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Kate HongView profile

Assistant Professor

• Sensory Perception
• Neural Circuits
• Cortical Injury Recovery
• +3 more

Dr. Kate Hong is an Assistant Professor in the Department of Biological Sciences and Neuroscience Institute at Carnegie Mellon University (CMU). She leads the Hong Lab, which investigates neural circuits underlying sensory-guided behavior, focusing on how cortical and subcortical regions mediate perception, decision-making, and recovery from injury. Her work combines electrophysiology, optogenetics, and behavioral assays using mouse models. Education: Ph.D. in Neuroscience, Harvard University Sc.B. in Biology, Brown University Postdoctoral Fellowships at Columbia University and Boston Children's Hospital Research Focus: Dr. Hong's lab explores two core questions: (1) How cortical-subcortical interactions drive sensory-motor decisions, and (2) How neural circuits reorganize after cortical injury. Recent studies include investigating texture discrimination mechanisms and recovery pathways in stroke models. Techniques used include high-speed imaging, optogenetics, and fMRI. Grants & Awards: Collaborative Grant from the RCSA (2023) SFARI Pilot Award to study autism-related tactile deficits Shurl and Kay Curci Foundation Grant (2022) Lab Team: PhD Students: Ben George, Alice Nam, Rawan Fakhreddine Undergraduates: Maggie Monahan (NASA Fellow), Baihe Zhang (SURFiN Scholar) Research Assistants: Morgan Tenney, Alex Lammers Future Directions: Ongoing projects include studying subcortical plasticity post-injury and developing new tools to map thalamocortical circuits in awake mice.

Josie ClowneyView profile

Associate Professor

• Neural Circuits
• Developmental Neuroscience
• Chemosensory Systems
• +3 more

Josie Clowney is an Associate Professor in the Department of Molecular, Cellular, and Developmental Biology at the University of Michigan, where she has held faculty position since 2017. Her research investigates the genomic algorithms that construct neural circuits during development, using Drosophila as a model to study how chemosensory systems drive both instinctual behaviors and learning. She teaches Bio 172 and an upper-level seminar on cellular diversity and scientific writing, and mentors graduate students through MCDB, CMB, NGP, and BIOINF PhD programs. Her educational background includes: Ph.D. in Biomedical Sciences (2012) from the University of California, San Francisco B.S. in Cellular and Molecular Biology (2005) from the University of Michigan, where she conducted research with Cunming Duan Clowney's research centers on understanding how definitive neuronal parameters are encoded in genomic information and translated into cellular architectures. Her lab hypothesizes that developmental algorithms for learning circuits versus instinctual circuits differ fundamentally in their genomic requirements, with chemosensory circuits serving as key models. Using fruit flies for their tractable brain organization, her work bridges computational principles and biological implementation to uncover universal brain organization rules. Analysis of her 15 most recent publications (2016-2025) reveals consistent focus on Drosophila mushroom body development, neural sexual differentiation, and spatial constraints in circuit formation. Key themes include non-deterministic mechanisms diversifying cell surface expression, chromatin dynamics in circadian regulation, and how input density tunes sensory responses. Her work integrates genomics, neuroanatomy, and behavior to model how compact genomic information generates complex neural architectures. No scientific awards were mentioned in the provided text. Dr. Clowney advises graduate students through multiple PhD programs at the University of Michigan, though specific student names and grant details are not provided in the source material. Her teaching includes foundational undergraduate coursework and advanced seminars emphasizing scientific writing. The active publication record spanning 2016-2025 indicates sustained research funding supporting her lab's investigations into neural circuit development. The Clowney Lab, housed in the Biological Sciences Building (4218 BSB), employs Drosophila genetics and neuroanatomical techniques to dissect developmental algorithms of brain wiring. Current projects explore how spatial constraints structure learning circuits, mechanisms of neural sexual differentiation, and the genomic encoding of circuit diversity. The lab collaborates within Michigan's neuroscience community through the Program in Biology and participates in interdisciplinary initiatives studying brain evolution and function.

