جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +۷ مورد دیگر

Vahid Behzadan is an Assistant Professor in Data Science and Computer Science at the University of New Haven's Tagliatela College of Engineering. He leads the SAIL Lab, focusing on AI safety and security, particularly in autonomous systems like driverless cars and smart cities. His work addresses adversarial attacks on machine learning and reinforcement learning, with applications in cybersecurity and healthcare. Behzadan has held prior positions at Kansas State University, University of Nevada Reno, and University of Birmingham, UK. He holds a Ph.D. in Computer Science and an M.S. from the University of Nevada Reno, and a B.Eng. from the University of Birmingham. His research spans AI ethics, cybersecurity, and complex systems. Behzadan advises the UNH hacking team and actively participates in policy initiatives, including Connecticut's AI Working Group and the Connecticut AI Alliance. He has contributed to over 30 peer-reviewed articles and frequently engages in media discussions on topics like AI safety, facial recognition, and cybersecurity threats. Key research areas include adversarial machine learning, AI forensics, and ethical AI design. His work bridges theoretical advancements with real-world applications in transportation, healthcare, and national security. Behzadan collaborates with organizations such as the Transportation Research Laboratory (TRL) and Open Web Application Security Project (OWASP).

• Neuroscience
• Artificial Intelligence
• Neuronal Modeling
• +۵ مورد دیگر

Eilif B. MULLER is a Professor in the Department of Neurosciences at Université de Montréal, Principal Investigator of the Architectures of Biological Learning Lab (ABL-Lab) at CHU Sainte-Justine Research Center, and Associate Faculty at Mila (Quebec AI Institute). His work bridges neuroscience and artificial intelligence, focusing on understanding how sensory perception is learned in the neocortex through biophysical simulations and deep learning models. He holds affiliations with IVADO (Institute for Data Valorization) and contributes to strategic initiatives like the UNIQUE Québec Center. His research integrates empirical neurophysiology with computational models, exploring dendritic processing and synaptic plasticity to inform both biological understanding and AI advancements. Teaches NSC-6044 and NSC-6045 (Neuroscience Colloquia) at Université de Montréal. Leads projects on neocortical learning mechanisms and their implications for neurodevelopmental disorders. Recipient of grants from CRSNG (Natural Sciences and Engineering Research Council), FRSQ (Health Research Fund), and institutional funding. Publications span topics in computational neuroscience, neural network modeling, and interdisciplinary AI-neuroscience research. Collaborates extensively across institutions to advance large-scale brain simulations and data-driven models.

Dana Brooksمشاهده پروفایل

استاد پژوهشی

• Biomedical Signal Processing
• Medical Imaging
• Statistical Signal Processing
• +۸ مورد دیگر

Dana Brooks is a Research Professor in the Department of Electrical and Computer Engineering at Northeastern University, with affiliations in Bioengineering. He holds a PhD from Northeastern University (1991) and has received the Søren Buus Outstanding Research Award (2006). His primary research focuses on biomedical signal and image processing, medical imaging techniques (including MRI and electrocardiography), and neuromodulation technologies such as transcranial magnetic stimulation (TMS). He is also involved in protein conformation estimation using X-ray scattering and optimization algorithms for medical applications. Dr. Brooks leads the Biomedical Signals Processing Lab and collaborates with the Center for Integrative Biomedical Computing . His work bridges engineering and medicine, with recent grants including a $400K NSF MRI grant for advanced TMS systems and a $600K NSF grant for motor cortical organization studies. He has advised students like Setareh Ariafar (PhD’20) and contributed to innovations in image mosaicking for confocal microscopy and machine learning applications in dermatology. His publications span computational neuroscience, cardiac imaging, and uncertainty quantification in biomedical simulations. Notable achievements include developing algorithms for ECG imaging, optimizing TMS protocols, and creating tools like UncertainSCI for simulation reliability assessment.

