جست‌وجوی استاد و پژوهشگر

Xiaofeng Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +۵ مورد دیگر

Xiaofeng Liu is an Assistant Professor at Yale University School of Medicine in the Departments of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science. He is also an Associate Member at the Broad Institute of MIT and Harvard. Previously, he held faculty positions at Harvard Medical School and research roles at Massachusetts General Hospital and Beth Israel Deaconess Medical Center. PhD in Mechatronics from University of Chinese Academy of Sciences Dual Bachelor's degrees in Automation (Wang-Daheng Elite Class) and Communication from University of Science and Technology of China His research integrates trustworthy AI, medical imaging, and data science to improve diagnosis, prognosis, and treatment monitoring for neurological disorders, cancer, and cardiovascular diseases. Key focus areas include domain adaptation techniques, diffusion models, and interpretable AI systems. Led special issues in IEEE Transactions on Pattern Analysis and Medical Image Analysis Developed novel frameworks like Ordinal UDA and Memory-Consistent Adaptation Scientific accolades include the Trailblazer R21 Award (NIBIB), OpenAI Research Award, and National Artificial Intelligence Research Resource Pilot Award. He serves as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively contributes to MICCAI and NIH review panels. His lab at Yale (XLiu Lab) investigates neural basis of intelligence to inspire AI development, with applications in brain tumor segmentation, cardiac imaging, and cross-modal medical diagnostics.

Pierre Maechlerمشاهده پروفایل

استاد

• Mitochondrial metabolism
• Beta-cell function
• Diabetes research
• +۵ مورد دیگر

Pierre Maechler is a Professor at the University of Geneva's Faculty of Medicine in the Department of Cell Physiology and Metabolism. His research focuses on mitochondrial metabolism in pancreatic beta cells and its critical role in diabetes pathogenesis, with particular emphasis on glutamate dehydrogenase function and regulation. His laboratory investigates the molecular mechanisms of insulin secretion and beta-cell failure in Type 2 diabetes. Maechler's research interests span mitochondrial metabolism, energy homeostasis, and the molecular pathways linking nutrient sensing to insulin secretion. His work has established crucial connections between glutamate metabolism, beta-cell function, and diabetes development. He investigates how metabolic stressors like glucotoxicity and lipotoxicity impair beta-cell function through mitochondrial dysfunction. His research has expanded to include the role of glutamate dehydrogenase in multiple organs including brain, liver, and muscle, revealing systemic metabolic implications. Analysis of Maechler's publication record shows a sustained focus on beta-cell metabolism with evolving scope. Early work established fundamental mechanisms of glutamate signaling in insulin secretion, while recent publications demonstrate expansion into multi-organ metabolic regulation. His research has identified novel biomarkers for beta-cell mass, explored therapeutic targets for diabetes, and revealed unexpected roles for glutamate dehydrogenase in diverse physiological processes from muscle regeneration to brain function. The consistent thread through his work is understanding how mitochondrial metabolism governs cellular and systemic energy homeostasis. Professor Maechler has mentored numerous PhD students and postdoctoral fellows who have gone on to publish significant work in diabetes research. His laboratory collaborates extensively with clinical researchers to translate basic findings into potential therapeutic approaches for diabetes. Funding for his work likely comes from Swiss National Science Foundation and other European research agencies, supporting investigations into metabolic diseases. Maechler leads the Mitochondria and Energy Metabolism research group at the University of Geneva. His team employs a multidisciplinary approach combining molecular biology, metabolomics, and physiology to investigate metabolic regulation in health and disease. The laboratory maintains strong connections with clinical diabetes researchers, facilitating translational applications of their findings. Current work focuses on identifying novel therapeutic targets for preserving beta-cell function in diabetes.

