جستجوی استاد راهنمای دانشگاهی

Alison L. Barthمشاهده پروفایل

استاد

• Neuroscience
• Neural Circuits
• Synaptic Plasticity
• +۶ مورد دیگر

Alison L. Barth is the Maxwell H. and Gloria C. Connan Professor in the Life Sciences at Carnegie Mellon University's Department of Biological Sciences, Mellon College of Science. She holds a Ph.D. from the University of California, Berkeley, and completed postdoctoral training at Stanford University School of Medicine. Her research focuses on how sensory experience shapes neural circuits in the cerebral cortex, particularly in mice. Key projects include studying synaptic plasticity mechanisms, developing fluorescence-based synapse analysis tools, and exploring neural network design principles for AI applications. Her lab employs advanced techniques like electrophysiology, electron microscopy, and computational modeling. Recent achievements include election as an AAAS Fellow (2024) and contributions to understanding PV interneuron roles, thalamocortical plasticity, and high-throughput connectomics. Ongoing work addresses learning algorithms, disease models (Alzheimer's/Parkinson's), and collaborative engineering initiatives for neural-inspired technologies. Publications span neuroscience, computational biology, and neuroengineering, with a focus on cortical circuits, synaptic mechanisms, and translational applications.

Vanessa Chenمشاهده پروفایل

استادیار

• Ubiquitous Sensing Systems
• Hardware Security for IoT Devices
• Analog/Mixed-Signal Integrated Circuits
• +۴ مورد دیگر

Vanessa Chen is an Assistant Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. She holds a Ph.D. from CMU (2013) and prior academic appointments at The Ohio State University and Qualcomm. Her research focuses on hardware security for IoT devices, analog/mixed-signal circuits, and ubiquitous sensing systems, with notable contributions in RF fingerprinting and machine learning integration. She leads the Energy-Efficient Circuits and Systems Lab and has received prestigious awards including the NSF CAREER Award (2019). Education : Ph.D., Electrical and Computer Engineering, Carnegie Mellon University (2013) M.S., Electrical Engineering, National Tsing Hua University, Taiwan (2005) B.S., Electrical Engineering, National Tsing Hua University, Taiwan (2003) Research Interests : Chen’s work spans secure analog/RF electronics, brain-machine interfaces, and energy-efficient data conversion systems. She pioneers methods to detect hardware Trojans via electromagnetic side-channels and designs lightweight, energy-efficient transceivers for IoT. Her projects integrate machine learning for real-time threat detection and system optimization. Awards & Grants : NSF CAREER Award (2019) EAGER Grant for Real-Time Learning in Secure RF Electronics (2020) SpecEES Grant for Trusted Wireless Transceivers (2019) Advising & Outreach : Collaborates with industry partners like Apple and Analog Devices. Develops educational initiatives integrating secure sensor design into undergraduate/graduate curricula. Her lab emphasizes interdisciplinary approaches to tackle emerging cybersecurity challenges in IoT and wireless systems.

Eric Yttriمشاهده پروفایل

دانشیار

• Neural Circuits
• Computational Neuroscience
• Motor Control
• +۴ مورد دیگر

Eric Yttri is an Associate Professor in the Department of Biological Sciences and a core faculty member of the Neuroscience Institute at Carnegie Mellon University (CMU), within the Mellon College of Science. His research focuses on neural circuits underlying motor control, decision-making, and learning, with a particular emphasis on basal ganglia and cortico-striatal interactions. He leads the Yttri Lab, which develops advanced tools like the B-SOiD/A-SOiD algorithms for automated behavioral analysis and the CMU Array for high-density neural recording. Education and Academic Background: Eric holds a Ph.D. in Systems Neuroscience (likely from CMU, though explicit details are not provided). His lab collaborates with institutions like the Howard Hughes Medical Institute, Rockefeller University, and international partners in Spain and Germany. Research Interests: His work spans computational neuroscience, motor control dynamics, neurotechnology, and translational research into Parkinson’s disease and Huntington’s disease. Key contributions include open-source tools for behavior quantification and 3D-printed neural interfaces. Awards and Recognition: Elected as Eberly Family Career Development Professor (2021), received NIH R01 and CRCNS grants, and recognized with the Kaufman New Investigator Award (2018). His lab’s innovations have been featured in Nature Communications, Neuron, and Science Advances. Labs/Teams: The Yttri Lab collaborates with the Verstynen, Rubin, and Vich Llompart groups on decision-making circuits, and with Oded Klav on dopamine-striatal pathways. They host rotating students from CMU, Pitt, and Haverford College, and engage in interdisciplinary initiatives like the Center for the Neural Basis of Cognition.