Mark Kramerمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Mathematical Modeling of Neural Activity
• +۵ مورد دیگر

Mark Kramer is a Professor in the Department of Mathematics & Statistics at Boston University. He belongs to the Applied Mathematics research group, focusing on mathematical, statistical, and machine learning approaches to characterize brain activity. His work bridges data-driven neuroscience with computational methods, exploring topics like biophysical models of neurons, field models of neural populations in epilepsy, and theoretical questions about brain rhythms. His research interests include: Biophysical modeling of single-neuron dynamics Neural population activity in pathological states Machine learning for detecting abnormal brain rhythms Analysis of cross-frequency coupling and coherence Kramer has developed educational resources like Case Studies in Neural Data Analysis using both MATLAB and Python. These materials teach practical data analysis techniques for spike trains and field data, emphasizing hands-on implementation over theoretical mathematics. He has received funding from NIH and NSF for computational neuroscience projects. His recent publications focus on epilepsy research, sleep spindle analysis, and neural signal processing. The work spans from developing statistical frameworks to understanding network dynamics in seizure termination and exploring phase consistency in neural data. Notably, his coherence studies revealed non-intuitive coupling patterns between brain regions, demonstrating that low-amplitude rhythms can be more informative than dominant ones.

• Cognitive Psychology
• Decision Making
• Metacognition
• +۳ مورد دیگر

Valerie Thompson is a Professor of Cognitive Psychology at the University of Saskatchewan, specializing in intuitive and analytic decision-making, metacognition, and reasoning processes. She holds roles as Past President of the Canadian Society of Brain, Behaviour, and Cognitive Science and Editor-in-Chief of *Thinking & Reasoning*. Her research has been continuously funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) since 1991. Education: BSc, University of Calgary, 1985 MA, University of Western Ontario, 1987 PhD, University of Western Ontario, 1991 Research Focus: Investigates when people rely on intuitive judgments versus analytical reasoning, metacognitive monitoring of decisions, and individual differences in reasoning strategies. Utilizes the Experimental Decision Lab (EDL) with eye-tracking and advanced experimental setups. Her work bridges cognitive science and applied decision-making, emphasizing dual-process theories. Grants & Facilities: Co-developed the Social Sciences Research Lab with a CFI grant, featuring 23 linked computers for cognitive experiments. This facility supports studies on reasoning, decision-making, and metacognition.

• Brain-Computer Interfaces
• Neuroscience
• Human-Computer Interaction
• +۳ مورد دیگر

Matt Russell is a PhD candidate in Computer Science at Tufts University, focusing on Brain-Computer Interfaces (BCI) within the Human-Computer Interaction Lab. His research emphasizes measuring mental workload via fNIRS and EEG, with applications in LLM-based interfaces and BCI design. He has taught Data Structures (C++) twice as a professor and served as a teaching assistant for multiple computer science courses. His work bridges neuroscience and engineering to enhance adaptive interface technologies. Education: PhD Candidate in Computer Science, Tufts University Research Interests: Russell’s multidisciplinary research explores implicit BCI design, mental workload analysis, and neuroergonomics. Key areas include fNIRS/EEG-based state classification, LLM interface integration, and real-time BCI systems for memory enhancement. His studies often involve human subject trials to evaluate cognitive and physiological responses. Publications: His articles span 2011 to 2025, focusing on neuroimaging techniques (fNIRS/EEG), BCI innovation, and HCI applications. Recent work explores AI collaboration impacts and low-cost EEG systems for cognitive task decoding. Teaching & Advising: Russell has instructed Data Structures (C++) and supported courses in graphics, cybersecurity, and concurrency. He actively contributes to pedagogical efforts in computer science education. Labs & Projects: His research is conducted in the Human-Computer Interaction Lab at Tufts, with open-source projects hosted on GitHub.

Keith D. Paulsenمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging Methodologies
• Cancer Therapeutics
• +۴ مورد دیگر

