جستجوی استاد راهنمای دانشگاهی

• Bioelectronics
• Soft Robotics
• Wearable Devices
• +۳ مورد دیگر

Young-Geun Park is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT) Department of Mechanical Engineering. His research focuses on advanced materials and soft electronics for biomedical applications, including neural interfaces, wearable sensors, and 3D-printed devices. He specializes in liquid metal-based technologies for implantable and flexible electronics, with applications in cardiac monitoring, neural engineering, and smart medical devices. His work bridges mechanical engineering, materials science, and biomedical innovation. Key areas of investigation include self-healing conductive materials, magnetically actuated sensors, and wireless neural recording systems. Park has pioneered techniques for high-resolution 3D printing of electronics and developed transparent, stretchable devices such as smart contact lenses for non-invasive health monitoring. His research emphasizes translating cutting-edge materials into practical clinical or consumer applications. Publications highlight interdisciplinary contributions to bioelectronics, wearable health tech, and additive manufacturing. While no scientific awards are explicitly mentioned, his prolific output since 2016 demonstrates sustained innovation in soft robotics and biomedical device engineering. Advising roles or grants are not detailed in current records, though his institutional affiliation suggests active participation in collaborative research projects at MIT. Labs/Teams: Affiliated with MIT's Mechanical Engineering Department, contributing to initiatives in bio-inspired robotics, flexible electronics, and healthcare technology development.

Xiaogan Liangمشاهده پروفایل

استاد

• Advanced Nanofabrication
• Nanomanufacturing
• Microsystem Technologies
• +۷ مورد دیگر

Professor Xiaogan Liang is a faculty member in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor, and serves as Associate Chair for Undergraduate Education. His research focuses on advanced nanofabrication, nanomanufacturing, and microsystem technologies, with applications in nanoelectronics, nanophotonics, and biosensing systems. He leads the Nanoengineering and Nanodevice Laboratory and holds a tenured position. Liang has been recognized with awards such as the Monroe-Brown Education Excellence Award (2018-19) and the NSF CAREER Award (2015). His work bridges nanotechnology and biomedical engineering, emphasizing scalable fabrication techniques and neuromorphic computing hardware. Education and Career: Liang joined the University of Michigan in 2012 as an Assistant Professor, advancing to his current rank. His academic background includes expertise in mechanical engineering and nanotechnology. Key research contributions include novel 2D material-based biosensors, memristive devices, and plasmonic systems for real-world applications. Research Interests: His lab develops nanoscale fabrication methods (e.g., nanoimprint lithography) for creating high-performance devices like MoS₂ photodetectors and Bi₂Se₃ memristors. Applications span environmental monitoring, medical diagnostics, and energy-efficient robotics. Recent innovations include brain-inspired computing hardware for robotics and bioinspired sensors for viral detection. Publications: Over 150 peer-reviewed articles highlight his work on neuromorphic systems, plasmonic colorimetric sensing, and scalable nanoassembly. Notable 2020-2025 publications address reservoir computing hardware, site-selective 2D material growth, and airborne particle monitoring systems. Awards: Monroe-Brown Award (2018), NSF CAREER (2015), CoE Awards (2016) Grants: Supported by NSF, U-M, and industry partnerships Advising: Mentors graduate students (e.g., Hongsuk Nam, Sungjin Wi) in nanofabrication and biosensor development. Collaborates with Professors Katsuo Kurabayashi and Allen Liu on interdisciplinary projects. Labs/Teams: Directs the Nanoengineering and Nanodevice Laboratory, fostering cross-disciplinary research in nanotechnology and biomedical engineering. Engages in collaborative efforts with materials science and electrical engineering groups.

Matt Thomsonمشاهده پروفایل

استاد

• Biological Engineering
• Cellular and Developmental Biology
• Molecular Biology
• +۸ مورد دیگر

