جست‌وجوی استاد و پژوهشگر

Pengtao Xieمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Machine Learning for Healthcare
• +۴ مورد دیگر

Pengtao Xie is an Associate Professor (tenured) in the Department of Electrical and Computer Engineering at UC San Diego, with cross-appointments in the Division of Biomedical Informatics and affiliations across multiple schools and institutes including the Halıcıoğlu Data Science Institute, School of Biological Sciences, and Skaggs School of Pharmacy. His research bridges human-inspired machine learning and healthcare applications. Education: PhD in Machine Learning, Carnegie Mellon University (2018) MS from Tsinghua University BS from Sichuan University Research Interests: His work focuses on machine learning inspired by human learning strategies , including learning by testing, interleaving, self-explanation, and teaching. These techniques are applied to large language models , foundation models , healthcare , and biomedicine . Recent efforts emphasize generative AI for medical image segmentation and protein function prediction. Scientific Awards: NIH MIRA Award (2025) NSF Career Award (2024) Best Graduate Teacher Award, ECE UCSD (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) Tencent Faculty Award (2021) AMIA Doctoral Dissertation Award Finalist (2020) Siebel Scholarship (2014) Teaching & Mentorship: He has developed and taught courses such as Deep Generative Models , Probabilistic Graphical Models , and Linear Algebra and Applications . He actively mentors PhD, master's, and undergraduate students, with alumni now at CMU, Stanford, Mila, and industry roles. Labs & Teams: He leads a research group within the Center for Machine-Intelligence, Computing and Security and collaborates with the Institute for Genomic Medicine and Institute of Engineering in Medicine at UC San Diego.

Tan Viet Tuyen Nguyenمشاهده پروفایل

مدرس

• Human-centered Artificial Intelligence
• Social Human-Robot Interaction
• Multimodal Perception and Interaction
• +۵ مورد دیگر

Dr. Tan Viet Tuyen Nguyen is a New Frontiers Fellow (Lecturer) in AI at the University of Southampton, specializing in Human-Centered Artificial Intelligence and Social Human-Robot Interaction. His research focuses on multimodal learning for robots to adapt their behavior to human social needs, with applications in healthcare, education, and service environments. Prior to this role, he was a Research Associate at King’s College London and a Research Assistant on the EU-funded CARESSES project, developing culturally-aware assistive robots for elderly support. Education: PhD in Information Science (Robotics) from Japan Advanced Institute of Science and Technology. He has organized conferences such as the IEEE RO-MAN 2022 special session on nonverbal communication and served as a reviewer for top-tier robotics and AI conferences. Research Interests include: Human-Robot Collaboration, Multimodal Perception, Generative AI for Social Interaction, and Context-Aware Robot Behavior Generation. His work has been recognized with awards including the Best Paper Award at ROMAN 2022 and the Prospective Research Award at ICServ 2023. Teaching Responsibilities include courses on Biologically Inspired Robotics, High-Level Programming, and MSc/Undergraduate project supervision. He currently oversees two PhD students and collaborates on projects like 'Exploring the impact of AI-driven writing of engagement in climate change' and 'Bridging Generations and Cultures through Generative AI.' Labs/Teams: Member of the Agents, Interaction and Complexity Centre and the Centre for Robotics Research at Southampton.

Rodrigo Venturaمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۴ مورد دیگر

Rodrigo Ventura is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), University of Lisbon. He is also a senior researcher at the Institute for Systems and Robotics (ISR-Lisbon), leading the Space and Aeronautics thematic line. His research focuses on the intersection of Robotics and Artificial Intelligence, emphasizing human-robot interaction, space robotics, and cognitive architectures. He coordinates the Minor in Space Sciences and Technologies at IST and the MBE on Space Systems for Tecnico+. As Adjoint Faculty at the International Space University (ISU), he contributes to global academic initiatives. His work includes experiments on the International Space Station and participation in analog space missions. Research interests span biologically inspired systems, machine learning, and teleoperation interfaces. Recent publications address reinforcement learning for UAVs, microgravity experiments, and pseudo-haptic feedback for robotic control. He teaches subjects like Artificial Intelligence and Decision Systems, Satellite Engineering, and Autonomous Systems.