Gary K. Fedderمشاهده پروفایل

استاد

• MEMS (Microelectromechanical Systems)
• Advanced Manufacturing
• Digital Twins
• +۷ مورد دیگر

Roles & Affiliations: Gary K. Fedder is the Howard M. Wilkoff Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with courtesy appointments in Mechanical Engineering and Biomedical Engineering. He serves as Faculty Director of the Manufacturing Futures Institute (MFI) and has held administrative roles including Vice Provost for Research, Associate Dean for Research in the College of Engineering, and Interim CEO of the Advanced Robotics for Manufacturing (ARM) Institute. Education: B.S. and M.S. in EECS from MIT (1982, 1984) Ph.D. in EECS from UC Berkeley (1994) Research Interests: Focuses on MEMS (microelectromechanical systems), advanced manufacturing, digital twins, aerosol jet printing, and stretchable electronics. His work spans microsensor design, implantable systems, and integration of MEMS with emerging technologies like nanomaterials and robotics. Current projects include generative manufacturing, smart manufacturing systems, and bioelectronics. Awards & Recognition: IEEE Fellow (2007) NSF CAREER Award (1996) Carnegie Ladd Research Award (1996) Ross Tucker Award (1993) Advising & Grants: Over 300 publications and 21 patents. Led the proposal for the $70M America Makes Institute and co-founded the ARM Institute. Serves on editorial boards of journals like Journal of Micromechanics and Microengineering and co-edits the Advanced Micro- and Nanosystems book series. Labs & Initiatives: Directs the MFI, which focuses on agile and sustainable manufacturing. Leads the MEMS Laboratory at CMU, advancing microsystem technologies and their applications in healthcare, robotics, and industry.

• Machine Learning
• Statistics
• Neuroscience
• +۴ مورد دیگر

Josue Orellana is a Research Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU) and serves as Managing Editor of the Computational Neuroscience Navigator initiative. He holds a PhD in Machine Learning and Neural Computation from CMU (2019) and a B.S. in Electrical Engineering from Washington State University. Previously, he worked as a Research Scientist at the National University of Singapore and Johns Hopkins University. His research integrates Statistics , Machine Learning , and Computational Neuroscience to model brain network interactions during cognitive tasks. He also investigates statistics education methodologies through the CMU Teach Stat research group. His work emphasizes graphical network analysis, phase coupling in neural circuits, and educational cognitive science. Publications span neuroscience, machine learning, and statistics education, with recent articles exploring neural oscillations, educational assessment tools, and multivariate phase coupling. His scholarly output demonstrates consistent focus on statistical methodology in neural systems and innovations in STEM pedagogy . Awards & Grants: CMU Simon Seed Grant (2020) GuSH cross-walk statistics education grant (2019) CMU Presidential Fellowship (2016-2018) President’s Honor Roll, WSU (2008-2012) He leads the Computational Neuroscience Navigator project at CMU’s Open Learning Initiative, developing concept-driven video resources. No advising relationships or lab affiliations are detailed beyond his educational research group.

Yuanning Liمشاهده پروفایل

استادیار

• Computational Neuroscience
• Cognitive Neuroscience
• Neural Encoding
• +۵ مورد دیگر

Yuanning Li is an Assistant Professor in the School of Biomedical Engineering at ShanghaiTech University and a visiting investigator at the Neurosurgical Institute of Fudan University, Huashan Hospital. He directs the Computational Cognitive and Translational Neuroscience Lab, focusing on human electrophysiology and computational methods to study high-level cognitive processes. He holds a PhD in Neural Computation and Machine Learning from Carnegie Mellon University and the University of Pittsburgh, a Master's in Electrical & Computer Engineering from Carnegie Mellon, and a Bachelor's in Electrical Engineering from Beihang University. His research explores computational and cognitive neuroscience, specifically neural representations of sensory/semantic information in speech, language, and vision. He develops machine learning models for neural data analysis using ECoG, fMRI, and M/EEG. Current lab members include 14 students, and he co-advises MD/PhD candidates at Huashan Hospital. His publications emphasize neural decoding, speech/language processing, and machine learning applications in neuroscience, with recent work on brain-to-text frameworks and multimodal neural representations.

