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Richard GoldenView profile

Professor

• Computational Psychometrics
• Statistical Machine Learning
• Learning Sciences
• +3 more

Richard Golden is Professor of Cognitive Science in the School of Behavioral and Brain Sciences at the University of Texas at Dallas, with a cross-appointment in the Erik Jonsson School of Engineering and Computer Science. He directs the Cognitive Informatics and Statistics (COINS) Laboratory and serves as Program Head for both the undergraduate Cognitive Science BS and graduate Applied Cognition and Neuroscience MS programs. Education: Ph.D. in Experimental Psychology (Computational Cognitive Science) - Brown University (1987) M.S. in Electrical Engineering (Statistical Pattern Recognition) - Brown University (1986) B.S. in Electrical Engineering and Psychology - University of California, San Diego (1982) Postdoctoral training at Stanford University and University of Pittsburgh Research Focus: Golden's work integrates psychometrics, learning science, and statistical machine learning to develop quantitative models of skill acquisition. His research spans: (1) Computational psychometrics for educational assessment, (2) Theoretical foundations of machine learning algorithms, and (3) Cognitive modeling of text comprehension and recall using Markov field approaches. Publication Trends: Recent work (2018-2023) focuses on cognitive diagnostic models, model misspecification detection, and statistical learning frameworks. His research demonstrates consistent innovation in psychometric methodologies with applications in education and healthcare. Awards & Honors: Invited keynote speaker for major conferences in mathematical psychology and neural networks IEEE Senior Member (1999) Secretary-Treasurer Service Award from Society for Mathematical Psychology (2017) Faculty Development Award for econometrics research (1998) Students & Funding: Actively advises graduate students in cognitive science and engineering. Recent grants include: SPARK: Information Matrix Tests for Cognitive Diagnostic Models ($50,000, UT Dallas) Neuroscience-Inspired Deep Learning ($16,000, Raytheon) Cognitive Diagnostic Knowledge Tracing research ($10,000, UT Dallas) Laboratory Leadership: Directs COINS Lab researching cognitive informatics and statistical methodologies. Maintains affiliations with Society for Mathematical Psychology, Psychometric Society, and International AI in Education Society.

Fei WangView profile

Professor

• Power Electronics
• Power Systems
• Motor Drives
• +3 more

Fei Wang is a Professor and Condra Chair of Excellence in Power Electronics at the University of Tennessee, Knoxville (UTK), affiliated with the Tickle College of Engineering and the Min H. Kao Department of Electrical Engineering and Computer Science. He serves as Technical Director of CURENT and holds a joint appointment at Oak Ridge National Lab. His expertise spans power electronics converters, motor drives, wide bandgap devices, and renewable energy systems. Education: B.S. in Electrical Engineering, Xi’an Jiaotong University, 1982 M.S. and Ph.D. in Electrical Engineering, University of Southern California, 1985 and 1990 Research Interests: Focuses on design, modeling, and control of advanced power electronics systems, including grid integration of renewable energy, electric vehicle drivetrains, and wide bandgap semiconductor applications. He has authored over 500 publications and holds 20 patents, with funding exceeding $65M and personal share over $18M. Key Contributions: Developed three-level NPC medium voltage drives at GE Co-founded CURENT and led its technical strategy Recipient of IEEE IAS Gerald Kliman Innovator Award (2018), Dushman Award (1998), and multiple research excellence awards Grants & Advising: Supervised 13 Ph.D. and 8 M.S. students, advised postdocs, and hosted over 35 visiting scholars. Active in IEEE standards development and editorial roles. Labs & Teams: Leads the CURENT Engineering Research Center, focusing on ultra-wide-area power grid resilience through advanced power electronics and system integration.

