of the Clinical Training Program and the Bay Area Predoctoral Training Consortium in Affective Science. Levenson earned a Ph.D. in Clinical Psychology from Vanderbilt University. His research examines emotional processes in marital interaction, cultural influences on emotion, and neural correlates of emotion in disorders like Alzheimer's and frontotemporal dementia. Key projects include longitudinal studies on marital dynamics and age-related emotional changes, supported by NIH grants. His research interests span psychophysiological measures of emotion, empathy, and emotional control, with notable contributions to understanding autonomic specificity in emotions. He trains students in psychophysiological methods, neuroanatomy, and emotion assessment.
Dr. Aaron Schurger is an Assistant Professor in the Psychology Department at Chapman University’s Crean College of Health and Behavioral Sciences. He is also a member of the Institute for Interdisciplinary Brain and Behavioral Sciences. Schurger holds a BA from Indiana University, and MA and PhD from Princeton University. His research focuses on the neuroscience of volition, consciousness, and decision-making, particularly exploring the readiness potential (RP) and its implications for free will debates. His work challenges classical interpretations of the RP using computational models, suggesting it reflects stochastic neural processes rather than preconscious decisions. Recent contributions include studies on the origins of the RP in spiking neural networks, critiques of causal structure theories of consciousness, and interdisciplinary analyses of free will. His findings emphasize that the RP may not indicate preconscious decision-making but instead arise from natural neural fluctuations during decision thresholds. Schurger collaborates across neuroscience, philosophy, and cognitive science, contributing to debates on consciousness, action initiation, and neural correlates of subjective experience. His research also addresses methodological rigor in studying unconscious processing and integrates computational models with empirical data, as seen in studies on movement timing and neural stability during perception. While no specific grants or labs are explicitly listed, his affiliations suggest involvement in interdisciplinary projects at Chapman.
