جستجوی استاد راهنمای دانشگاهی

Joshua B Tenenbaumمشاهده پروفایل

استاد

• Computational Cognitive Science
• Artificial Intelligence
• Human-Machine Intelligence
• +۴ مورد دیگر

Joshua B. Tenenbaum is a Professor of Computational Cognitive Science in MIT's Department of Brain and Cognitive Sciences, a Principal Investigator at CSAIL, and Research Thrust Leader at CBMM. His work bridges human cognition and machine intelligence, focusing on perception, learning, and reasoning. He holds a PhD from MIT (1999) and previously taught at Stanford (1999–2002). His research develops algorithms used globally in science/engineering, emphasizing human-like AI grounded in computational models of mind. Awards include the Troland Research Award (NAS), APA's Early Career Contribution Award, and Society of Experimental Psychologists' Early Investigator Award. He is a Fellow of the Cognitive Science Society and Society of Experimental Psychologists. Key research interests span intuitive physics, theory of mind, probabilistic models of cognition, and neuro-symbolic AI. His lab explores how humans and machines learn from limited data, reason with abstract concepts, and generalize knowledge. Publications emphasize interdisciplinary approaches to intelligence, with recent work on neuro-symbolic systems, embodied reasoning, and ethical AI frameworks. He advises MIT's AI initiatives and collaborates internationally on cognitive science projects. His lab's work has led to open-source tools like probabilistic programming frameworks and benchmark datasets for evaluating human-like capabilities in machines. Current projects include understanding moral judgment mechanisms and developing physically plausible AI systems.

• Mathematical Biology
• Genomics
• Bioinformatics
• +۳ مورد دیگر

Indika Rajapakse is a Professor of Mathematics and affiliated as a Professor of Computational Medicine and Bioinformatics at the University of Michigan. His primary affiliation is with the Department of Mathematics within the College of Literature, Science, and the Arts (LSA). He can be reached at indikar@umich.edu . Rajapakse’s research bridges mathematical theory and biomedical applications, focusing on genomic organization, hypergraph modeling, and computational methods for cell reprogramming and disease analysis. His research interests include mathematical biology , genomics , and systems biology , with a particular emphasis on understanding how genome architecture influences cellular identity and function. He develops tools like the 4DNvestigator and Bioinformatics Retrieval Augmentation Data (BRAD) system to analyze genomic and transcriptomic data. His work also explores machine learning applications in medicine, such as adversarial immune-inspired learning systems and language model integration in digital biology. Rajapakse’s publications highlight trends in hypergraph theory for biological networks, dynamic sensor selection for biomarkers, and computational platforms for medical research. He has contributed to studies on obesity-driven immune network disruptions, lineage-specific chromosomal topologies, and the role of macrophages in diabetes. His research often combines mathematical frameworks with experimental biology to address clinical and theoretical challenges. He advises students through the Mathematics department at U-M and collaborates on grants related to genome dynamics and computational medicine. Notable projects include developing the SEWAL platform for genomic data analysis and exploring the 4D Nucleome’s functional organization. Rajapakse’s interdisciplinary work advances both fundamental biology and translational medical research.

Keith Evan Greenمشاهده پروفایل

استاد

• Architectural Robotics
• Human-Robot Interaction (HRI)
• Continuum Robotics
• +۵ مورد دیگر

Keith Evan Green is a Professor at Cornell University with dual appointments in the Department of Human Centered Design (College of Human Ecology) and the Sibley School of Mechanical and Aerospace Engineering (College of Engineering). He also holds graduate faculty roles in Information Science and Robotics. His transdisciplinary research focuses on architectural robotics, exploring how cyber-physical environments can augment human experiences through reconfigurable spaces and robotic furnishings. Green earned degrees from the University of Pennsylvania and University of Illinois at Chicago, and previously served as a tenured faculty member at Clemson University and the University of Auckland. He directs the Architectural Robotics Lab (ARL), which pioneered concepts like 'robot-rooms' and has received significant NSF funding. Key honors include the Jean and Douglas McLean Professorship and multiple best paper awards at CHI, DIS, and IDC conferences. His lab collaborates globally with institutions like TU Delft, and his work spans healthcare robotics, educational tools, and community-focused design. Current research emphasizes socio-spatial interfaces, emotion regulation systems, and child-robot interaction frameworks. Education: B.A. Psychology, University of Pennsylvania (1985) M.Arch., University of Illinois at Chicago (1990) Ph.D. Architecture, University of Pennsylvania Research Interests: Architectural Robotics integrates design, robotics, and psychology to create adaptive environments. Current projects include socio-spatial interfaces fostering human connections, soft robotic surfaces for stress regulation, and educational tools like CyberPLAYce. Green's work emphasizes ethical design for under-served populations including post-stroke patients and children. Grants & Funding: Recent projects include a $600K NSF grant for 'Robot Rooms' and collaborations with NASA JPL, Sandia Labs, and industry partners like Microsoft Research. The ARL also developed the LIT ROOM literacy tool supported by NSF grants. Lab & Teams: The ARL employs a radical collaboration model, involving researchers from robotics, psychology, and design. Notable projects include the Animated Work Environment (AWE), communIT, and the home+ furniture suite. Partnerships span TU Delft, Politecnico di Milano, and the Bartlett UCL.