Jean-Pierre R. FaletView profile

Assistant Professor

• Machine Learning
• Neurology
• Healthcare AI
• +3 more

Jean-Pierre R. Falet is an Assistant Professor in the Department of Neurology and Neurosurgery at McGill University, combining roles as an attending neurologist at the McGill University Health Centre and a PhD Candidate in Computer Science at Mila/Université de Montréal. He also serves as a part-time Machine Learning Research Scientist at Mila's Safe AI for Humanity (SAIFH) group, co-supervised by Yoshua Bengio. Education includes an MD (2017), neurology residency (2022), and a master’s in neuroscience (2023) from McGill University. His master’s research focused on causal machine learning for precision medicine and clinical trial optimization in multiple sclerosis (MS), supervised by Douglas Arnold and Tal Arbel. Current PhD work centers on model-based AI systems with safety guarantees, particularly probabilistic inference inspired by human cognition. Research bridges machine learning theory and clinical applications, emphasizing healthcare safety-critical systems. Collaborations include work with Yoshua Bengio and Joumana Ghosn at Mila’s SAIFH. His GitHub repository ms-predictive-enrichment supports clinical trial enrichment via deep learning for MS patients.

Manar SamadView profile

Associate Professor

• Machine Learning
• Deep Neural Networks
• Computer Vision
• +3 more

Dr. Manar Samad is Associate Professor of Computer Science at Tennessee State University's College of Engineering. His multidisciplinary research spans machine learning, computer vision, natural language processing, health informatics, and explainable AI, with projects funded by NSF, NIH, DoD, and Amazon. Dr. Samad leads research on deep representation learning for tabular data, domain adaptation in computer vision, missing data imputation techniques, and multimodal learning. His health informatics work focuses on clinical decision support systems using electronic health records. Recent publications demonstrate specialization in self-supervised learning, clustering algorithms, and cross-domain adaptation for medical applications. Current projects include mathematically-inspired representation learning for heterogeneous data (DoD), deep clustering of EHR data (NSF), and cross-domain computer vision applications (Amazon). Dr. Samad directs the Computational Intelligence and Data Analytics Lab (CIDALab), advancing fundamental algorithms and applied AI systems. Education: Postdoc (Geisinger Medical Center), PhD (Old Dominion University), MSc (University of Calgary), BSc (Bangladesh University of Engineering & Technology) Awards: 2016 Outstanding PhD Researcher Award (Old Dominion University) Major Grants: NSF ($200K), DoD ($800K), NIH ($421K), USDA ($500K), Amazon ($80K)

Joshua B TenenbaumView profile

Professor

• Computational Cognitive Science
• Artificial Intelligence
• Human-Machine Intelligence
• +4 more

Joshua B. Tenenbaum is a Professor of Computational Cognitive Science in MIT's Department of Brain and Cognitive Sciences, a Principal Investigator at CSAIL, and Research Thrust Leader at CBMM. His work bridges human cognition and machine intelligence, focusing on perception, learning, and reasoning. He holds a PhD from MIT (1999) and previously taught at Stanford (1999–2002). His research develops algorithms used globally in science/engineering, emphasizing human-like AI grounded in computational models of mind. Awards include the Troland Research Award (NAS), APA's Early Career Contribution Award, and Society of Experimental Psychologists' Early Investigator Award. He is a Fellow of the Cognitive Science Society and Society of Experimental Psychologists. Key research interests span intuitive physics, theory of mind, probabilistic models of cognition, and neuro-symbolic AI. His lab explores how humans and machines learn from limited data, reason with abstract concepts, and generalize knowledge. Publications emphasize interdisciplinary approaches to intelligence, with recent work on neuro-symbolic systems, embodied reasoning, and ethical AI frameworks. He advises MIT's AI initiatives and collaborates internationally on cognitive science projects. His lab's work has led to open-source tools like probabilistic programming frameworks and benchmark datasets for evaluating human-like capabilities in machines. Current projects include understanding moral judgment mechanisms and developing physically plausible AI systems.

• Neuroengineering
• Brain-Machine Interfaces
• Neurorehabilitation
• +3 more

Nicolas Berberich is a Lecturer and senior PhD candidate at the Technical University of Munich's Chair of Cognitive Systems, where he leads the neuroengineering research team. He teaches courses on neuro-inspired systems engineering and neurorehabilitation technologies, and mentors young researchers. His interdisciplinary research bridges robotics, neuroscience, and philosophy. Research interests include: Intention prediction and sensory feedback in brain-machine interfaces EEG/fMRI studies of embodiment and agency Neuroengineering applications in rehabilitation Neuroethics and AI ethics frameworks His publications focus on neurorehabilitation technologies, exoskeleton design, and motor learning. Recent work explores tremor suppression, sensory substitution systems, and neural correlates of motor imagery. Awards and fellowships: Wolfgang-Heilmann Award (2020) for AI education initiative 'KI macht Schule' Finalist for IROS Best Paper Award (2020) RIKEN Research Scholarship (2019) Best Poster Award at Interdisciplinary College (2018) SmartStart Fellowship (2017-2018)