Kevin W. Plaxcoمشاهده پروفایل

استاد

• Protein Folding
• Biomolecular Engineering
• Electrochemical Sensors
• +۳ مورد دیگر

Kevin W. Plaxco is a Professor in the Department of Chemistry & Biochemistry at the University of California, Santa Barbara (UCSB), leading the Plaxco Group. His research focuses on protein folding, biomolecular engineering, and the development of electrochemical aptamer-based (EAB) sensors for real-time molecular monitoring in vivo. These sensors enable high-resolution measurements of drugs and biomarkers in biological fluids, with applications in pharmacokinetic analysis, feedback-controlled drug delivery, and biomedical diagnostics. The lab also investigates protein-surface interactions to enhance biotechnological applications. Research interests include: Protein folding mechanisms and their application to sensor design Electrochemical sensor technology for in vivo diagnostics Real-time pharmacokinetic monitoring and closed-loop drug delivery systems Biophysics of biomolecules at surfaces Advising and Lab Contributions: The Plaxco Group has mentored numerous graduate students, postdoctoral researchers, and visiting scholars, contributing to over 200 publications. The lab is affiliated with UCSB’s Center for Bioengineering and collaborates across disciplines to advance sensor innovation and biophysical studies. Labs/Teams: The Plaxco Group operates within the Department of Chemistry & Biochemistry, emphasizing interdisciplinary approaches to biomedical engineering and molecular sensing.

Aurélie Labbeمشاهده پروفایل

استاد

• Data Science
• Statistical Learning
• Machine Learning
• +۷ مورد دیگر

Aurélie Labbe is a Full Professor in the Department of Decision Sciences at HEC Montréal, holding the prestigious FRQ-IVADO Chair in Data Science. Appointed as Co-Scientific Director – Academic Partnerships at IVADO in October 2023, she plays a key leadership role in establishing connections between IVADO and partner universities. Her academic journey includes a PhD in Statistics from the University of Waterloo, a Master's degree in Statistics from the University of Montreal, and dual Bachelor's degrees in Applied Mathematics and Social Sciences from Paris-Dauphine University and Pure Mathematics from Versailles-St Quentin University. Her research spans multiple interdisciplinary domains with a focus on developing advanced statistical and machine learning methodologies for big data analysis. Labbe's work bridges theoretical statistics with practical applications across diverse fields including genomics, neuroscience, transportation systems, and health informatics. She has made significant contributions to kernel methods, matrix factorization techniques, random forest applications, and spatiotemporal data analysis, with publications appearing in top journals across multiple disciplines. Analyzing her recent publications reveals a clear trend toward methodological innovation applied to complex real-world problems. Her work demonstrates expertise in handling high-dimensional data from diverse sources including neuroimaging, transportation networks, and genomic studies. The interdisciplinary nature of her research connects statistical theory with applications in healthcare, transportation safety, and biological sciences, reflecting her ability to develop methods that address domain-specific challenges while advancing statistical methodology. Holder of the FRQ-IVADO Chair in Data Science Member of the Center for Mathematical Research Training Professor Labbe actively mentors the next generation of data scientists, supervising numerous doctoral and master's students. Her supervision portfolio includes 1 doctoral thesis (2023), 4 master's theses (2022-2024), and 32 supervised projects spanning 2019-2025. Her students' work covers diverse applications including transportation safety, healthcare analytics, financial modeling, and environmental analysis. Through her leadership of the FRQ-IVADO Chair in Data Science, she coordinates research activities that integrate mathematical, statistical, and computer science expertise with domain knowledge from various data-generating fields. As Co-Scientific Director at IVADO, Professor Labbe leads efforts to establish connections with faculties and departments across five partner universities, integrating them into IVADO's research and knowledge transfer activities. Her leadership role positions her at the forefront of advancing data science research and applications in Quebec's academic ecosystem.

