جست‌وجوی استاد و پژوهشگر

Kohei Nakajimaمشاهده پروفایل

دانشیار

• Soft Robotics
• Physical Reservoir Computing
• Physical Deep Learning
• +۵ مورد دیگر

Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.

Christina Tzengمشاهده پروفایل

استادیار

• Cognition
• Cognitive Science
• Spoken Language Perception
• +۳ مورد دیگر

Christina Tzeng serves as an Assistant Professor in the Department of Psychology within San Jose State University's College of Social Sciences. Her research investigates the cognitive mechanisms enabling human comprehension of spoken language despite inherent variability from social factors including age, gender, and regional origin. She examines how listeners rapidly decode speech signals while simultaneously processing speaker identity and contextual information. Her primary research focuses on cognition, cognitive science, and spoken language perception, with emphasis on how social diversity in speech inputs shapes perceptual organization and auditory expectations. Key investigations include perceptual learning of non-native accents, lexically guided recalibration, integration of linguistic/non-linguistic speech properties, and sound symbolism. Her work demonstrates that speech variability—often perceived as noise—actually facilitates memory organization and predictive processing in human cognition. Dr. Tzeng's publication record (2010-2024) reveals consistent exploration of perceptual adaptation mechanisms, with recent work emphasizing attentional modulation in accent processing (2024), cumulative statistical learning in speech perception (2021-2023), and cross-modal correspondences in prosody (2016-2018). Her research trajectory shows increasing sophistication in modeling how social context interfaces with core speech processing systems. Scientific Awards: No awards documented in source material Advising and Grants: Source text provides no details regarding graduate student mentorship or research funding. Her background notes undergraduate research experience in human communication and memory labs, suggesting potential mentorship focus areas. Dr. Tzeng directs the Cognition of Language Processing (CLAP) Lab, which employs experimental methodologies to investigate spoken language perception. The lab's research framework positions speech diversity not as interference but as fundamental to human perceptual systems, with implications for understanding communication in multicultural contexts and developing more robust speech recognition technologies.

Justin Haldarمشاهده پروفایل

استاد

• Computational Imaging
• Inverse Problems
• Magnetic Resonance Imaging
• +۳ مورد دیگر

Justin Haldar is a Professor in the Ming Hsieh Department of Electrical and Computer Engineering at the University of Southern California (USC), where he serves as Director of the Signal and Image Processing Institute and Co-Director of the Biomedical Imaging Group. He holds a joint appointment in the Department of Biomedical Engineering and maintains affiliations with the Dornsife Cognitive Neuroscience Imaging Center, Brain and Creativity Institute, and Dynamic Imaging Science Center. His research focuses on computational imaging and inverse problems, with particular emphasis on developing novel data acquisition and signal processing methods for magnetic resonance imaging (MRI). Haldar's work addresses critical limitations in MRI technology including long acquisition times, limited signal-to-noise ratio, and high costs, developing approaches that combine physical imaging process modeling, constrained signal models, theoretical frameworks, and fast computational algorithms. Haldar's publication record demonstrates consistent innovation in MRI technology, with research spanning from fundamental reconstruction algorithms to practical clinical applications. His work shows a clear progression from theoretical advances in spatiotemporal imaging to real-world implementations that accelerate MRI exams and enable previously impractical next-generation imaging techniques. The breadth of his research connects electrical engineering principles with biomedical applications, particularly in neuroscience and medical diagnostics. NSF CAREER Award recipient IEEE ISBI Best Paper Award winner IEEE EMBC First-Place Student Paper Award recipient Current Chair of IEEE Signal Processing Society's Technical Committee on Computational Imaging Deputy Editor-in-Chief for IEEE Transactions on Computational Imaging Associate Editor for IEEE Transactions on Medical Imaging Haldar maintains active mentorship and collaboration through his leadership of the Biomedical Imaging Group and Signal and Image Processing Institute. His editorial roles in major imaging journals demonstrate significant influence in the field. He has secured substantial research funding including NSF grants that support his innovative work in computational imaging. Haldar's laboratory focuses on developing next-generation MRI techniques that leverage the 'blessings of dimensionality' while mitigating associated challenges, with particular emphasis on jointly designing data acquisition and reconstruction methods to exploit inherent structure within high-dimensional data.

• Functional Analysis (applied)
• Operator/Spectral Theory
• Compressed Sensing
• +۱۲ مورد دیگر

Professor Anders Christian Hansen is a leading mathematician at the University of Cambridge and University of Oslo , specializing in Functional Analysis , Artificial Intelligence , and Compressed Sensing . As head of the Applied Functional and Harmonic Analysis group and a Royal Society University Research Fellow , his work bridges foundational mathematics with cutting-edge data science. Current affiliations: University of Cambridge (since 2013), University of Oslo (Professor II since 2012) Previous roles: Von Karman Instructor (Caltech), Marie Curie Fellow (Vienna), Junior Research Fellow (Cambridge) Research Interests : Functional Analysis (applied), Operator/Spectral Theory, Compressed Sensing Artificial Intelligence, Computational Barriers, Foundations of Mathematics Medical Imaging, Inverse Problems, Numerical Analysis Article Trends : His recent work explores the limits of AI through the Solvability Complexity Index (SCI), revealing inherent instabilities in deep learning despite theoretical guarantees. Collaborations with B. Adcock and M. Colbrook address structured sampling and numerical algorithms for spectral problems in infinite dimensions. Scientific Awards : PROSE Award Finalist (2022) for Compressive Imaging Whitehead Prize (2019), IMA Prize (2018) Leverhulme Prize (2017) for contributions to applied analysis Advising & Grants : Supervised 17 PhD students and 8 postdocs. Active in grants related to medical imaging , AI safety , and computational mathematics .