• Auditory Processing
• Visual Processing of Faces
• Deep Neural Networks
• +4 more

Dr. Christoph Daube is a Lecturer at the School of Psychology & Neuroscience, University of Glasgow. His research focuses on understanding how sensory inputs—such as speech sounds and visual face stimuli—are processed into abstract perceptual representations. He employs methods including deep neural networks (DNNs), magnetoencephalography (MEG), and information theory to explore these processes. Key interests include auditory speech processing in the brain, visual face perception, and the algorithmic equivalence between neural and artificial intelligence models. His work integrates interdisciplinary approaches, such as analyzing how speech envelope tracking occurs across auditory and non-auditory cortices, and investigating cultural influences on facial beauty perception. Recent studies include causal experiments on facial expression alignment in social interactions and theoretical frameworks for comparing brain and DNN models. Dr. Daube has contributed to over 10 peer-reviewed articles since 2019, spanning journals like *Current Biology*, *Cerebral Cortex*, and *Trends in Cognitive Sciences*. His research highlights the interplay between sensory processing, computational modeling, and human behavior.

Matthew CookView profile

Professor

• Computer Science
• Artificial Intelligence
• Neural Networks
• +6 more

M.M. (Matthew) Cook, PhD is a Full Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence group at the Bernoulli Institute. His research focuses on theoretical computer science, neural networks, and biologically inspired computation. He holds a PhD and has extensive experience in neuromorphic engineering, cellular automata, and computational neuroscience. His work spans interdisciplinary areas including spiking neural networks for biosignal processing, self-assembly algorithms, and paradox resolution in causal inference. Key contributions include foundational studies on universality in cellular automata (e.g., Rule 110), neuromorphic hardware implementations, and neuroimaging analysis techniques for synaptic partner detection in Drosophila brain data. Recent publications highlight advancements in event-driven neural networks for epilepsy monitoring, low-power neuromorphic systems, and algorithmic exploration strategies. His research bridges theoretical computer science with practical applications in biomedical engineering and cognitive modeling.

Michael ManganView profile

Senior Lecturer

• Machine Learning
• Robotics
• Biorobotics
• +5 more

Dr. Michael Mangan is a Senior Lecturer in Machine Learning and Robotics at the University of Sheffield's School of Computer Science, and Operations Director of Sheffield Robotics. He holds an MEng in Avionics from the University of Glasgow (2004), an MSc in Neuroinformatics from the University of Edinburgh (2006), and a PhD in Biorobotics (2011). His research focuses on understanding insect navigation and applying these insights to robotics, emphasizing bio-inspired systems for autonomous navigation and smart agriculture. Research interests include modeling insect vision, navigation strategies, and neural mechanisms, with applications to robotics and pest control. Mangan leads the ManganLab, exploring how simple nervous systems enable complex behaviors through computational and robotic models. His work integrates biology, computer science, and engineering to develop robust autonomous systems. Key contributions include tools like CompoundRay for simulating compound eyes and I2Bot for multi-modal insect navigation simulations. He has secured grants such as ActiveAI (£953k) and SkyEye (£25k), focusing on AI transparency, neuromorphic control, and vision-based localization. Professional memberships include the Machine Learning research group and involvement in Living Machines conferences. Current projects explore fruit detection in agriculture, neuromorphic sequence learning, and animal tracking in natural environments.