Carlos Brodyمشاهده پروفایل

استاد

• Systems Neuroscience
• Cognitive Neuroscience
• Decision-making
• +۶ مورد دیگر

Carlos Brody is the Wilbur H. Gantz III '59 Professor of Neuroscience at Princeton University, where he leads a research group at the Princeton Neuroscience Institute. His laboratory employs a unique combination of computational, behavioral, and electrophysiological techniques to investigate the neural mechanisms underlying cognitive abilities. Dr. Brody's research focuses on understanding how the brain processes information during cognitive tasks, particularly examining short-term memory, decision-making, and time perception. His lab trains rats to perform complex cognitive tasks while recording neural activity, and develops computational models to explain the experimental findings. They have pioneered the study of 'internal signals' in neural activity that constitute 'the internal conversation of the mind,' with their key discovery being 'nTc' (Neurally-inferred Time of Commitment), a biomarker that indicates decision commitment before overt behavioral responses. Dr. Brody's laboratory has been continuously supported by HHMI (Howard Hughes Medical Institute) with renewal until 2032. They are currently conducting groundbreaking research using multiple Neuropixels probes for large-scale recordings across the brain while rats perform cognitive behaviors, representing what Dr. Brody considers the future of cognitive systems neuroscience that combines advanced recording technology, AI-based analysis, and well-controlled behavioral paradigms. Scientific Awards HHMI Investigator (renewed until 2032) Advising and Research Support Dr. Brody has mentored numerous successful researchers who have secured faculty positions and leadership roles: Marino Pagan (Nature publication, SFARI Bridge to Independence Award) Edward Nieh (faculty position at University of Virginia) Manuel Schottdorf (Nature publication) Sue Ann Koay (publications in Neuron and eLife, Group Leader at Janelia) Brian DePasquale (faculty position at Boston University) Emily Dennis (Group Leader at HHMI's Janelia) Ahmed El Hady (Group Leader at Max Planck Institute) Abby Russo (joined CTRL-Labs startup) Diksha Gupta (Best Paper Award at RLDM conference) His lab is currently supported by HHMI funding and is planning to implement next-generation Neuropixels probes in Spring 2025 to record from 6,000-12,000 neurons simultaneously across multiple brain regions. Research Team and Facilities The Brody Lab features a diverse team ranging from purely computational to purely experimental researchers. The lab emphasizes minimizing barriers between computational and experimental approaches, encouraging researchers to move freely along this spectrum based on their interests. They maintain state-of-the-art facilities for behavioral training, electrophysiological recordings, and computational analysis, with plans to implement next-generation Neuropixels recording technology in Spring 2025.

Dave Bridgesمشاهده پروفایل

دانشیار

• Nutrient sensing
• Cellular signaling
• Metabolic regulation
• +۷ مورد دیگر

Dr. Dave Bridges is an Associate Professor in the Department of Nutritional Sciences at the University of Michigan School of Public Health . Trained as a molecular biologist and biochemist, his research focuses on the regulation of macronutrient metabolism with emphasis on obesity, diabetes, and cardiovascular disease. The Bridges Lab employs Drosophila , mouse, and human clinical models to investigate nutrient sensing pathways like mTORC1, glucocorticoid signaling, and precision nutrition approaches. Education: PhD, University of Calgary (2005) BS, University of Calgary (2000) Research areas include nutrient signaling pathways , stress-hormone interactions , and precision nutrition through ketogenic diet studies. Collaborations with Dr. Brigid Gregg examine breastmilk lipid variability and maternal health impacts. Recent work explores Bayesian statistical approaches for nutritional research, advocating for brms package implementation. Contact: Email: davebrid@umich.edu Phone: 734-764-1266 Address: 1415 Washington Heights, Ann Arbor, MI 48109