Stephanie RièsView profile

Associate Professor

• Cognitive Neuroscience
• Psycholinguistics
• Speech and Language Disorders
• +4 more

Stephanie Riès is an Associate Professor in the Department of Speech, Language, and Hearing Sciences at San Diego State University, where she directs the Laboratory for the Brain Dynamics of Language. Her research specializes in the cognitive neuroscience of human language, examining brain dynamics in language production, control processes, and neural reorganization following brain disorders. Education: BS in Biology from University of Rennes 1 MS in Cognitive Neuroscience from Aix-Marseille University MS in Cellular Biology and Animal Physiology from University of Rennes 1 PhD in Cognitive Neuroscience from Aix-Marseille University Her work integrates electrophysiology (EEG, EMG) and neuroimaging to study language production mechanisms and compensatory plasticity in conditions like stroke, epilepsy, and brain tumors. Key research areas include bilingual interference resolution, speech monitoring mechanisms, and neural reorganization patterns. Recent publications focus on neural hierarchies in speech production, neuroplasticity in language recovery, and advanced brain mapping techniques. Article analyses reveal strong emphasis on electrophysiological methods (sEEG, ERP), bilingual cognition, and clinical translation of neuroscience findings. Dr. Riès leads significant research initiatives through her laboratory, investigating brain-behavior relationships using intracranial recordings and developing novel approaches for speech activity detection across neurological populations.

Aftab AhmadView profile

Professor

• Machine Learning
• Cyber Security
• Public Health Prediction
• +5 more

Aftab Ahmad is a Professor in the Department of Computer Science at the City University of New York (CUNY), specializing in cybersecurity and machine learning applications. He holds a Doctor of Science from George Washington University. His research focuses on developing machine learning algorithms for cyber threat intelligence (CTI) and public health prediction, along with designing secure generative deep learning models resistant to reverse-engineering. Key research areas include: Cybersecurity frameworks and privacy-preserving architectures Generative adversarial networks (GANs) with embedded security features Biomedical signal analysis and human body channel modeling Secure wireless protocols for critical infrastructure His publication trends emphasize: Privacy metrics and data protection mechanisms Smart grid and IoT security Neuroscience-inspired machine learning models Wireless network vulnerability assessments No scientific awards or grants were explicitly mentioned in the provided texts. He teaches advanced courses in computer security and network forensics at undergraduate and graduate levels. No advising relationships or lab affiliations were detailed in the available information.

• Computer Vision
• Machine Learning
• Image and Video Processing
• +3 more

Fouad Khelifi is an Associate Professor in the Department of Computer and Information Sciences at Northumbria University. His research focuses on computer vision, machine learning, image/video processing, biometrics, multimedia forensics, and medical image analysis. He obtained his PhD in Computing Science from Queen's University Belfast (2007) and held prior research roles at the University of Bradford (2007–2009) before joining Northumbria in 2010. He supervises PhD students in cybersecurity applications and palm-vein recognition systems. Education: PhD in Computing Science, Queen's University Belfast (2004–2007) Fellow of the Higher Education Academy (FHEA, 2014) Research Interests: Khelifi’s work spans advanced deep learning techniques for medical imaging (e.g., cancer detection, retinal disease analysis), source camera identification in digital forensics, and biometric authentication systems. He develops novel algorithms for feature extraction, fusion networks, and transformer-based models in healthcare and multimedia security. Advising: Supervising Egallekanda Perera (PhD, 2019–2025): Efficient Keypoint-based Palm-vein Recognition Co-supervising Ikechukwu Ikpeama (PhD, 2024–): Cybersecurity for Industrial Control Systems Labs/Teams: Active in Northumbria’s Digital Media and Systems Research groups, contributing to interdisciplinary projects in AI-driven medical imaging and multimedia forensics.