Stuart A. Newmanمشاهده پروفایل

استاد

• Developmental Biology
• Evolutionary Biology
• Vertebrate Limb Development
• +۵ مورد دیگر

Stuart A. Newman serves as Professor of Cell Biology and Anatomy at New York Medical College's Graduate School of Biomedical Sciences and holds a secondary appointment in the School of Medicine. He concurrently serves as External Faculty at Austria's Konrad Lorenz Institute for Evolution and Cognition Research and editor-in-chief of the journal Biological Theory . His career spans developmental and evolutionary biology with emphasis on physical mechanisms underlying morphogenesis. Dr. Newman earned his A.B. from Columbia University, Ph.D. in Chemical Physics from the University of Chicago, followed by postdoctoral fellowships in Theoretical Biology (University of Chicago) and Biological Sciences (University of Sussex). His scholarly journey includes visiting professorships at the Pasteur Institute, Indian Institute of Science, and University of Tokyo. His research centers on self-organizing mechanisms in vertebrate limb development, evolutionary transitions in multicellularity, and physical bases for animal body plan origination. He pioneers theories on how developmental toolkit genes mobilize physical effects in cell aggregates to generate evolutionary novelty, while critically examining sociopolitical implications of biotechnology through works like Biotech Juggernaut . Analysis of his 15 most recent publications (2022-2025) reveals three dominant thematic clusters: 1) Physical mechanisms in developmental patterning (particularly limb skeletogenesis), 2) Evolutionary transitions in multicellularity and agency emergence, and 3) Critical bioethics addressing genetic engineering and biocapitalism. His work consistently bridges theoretical biology, experimental embryology, and sociopolitical critique. Dr. Newman's scientific recognition includes: Dean's Distinguished Research Award (1994) Fogarty Senior International Fellowship (1989) He co-founded the Council for Responsible Genetics, serves on the Alliance for Human Biotechnology advisory board, and has co-authored foundational texts including Biological Physics of the Developing Embryo . His editorial leadership spans the Journal of Biosciences and MIT Press's Multicellularity: Origins and Evolution . Current projects focus on theoretical models of evolutionary transitions and policy advocacy regarding human genetic engineering.

Mark Hindersمشاهده پروفایل

استاد

• Medical Imaging
• Intelligent Robotics
• Security Screening
• +۷ مورد دیگر

Mark Hinders is Professor of Applied Science at William & Mary, affiliated with the College of Arts & Sciences. His research integrates novel instrumentation and machine learning to advance measurement science, particularly in interpreting multi-modal imaging data for diverse applications. His research interests lie at the intersection of physical wave-based sensing and intelligent data interpretation. He focuses on the interaction of acoustic, ultrasonic, elastic, thermal, electromagnetic, and optical waves with materials, tissues, and structures. These investigations support applications in medical imaging , intelligent robotics , security screening , remote sensing , and nondestructive evaluation . His approach emphasizes interdisciplinary training, enabling students to gain experience across multiple industries. The absence of recent publications in the provided text limits analysis of current article trends, but the described research direction suggests strong emphasis on sensor fusion, automation, and applied artificial intelligence in physical systems. While no scientific awards are listed in the current text, his sustained research program and industry collaborations indicate recognition within applied science and engineering communities. Professor Hinders actively mentors students, with many graduates pursuing diverse careers. His current research projects involve close collaboration with former students, reflecting a lasting mentorship network and real-world impact. Although no specific grants are mentioned, the scope of instrumentation and interdisciplinary work implies external funding support. Research appears to be conducted in a student-centered lab environment focused on practical implementation and innovation. His work is inherently team-based, involving current and former students in applied research projects. The integration of machine learning with physical measurements suggests a lab culture combining experimental engineering, data science, and cross-industry application development.

Ron Daboraمشاهده پروفایل

دانشیار

• Wireless Communications
• Cooperation and Relaying in Wireless Networks
• Secure Communications
• +۷ مورد دیگر

Ron Dabora is an Associate Professor at the Department of Electrical and Computer Engineering, Ben-Gurion University, Israel. He is currently a Visiting Research Collaborator at Princeton University. His research focuses on wireless communications, secure communications, channels with memory, signal processing for communications, and machine learning for communications. Education : B.Sc. and M.Sc. from Tel-Aviv University (1994, 2000), Ph.D. from Cornell University (2007). Past Roles : Postdoctoral researcher at Stanford University (2007–2009), R&D engineer at Communications Research Institute (1994–2000), Algorithms Group at Millimetrix Broadband Networks (2000–2003). Research Interests include wireless communications, cooperation and relaying in networks, secure communications, feedback and channel coding, machine learning for communications, and power line communications. His current projects are Resilient UAV Communications (2025–2027) and Inherent Limitations and Signal Processing for Communications with Asynchronous Sampling (2020–2025). Past projects span 5G algorithm design, wireless network optimization, and power line communications. Advising : Currently supervising Zikun Tan (Ph.D., Signal Processing and Machine Learning for Communications), Itai Zino (Ph.D., Machine Learning for Communications), and others. Former students include Nir Shlezinger (Weizmann Institute), Daniel Zahavi (LEVL Technologies), and Yoel Bokobza (Samsung Research). Honors : IEEE Senior Member. Teaching : Courses in digital wireless communications, network information theory, signal processing, and modern communications.