Keith D. Paulsen is the MacLean Professor of Engineering at Dartmouth College’s Thayer School of Engineering and holds the title of Professor of Radiology & Surgery at the Geisel School of Medicine. He serves as Scientific Director of the Center for Surgical Innovation at Dartmouth-Hitchcock Medical Center and Co-Director of the Translational Engineering in Cancer Research Program at the Norris Cotton Cancer Center. His roles emphasize interdisciplinary collaboration between engineering, medicine, and oncology. Paulsen earned a BSc in Biomedical Engineering from Duke University (1981), followed by MS (1984) and PhD (1986) degrees in Engineering Sciences from Dartmouth College. His research focuses on biomedical imaging, cancer therapeutics, and image-guided surgery, with particular expertise in optical and electromagnetic methodologies. He has pioneered technologies such as fluorescence-guided surgery, quantitative scatter imaging, and non-linear image reconstruction techniques, aiming to enhance surgical precision and cancer diagnosis. His awards include fellowships from OSA, SPIE, AIMBE, IEEE, and the National Academy of Inventors. Paulsen’s work has led to startups like CairnSurgical (where he serves as CTO) and InSight Surgical Technologies, translating research into clinical tools. Key projects include intraoperative imaging systems for brain and spine surgery, microwave imaging for breast cancer, and optical molecular imaging for real-time surgical guidance. Paulsen teaches advanced computational methods (ENGS 205, 105) and courses on medical device innovation (ENGM 189.1/2). His lab, part of Dartmouth’s Optics in Medicine cluster, collaborates with radiology, surgery, and oncology departments to develop clinical technologies funded by NIH, NCI, and DoD grants.

Elena A. Eroshevaمشاهده پروفایل

استاد

• Hierarchical Modeling
• Longitudinal Data Analysis
• Latent Variable Modeling
• +۴ مورد دیگر

Elena A. Erosheva is a Professor in the Department of Statistics and Social Work at the University of Washington , where she also serves as the Associate Director of the Center for Statistics and the Social Sciences (CSSS). Her research focuses on hierarchical modeling , longitudinal data analysis , and latent variable models , with applications in social sciences, disability research, and health statistics. She has taught courses like Multivariate Data Analysis, Sample Survey Techniques, and Statistical Modeling with Latent Variables. Education : PhD in Statistics, University of Washington (2002) Current Research : Measuring gender-based homophily in collaborations, modeling life course transitions, partial mastery cognitive diagnosis models, and bias detection in grant review Scientific Awards : 2019 NIH Center for Scientific Review First Prize for Most Creative Idea for Detection of Bias in Peer Review 2013 Mitchell Prize, International Society for Bayesian Analysis 2015 ASA Student Paper Award (with Maryclare Griffin) Her students and postdoctoral researchers include Sheridan Grant, Y. Samuel Wang, and Jonathan Gruhl. She uses R software extensively in her teaching and research. Contact: erosheva@uw.edu | Personal Website

• Internet of Things
• Edge Computing
• Artificial Intelligence
• +۶ مورد دیگر

Professor Xiaodong Liu is a faculty member at Edinburgh Napier University, affiliated with the School of Computing, Engineering and the Built Environment . His research spans Internet of Things , Edge Computing , Artificial Intelligence , and Cybersecurity , with a focus on decentralized systems and data-driven decision-making. Research Themes : IoT orchestration, federated learning, smart city infrastructure, building maintenance optimization, and automotive cybersecurity. Current Projects : Leading Swarmchestrate (EU-funded), Long-range Perceptive Autonomous Vehicles (Royal Society), and Met-Bot for Disaster Surveillance (Royal Society). His recent publications emphasize privacy-preserving edge learning , semantic IoT data validation , and deep learning for weather prediction . As a supervisor, he has guided PhD students in areas like federated learning, smart building systems, and IoT security. Collaborations include partnerships with institutions in Scotland, China, and Italy, alongside funding from European Commission , Royal Society , and Scottish Funding Council . He contributes to international conferences and journals, with notable work in IEEE Transactions , ACM TAAS , and MDPI publications.

John O'Dohertyمشاهده پروفایل

استاد

• Decision Neuroscience
• Reward Learning
• Neural Mechanisms of Decision-Making
• +۴ مورد دیگر