Soham JanaView profile

Assistant Professor

• High-dimensional statistics
• Robust estimation
• Statistical Machine Learning
• +5 more

Soham Jana is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, part of the College of Science. He holds a Ph.D. in Statistics and Data Science from Yale University (2022), an M.Stat. and B.Stat. from the Indian Statistical Institute (ISI), Kolkata. His research focuses on high-dimensional statistics, robust estimation, statistical machine learning, causal inference, and empirical Bayes methods. Previously, he was a Post-doctoral Research Associate at Princeton University's Operations Research and Financial Engineering department. Education: Ph.D., Yale University, 2022 (Thesis: Learning non-parametric and high-dimensional distributions via information-theoretic methods) M.Stat. (Hons.), Indian Statistical Institute, 2017 (Dissertation: Characterization of single-integral non-kernel divergences) B.Stat. (Hons.), Indian Statistical Institute, 2015 Research interests include theoretical and methodological aspects of high-dimensional statistics, robust estimation, neural networks, causal inference, and empirical Bayes. He has received awards such as the INSPIRE Scholarship and the Kaneb Center Course Design Academy Award (2024–2025, $5000). Grants and professional contributions include organizing sessions at conferences like JSM and CFE-CMStatistics. His work spans collaborations with institutions like Princeton, Yale, and the First Republic Bank Research Program. He teaches courses such as Introduction to Probability and Modern Machine Learning Techniques at Notre Dame.

Joshua VogelsteinView profile

Associate Professor

• Systems Neuroscience
• Computational Neuroscience
• Neural Circuits
• +7 more

Joshua Vogelstein, PhD, is an Associate Professor of Biomedical Engineering at Johns Hopkins University, affiliated with the Whiting School of Engineering. His research focuses on understanding intelligence—both biological and artificial—through the lens of shared principles. He develops statistical machine learning and big data tools to analyze neural circuits, connectomes, and large-scale biological datasets. Key collaborations include experimental neuroscientists worldwide, driving advances in connectomics and computational neuroscience. Research interests include systems neuroscience, cognitive and computational models of intelligence, and applying machine learning to understand neural communication across scales. His lab emphasizes rigorous statistical methods and reproducible neuroimaging analysis, addressing challenges like batch effects and data reliability. Notable contributions include CloudReg for cross-modal brain registration and GraSPy for graph statistics. Recent work explores organoid intelligence, leveraging organoid networks to model brain development and disease. Vogelstein’s team also investigates prospective learning frameworks for dynamic systems and causal inference in observational health studies. His lab maintains open-source tools like mvlearn and Neurodata’s ecosystem, advancing reproducible neuroscientific research.

Zhaodan KongView profile

Associate Professor

• Control Theory
• Machine Learning
• Formal Methods
• +4 more

Zhaodan Kong is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California, Davis. He leads the Cyber-Human-Physical Systems Lab , focusing on interdisciplinary research at the intersection of control theory, machine learning, and formal methods. His work applies these techniques to autonomous systems, human-autonomy teaming, cyber-physical systems, and neural engineering. His research emphasizes interpretable and safe systems, with applications in robotics, healthcare, and environmental monitoring. Research interests include control theory, machine learning, formal methods, and their applications to autonomous systems. Recent efforts explore energy-efficient UAV trajectory optimization, human-AI collaboration frameworks, and fault diagnosis in cyber-physical systems. His lab develops frameworks for cohort explanation in machine learning models and studies trust dynamics in human-autonomy interactions. Publications span topics such as UAV mission planning, neural dynamics modeling, and interpretable AI. His work bridges theoretical foundations with practical applications, aiming to advance autonomous systems' reliability and human-system integration. Office hours are held during Winter 2025, indicating active academic engagement.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Dr. Varuna De Silva is a Reader (Associate Professor) in Machine Intelligence at Loughborough University London, where he serves as Programme Director for the Artificial Intelligence and Data Analytics MSc programme. He joined as a Lecturer in 2016 after industry experience at Apical Ltd, where his patented algorithms were deployed in over 500 million devices. His research focuses on scaling AI to real-world engineering systems through multi-agent reinforcement learning, multimodal computer vision, and simulation modeling. Education: B.Sc. (Hons) Engineering, University of Moratuwa, Sri Lanka (2007) Ph.D. Electronic Engineering, University of Surrey (2011) PG Cert in Academic Practice, Loughborough University (2018) His work spans reinforcement learning architectures, neuromorphic computing, and AI applications in healthcare, environmental systems, and autonomous vehicles. Recent publications demonstrate strong emphasis on interpretable AI, brain-computer interfaces, and causal reasoning in multi-agent environments, with consistent cross-disciplinary integration of machine learning techniques. Awards: IEEE Chester Sall Award, Consumer Electronics Society (2010) Vice Chancellor's Award for Best Post Graduate Research Student (2011) Overseas Research Scholarships Award (2008) He leads EPSRC-funded projects on next-generation machine intelligence and maintains industrial collaborations. As module leader for Bayesian Methods in Deep Learning and Statistical Methods in Finance, he drives curriculum development in AI. His team actively researches autonomous systems and human-AI interaction frameworks.