Robert Kassمشاهده پروفایل

استاد

• Computational Neuroscience
• Bayesian Inference
• Graphical Models
• +۷ مورد دیگر

Robert Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, with affiliations in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. He has served as Department Head of Statistics for 9 years and held leadership roles in academic societies. Ph.D. in Statistics (University of Chicago, 1980) B.A. in Mathematics (Antioch College) Postdoctoral training at Princeton University His research bridges statistics and neuroscience, focusing on: Statistical methods for neural data analysis Bayesian inference and graphical models Point process modeling of spike trains High-dimensional and time-series analysis Computational neuroscience education Recent publications span computational neuroscience, emphasizing phase coupling analysis, latent variable modeling, and cross-population dynamics, with methodological innovations in neural data interpretation. As founding Editor-in-Chief of Bayesian Analysis and Executive Editor of Statistical Science , he shaped academic discourse in statistics. Key scientific recognitions: Outstanding Statistical Application Award (ASA) Distinguished Achievement Award (COPSS) National Academy of Sciences member (2023) He co-authored the influential textbook Analysis of Neural Data and developed international workshops like Statistical Analysis of Neuronal Data (2002-2017), advancing neuroscience methodology globally.

Alireza Chamanzarمشاهده پروفایل

پژوهشگر ارشد

• Neural Engineering
• Medical Diagnostics
• Brain-Computer Interfaces
• +۲ مورد دیگر

Alireza Chamanzar is a Research Fellow at Carnegie Mellon University's Neuroscience Institute with a joint appointment at Massachusetts General Hospital's ICU Precision Medicine Lab. His research integrates signal processing, AI, and statistical modeling to develop non-invasive neural monitoring systems. Chamanzar's work focuses on automated detection of neurological emergencies like spreading depolarizations and brain silences using scalp EEG. Current projects include lesion-network mapping, hyperensemble learning for outcome prediction in critical care, and generative AI applications in medicine. Key Innovations: SilenceMap framework for EEG-based brain silence localization Non-invasive suppression techniques for cortical spreading depolarizations Multimodal biosignal integration for neurocritical care prognosis As CTO of Precision Neuroscopics, he leads development of FDA-cleared monitoring systems for traumatic brain injury. His Roche Fellowship supports translational research in neurocritical care diagnostics.

Richard Johnstonمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Biomedical Engineering
• Computational Neuroscience

Richard Johnston is a Research Fellow in the Department of Biomedical Engineering at Carnegie Mellon University, affiliated with the Neuroscience Institute. His work bridges neural computation and biomedical engineering within Pittsburgh's academic research ecosystem. His educational credentials include: PhD, The University of Nottingham, 2017 MSc, The University of Nottingham, 2014 BSc, Queen's University Belfast, 2013 Johnston's research spans core neuroscience with engineering applications, focusing on computational modeling of neural systems and biomedical technology development. His interdisciplinary approach integrates neural computation principles with biomedical engineering methodologies to advance understanding of brain function and develop neurotechnologies. Current initiatives within the Neuroscience Institute emphasize collaborative projects in neural data analysis and neural interface systems.

Justin Patelمشاهده پروفایل

پژوهشگر ارشد

• Neural Engineering
• Computational Neuroscience
• Signal Processing
• +۱ مورد دیگر

Justin Patel is a Postdoctoral Research Associate in Biomedical Engineering at the University of Pittsburgh's Neuroscience Institute. His research focuses on developing computational approaches and signal processing methods for neural engineering applications. His work bridges electrical engineering principles with neuroscience challenges, particularly in neural data analysis and brain-computer interface development. Research interests include neural signal processing, computational neuroscience modeling, and algorithms for understanding neural coding principles.