John O'Doherty is the Fletcher Jones Professor of Decision Neuroscience at the California Institute of Technology (Caltech). He holds a B.A. from Trinity College Dublin (1996) and a D.Phil. from the University of Oxford (2000). His academic trajectory includes roles as Assistant Professor (2004–2007), Associate Professor (2007–2009), and Professor (2009–present), culminating in his current endowed chair since 2021. From 2013–2017, he served as Director of the Caltech Brain Imaging Center. Research Focus: O'Doherty’s work investigates how the brain processes decisions under uncertainty, encodes reward values, and learns from experience. His group studies neural systems involved in decision-making, including arbitration between model-based and model-free learning strategies, the role of prefrontal and striatal circuits in value representation, and social learning mechanisms. They utilize computational modeling combined with neuroimaging (fMRI) and electrophysiological techniques. Key Contributions: His research bridges cognitive neuroscience and psychiatry, addressing topics like gambling disorder, autism spectrum traits, anorexia nervosa, and obsessive-compulsive behaviors. He has pioneered studies on the neural substrates of reward prediction errors, hierarchical decision-making, and the interplay between habitual and goal-directed actions. Affiliations: Caltech Brain Imaging Center (Former Director) Leading international collaborations in neuroeconomics and computational psychiatry Grants & Funding: Extensive support from NIH, NSF, and private foundations for projects on decision neuroscience and translational mental health research.

• Computational Neuroscience
• Visual System
• Sensory Coding
• +۷ مورد دیگر

Thomas Wachtler-Kulla is a Professor in the Department of Biology II at Ludwig-Maximilians-University Munich, where he leads the Computational Neuroscience research group. He is a GSN full member and serves as the GSN Ombudsperson, offering neutral and confidential counseling for students. He is also the Group Leader and Scientific Director at the German Neuroinformatics Node (G-Node), contributing significantly to neuroscience data infrastructure. His research focuses on how the brain processes sensory signals, particularly in the visual system, aiming to understand neural coding, perceptual stability, and color vision under natural conditions. He employs neurophysiology, psychophysics, and computational modeling to investigate sensory processing, eye movement compensation, and efficient coding mechanisms. At G-Node, he develops software and hardware tools for organizing, storing, analyzing, and sharing neurophysiological data, promoting reproducible research. His recent work spans Bayesian models of hue perception, data management frameworks like DataLad and odML, and studies on honeybee neuroethology. He actively supervises graduate students and contributes to major initiatives such as NFDI-Neuro and the International Neuroinformatics Coordinating Facility, advancing standards for open and FAIR neuroscience. Scientific Contributions and Leadership: Scientific Director, German Neuroinformatics Node (G-Node) GSN Ombudsperson for student conflict resolution Key contributor to NFDI-Neuro and INCF Developer of tools for metadata management and data sharing He advises several current and former graduate students and is deeply involved in shaping data policies and infrastructure for the neuroscience community, ensuring scientific rigor and accessibility.

Psyche Louiمشاهده پروفایل

دانشیار

• Music Perception and Cognition
• Auditory Cognitive Neuroscience
• Creativity and Creative Practice
• +۳ مورد دیگر

Psyche Loui is an Associate Professor of Creativity and Creative Practice in the Department of Music at Northeastern University, leading the Music, Imaging, and Neural Dynamics (MIND) Lab. She holds a PhD in Psychology from UC Berkeley and dual bachelor’s degrees in Psychology and Music from Duke University. Her research focuses on the neuroscience of music perception, including how music influences attention, emotion, and neurological conditions like ADHD. Her work has been supported by major grants from the NSF and NIH, and she has received awards such as the Templeton Positive Neuroscience Award and a NSF CAREER Award. Education : PhD in Psychology (Cognition, Brain, and Behavior), University of California, Berkeley Bachelor’s in Psychology and Music, Duke University Research & Grants : Loui’s research explores how neural mechanisms underpin music perception and cognition, with recent studies investigating the efficacy of algorithmically generated music for improving focus in ADHD. Her grants include NIH funding for projects like ‘Gamma-Music-Based Intervention for Alzheimer’s’ ($2.7M) and NSF support for ‘Reward Prediction in Creative Perception.’ Awards : National Science Foundation CAREER Award (2020) Templeton Positive Neuroscience Award (2010) Multiple Grammy Awards (text reference) Labs & Teams : Loui directs the MIND Lab, which uses neuroimaging and electrophysiology to study music-brain interactions. Collaborations include work with Brain.fm on attention-focused music interventions and global studies on cross-cultural music patterns. Teaching : Courses include Music Cognition , Acoustics and Psychoacoustics , and Creative Cognition , bridging theory with practical applications in neuroscience and education.