Lara A. Estroffمشاهده پروفایل

استاد

• Biomineralization
• Crystal Growth Mechanisms
• Bio-inspired Materials Synthesis
• +۷ مورد دیگر

Lara A. Estroff is a Full Professor and the current Chair of the Department of Materials Science and Engineering at Cornell University's College of Engineering. She has been a faculty member since 2005 and served as Director of Graduate Studies from 2015 to 2019. Her academic leadership and research excellence position her at the forefront of bio-inspired materials and biomineralization research. Her educational background includes a B.A. in Chemistry from Swarthmore College (1997) and a Ph.D. in Chemistry from Yale University (2003), followed by an NIH-funded postdoctoral fellowship at Harvard University in the lab of Prof. George M. Whitesides. Dr. Estroff's research centers on the fundamental mechanisms of crystal growth, biomineralization, and pathological mineralization. She investigates how organisms control mineral formation and applies these principles to engineer synthetic materials with complex structures and functionalities. Her work spans biomaterials, tissue engineering, and energy materials—particularly hybrid organic-inorganic perovskites for photovoltaics. She employs advanced characterization techniques and has pioneered in situ methods to monitor crystallization dynamics. Her recent publications reveal a strong trend toward interdisciplinary research, integrating materials science with cancer biology, immunology, and machine learning. The articles emphasize bio-inspired synthesis, mineral-tissue interactions, and the development of functional crystalline materials for medical and energy applications. Faculty Early CAREER Award, National Science Foundation (2009) Fiona Ip Li '78 and Donald Li '75 Excellence in Teaching Award, Cornell College of Engineering (2007) Marilyn Emmons Williams Award, Cornell Undergraduate Research Board (2009) Keynote Speaker, Gordon Research Seminar on Biomineralization (2012) Lawrence Berkeley National Lab Affiliate (2013) Dr. Estroff leads a major DOE-funded project titled “Formulation Engineering of Energy Materials via Multiscale Learning Spirals,” a $3 million, three-year initiative using machine learning to optimize perovskite synthesis for solar cells. She has advised numerous graduate students and postdoctoral researchers, and her lab is known for fostering collaborative, cross-disciplinary research. She has also contributed to educational initiatives at Cornell, particularly in undergraduate research and materials education. Her research group operates at the intersection of chemistry, engineering, and biology, focusing on high-resolution characterization of biominerals, in situ crystal growth studies, and the design of in vitro models for cell-mineral interactions. The lab actively collaborates with institutions including Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, and Johns Hopkins University.

Maartje Bastingsمشاهده پروفایل

دانشیار

• Biomaterials Engineering
• DNA Nanotechnology
• Supramolecular Materials
• +۵ مورد دیگر

Maartje Bastings is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , leading the Programmable Biomaterials Laboratory (PBL) within the School of Engineering (STI) . She holds additional affiliations with the Institute of Materials (IMX) , IBI-STI (Bioengineering), and supervises doctoral programs in Biotechnology and Biological Engineering ( EDBB-GE ) and Materials Science and Engineering ( EDMX-GE ). Her research focuses on DNA-based supramolecular materials engineered to achieve dynamic reciprocity —a two-way interaction between synthetic materials and biological systems. By leveraging DNA as a programmable scaffold, she investigates structural mechanics, valency control, and geometric constraints governing self-assembly and cell communication at bio-interfaces. Key applications include immune system modulation, diagnostics , and vaccine development . Analysis of her 15 most recent publications reveals a focus on multivalent interactions for T-cell activation, spatial patterning in immune signaling, and engineered coatings for DNA origami stability. Subfields span T cell receptor engineering , nanoscale protein dynamics , stimuli-responsive biomaterials , and bio-inspired therapeutic design . PhD Students: Chen Yuduo Hendrickx Pauline Bart M. Kononenko Artem Li Shujie Lou Yameng Meyer Pitt Narita Minako Rousseau Benjamin Bila Hale Caroprese Vincenzo Comberlato Alice Kurisinkal Eva Eugene Paloja Kaltrina Rodríguez Franco Hugo José Tekin Cem Wong Siu Ho Contact: maartje.bastings@epfl.ch

• Computational Neuroscience
• Neural Networks
• Retinal Computation
• +۷ مورد دیگر

Joel Zylberberg is an Adjunct Assistant Professor at the University of California, Los Angeles (UCLA), affiliated with the Department of Ophthalmology within the School of Medicine . His research bridges Computational Neuroscience , Neural Networks , and Machine Learning , focusing on how neural activity and biological mechanisms inform artificial intelligence and visual cortex dynamics . Joel's work explores retinal computation , population coding , and neural adaptation , often analyzing mouse visual cortex and neurophysiological data . His recent publications highlight trends in dynamic retinal processes , stimulus-driven network topology , and brain-inspired machine learning , emphasizing the interplay between biophysics and computational modeling . Collaborators include Greg Field (UCLA), Richard Born (Harvard), and Michael DeWeese (UC Berkeley), with affiliations spanning institutions like University of Washington and University of California, San Diego (UCSD). His work appears in journals such as Nature Neuroscience , Neuron , and PLOS Computational Biology .