Vincent Leungمشاهده پروفایل

پژوهشگر

• Artificial Intelligence and Image Processing
• Signal Processing
• Neurosciences
• +۳ مورد دیگر

Vincent Leung is a Postdoctoral Research Associate in the Communications and Signal Processing Group at the Department of Electrical and Electronic Engineering, Imperial College London. He holds a PhD (2023) and a first-class MEng (2018) in Electrical and Electronic Engineering from Imperial College London, supported by the President's PhD Scholarship. His research focuses on deep learning for 3D reconstruction of dynamic protein complexes from Cryo-EM data and signal processing methodologies such as FRI signal reconstruction. He is affiliated with the Faculty of Engineering and actively contributes to interdisciplinary projects at the intersection of AI and biomedical engineering. Education: PhD in Electrical and Electronic Engineering, Imperial College London (2023) MEng (First Class) in Electrical and Electronic Engineering, Imperial College London (2018) Research Interests: His work combines deep learning, signal processing, and neuroscience to develop innovative solutions for computational imaging and biomedical data analysis. Key areas include FRI signal reconstruction using autoencoders, spiking neural networks, and heterogeneous neural architectures for robust learning. Recent projects involve advancing Cryo-EM data analysis through deep neural networks. Grants & Collaborations: Recipient of the BBSRC Grant (2024): 'Deep Learning for 3-D Reconstruction of Heterogeneous Molecular Structures from Cryo-EM Data' Collaborations with Prof. Pier Luigi Dragotti and international researchers in signal processing and AI Publications: His work spans 5+ peer-reviewed articles in top-tier journals like IEEE Transactions on Signal Processing, focusing on signal reconstruction, spiking neural networks, and FRI methodologies. Notable contributions include learning-based FRI reconstruction frameworks and neural heterogeneity studies.

• Wireless Communications
• Deep Learning
• Signal Processing
• +۴ مورد دیگر

Dr. Geoffrey Ye Li is the Chair Professor in Wireless Systems at Imperial College London's Department of Electrical and Electronic Engineering, Faculty of Engineering. Prior to joining Imperial in 2020, he held a Professorship at Georgia Institute of Technology (2000–2020) and was a Principal Technical Staff Member at AT&T Labs (1995–2000). His research focuses on wireless communications, deep learning integration, and signal processing, with over 700 publications and 40 patents. He leads the Intelligent Transmission and Processing Laboratory . Education & Career: PhD in Electrical Engineering (Assumed based on career trajectory) Georgia Institute of Technology: 20-year tenure as Professor (1995–2020) AT&T Labs: Principal Technical Staff (1990–1995) Research Interests: OFDM systems and resource cooperation frameworks Deep learning applications in wireless communications Integrated sensing and communication (ISAC) systems AI-driven semantic communication protocols Energy-efficient and green wireless networks Awards & Recognition: Fellow of Royal Academy of Engineering (2024) IEEE Fellow (2006) IET Fellow (2021) 2024 IEEE Eric E. Sumner Award Clarivate Highly Cited Researcher (2001, 2014–2023) Grants & Labs: Pioneered the Intelligent Transmission and Processing Laboratory at Imperial Secured funding for projects in AI-semantic communication, ISAC, and neuromorphic computing Contributed to global standards in OFDM and wireless system design Current Focus: Advancing semantic communication frameworks, AI-driven signal processing, and low-complexity machine learning systems for next-generation wireless networks.

