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Luca DidaciView profile

Associate Professor

• Statistical Pattern Recognition
• Biometric Recognition and Security
• Semi-Supervised Learning
• +4 more

Luca Didaci is an Associate Professor of Computer Engineering at the University of Cagliari's Department of Electrical and Electronic Engineering (DIEE), within the Faculty of Engineering and Architecture. He leads research in statistical pattern recognition, biometric systems, and EEG signal analysis, focusing on applications in security, healthcare, and machine learning. His work includes developing EEG-based personal identification techniques, semi-supervised learning methods, and advanced tools for histopathological image analysis. With an h-index of 16, his research has been cited over 950 times. He teaches courses on programming, software development, and computer science fundamentals across undergraduate and graduate programs. Didaci is affiliated with the Pattern Recognition and Application Lab at DIEE and contributes to the PhD Program in Electronic and Computer Engineering. His research interests span multiple classifier systems, biometric authentication, and cybersecurity. Recent projects include studying ADaM/SDTM clinical data formats and investigating adversarial machine learning threats to biometric systems. He actively publishes in top-tier journals and conferences, emphasizing interdisciplinary applications of AI in engineering and medicine.

Matt ThomsonView profile

Professor

• Biological Engineering
• Cellular and Developmental Biology
• Molecular Biology
• +8 more

Matt Thomson is a Professor of Computational Biology and Investigator at the Heritage Medical Research Institute (HMRI) at the California Institute of Technology (Caltech). He holds a B.A. from Harvard University (2001) and a Ph.D. from Caltech (2011). His academic progression includes roles as Visiting Associate (2016), Assistant Professor (2017-24), and his current full Professor position since 2024. His research focuses on applying quantitative methods to engineer cellular differentiation and control biological systems. Key areas include synthetic biology, single-cell genomics, and active matter systems. Techniques employed include mathematical modeling, machine learning, and single-cell RNA sequencing. Notable achievements include developing optical differentiation systems, computational tools for cell trajectory analysis, and stochastic models of cellular self-organization. Thomson's work bridges biology and engineering, with applications in regenerative medicine, cancer immunotherapy, and foundational research in developmental biology. His lab, the Thomson Lab, collaborates with the SPEC initiative to advance programmable biological systems. He also explores AI-driven approaches for protein engineering and large language model optimization in biological contexts. No scientific awards are explicitly listed in the provided materials. His research is supported by grants and institutional resources at Caltech and HMRI. He advises students and researchers in his lab but no specific names are provided in the text.

• Neuroscience
• Statistics
• Machine Learning
• +3 more

David Zoltowski is a Postdoctoral Researcher in the Department of Statistics at Stanford University, advised by Scott Linderman. His research focuses on developing and applying statistical and machine learning methods to understand neural dynamics, decision-making processes, and multi-region neural communication. He specializes in latent variable models, Bayesian inference techniques, and dynamical systems analysis. Key contributions include benchmarking frameworks for neural activity models and methods for uncovering structure in complex neural datasets. His work bridges computational neuroscience and statistics, with applications spanning neural decoding, state-space modeling, and neuromodulation. Recent projects address the integration of time and reward signals in decision-making circuits and the development of scalable algorithms for high-dimensional spike train analysis. David collaborates on interdisciplinary projects involving neuroimaging, behavioral neuroscience, and probabilistic modeling. His tools and benchmarks are widely used in the computational neuroscience community for evaluating latent variable models and neural decoding systems.

Thomas A. ClelandView profile

Professor

• Neuroscience
• Cognitive Neuroscience
• Olfactory Systems
• +3 more

Thomas A. Cleland is a Professor of Psychology at Cornell University, affiliated with the College of Arts and Sciences. His research focuses on how cognitive and perceptual processes emerge from neural circuit properties, particularly in olfactory systems. He explores learning, memory, and sensory processing using techniques like electrophysiology, computational modeling, and neuromorphic algorithms. Cleland's work bridges neuroscience and artificial intelligence, aiming to develop biologically inspired solutions for AI systems. He teaches courses such as PSYCH 4600 (Neural Representations) and leads the Computational Physiology Laboratory. His research integrates interdisciplinary methods, including brain imaging (e.g., light-sheet imaging), behavior genetics, and theoretical studies. Cleland’s lab collaborates on projects like odor recognition, neuromorphic circuits, and neural oscillations, with publications spanning journals like Neuroscience and Artificial Intelligence . His contributions include developing tools such as the Annolid segmentation framework and advancing understanding of olfactory bulb dynamics. Cleland’s work has implications for AI, sensory neuroscience, and neurotechnology, reflecting his commitment to linking biological insights with computational innovation.

Aran NayebiView profile

Assistant Professor

• Machine Learning
• Neuroscience
• Artificial Intelligence
• +5 more

Aran Nayebi is an Assistant Professor at Carnegie Mellon University, affiliated with the Machine Learning & Neuroscience Institute. His research focuses on integrating machine learning and neuroscience to understand biological intelligence and develop biologically plausible AI models. He explores embodied agents, neural network dynamics, and human-AI alignment through game-theoretic frameworks. Key research interests include reverse-engineering natural intelligence through embodied agents, developing models that align with brain dynamics (e.g., zebrafish agents, rodent tactile processing), and addressing ethical challenges in AI alignment. His work bridges computational neuroscience and neural network design, emphasizing scalable models and biologically inspired algorithms. Recent publications span topics such as AI-driven economic policy (Universal Basic Income), neuroAI turing tests, and model-brain comparison techniques. His interdisciplinary approach addresses both technical challenges (e.g., neural network scaling) and societal implications of AI.