Colleen BaileyView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +5 more

Colleen Bailey is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas. Her research focuses on the intersection of machine learning, signal processing, and energy systems, with applications spanning biomedical imaging, environmental monitoring, and edge computing. Research Interests: Machine learning optimization for edge devices Entropy-based image compression techniques Attention mechanisms in vision transformers Urban air pollution prediction models Land surface temperature super-resolution Publication Trends: Recent works emphasize compact AI architectures (e.g., MHATT network, entropy bottleneck models) for efficient processing in resource-constrained scenarios. Applications include medical imaging (Chest X-ray analysis), environmental monitoring (air quality, Martian dust storms), and energy systems (household prediction, power quality classification). Contact: Email: Colleen.Bailey@unt.edu Office: Discovery Park B252 Phone: 940-891-6874

Rosanne RademakerView profile

Research Professor

• Perception
• Working Memory
• Attention
• +3 more

Dr. Rosanne Rademaker is a Research Professor and Group Leader at the Rademaker Lab, part of the Ernst Strüngmann Institute (ESI) in Frankfurt, Germany, affiliated with Goethe University’s Department of Psychology. Her research focuses on understanding how sensation and cognition interact to shape human perception, particularly in visual working memory, attention, and physiological arousal states. Her lab employs behavioral, computational, and neuroimaging techniques (fMRI, M/EEG) to explore how the brain balances perceptual input with stored memories. In addition to foundational work on memory and attention, the lab investigates context effects on perception, motor-output impacts on visual processing, and computational neural principles. Rosanne emphasizes collaborative, fun science, fostering an inclusive environment through outreach and international collaborations. Key recent work includes studies on categorical representations in the visual hierarchy and neural dynamics during memory recall. Lab Members: Giuliana Giorjiani (PhD), Noa Noelle Krause (MSc), Amit Rawal (PhD), Maria Servetnik (PhD), Nursima Ünver Aydingül (PhD). Grants & Collaborations: Mishal Qubad’s “Junior Clinician Scientist” grant on schizophrenia visual maps, international collaborations with Toronto and the Max Planck School of Cognition. Teaching: Lectures on “Introduction to Cognitive Psychology” at Goethe University. Publications highlight her work in Nature Neuroscience , eLife , and Journal of Cognitive Neuroscience , with over 30 peer-reviewed articles. The lab actively engages in conferences (VSS, ECVP) and hosts annual retreats to promote scientific exchange.

Wenwen WangView profile

Associate Professor

• Computer Systems
• Compiler Design
• Computer Architecture
• +4 more

Wenwen Wang is an Associate Professor in the School of Computing at the University of Georgia's Franklin College of Arts & Sciences. His research focuses on computer systems, compiler design, and embedded systems security. He holds a Ph.D. in Computer Science from the University of Chinese Academy of Sciences (2014). Education: Ph.D., Computer Science, University of Chinese Academy of Sciences, 2014 His research emphasizes dynamic binary translation, compiler optimization, and secure embedded systems. Notable contributions include frameworks like JavART (JIT compiler optimization) and BSan (memory error detection). He received the 2021 M. G. Michael Award for Sciences from the Franklin College. Wang has secured two NSF grants totaling $1.2 million, including CSR: Small grants for FALCON (2023–2027) and Modernizing Dynamic Binary Translation Systems (2023–2027). He advises three graduate students: Ruili Fang, Yage Hu, and Boyang Yi. His work addresses challenges in cross-architecture virtualization, GPU-based graph computing, and hardware-triggered security mechanisms. Recent projects include Liberator (GPU graph processing) and InvisiGuard (embedded device integrity).

Demetrios M. CotsovosView profile

Associate Professor

• Structural Engineering
• Concrete Behavior
• Finite Element Analysis
• +5 more

Demetrios M. Cotsovos is an Associate Professor at the School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University. His research focuses on structural concrete behavior under static and dynamic loads, including seismic, impact, and blast scenarios. He holds a PhD from Imperial College London (2005 Unwin Award) and has extensive experience in numerical modeling, experimental testing, and structural assessment. Education: PhD in Civil and Environmental Engineering (Imperial College London), MSc in Structural Design and Analysis (National Technical University of Athens), BEng in Civil Engineering (University of Patras). Research interests include: constitutive modeling of structural materials, nonlinear finite element analysis, fiber-reinforced concrete performance, and seismic design methodologies. Notable contributions involve developing FE tools for predicting structural responses under high-rate loading and investigating the impact of steel fibers and infill walls on structural integrity. He manages specialized equipment such as drop-test rigs and high-capacity load frames (up to 3000kN). Awards include the 2005 Unwin Award for doctoral research. His work aligns with UN Sustainable Development Goals related to resilient infrastructure and climate action. Collaborations include projects on railway infrastructure dynamics and litigation support for structural integrity assessments. He has supervised multiple research projects and contributed to international conferences, producing over 70 peer-reviewed publications.