Mario A. Svirskyمشاهده پروفایل

استاد

• Auditory Neuroscience
• Cochlear Implants
• Speech Perception
• +۳ مورد دیگر

Mario A. Svirsky is the Noel L. Cohen Professor of Hearing Science and Professor of Neuroscience at NYU Grossman School of Medicine. He leads the Laboratory for Translational Auditory Research, focusing on auditory neural prostheses like cochlear implants and their impact on speech perception and neuroplasticity. His work bridges clinical care and scientific discovery, addressing how the brain adapts to sensory deprivation and degraded auditory input. Education: PhD in Biomedical Engineering from Tulane University (1988). Postdoctoral training at MIT and prior academic appointments at Indiana University and Purdue University before joining NYU in 2005. Research: Explores cochlear implant performance optimization, speech perception in hearing-impaired individuals, and neuroplasticity mechanisms. Collaborates with the Froemke Lab on animal models of cochlear implantation. Active in developing computational models and signal processing techniques to improve implant efficacy. Funding: Principal investigator on multiple NIH grants (e.g., R01 DC016839, R01 DC016834) and industry partnerships. His lab’s work has advanced clinical management strategies for cochlear implant users, including those with contralateral hearing aids. Labs/Teams: Directs the Laboratory for Translational Auditory Research, collaborating with multidisciplinary teams including engineers, neuroscientists, and clinical audiologists. Mentors postdocs, audiologists, and medical students in auditory research.

Brett J. Borghettiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۱۲ مورد دیگر

Dr. Brett J. Borghetti is a Professor of Computer Science in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), Graduate School of Engineering and Management, Wright-Patterson AFB, OH. He was promoted to Professor in July 2022, following prior appointments as Associate Professor (2017) and Assistant Professor (2008/2013). His expertise lies in artificial intelligence, machine learning, deep learning, cybersecurity, and human-machine teaming. Education: Ph.D. in Computer Science, University of Minnesota, Twin Cities (2008) M.S. in Computer Systems, Air Force Institute of Technology (1996) B.S. in Electrical Engineering, Worcester Polytechnic Institute (1992) Dr. Borghetti's research focuses on applying machine learning to physical science sensors (hyperspectral, seismic, RF), cybersecurity, and enhancing human-machine team performance. He teaches graduate courses in machine learning, AI, data security, and algorithm design, and advises numerous MS and PhD students in areas such as sensor exploitation, cognitive workload, and cyber situational awareness. His recent publications demonstrate strong trends in deep learning for multimodal sensor fusion, nuclear security, and neuroergonomics. Scientific Awards: AETC Educator of the Year (2021, Civilian) AFIT Ezra Kotcher Teaching Award (2021) AFIT Teaching Excellence Award (2019) AF STEM Outstanding Science and Educator Award (2015) Multiple Eta Kappa Nu Outstanding Instructor Awards Air Force Meritorious Service Medal and other military honors Dr. Borghetti has advised numerous graduate students and led research projects with significant funding and applications in defense and national security. He has directed research in AI-driven sensor analysis, cyber defense systems, and adaptive automation. His work often involves collaboration with national labs and DoD agencies. He has contributed to major research initiatives in human factors, cyber intruder detection, and machine learning for operational environments. Labs and Research Teams: His work is associated with AFIT's research in cyber security, sensor exploitation, and human-machine systems. He collaborates with teams working on the Cyber Intruder Alert Testbed (CIAT), neuroergonomic modeling, and machine learning for defense applications.

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

Mark Churchland is an Associate Professor of Neuroscience at Columbia University's Mortimer B Zuckerman Mind Behavior Institute. His research focuses on understanding neural dynamics in motor cortex, particularly how these dynamics generate movement patterns and contribute to neural prosthetics development. He is also a Co-director of the Grossman Center for the Statistics of Mind and a member of the Kavli Institute for Brain Science. Co-director, Grossman Center for the Statistics of Mind Member, Kavli Institute for Brain Science Member, Behavior Research Churchland's research investigates the computational properties of neural circuits in motor cortex, emphasizing how self-generated neural activity underpins movement. His work bridges theoretical neuroscience with practical applications in brain-machine interfaces (BMIs), translating basic science into technologies that restore movement capabilities by bypassing spinal cord injuries. Key themes include neural population dynamics, movement variability, and cortical preparation mechanisms. His publications reveal consistent exploration of neural dynamics in motor systems, focusing on population coding, variability suppression, and BMI algorithm design. Research spans both fundamental questions about cortical computation and translational efforts to enhance prosthetic devices through improved understanding of preparatory neural activity. McKnight Scholar Award Sloan Research Fellow NIH Director's New Innovator Award Program Searle Scholars Program Burroughs Wellcome Fund Career Award in the Basic Biomedical Sciences Churchland has secured NIH grants for projects on dynamical systems in neuroscience (2012-2017). His collaborative work with Krishna Shenoy and colleagues at Stanford has produced foundational studies on cortical activity patterns and motor control. He actively participates in academic committees and research networks focused on computational neuroscience and mind-brain relationships.