Matt Thomson is a Professor of Computational Biology and Investigator at the Heritage Medical Research Institute (HMRI) at the California Institute of Technology (Caltech). He holds a B.A. from Harvard University (2001) and a Ph.D. from Caltech (2011). His academic progression includes roles as Visiting Associate (2016), Assistant Professor (2017-24), and his current full Professor position since 2024. His research focuses on applying quantitative methods to engineer cellular differentiation and control biological systems. Key areas include synthetic biology, single-cell genomics, and active matter systems. Techniques employed include mathematical modeling, machine learning, and single-cell RNA sequencing. Notable achievements include developing optical differentiation systems, computational tools for cell trajectory analysis, and stochastic models of cellular self-organization. Thomson's work bridges biology and engineering, with applications in regenerative medicine, cancer immunotherapy, and foundational research in developmental biology. His lab, the Thomson Lab, collaborates with the SPEC initiative to advance programmable biological systems. He also explores AI-driven approaches for protein engineering and large language model optimization in biological contexts. No scientific awards are explicitly listed in the provided materials. His research is supported by grants and institutional resources at Caltech and HMRI. He advises students and researchers in his lab but no specific names are provided in the text.

• Neuroscience
• Statistics
• Machine Learning
• +۳ مورد دیگر

David Zoltowski is a Postdoctoral Researcher in the Department of Statistics at Stanford University, advised by Scott Linderman. His research focuses on developing and applying statistical and machine learning methods to understand neural dynamics, decision-making processes, and multi-region neural communication. He specializes in latent variable models, Bayesian inference techniques, and dynamical systems analysis. Key contributions include benchmarking frameworks for neural activity models and methods for uncovering structure in complex neural datasets. His work bridges computational neuroscience and statistics, with applications spanning neural decoding, state-space modeling, and neuromodulation. Recent projects address the integration of time and reward signals in decision-making circuits and the development of scalable algorithms for high-dimensional spike train analysis. David collaborates on interdisciplinary projects involving neuroimaging, behavioral neuroscience, and probabilistic modeling. His tools and benchmarks are widely used in the computational neuroscience community for evaluating latent variable models and neural decoding systems.

• Neuroscience
• Systems Neuroscience
• Computational Neuroscience
• +۷ مورد دیگر

Anne Kathryn Churchland is Professor of Neurobiology at the University of California, Los Angeles David Geffen School of Medicine, where she holds the Arnold B. Scheibel, M.D. Chair for Brain Research. She leads a multidisciplinary laboratory that leverages large-scale electrophysiology, wide-field calcium imaging, and computational modeling to understand how cortical and sub-cortical circuits transform multisensory evidence into flexible decisions. Churchland co-founded and co-directs the International Brain Laboratory (IBL), a global consortium that has released the first standardized, brain-wide dataset of mouse decision-making. Her work has revealed fundamental principles such as choice-selective inhibition, category-free mixed selectivity, and the influence of spontaneous movements on cortical dynamics, while simultaneously producing open-source tools for chronic Neuropixels recordings, motion correction (DREDge), and web-based data exploration. Education: Ph.D. in Neuroscience, University of California, San Francisco, November 2003 Research Focus: Churchland’s group investigates how distributed neural populations encode and integrate sensory information across modalities, accumulate evidence over time, and generate choice. Using high-density Neuropixels probes and cortex-wide calcium imaging in head-fixed and freely moving mice, her team links trial-by-trial neural dynamics to sophisticated behavioral models. A unifying theme is the role of latent behavioral states—such as arousal, engagement, and spontaneous movement—in shaping sensory representations and decision computations. Technological & Open-Science Contributions: Beyond scientific discoveries, the lab develops and openly shares hardware (lightweight reusable Neuropixels implants), algorithms (DREDge motion correction), and data platforms (IBL Data & Atlas websites). These resources are already accelerating labs worldwide and exemplify her commitment to reproducible, collaborative neuroscience. Selected Honors: UCLA Excellence in Postdoc Mentoring Award (2024) James M. and Cathleen D. Stone Faculty Research Award, CSHL (2020) Louise Hanson Marshall Special Recognition Award, Society for Neuroscience (2017) Janett Rosenberg Trubatch Career Development Award, SfN (2012) Funding & Leadership: Churchland has served as Principal or Co-Principal Investigator on continuous NIH support since 2008, including a current U19 “State-dependent Decision-making in Brain-wide Neural Circuits” and prior R01 and K99/R00 awards. She is an active member of the NeuroAI initiative, promoting two-way interactions between neuroscience and next-generation artificial intelligence. Training Environment: The Churchland lab at UCLA provides a vibrant training ground for postdoctoral fellows and graduate students, integrating experimental design, advanced data acquisition, large-scale compute, and theoretical analysis. Trainees leave with broad expertise valued in both academic and tech sectors, as evidenced by her 2024 UCLA mentoring award.