• Reliable Hardware Design
• Electronic Design Automation
• Edge Computing
• +4 more

Dr. Abusaleh Jabir is a University Reader at the School of Engineering, Computing and Mathematics, Oxford Brookes University. He holds a DPhil in Computing from the University of Oxford and leads the Advanced Reliable Computer Systems (ARCoS) group. His research focuses on reliable hardware design, memristive nanotechnology, edge computing, and secure authentication systems. He has over 80 peer-reviewed publications and multiple patents, including innovations in error-tolerant circuits and memristive architectures. **Education**: DPhil in Computing (University of Oxford). **Research Interests**: Reliable hardware design, electronic design automation, sensing at the edge, physical uncloneable authentication, and emerging memristor technologies. His work addresses challenges in IoT, edge computing, and cybersecurity through innovative electronic systems. **Funding & Projects**: Current projects include the Leverhulme Trust-funded MONITOR gas sensor array initiative. His research has been supported by the UK Ministry of Defence, EPSRC, and Finance South East. **Awards & Patents**: Multiple patents granted, including EU 17706875.6 (memristive logic) and GB 1914221.5 (reconfigurable memristive logic). Recognized with best paper awards. **Advising & Impact**: Supervised PhD students now leading semiconductor and automotive industries (e.g., Infineon Technologies, Continental Teves AG). Collaborates with academic and industrial partners globally. **Labs & Teams**: Leads the ARCoS group within the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute, fostering interdisciplinary innovation in secure and reliable electronics.

Jozsef CsicsvariView profile

Professor

• Hippocampal physiology
• Large scale neuronal recordings and their analysis
• Learning and memory
• +3 more

Jozsef Csicsvari is a Professor at the Institute of Science and Technology Austria (ISTA), where he leads a research group focused on Systems Neuroscience since 2011. Prior to this, he held positions as MRC Senior Scientist at the University of Oxford (2003-2011) and Postdoctoral Fellow at Rutgers University (1999-2002). His academic journey began with a PhD in Behavioral and Neural Sciences from Rutgers University in 1999 and a Diploma in Informatics from the Technical University of Budapest. His research interests center on memory formation and neuronal circuit mechanisms , particularly how hippocampal networks implement spatial learning and memory stabilization . His group investigates the mnenomic role of neuronal populations in brain regions involved in spatial memory, using optogenetics and multi-neuronal recordings during spatial tasks and sleep. Key themes include hippocampal oscillations , cell assembly interactions , and neuronal plasticity . Scientific trends in his recent publications reveal a deep exploration of theta oscillations , hippocampal-prefrontal interactions , and spatial memory coding . His work employs optogenetic techniques to dissect neuronal circuit dynamics and memory reactivation mechanisms. Honors and Awards include the ERC Starting Grant (Consolidator level) and the Title of Professor in Neuroscience at the University of Oxford. His advisory role extends to 6 PhD students and postdoctoral researchers at ISTA. He also contributes to scientific governance as Head of IST Animal Welfare Council and Editorial Board member of PLOS Biology.