• Neuroscience
• Behavioral Genetics
• Electrophysiology
• +۳ مورد دیگر

Rui M. Costa is a Professor of Neuroscience at Columbia University, with appointments in Neurology and the Mortimer B. Zuckerman Mind Brain Behavior Institute. His research focuses on understanding how molecular networks in the brain influence neural circuits to shape behaviors, particularly in action control and disorders like Autism Spectrum Disorders and Obsessive-Compulsive Disorder. Primary Affiliation: Columbia University Departments: Neuroscience, Neurology Institute: Mortimer B. Zuckerman Mind Brain Behavior Institute Research Interests Molecular and neural circuit mechanisms of action control Goal-directed actions vs. habits Behavioral disorders: Autism and OCD Neural plasticity in motor learning Publication Trends His recent work explores corticostriatal dynamics, basal ganglia pathways, and endocannabinoid modulation in habit formation. Studies also investigate Foxp2 mutations, motor learning consolidation, and two-photon imaging of neural activity in rodents.

Christian Stilpمشاهده پروفایل

دانشیار

• Auditory perception
• Speech perception
• Music perception
• +۵ مورد دیگر

Christian Stilp, PhD, is an Associate Professor in the Department of Psychological and Brain Sciences at the University of Louisville. His research focuses on auditory perception, speech perception, and music perception, with an emphasis on how acoustic context and natural signal statistics influence perceptual processes. He leads the Auditory Perception and Processing (APP) Lab, which investigates perceptual learning, efficient coding, and the role of variability in speech and music perception. Dr. Stilp earned his B.S. in Psychology from the University of Wisconsin in 2003 and his Ph.D. in Perceptual Psychology in 2011, also from the University of Wisconsin. He completed a postdoctoral fellowship at Case Western Reserve University before joining the University of Louisville faculty in 2012. His research interests include spectral contrast effects, talker adaptation, expertise in speech and music perception, and the application of these principles to hearing-impaired listeners. He teaches courses such as Sensation and Perception, Music Perception and Cognition, and Advanced Statistics. Dr. Stilp has advised numerous graduate and undergraduate students, many of whom have contributed to his lab’s publications. His lab collaborates with researchers at institutions including the University of Connecticut, University of Minnesota, and University of Zurich.

Lee M. Millerمشاهده پروفایل

استاد

• Neuroengineering
• Computational Neuroscience
• Auditory and Speech Processing
• +۴ مورد دیگر

Lee M. Miller is a Professor and Vice Chair of Academic Affairs in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis, affiliated with the Center for Mind and Brain. His research focuses on neuroengineering, computational neuroscience, and neural mechanisms underlying attention, speech processing, and multisensory integration. Research interests include the development of neural prosthetics, decoding of neuromuscular signals for prosthetic control, and understanding how auditory and visual systems interact during speech perception and attentional processes. His work bridges clinical applications (e.g., cochlear implants) with fundamental neuroscience, leveraging tools like electrophysiological recordings, EEG/MEG, and advanced signal processing techniques. Recent publications highlight innovations in electromyographic speech neuroprosthetics, the topology of neuromuscular signals, and the neural basis of speech-in-noise processing. Miller’s studies emphasize translational potential, such as improving speech synthesis from brain signals and designing haptic feedback systems for motor coordination. His contributions have advanced understanding of neural mechanisms in sensory integration, auditory attention, and the impact of cognitive factors on perception. Miller maintains a lab dedicated to these interdisciplinary efforts, with a focus on both basic science and clinical applications.