Thomas A. Clelandمشاهده پروفایل

استاد

• Neuroscience
• Cognitive Neuroscience
• Olfactory Systems
• +۳ مورد دیگر

Thomas A. Cleland is a Professor of Psychology at Cornell University, affiliated with the College of Arts and Sciences. His research focuses on how cognitive and perceptual processes emerge from neural circuit properties, particularly in olfactory systems. He explores learning, memory, and sensory processing using techniques like electrophysiology, computational modeling, and neuromorphic algorithms. Cleland's work bridges neuroscience and artificial intelligence, aiming to develop biologically inspired solutions for AI systems. He teaches courses such as PSYCH 4600 (Neural Representations) and leads the Computational Physiology Laboratory. His research integrates interdisciplinary methods, including brain imaging (e.g., light-sheet imaging), behavior genetics, and theoretical studies. Cleland’s lab collaborates on projects like odor recognition, neuromorphic circuits, and neural oscillations, with publications spanning journals like Neuroscience and Artificial Intelligence . His contributions include developing tools such as the Annolid segmentation framework and advancing understanding of olfactory bulb dynamics. Cleland’s work has implications for AI, sensory neuroscience, and neurotechnology, reflecting his commitment to linking biological insights with computational innovation.

Aran Nayebiمشاهده پروفایل

استادیار

• Machine Learning
• Neuroscience
• Artificial Intelligence
• +۵ مورد دیگر

Aran Nayebi is an Assistant Professor at Carnegie Mellon University, affiliated with the Machine Learning & Neuroscience Institute. His research focuses on integrating machine learning and neuroscience to understand biological intelligence and develop biologically plausible AI models. He explores embodied agents, neural network dynamics, and human-AI alignment through game-theoretic frameworks. Key research interests include reverse-engineering natural intelligence through embodied agents, developing models that align with brain dynamics (e.g., zebrafish agents, rodent tactile processing), and addressing ethical challenges in AI alignment. His work bridges computational neuroscience and neural network design, emphasizing scalable models and biologically inspired algorithms. Recent publications span topics such as AI-driven economic policy (Universal Basic Income), neuroAI turing tests, and model-brain comparison techniques. His interdisciplinary approach addresses both technical challenges (e.g., neural network scaling) and societal implications of AI.

Ramana Pidapartiمشاهده پروفایل

استاد

• Multidisciplinary Design Innovation
• Computational Biomechanics
• Design Informatics
• +۱ مورد دیگر

Ramana Pidaparti is a Professor at the University of Georgia's College of Engineering, specializing in multidisciplinary design innovation, computational biomechanics, design informatics, and STEM education. He earned his Ph.D. in Aeronautics & Astronautics from Purdue University and employs project-based, collaborative, and experiential teaching strategies. His professional affiliations include fellowships with the American Association for the Advancement of Science, Royal Aeronautical Society, American Society of Mechanical Engineers, and American Institute of Aeronautics and Astronautics. Research interests span: Multidisciplinary design optimization Computational modeling of biological systems Educational technology integration in STEM Bio-inspired engineering solutions Recent publications (2023-2025) focus on AI applications in robotics, biomechanics, and middle-school education, with emphasis on knowledge-transfer frameworks, biomaterial characterization, and 3D learning modules. Awards recognize contributions to aerospace and mechanical engineering sciences.

Rebecca E. Taylorمشاهده پروفایل

دانشیار

• DNA nanotechnology
• molecular and cellular mechanobiology
• bio-inspired micro- and nanosystems
• +۷ مورد دیگر