Regina LapateView profile

Assistant Professor

• Affective Neuroscience
• Emotional Processing
• Cognitive Control
• +3 more

Dr. Regina Lapate serves as an Assistant Professor in the Department of Psychological and Brain Sciences at the University of California, Santa Barbara (UCSB), within the College of Letters & Science. She directs the LEAP Neuro Lab (Lapate Experimental Affective Psychophysiology and Neuroscience Laboratory), where her team investigates neural mechanisms of emotion-cognition interactions using multimodal methodologies including TMS, EEG, fMRI, and psychophysiology. Her educational trajectory includes a PhD from the University of Wisconsin-Madison (2015) under Dr. Richard Davidson, followed by an NIH postdoctoral fellowship at UC Berkeley (2016) with Dr. Mark D'Esposito. She co-edited the 2nd edition of The Nature of Emotion: Fundamental Questions in 2018. Dr. Lapate's research centers on emotional processing, cognitive control, and adaptive emotion regulation, with particular emphasis on temporal memory dynamics and prefrontal cortex function. Her lab employs causal inference approaches to explore how emotional states sculpt sensory processing and behavioral goals, aiming to uncover mechanisms that promote resilience against psychopathology. Recent work demonstrates how emotional context modulates temporal memory and action goal representations, revealing critical prefrontal circuitry involved in emotional adaptation. Her publication trends reveal deep integration of time perception with emotional processing, neurostimulation-based causal testing of prefrontal mechanisms, and translational applications for mood and anxiety disorders. The 2024-2025 publications particularly highlight anorexia nervosa symptomatology, temporal coding in emotion, and open-loop motor circuit dissociations. Scientific recognition includes: Hellman Fellowship (2024) Prestigious NIH grant for prefrontal mechanism research (2023) Regent’s Junior Faculty Award (2023) Dr. Lapate has secured substantial grant funding including an NIH award to elucidate prefrontal mechanisms of emotional processing and regulation. Her LEAP Neuro Lab functions as a collaborative hub where graduate and undergraduate researchers investigate fundamental questions about how emotional experiences dynamically interact with cognitive processes to shape adaptive functioning and psychopathology vulnerability.

Michael BronsteinView profile

Professor

• Geometric Deep Learning
• Graph Representation Learning
• Computer Vision
• +4 more

Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.

Nick AudetteView profile

Assistant Professor

• Flexible Sensory Processing
• Thalamocortical Circuits
• Learning and Memory
• +3 more

Nick Audette is an Assistant Professor in the Department of Psychological Sciences at the University of Connecticut (UConn). He leads the Audette Lab, established in January 2025 at the Storrs campus, where his team investigates how the brain integrates sensory input with environmental context and experience to enable perception. His research employs large-scale neural recordings in mice during acoustically enriched behaviors. Dr. Audette earned his Ph.D. in 2018 from Carnegie Mellon University. His primary research areas include: Flexible sensory processing in thalamocortical circuits Neural mechanisms of learning and memory consolidation Movement-based predictions in auditory cortex Stimulus-specific prediction error encoding His recent publications (2014-2025) demonstrate a consistent focus on predictive processing mechanisms in sensory systems. Key trends include: Thalamocortical plasticity during sensory learning Movement-related neural predictions in auditory cortex Stimulus-specific error detection neurons Translaminar circuit organization High-throughput analysis of neural plasticity Methodologically, his work combines electrophysiology, behavioral paradigms, and advanced imaging techniques. Dr. Audette currently advises two undergraduate researchers, Claudia and Ava, on a project developing high-throughput behavioral assays linking lever-press movements to auditory predictions in mice. The lab has secured space in UConn's Bousfield Psychology Building and plans electrophysiological investigations of sensory prediction encoding.

Tim FoskerView profile

Senior Lecturer

• Developmental Psychology
• Cognitive Neuroscience
• Language Development
• +5 more

Tim Fosker is a Senior Lecturer in the School of Psychology at Queen’s University Belfast, specializing in developmental cognitive neuroscience and psychology. He focuses on the auditory precursors of speech processing, literacy development, and neural development in children. Fosker directs the Language, Learning and Literacy Lab (L3Lab@QUB), which includes EEG and audiometric facilities. He holds a PhD from Bangor University and completed a postdoctoral fellowship at the University of Cambridge under Professor Usha Goswami. His research interests span auditory processing deficits in dyslexia, classroom communication methods, and psycholinguistics. He teaches Developmental Psychology at undergraduate and postgraduate levels and serves as Advisor of Studies, Personal Tutor, and Disability Advisor in his department. Fosker has secured grants, including the 2010–2013 project on auditory discrimination and children’s reading development. His awards include the Staff Excellence Award (2021) and the School of Psychology ICARE Award (2022). He actively collaborates on initiatives like fostering screen time interventions and hosting academic visitors. His recent work emphasizes systematic reviews on screen time effects in neurodiverse children and longitudinal studies on phonological development. Fosker’s lab also engages in outreach, such as the Knocknagoney Primary School initiative, promoting child development research.