Wendy Meiringمشاهده پروفایل

استاد

• Statistical Methods for Brain Imaging
• Spatial and Temporal Processes
• Computational Statistics
• +۴ مورد دیگر

Wendy Meiring is a Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. Her research focuses on statistical methods for analyzing complex data in neuroscience, environmental science, and biomedical applications. She specializes in spatial and temporal processes, computational statistics, machine learning, and uncertainty quantification. Her work integrates advanced statistical techniques with real-world challenges, such as analyzing brain imaging data, pharmacokinetic models, and environmental monitoring. She has contributed to methodologies for functional data analysis, clustering-based correlation estimation, and spatial-temporal modeling. Dr. Meiring has published extensively in top journals, including The Journal of Computational and Graphical Statistics , focusing on topics likeFréchet regression, pharmacokinetic modeling, and environmental phenology. Her research bridges theoretical statistics with practical applications in health, neuroscience, and ecology. She collaborates across disciplines, contributing to programs like the Interdepartmental Graduate Program in Dynamical Neuroscience at UCSB. Her lab develops innovative statistical tools for analyzing high-resolution datasets, emphasizing reproducibility and methodological rigor.

Abhijit Patelمشاهده پروفایل

دانشیار

• Liquid Biopsy
• Circulating Tumor DNA
• Cancer Detection
• +۶ مورد دیگر

Abhijit Patel, MD, PhD is an Associate Professor of Therapeutic Radiology at Yale School of Medicine and serves as the Medical Director of the Lawrence & Memorial Cancer Care Center in Waterford. His work focuses on developing innovative liquid biopsy approaches for cancer detection and monitoring, with particular emphasis on circulating tumor DNA analysis. Dr. Patel received his medical education at Yale University School of Medicine (MD, 2003) and completed his PhD in Molecular Biophysics and Biochemistry from Yale University Graduate School (2002). His training includes an internship at Memorial Sloan-Kettering Cancer Center (2004), residency in Radiation Oncology at Harvard Medical School (2008), and postdoctoral research at the Howard Hughes Medical Institute, Massachusetts General Hospital (2008). His research program centers on developing ultrasensitive diagnostic tools to measure trace amounts of circulating tumor DNA molecules through analysis of genomic and epigenomic signatures. Dr. Patel's work addresses key challenges in liquid biopsy technology, including assay development for circulating tumor DNA detection, high-throughput RNA profiling, and clinical applications for early cancer detection, treatment response assessment, and monitoring treatment resistance. His research draws upon expertise from clinical oncology, molecular biology, chemistry, computer science, and microfluidics. Analysis of Dr. Patel's recent publications reveals a strong focus on advancing liquid biopsy technologies for cancer detection and monitoring. His work spans technical innovations in DNA sequencing and analysis, clinical validation studies across multiple cancer types (particularly lung cancer), and collaborative efforts through the Liquid Biopsy Consortium to address challenges in early cancer detection. His research consistently aims to bridge the gap between molecular diagnostics and clinical oncology practice. Lungevity Foundation Early Detection Award (2015) Tina's Wish Foundation Research Award (2015) ASTRO Junior Faculty Career Research Training Award (2011) Brozman Foundation Fellowship (2011) American Cancer Society Institutional Research Grant (2010) Dr. Patel serves as a sub-investigator on multiple clinical trials focused on cancer detection and treatment, including studies on adjuvant radiotherapy for head and neck cancer, mechanisms of treatment resistance in lung cancer, and peripheral blood profiling for lung cancer screening. He leads the Patel Lab at Yale, which collaborates extensively with scientists and clinicians across disciplines to advance liquid biopsy technologies for clinical implementation.

Marlen Gonzalezمشاهده پروفایل

استادیار

• Neuroscience
• Developmental Psychology
• Mental Health
• +۴ مورد دیگر

Marlen Gonzalez is an Assistant Professor in Psychology at Cornell University and founder/director of the Community Neuroscience Initiative . Her research explores how developmental contexts shape adult brain function and health outcomes. Her work bridges Neuroscience , Developmental Psychology , and Social Cognition , focusing on neural pathways linked to stress, reward, and mental health. Recent articles highlight interdisciplinary approaches combining fMRI, TMS, and epigenetics. Key trends in her publications include social determinants of health, transdiagnostic neurocircuitry, and behavioral ecology. She also explores methodological innovations like brain imaging and computational modeling. Her lab engages in community-based research, addressing low-income populations and environmental influences on brain development.