Rebecca E. Taylor is an Associate Professor in the College of Engineering at Carnegie Mellon University, holding joint appointments in Mechanical Engineering, Biomedical Engineering, and Electrical and Computer Engineering. She leads the Microsystems and Mechanobiology Lab, where her interdisciplinary research spans molecular, cellular, microscale, and macroscale systems. Her work bridges engineering principles with biological understanding to create novel nanotechnologies. Dr. Taylor's educational background includes: B.S.E. in Mechanical Engineering from Princeton University (2001) M.S. in Mechanical Engineering from Stanford University (2010) Dual Ph.D. degrees in Mechanical Engineering and Bioengineering from Stanford University (2013) Her research focuses on three primary domains: DNA nanotechnology for molecular and cellular mechanobiology Bio-inspired micro- and nanosystems Advanced manufacturing across multiple scales Dr. Taylor's work leverages structural DNA nanotechnology to create tools for measuring stress and strain in soft materials, investigating molecular and cellular mechanobiology, and developing novel approaches for drug delivery and gene therapy. Her lab has made significant contributions in creating self-assembling nanofilaments from gamma-modified peptide nucleic acid (gPNA), which form stable structures in harsh environments. She also investigates the use of DNA-based nanostructures as flexible connectors for microscale swimmer robots and as bridging materials for enhancing self-assembly processes. Her recent publications reveal a strong focus on microswimmers, DNA origami, cell encapsulation, and computational design methods for nanostructures. The research demonstrates increasing sophistication in combining top-down engineering processes with bottom-up self-assembly approaches. Dr. Taylor has received numerous prestigious awards: NSF CAREER Award (2020) AFOSR Young Investigator Program (YIP) Award (2017) Donald L. and Rhonda Struminger Faculty Fellow (2016) Dr. Taylor actively mentors a large group of students across multiple levels. Her lab currently includes multiple Ph.D. students, postdoctoral researchers, M.S. students, and undergraduates. She has successfully guided numerous students to completion of their degrees, with many alumni now working at leading institutions and companies. Her research is supported by multiple grants from agencies including NSF, NIH, and AFOSR, reflecting the interdisciplinary and high-impact nature of her work. The Microsystems and Mechanobiology Lab collaborates extensively with researchers in Chemistry, Biomedical Engineering, Physics, Developmental Biology, and Cardiovascular Medicine, highlighting the cross-disciplinary nature of Dr. Taylor's research program. Her work on workforce training tools like voice assistants for advanced manufacturing demonstrates her commitment to translating research into practical applications.

Daniela Rusمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence/ML
• Data Science
• +۵ مورد دیگر

Daniela Rus is the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL). She leads research in robotics, AI/ML, and autonomous systems, emphasizing soft robotics, mobile computing, and data-driven solutions. Her work spans theoretical and applied domains, including self-reconfiguring robots, AI for healthcare, and transportation optimization. Education: Earned her PhD in Computer Science from Cornell University. Recognized as a 2002 MacArthur Fellow, ACM Fellow, IEEE Fellow, and member of the National Academy of Engineering and American Academy of Arts and Sciences. Research focuses on robotics (soft robots, modular systems), AI applications (neural networks, generative models), and societal impacts (autonomous vehicles, data privacy). Projects include the Robot Garden educational platform, M-block modular robots, and AI-driven traffic optimization systems. Her lab, CSAIL, fosters interdisciplinary innovation across computer science, engineering, and biology. Publications highlight breakthroughs in robot design, algorithmic efficiency, and human-robot interaction. Current efforts explore AI for environmental monitoring, surgical robotics, and ethical autonomous systems. CSAIL hosts 100+ research groups, including the Distributed Robotics Lab and Embodied Intelligence Community.

• Cognitive Neuroscience
• Computational Modeling
• Neural Computation
• +۱ مورد دیگر

Ruiyi Zhang is a Postdoctoral Research Associate at the Neuroscience Institute, focusing on neural computation and AI integration. Her research bridges cognitive neuroscience and machine learning, examining prefrontal cortex functions in decision-making and neural network architectures for spatial navigation. Recent publications explore strategic cognitive processes, modular neural systems, and brain-inspired AI frameworks. Her work emphasizes computational approaches to understanding neural mechanisms and enhancing artificial intelligence systems.

Tian-Ming Fuمشاهده پروفایل

استادیار

• Neurotechnology
• Biomedical Engineering
• Nanoelectronics
• +۳ مورد دیگر

Tian-Ming Fu is an Assistant Professor in the Department of Electrical and Computer Engineering at Princeton University. His research bridges neurotechnology, biomedical engineering, and nanoelectronics, focusing on developing implantable electronics and advanced imaging systems for neuroscience applications. B.S. in Math and Physics, Tsinghua University Ph.D. in Chemistry, Harvard University Postdoctoral training at HHMI Janelia His work centers on creating syringe-injectable mesh electronics for chronic brain monitoring and MOSAIC optical scopes for high-resolution subcellular imaging. Applications span neural regeneration studies, immune synapse analysis, and olfactory circuit mapping. Recent publications highlight innovations in nanoelectronic design , multimodal imaging , and neuroimmune interventions . Fu has received significant recognition including the Top 10 World Changing Idea award from Scientific American and selection as a Most Notable Research Advance by Chemical & Engineering News. His research involves collaborations with interdisciplinary teams and has secured substantial grants for advancing neurotechnology and regenerative medicine.