Stefano Noventaمشاهده پروفایل

پژوهشگر ارشد

• Mathematical Psychology
• Psychometrics
• Psychophysics
• +۷ مورد دیگر

Stefano Noventa is a Research Fellow at the Methods Center, Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has held multiple postdoctoral positions at the University of Tübingen and previously at the University of Verona and the University of Padova. Education: Ph.D. in Cognitive Psychology, University of Padova (2011) M.Sc. in Physics, University of Padova (2006) Studies in Physics, University of Padova (1999–2006) International Visiting Graduate Student, University of Toronto (2009, 2010) Dr. Noventa's research lies at the intersection of mathematical psychology, psychometrics, and psychophysics, with a focus on developing and unifying quantitative models of human cognition and assessment. His work integrates Item Response Theory (IRT) and Knowledge Space Theory (KST) to create more robust frameworks for educational and psychological measurement. He investigates latent variable models, probabilistic knowledge structures, and the identifiability of complex psychometric models, often applying these to domains such as education, organizational psychology, and entrepreneurship. His recent publications (2020–2024) demonstrate a strong trend toward theoretical integration, particularly in bridging cognitive diagnosis models with traditional psychometric frameworks. The articles emphasize mathematical rigor, model generalization, and empirical validation, with applications in both cognitive science and applied psychology. Topics include the unification of assessment models, parameter estimation under local dependence, and the modeling of intuitive physical reasoning. Scientific Awards: No awards or honors listed in the provided text. Dr. Noventa has not been explicitly mentioned as an advisor to students, but he has served as a corresponding author and collaborator on multiple research projects, indicating a leadership role in research teams. He has been involved in a DFG-funded project (GLI NON-NORM) since 2019, suggesting active grant participation. His work is highly collaborative, involving researchers from Germany, Italy, Austria, and Canada. Labs and Research Groups: Methods Center, University of Tübingen Hector Institute of Education Science and Psychology, University of Tübingen Center of Assessment, University of Verona Department of General Psychology, University of Padova

Giang Tranمشاهده پروفایل

دانشیار

• Sparse Modeling
• Compressed Sensing
• Image Processing
• +۲ مورد دیگر

Dr. Giang Tran is an Associate Professor in the Department of Applied Mathematics at the University of Waterloo, where she leads research in sparse modeling and computational mathematics. She holds a PhD from UCLA and previously served as a Bing Instructor at the University of Texas at Austin. Her research explores sparse optimization techniques with applications in medical imaging, dynamical systems, and data science. Recent publications focus on developing novel algorithms for sparse random feature expansions and dynamical system identification. She mentors numerous graduate and undergraduate researchers through projects on neural networks, transformers, and epidemic forecasting. Awards include the NSERC Discovery Grant and SIAM Student Paper Prize. Dr. Tran teaches advanced courses in numerical methods and functional analysis, contributing to curriculum development in computational mathematics.

Lei Liuمشاهده پروفایل

استاد

• Biostatistics
• Data Science
• Survival Analysis
• +۵ مورد دیگر

Lei Liu, PhD, is a Professor of Biostatistics, Medicine, and Statistics and Data Science at Washington University in St. Louis. He holds positions in the Roy and Diana Vagelos Division of Biology & Biomedical Sciences (DBBS), the Institute for Informatics, Data Science and Biostatistics (I2DB), and the Center for Biostatistics and Data Science (CBDS). His research focuses on biostatistical and data science methods, including survival analysis, longitudinal data modeling, and machine learning applications in healthcare. He collaborates with clinicians across disciplines like cardiology, ophthalmology, and addiction medicine. Dr. Liu’s work emphasizes high-dimensional omics data analysis, medical cost modeling, and joint multi-outcome models. He is an Associate Editor of Biometrics and a former member of the NIH Biostatistical Methods and Research Design Study Section. He mentors underrepresented minority researchers through the NHLBI PRIDE program.