Ben Hankamerمشاهده پروفایل

پژوهشگر ارشد

• Biological Sciences
• Industrial Biotechnology
• Plant Biology
• +۳ مورد دیگر

Professor Ben Hankamer is a Professorial Research Fellow at The University of Queensland's Institute for Molecular Bioscience (IMB), affiliated with the School of Chemistry and Molecular Biosciences and the Centre for Solar Biotechnology. His research focuses on leveraging microalgae for sustainable solutions in energy, environment, and health. Education: Bachelor of Science (Honours), Advanced, University of Liverpool Masters of Science, University College London Doctor of Philosophy, University College London Research Interests: Structural biology of photosynthesis using cryo-EM to study microalgal photosynthetic machinery Microalgae biotechnology for solar fuels, bioremediation, and high-value products (e.g., vaccines, nutraceuticals) Sustainable biomanufacturing and circular bioeconomy Research Impact: His work addresses global challenges like climate change and resource scarcity through algae-based systems for CO₂ sequestration, renewable energy, and eco-services. Grants & Supervision: Available for PhD supervision and actively collaborates with industry on biotechnology and sustainability projects. Labs & Teams: Leads the Centre for Solar Biotechnology, a multidisciplinary hub advancing bio-inspired solar technologies for health and environmental resilience.

M. Khalid Jawedمشاهده پروفایل

دانشیار

• Robotics
• Mechanics
• Machine Learning
• +۵ مورد دیگر

M. Khalid Jawed is an Associate Professor in the Department of Mechanical and Aerospace Engineering at UCLA. His research focuses on the intersection of mechanics, robotics, machine learning, and computer graphics. The Structures-Computer Interaction Lab under his leadership explores smart structures, soft robotics, and physics-based modeling of flexible systems. Applications include precision agriculture robotics and bio-inspired locomotion systems. He holds a Ph.D. from MIT and postdoctoral training at Carnegie Mellon University. Education: Postdoctoral Research: Soft Robotics, Carnegie Mellon University (2016-17) Ph.D. (Mechanical Engineering: Mechanics), MIT (2016) S.M. (Mechanical Engineering), MIT (2014) B.S.E. in Aerospace Engineering and Engineering Physics, University of Michigan (2012) Research Interests: Professor Jawed’s work emphasizes computational mechanics, soft robotic design, and bio-inspired systems. His lab develops algorithms for real-time physics engines and explores novel materials like kirigami-enhanced composites. Key areas include robotic locomotion in complex environments, adaptive structures, and machine learning-driven simulations for deployable systems. Recent studies on spider ballooning mechanics and bacterial flagella-inspired propulsion highlight his interdisciplinary approach. Publications & Trends: His recent work emphasizes soft robotics, material innovation, and real-time control systems. Notable themes include kirigami-based morphing structures , machine learning for inverse design , and bio-inspired locomotion mechanisms . Over 50 publications span journals like Soft Matter and conference proceedings in robotics (e.g., IROS). Awards: NSF CAREER Award (2021) Outstanding Teaching Award, UCLA (2019) Multiple IROS Best Paper Finalist recognitions (2021-2023) Advising & Labs: He mentors students in robotics, mechanics, and computational modeling. The Structures-Computer Interaction Lab collaborates on projects with industry partners, focusing on precision agriculture robots and sustainable soft robotics. No specific students are listed in the provided materials. Key Projects: Developed the first real-time physics engine for soft robotics (2020) and explored sunlight-powered autonomous systems (2023). His work on spider flight mechanics was featured in multiple news outlets in 2022-2023.

Janet Wilesمشاهده پروفایل

استاد

• Human-Centered Computing
• Artificial Intelligence
• Social Robotics
• +۵ مورد دیگر

Professor Janet Wiles is a leading academic in Human-Centered Computing at the University of Queensland. She holds a PhD in Computer Science from the University of Sydney and completed a postdoctoral fellowship in Psychology. Her work bridges engineering, neuroscience, and social sciences, focusing on AI-driven technologies for dementia care, social robotics, and bio-inspired computation. She leads multidisciplinary teams developing tools like the Florence communication system and the Discursis analytics platform. Wiles teaches advanced research methods and has pioneered courses in human-centered AI, emphasizing cross-disciplinary collaboration. Her research explores complex systems, visualization, and the ethical integration of technology in healthcare and education. Notable projects include citizen science initiatives studying mycelial networks and AI-driven language technology co-design with Indigenous communities. Education: PhD in Computer Science (University of Sydney), Postdoctoral Fellowship in Psychology Research Interests: Human-Centered AI, Social Robotics, Neurosymbolic Systems, Assistive Technologies, Bio-inspired Computation Key Projects: Florence Project, Lingodroids, RatSLAM, Discursis Her publications span AI ethics, robotics, and computational neuroscience, with a focus on real-world applications. Wiles has contributed to over 200 peer-reviewed articles and actively engages in public outreach through initiatives like Visualising AI exhibitions.