• Control Systems
• Robotics
• Machine Learning
• +۳ مورد دیگر

Professor Bing Chu is an academic at the University of Southampton, actively contributing to research in control systems, robotics, and machine learning. They are a member of the Vision, Learning and Control Centre for Internet of Things and Pervasive Systems and the Centre for Robotics, focusing on interdisciplinary approaches that combine control theory with data-driven methodologies. Current research interests include: Iterative learning control Human-robot interaction Wind farm power optimization Robot behavior modeling Control system architectures Collaborative learning systems Recent publications highlight trends in data-driven control systems, human-robot interaction datasets, and optimization techniques for both continuous-time systems and wind energy applications. Professor Chu supervises multiple PhD students across robotics and electronic engineering, including Balint Gucsi, Haonan Shen, and Aleksander Wolski, while leading projects funded by Zhengzhou University and the Royal Society.

Amit Lalمشاهده پروفایل

استاد

• Microsystem Engineering
• MEMS and NEMS
• Ultrasonic Sensors
• +۱۲ مورد دیگر

Amit Lal is a Professor in the School of Electrical and Computer Engineering at Cornell University, with affiliations in Biomedical Engineering, Applied Engineering Physics, and Mechanical and Aerospace Engineering. He is a member of key research centers including Cornell CCMR, NBTC, and KAUST-CU. Education: B.S. in Electrical Engineering, California Institute of Technology, 1990 Ph.D. in Electrical Engineering, University of California, Berkeley, 1996 Prof. Lal's research focuses on the development of integrated microsystems using micro- and nanoscale fabrication. His work spans ultrasonic MEMS, low-power IoT sensors, atomic microsystems, and bio-robotics. He directs the SonicMEMS Laboratory, advancing technologies in GHz ultrasonics, inertial sensing, and chip-scale manipulation of particles. His interdisciplinary interests include biomedical imaging, solid-state devices, nanotechnology, and plasma science. His recent publications highlight innovation in energy harvesting, MEMS gyroscopes, and biologically integrated systems. The works reflect strong trends in autonomous sensing, miniaturized power sources, and hybrid bio-electromechanical systems, particularly for medical and navigation applications. Scientific Awards and Honors: NSF CAREER Award Whitaker Foundation Award Department of Defense Exceptional Service Award Best Program Manager Award, DARPA IEEE Ultrasonics and Frequency Control Symposium Best Paper Award IEEE NEMS Best Paper Award Robert M. Scharf 1977 Professor, Cornell Engineering HHMI Visiting Scientist, Janelia Farms Intel Fellowship (awarded to advisee) Prof. Lal has advised numerous students who have gone on to win awards and publish impactful research. He has secured significant research funding through DARPA and other agencies, managing and initiating multiple high-impact programs. His leadership extends to service on technical committees for IEEE conferences and journals, including Transducers and the IEEE Sensors Council. He has also contributed to academic recruiting within ECE. He leads the SonicMEMS Laboratory , a multidisciplinary research group focused on transforming sensing, communication, and computation at the microscale. The lab fosters collaboration across engineering and life sciences, pushing the boundaries of what integrated microsystems can achieve.

Tomaso A. Poggioمشاهده پروفایل

استاد

• Computational Neuroscience
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology , an investigator at the McGovern Institute for Brain Research , a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) , and the director of both the MIT Center for Biological and Computational Learning (CBCL) and the multi-institutional Center for Brains, Minds and Machines (CBMM) . Research Interests Poggio’s research is fundamentally interdisciplinary, sitting at the intersection of computational neuroscience , machine learning , and computer vision . His work is driven by the conviction that learning is the core gateway to both biological intelligence and artificial systems. Current themes include: Mathematical foundations of statistical learning theory Engineering applications in computer vision, graphics, bioinformatics, and intelligent search Computational neuroscience of visual object recognition and the ventral stream of the visual cortex Scientific Awards & Honors Eugene McDermott Professorship, MIT Advising & Grants Over three decades, Poggio has advised a large cohort of doctoral and master’s students whose theses span machine learning, computer vision, neuroscience, and bioinformatics. Representative graduates include H. Jhuang, Stanley Bileschi, Jacob Bouvrie, Jennifer Louie, M. Kouh, Sayan Mukherjee, Ryan Rifkin, Alexander Rakhlin, Thomas Serre, Gene Yeo, and many others. His research has been continuously supported by major federal and private funding initiatives, most recently through the multi-institutional Center for Brains, Minds and Machines (CBMM) headquartered at the McGovern Institute since 2013. Laboratories & Teams Poggio directs the Poggio Lab (CBCL) at MIT, an interdisciplinary group comprising neuroscientists, computer scientists, mathematicians, and engineers. The lab collaborates closely with experimental neuroscientists to develop predictive computational theories of visual cortex function and to translate those insights into practical algorithms for computer vision and machine learning.