Patrick Mayoمشاهده پروفایل

استادیار

• Systems Neuroscience
• Visual Neuroscience
• Ophthalmology
• +۳ مورد دیگر

Dr. Patrick Mayo is an Assistant Professor in the Department of Ophthalmology at the University of Pittsburgh School of Medicine. His research focuses on visual-motor interactions, particularly how eye movements influence cortical visual processing. He leads the Mayo Lab, which bridges laboratory experiments with natural primate vision to understand neural mechanisms underlying adaptive eye movements and visual perception. Education & Training : PhD in Neurobiology, Duke University Postdoctoral Research, Harvard Medical School (Neurobiology) Postdoctoral Fellowship, University of Pittsburgh (Neuroscience) Research Interests : Exploring how visual information from the retina is transformed into eye movement commands through population-level neural activity in the cerebral cortex. Key themes include visual-motor integration, perceptual stability during eye movements, and clinical applications for visual impairment treatments. Publications : Recent work includes studies on attentional decoding via local field potentials, presaccadic visual remapping circuits, and the neurophysiology of eye blinks. His research spans systems neuroscience, neural coding, and translational ophthalmology. Lab & Teams : The Mayo Lab collaborates across disciplines within the Neuroscience Institute, Eye & Ear Institute, and UPMC Vision Institute. Their work integrates electrophysiological recordings, computational modeling, and clinical insights to advance understanding of brain-behavior relationships.

Aaron Batistaمشاهده پروفایل

دانشیار

• Neural Engineering
• Computational Neuroscience
• Brain-Computer Interfaces
• +۲ مورد دیگر

Dr. Aaron Batista serves as an Associate Professor in the Department of Bioengineering at the University of Pittsburgh. He is affiliated with the Neuroscience Institute and participates in the PNC Training Faculty, contributing to the Ph.D. in Neural Computation program. His research focuses on neural engineering and computational neuroscience, particularly in the development of brain-computer interfaces (BCIs) and understanding neural mechanisms underlying movement and cognition. Dr. Batista’s academic contributions include advancing technologies that decode neural activity for prosthetic control and investigating how neural signals translate into functional behaviors. While specific grants or awards are not listed, his involvement with interdisciplinary programs reflects his commitment to bridging engineering and neuroscience. He is based at BST3 4074 and can be reached via the provided contact numbers. His work aligns with the broader goals of the University of Pittsburgh’s neural computation and systems neuroscience initiatives.

Jennifer Collingerمشاهده پروفایل

استادیار

• Neural Engineering
• Neurorehabilitation
• Motor Control
• +۳ مورد دیگر

Jennifer Collinger is an Assistant Professor in the Department of Physical Medicine and Rehabilitation at the University of Pittsburgh. She is affiliated with the Neuroscience Institute and serves as part of the PNC (Pittsburgh Neural Computation) Training Faculty. Her work focuses on neural engineering and neurorehabilitation, particularly in advancing technologies for motor function recovery and neural prosthetics. Dr. Collinger is actively involved in interdisciplinary research bridging clinical neuroscience, biomedical engineering, and clinical practice. As part of the PNC Training Faculty, she contributes to graduate education in neural computation, emphasizing computational approaches to understanding and restoring neurological function. Her research interests include motor control, brain-computer interfaces, and translational applications of neural technologies in clinical settings.

Elias Toweمشاهده پروفایل

استاد

• Quantum Computing
• Photonics
• Electronic Materials
• +۵ مورد دیگر

Elias Towe is the Albert & Ethel Grobstein Professor and University Professor at Carnegie Mellon University's College of Engineering. He directs the NSF Quantum Computing and Information Technologies (QCiT) Center. Previously, he served as a Program Manager at DARPA, leading initiatives in photonics and electronics, including the VLSI Photonics Program. He earned his S.B., S.M., and Ph.D. in EECS from MIT. His research focuses on quantum and optical phenomena for next-generation photonic devices, with system-level projects in quantum processors (quantum-dot qubits) and quantum networks. He also addresses energy infrastructure, climate resilience, and public policy related to technology. Notable recognitions include membership in the National Academy of Engineering and leadership roles in major initiatives like the QCiT Center. His work has been highlighted at events such as the TMS Annual Meeting, and he collaborates on interdisciplinary projects like brain-interface technologies and advanced neural probes. Education: Ph.D., S.M., and B.S. in EECS from MIT (1981-1987) Labs/Teams: Director of NSF QCiT Center, Materials Science collaborations