Ruohan ZhangView profile

Researcher

• Robotics
• Human-Robot Interaction
• Brain-Machine Interface
• +5 more

Ruohan Zhang is a postdoctoral researcher at Stanford Vision and Learning Lab , Stanford Institute for Human-Centered Artificial Intelligence , and Wu Tsai Human Performance Alliance . They will join Northwestern University , Department of Computer Science in Fall 2026. Research Focus: Human-centered artificial intelligence Robotic manipulation and learning Human-robot interaction Brain-machine interfaces Neuroscience-inspired AI Key Contributions: Developed BEHAVIOR Robot Suite for whole-body manipulation tasks Created UAD framework for unsupervised affordance distillation Advanced Chain-of-Modality approach for multimodal robotic learning Recent Scientific Awards: Wu Tsai Human Performance Alliance Fellowship (2022-24) AAAI/SIGAI Doctoral Consortium Scholarship (2019) Google AR/VR Research Award (2017-18) College Continuing Fellowship (2014-17) Teaching & Service: Organizer of ICCV, ECCV, NeurIPS workshops Instructor for CS231n: Deep Learning for Computer Vision at Stanford (2023) Teaching Assistant experience in Computational Brain , Machine Learning , and Discrete Mathematics at Stanford and UT Austin

• Neural Signal Processing
• Machine Learning for Neuroscience
• Brain Network Modeling
• +5 more

Dr. Quanying Liu serves as Associate Professor in the Department of Biomedical Engineering at Southern University of Science and Technology (SUSTech), where she leads the Neural Computing and Control Laboratory (NCC lab) as Principal Investigator and Doctoral Supervisor. Her academic journey includes a PhD from ETH Zurich in Biomedical Engineering, postdoctoral training at Caltech, and research positions at Huntington Medical Research Institute, KU Leuven, and Oxford University. PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Dr. Liu's research bridges neuroscience, machine learning, and control theory with focus on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for brain interpretation, and optimization frameworks for neuromodulation (tES, TMS). Her work develops high-density EEG source imaging algorithms, data-driven brain network modeling, and control-theoretic approaches for neural stimulation. The NCC lab specializes in machine learning algorithms, neural computation, multimodal data fusion, network control theory, and bidirectional brain-computer interfaces. Analysis of her recent publications reveals strong trends in EEG-based visual decoding, multimodal neural embeddings, and AI-driven brain network modeling. Her work increasingly integrates generative models for neural data synthesis, control theory for precise neuromodulation, and cross-modal approaches connecting neural signals with cognitive functions. The New Brain 30 (2023) AAIC travel award (2019) Estes Stars Award (2018) Shenzhen Peacock Talent Plan C Dr. Liu actively mentors students through SUSTech's doctoral program and leads multiple significant research initiatives including a National Natural Science Foundation Youth Project, a National Key R&D Program in Bio-Information Fusion, and several Shenzhen municipal projects. Her lab receives funding from Guangdong Provincial Basic Research Fund, Shenzhen Science and Technology Innovation Commission, and international fellowships including Boswell Postdoctoral Fellowship and Swiss National Science Foundation grants. The NCC lab maintains strong international collaborations with Caltech, ETH Zurich, and Oxford University while developing novel platforms like WheelCon for sensorimotor control studies. The Neural Computing and Control Laboratory operates as an interdisciplinary hub integrating computational neuroscience, machine learning, and control engineering. The lab develops specialized hardware-software systems for closed-loop neurostimulation, maintains the EEGdenoiseNET benchmark dataset, and pioneers methods like MOVEA for transcranial electrical stimulation optimization. Current research directions include generative models for neural decoding, network control theory applications, and AI-human cognitive interaction frameworks.