Euisik Yoonمشاهده پروفایل

استاد

• Bio-MEMS
• Microfluidics
• Bio-Nanotechnology
• +۶ مورد دیگر

Euisik Yoon is a **Professor of Electrical Engineering & Computer Science** at the **University of Michigan**'s **College of Engineering**. His research focuses on **Bio-MEMS, Microfluidics, Neural Engineering, and Cancer Treatment Technologies**, with a lab dedicated to developing advanced microsystems for neuroscience and biomedical applications. He leads the **Yoon Lab**, which innovates in neural probes, optogenetic interfaces, and single-cell analysis platforms. Key achievements include the **Monroe-Brown Foundation Research Excellence Award (2023)** and the **Best Paper Award at IEEE BIOCAS 2022**. His work spans **neural recording systems**, **microLED-based optoelectrodes**, and **high-throughput cancer cell analysis** techniques like **Hydro-Seq** and **MiniSTAR**. Collaborations with clinicians and engineers have produced breakthroughs in **brain-machine interfaces**, **prostate cancer diagnostics**, and **metastasis research**. Notable projects include **self-assembled origami neural probes**, **ultra-compliant carbon nanotube devices**, and **scalable optogenetic tools** for studying neural networks in vivo. His lab also develops **3D-printed electrophysiology systems** and **low-power biomedical sensors**. Key Awards: Monroe-Brown Award, PCF Challenge Award, Nature Communications Publications Research Themes: Neural interfaces, cancer diagnostics, optogenetics, microfluidics Lab Innovations: Yoon Lab designs bio-inspired sensors, neural probes, and cancer analysis platforms

• Optoelectronics
• Organic Electronics
• Perovskite Solar Cells
• +۳ مورد دیگر

Thomas Anthopoulos is Professor of Emerging Optoelectronics at the University of Manchester's Department of Electrical and Electronic Engineering. His research focuses on next-generation optoelectronic technologies including organic and perovskite photovoltaics, printed electronics, and neuromorphic computing systems. Research interests span scalable nanomanufacturing of electronic devices, with emphasis on organic semiconductors, metal-oxide transistors, graphene diodes, and perovskite-based computing architectures. Key application areas include renewable energy, biosensors, flexible electronics, and neuromorphic systems. Recent publications demonstrate strong focus on advanced materials for optoelectronics, including record-efficiency organic photovoltaics (20.5%), neuromorphic computing elements, flexible oxide transistors, and novel biosensing platforms. Research consistently bridges fundamental materials science with device engineering. Supervises PhD projects on third-generation photovoltaics and scalable nanomanufacturing. Organizes international conferences including IEC TC 119 Printed Electronics events at the Henry Royce Institute. Research contributes to UN Sustainable Development Goals through renewable energy and advanced materials innovation.

Stephanie Nelliمشاهده پروفایل

استادیار

• Cognitive Science
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Stephanie Nelli is an Assistant Professor of Cognitive Science at Occidental College, appointed in 2021. Her research integrates neuroscience and machine learning to explore computational principles underlying human learning and decision-making processes. She holds a B.A. from the University of North Carolina at Chapel Hill and a Ph.D. from the University of California, San Diego. Dr. Nelli's research focuses on understanding how the brain's neural networks enable rapid learning and adaptive decision-making. Key areas include studying alpha oscillations' role in visual processing, the interplay between sensory evidence accumulation and cognitive control, and the theoretical implications of deep learning for cognitive models. Her work often employs EEG and computational modeling to bridge gaps between human neuroscience and artificial intelligence. Recent publications highlight topics such as neural knowledge assembly in humans and artificial systems, the impact of alpha frequency on visual perception, and the neurochemical effects of ethanol on subcortical brain regions. While no specific awards are listed, her contributions reflect interdisciplinary innovation at the intersection of cognitive science and computational neuroscience. Office hours are held weekly on Tuesdays from 4:30-5:30pm and Wednesdays from 2:00-4:00pm in Swan Hall #223. She can be reached via email at nelli@oxy.edu .