• Physics
• Biomedical Engineering
• Neuroscience
• +۳ مورد دیگر

Prof. Zdenka Kuncic is a Professor of Physics at the School of Physics , University of Sydney. She holds a BSc (Hons I) from the University of Sydney and a PhD in theoretical astrophysics from the University of Cambridge. Her research bridges physics, medicine, biology, neuroscience, and engineering, focusing on interdisciplinary applications of physics-based approaches to solve complex problems. Key affiliations include the University of Sydney Nano Institute and the Charles Perkins Centre. Her research interests include neuromorphic computing, nanoparticle-based medical imaging, and quantum technologies. Notable grants include the 2024 Reconfigurable Neuromorphic Compute System (ARC LIEF) and the AMTAR Hub (2023). Media highlights include features in The Conversation , Financial Times , and ABC Science . Recent innovations include nanowire networks mimicking brain-like learning and memory, and advancements in MRI-guided radiotherapy using nanoparticles. Her work emphasizes translational research with potential impacts on healthcare and technology.

• Insect Behavior
• Ecology
• Entomology
• +۵ مورد دیگر

Associate Professor Tanya Latty is an entomologist at the University of Sydney's School of Life and Environmental Sciences. She holds a BSc in Biology and Environmental Science from Trent University (Canada) and a PhD in Insect Ecology from the University of Calgary (Canada). Her research focuses on insect behavior and ecology, with interdisciplinary collaborations spanning mathematics, computer science, and operations research. She is a member of the Sydney Institute of Agriculture and leads studies on pollination systems, swarm intelligence, and sustainable insect management. Her current projects include a systems approach to optimizing crop pollination via companion flowers (funded by ARC Future Fellowship) and assessing impacts of prescribed burning on honeybees (AgriFutures Australia grant). She has published extensively on topics like ant transportation networks, slime mould decision-making, and urban bee ecology. Media engagements include features on Quirks & Quarks discussing slime mould cognition and ant navigation. Research themes include bio-inspired technology development, pollination ecology, and invertebrate conservation. Her lab emphasizes Indigenous land acknowledgments, recognizing Gadigal traditional custodianship. Grants total over $2M in recent years, supporting both fundamental and applied ecological research.

• Auditory Processing
• Visual Processing of Faces
• Deep Neural Networks
• +۴ مورد دیگر

Dr. Christoph Daube is a Lecturer at the School of Psychology & Neuroscience, University of Glasgow. His research focuses on understanding how sensory inputs—such as speech sounds and visual face stimuli—are processed into abstract perceptual representations. He employs methods including deep neural networks (DNNs), magnetoencephalography (MEG), and information theory to explore these processes. Key interests include auditory speech processing in the brain, visual face perception, and the algorithmic equivalence between neural and artificial intelligence models. His work integrates interdisciplinary approaches, such as analyzing how speech envelope tracking occurs across auditory and non-auditory cortices, and investigating cultural influences on facial beauty perception. Recent studies include causal experiments on facial expression alignment in social interactions and theoretical frameworks for comparing brain and DNN models. Dr. Daube has contributed to over 10 peer-reviewed articles since 2019, spanning journals like *Current Biology*, *Cerebral Cortex*, and *Trends in Cognitive Sciences*. His research highlights the interplay between sensory processing, computational modeling, and human behavior.