• Biomaterials
• Tissue Engineering
• Regenerative Medicine
• +۵ مورد دیگر

João F. Mano is a Full Professor at the Department of Chemistry, University of Aveiro, and Director of the Doctoral Program on Biotechnology. He leads the COMPASS Research Group and serves as Vice-Director at CICECO - Aveiro Institute of Materials. His academic appointments include Invited Professor at University of Lorraine (France), Visiting Professor at KAIST (South Korea), and Adjunct Professor at Ajou University (South Korea). Education: PhD in Chemistry (1996, Technical University of Lisbon); D.Sc. in Tissue Engineering, Regenerative Medicine and Stem Cells (2012, University of Minho) Research Interests focus on Biomaterials for Regenerative Medicine , integrating Nanotechnology , Microtechnology , and Biofabrication . His group develops Bioinspired Materials using polymer chemistry, Decellularized Extracellular Matrix , and 3D Bioprinting to engineer Cell Microenvironments for therapeutic applications. Recent Publications highlight advancements in Human-Derived Hydrogels , Photopolymerizable Scaffolds , Magneto-Responsive Biomaterials , and Programmable Bioinks . Trends show emphasis on Organ-on-a-Chip integration, Smart Living Materials , and Green Bioprinting methodologies. Scientific Awards include: European Research Council Advanced Grants (2015, 2020) Fellow at IUPAC, European Academy of Sciences, and American Institute of Medical and Biological Engineering ERC Proof of Concept Grants Doctor Honoris Causa from University of Lorraine and Utrecht UNESCO Chair on Biomaterials George Winter Award (European Society for Biomaterials) Supervisions & Collaborations encompass 74+ MSc, 26+ PhD students, and 40+ postdocs. He co-founded METATISSUE and CELLULARIS Biomodels , and serves as Editor-in-Chief of Materials Today Bio .

Wolfgang Nejdlمشاهده پروفایل

استاد

• Web Science
• Information Retrieval
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .

• Computational Neuroscience
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Prof. Sander Bohte is a part-time full professor of Computational Neuroscience at the University of Amsterdam (Swammerdam Institute of Life Sciences) and an honorary full professor of Bio-inspired Neural Networks at the University of Groningen. He serves as a Scientific Staff Member and Group Leader in the Machine Learning department at CWI, Amsterdam. His work bridges computational neuroscience and machine learning with a focus on continuous-time information processing. Key research interests include: Spiking Neural Networks with predictive coding and multi-compartment models Biologically Plausible Learning in recurrent and deep architectures Working Memory modeling via reinforcement learning Neuromorphic Computing for real-time systems and GPU acceleration His recent publications highlight trends in neural adaptation , predictive coding , and SNN hardware-software co-design . Awards include the Veni Innovational Research Grant (2004) and ERCIM grant (2013) . He actively supervises MSc theses and leads grants like the NWO KIC project 'Selfhealing Neuromorphic Systems' (2024).

Youngchan Kimمشاهده پروفایل

مدرس

• Quantum Biology
• Biophotonics
• Bioimaging
• +۳ مورد دیگر

Dr. Youngchan Kim is a Lecturer in Quantum Biology at the University of Surrey , serving as Director of the Quantum Biology Doctoral Training Centre (QB-DTC). He is affiliated with multiple departments including the School of Biosciences, Advanced Technology Institute, and Quantum Sciences Group. PhD in Physics (2011), Korea Advanced Institute of Science and Technology MSc in Physics (2008), KAIST BSc in Physics (2006), Chung-Ang University Graduate Certificate in Learning and Teaching (2022), Advance HE His research focuses on quantum phenomena in biological systems at physiological temperatures, particularly using femtosecond optical spectroscopy and genetically engineered fluorescent proteins to explore evolutionary adaptations and develop quantum-bio-inspired technologies like room-temperature single-photon sources. The 15 most recent publications span quantum biology, biophotonics, and optical spectroscopy, with particular emphasis on quantum coherence in biological systems , terahertz birefringence , fluorescent protein dynamics , and biomedical imaging innovations . These works demonstrate his interdisciplinary approach bridging physics, biology, and medical applications. As QB-DTC Director, he leads transdisciplinary initiatives fostering collaboration between quantum physics and biosciences. His technical expertise includes time-correlated single-photon counting , common-path interferometry , and ultrafast fluorescence depolarization techniques.