Gregory Sawickiمشاهده پروفایل

استاد

• Wearable Robotics
• Biomechanics
• Neurophysiology
• +۳ مورد دیگر

Gregory Sawicki is a Professor and Joseph Anderer Faculty Fellow at Georgia Tech, holding joint appointments in the School of Mechanical Engineering and School of Biological Sciences. Effective 2025, he will serve as Interim Executive Director of Georgia Tech's Institute on Robotics and Intelligent Machines (IRIM). His academic journey includes a B.S. from Cornell University (1999), an M.S. from University of California-Davis (2001), and a Ph.D. in Human Neuromechanics from the University of Michigan (2007). Postdoctoral training at Brown University (2007–2009) followed, before joining NC State/UNC Chapel Hill as faculty (2009–2017) and Georgia Tech in 2017. His research focuses on the Human Physiology of Wearable Robotics (PoWeR Lab), exploring neuromechanical principles to design lower-limb exoskeletons that synergize with human physiology. Key areas include gait stabilization, aging locomotion, and post-stroke rehabilitation. Notable contributions include bio-inspired exoskeleton control frameworks and insights into muscle-tendon dynamics during perturbed movements. Education: Cornell University (B.S., 1999), UC Davis (M.S., 2001), University of Michigan (Ph.D., 2007) Awards: Joseph Anderer Faculty Fellow, 2021 Journal of Biomechanics Award (American Society of Biomechanics) Current projects emphasize symbiotic human-machine systems, leveraging real-time data for adaptive control. His lab’s work spans从 biomechanical modeling to clinical applications, aiming to enhance mobility for healthy and impaired populations.

Tansu Celikelمشاهده پروفایل

استاد

• Neural Information Processing
• Cognitive Artificial Intelligence
• Computational Modeling
• +۳ مورد دیگر

Tansu Celikel is a Professor and School Chair at the School of Psychology , Georgia Institute of Technology. His research focuses on neural information processing, cognitive AI, computational modeling, and neuroplasticity. He leads the Neuroloops lab, which explores perceptual learning through experimental and computational approaches in animals and humans. Key research contributions include identifying mechanisms of sensory-to-action transformation, developing real-time neuromodulation tools, and bridging animal/human studies through robotic control architectures. His lab has pioneered methods for millisecond-resolution behavioral quantification and neural interface systems. Publications emphasize interdisciplinary approaches spanning neuroscience, engineering, and AI. Current work targets closed-loop control of perceptual learning stages, from sensory input to motor execution. The lab's translational focus aims to apply neurobiological principles to robotics and cognitive enhancement technologies. Laboratory activities include hardware/software development for neural interfaces, computational modeling of sensory systems, and collaborative projects with robotics engineers. Experimental platforms involve rodents and human subjects to study shared perceptual mechanisms across species.

Dario Zancaمشاهده پروفایل

پژوهشگر

• Visual Perception
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Dario Zanca is a Postdoctoral Researcher in the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He is affiliated with the Machine Learning and Data Analytics (MaD) Lab and focuses on visual perception, eye movement analysis, and machine learning applications. His work bridges computational models of human attention with neural network behavior, contributing datasets like FixaTons and CapMIT1003. Research areas include: Biologically-inspired visual attention models Eye tracking and gaze analysis in clinical and computational contexts Deep learning interpretability and explainable AI Time series analysis and predictive process monitoring Key contributions include tools like the SaccadeAnalysisMatlabApp for gaze data processing and participation in initiatives like the PolarBearVidID dataset for wildlife behavior analysis. His interdisciplinary projects span healthcare, robotics, and environmental science, emphasizing real-world applications of AI.

Grace Lindsayمشاهده پروفایل

استادیار

• Artificial Neural Networks
• Neuroscience
• Psychology
• +۳ مورد دیگر

Grace Lindsay is an Assistant Professor of Psychology, Neural Science, and Data Science at New York University. Her research focuses on using artificial neural networks to model biological information processing and address climate change challenges. She explores attention mechanisms, tools for neuroscience analysis, and machine learning applications in environmental science, such as remote sensing for climate mitigation. Lindsay’s work bridges computational neuroscience and real-world problem-solving, emphasizing interdisciplinary collaboration. Education: B.S. in Neuroscience (University of Pittsburgh), Postdoctoral Research (University College London), Ph.D. in Theoretical Neuroscience (Columbia University). Current affiliations include the NYU Center for Data Science and collaborations with organizations like Collaborative Earth’s Beaver Lab, studying beaver ecosystems. Research interests span neural network architectures, climate data analysis, and the ethical application of AI. Her lab actively develops tools for climate adaptation, leveraging machine learning to analyze satellite imagery and environmental datasets. Lindsay also advocates for the responsible integration of AI in climate science, emphasizing the need for cross-disciplinary solutions. Grants and advising: While specific grants are not listed, her work is supported by NYU and partnerships with environmental organizations. She mentors students in both neuroscience and climate technology, fostering innovation at the intersection of these fields. Labs/Teams: The Lindsay Lab at NYU, focusing on dual tracks of neuroscience modeling and climate change solutions. Future work includes expanding climate-related projects and advancing interpretable AI for ecological systems.