Sina Sarehمشاهده پروفایل

استاد

• Soft robotics
• Multi-modal mobility
• Manipulation and attachment
• +۱ مورد دیگر

Sina Sareh is a robotics researcher at the Royal College of Art (RCA), where he leads the RCA Robotics Laboratory within the School of Design. He has established himself as an expert in soft robotics and multimodal sensing, developing innovative solutions for human safety and access problems in industrial operations. Dr. Sareh's educational background includes: PhD in Robotics from the University of Bristol, where he worked on monolithic design of flexible actuators for operation in confined liquid environments MSc in Control Systems from the University of Sheffield BSc in Electrical Engineering from Amirkabir University of Technology, Tehran Dr. Sareh's research focuses on soft robotics, multi-modal mobility, manipulation and attachment, and multimodal sensing. His work bridges the gap between robotics engineering and practical applications, particularly in medical and industrial settings. He has developed novel approaches to robotic attachment inspired by octopus biology, created haptic interfaces that mimic the feeling of touching human internal organs, and designed soft robotic technologies to help articulate pain symptoms. His research consistently demonstrates innovation in creating adaptable robotic systems that can operate effectively in complex, unstructured environments where traditional rigid robots face limitations. His publication record demonstrates a strong trajectory in robotics research, with emphasis on soft robotics, medical applications, and novel sensing techniques. The research shows progression from fundamental soft actuator design to practical applications in surgery, industrial operations, and human-robot interaction, with a consistent focus on solving real-world problems through biologically inspired approaches. Dr. Sareh has successfully secured multiple research grants, including EPSRC funding for 'Getting a Grip' and 'Multi-vendor Interoperability in Robotics,' as well as InnoHK funding for 'Intelligent Medicine Warehousing.' He has also served as an impact assessor for the Research Excellence Framework (REF) 2021 in Engineering and is a member of the editorial board at IET Cyber-physical Systems and Robotics Journal. Currently, Dr. Sareh advises research students including Filippo Sanzeni, and maintains active collaborations with industry and academic partners through the RCA Robotics Laboratory, which serves as a hub for interdisciplinary robotics research at the intersection of design, engineering, and human-centered applications. His work on projects like 'Topographies of Pain' and 'Reminisys' demonstrates a commitment to applying robotics technology to improve healthcare outcomes and quality of life.

Nikolaus Kriegeskorteمشاهده پروفایل

استاد

• Computational Neuroscience
• Artificial Neural Networks
• Visual Perception
• +۳ مورد دیگر

Nikolaus Kriegeskorte is a Professor of Psychology and Neuroscience, and Director of Cognitive Imaging at the Mortimer B. Zuckerman Mind Brain Behavior Institute at Columbia University. He is affiliated with the Departments of Psychology, Neuroscience, and Electrical Engineering. Institution: Columbia University Academic Roles: Professor of Psychology and Neuroscience; Director of Cognitive Imaging Email: nk2765@columbia.edu Location: Jerome L. Greene Science Center, 3227 Broadway, L3-064 Research Focus: The lab explores the cognitive neuroscience of vision, modeling biological visual systems with artificial neural networks. Key areas include developing statistical inference and visualization techniques to bridge theory and experimental data, understanding representational geometry in neural systems, and optimizing deep learning frameworks for neuroscience. Recent Publications: Highlighted work spans neural network modeling of visual perception, representational similarity analysis, and the topology of brain representations. The lab's methods, such as the TorchLens Python package, enable transparent extraction and visualization of hidden layer activations in neural networks. Grants: Projects are supported by funding from the National Science Foundation (NSF) - Cognitive Neuroscience and the National Institutes of Health (NIH) - NIMH. Laboratory: The Visual Inference Lab (kriegeskortelab.zuckermaninstitute.columbia.edu) is located at Quad 3D, Zuckerman Institute, 3227 Broadway.