Ramana PidapartiView profile

Professor

• Multidisciplinary Design Innovation
• Computational Biomechanics
• Design Informatics
• +1 more

Ramana Pidaparti is a Professor at the University of Georgia's College of Engineering, specializing in multidisciplinary design innovation, computational biomechanics, design informatics, and STEM education. He earned his Ph.D. in Aeronautics & Astronautics from Purdue University and employs project-based, collaborative, and experiential teaching strategies. His professional affiliations include fellowships with the American Association for the Advancement of Science, Royal Aeronautical Society, American Society of Mechanical Engineers, and American Institute of Aeronautics and Astronautics. Research interests span: Multidisciplinary design optimization Computational modeling of biological systems Educational technology integration in STEM Bio-inspired engineering solutions Recent publications (2023-2025) focus on AI applications in robotics, biomechanics, and middle-school education, with emphasis on knowledge-transfer frameworks, biomaterial characterization, and 3D learning modules. Awards recognize contributions to aerospace and mechanical engineering sciences.

Daniela RusView profile

Professor

• Robotics
• Artificial Intelligence/ML
• Data Science
• +5 more

Daniela Rus is the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL). She leads research in robotics, AI/ML, and autonomous systems, emphasizing soft robotics, mobile computing, and data-driven solutions. Her work spans theoretical and applied domains, including self-reconfiguring robots, AI for healthcare, and transportation optimization. Education: Earned her PhD in Computer Science from Cornell University. Recognized as a 2002 MacArthur Fellow, ACM Fellow, IEEE Fellow, and member of the National Academy of Engineering and American Academy of Arts and Sciences. Research focuses on robotics (soft robots, modular systems), AI applications (neural networks, generative models), and societal impacts (autonomous vehicles, data privacy). Projects include the Robot Garden educational platform, M-block modular robots, and AI-driven traffic optimization systems. Her lab, CSAIL, fosters interdisciplinary innovation across computer science, engineering, and biology. Publications highlight breakthroughs in robot design, algorithmic efficiency, and human-robot interaction. Current efforts explore AI for environmental monitoring, surgical robotics, and ethical autonomous systems. CSAIL hosts 100+ research groups, including the Distributed Robotics Lab and Embodied Intelligence Community.

• Cognitive Neuroscience
• Computational Modeling
• Neural Computation
• +1 more

Ruiyi Zhang is a Postdoctoral Research Associate at the Neuroscience Institute, focusing on neural computation and AI integration. Her research bridges cognitive neuroscience and machine learning, examining prefrontal cortex functions in decision-making and neural network architectures for spatial navigation. Recent publications explore strategic cognitive processes, modular neural systems, and brain-inspired AI frameworks. Her work emphasizes computational approaches to understanding neural mechanisms and enhancing artificial intelligence systems.

Dan J. WangView profile

Associate Professor

• Social Networks
• Entrepreneurship
• Innovation
• +4 more

Dan J. Wang is the David W. Zalaznick Associate Professor of Business at Columbia Business School, with a courtesy appointment in Sociology. He teaches Strategy, Technology Strategy, and Organizational Theory. His research explores social networks' role in knowledge transfer, entrepreneurship, and innovation, particularly focusing on reverse brain drain and venture capital dynamics. Recognized with awards like the Singvhi Prize and inclusion in Poets & Quants' 'Best 40 Under 40,' his work appears in top journals like Administrative Science Quarterly and American Sociological Review . Education: BA in Sociology and Comparative Literature from Columbia University (2007), PhD in Sociology from Stanford University (2013). Research interests span migration's impact on innovation, venture capital networks, and social movements. His studies include how return migrants bridge global knowledge gaps and how protest tactics diffuse across organizations. He also examines mentorship's role in labor markets and inequality in professional networking. Awards include the Kauffman Foundation's Dissertation and Junior Faculty Fellowships. His research has been featured in Strategy+Business , CNN Money , and NPR . He serves as a consulting editor for the American Journal of Sociology . Notable grants and collaborations include studies on migrant entrepreneurship, venture capital syndicates, and intercultural exchange. His work bridges academia and practice, influencing both strategic management education and real-world innovation strategies.

Stephanie NelliView profile

Assistant Professor

• Cognitive Science
• Neuroscience
• Machine Learning
• +3 more

Stephanie Nelli is an Assistant Professor of Cognitive Science at Occidental College, appointed in 2021. Her research integrates neuroscience and machine learning to explore computational principles underlying human learning and decision-making processes. She holds a B.A. from the University of North Carolina at Chapel Hill and a Ph.D. from the University of California, San Diego. Dr. Nelli's research focuses on understanding how the brain's neural networks enable rapid learning and adaptive decision-making. Key areas include studying alpha oscillations' role in visual processing, the interplay between sensory evidence accumulation and cognitive control, and the theoretical implications of deep learning for cognitive models. Her work often employs EEG and computational modeling to bridge gaps between human neuroscience and artificial intelligence. Recent publications highlight topics such as neural knowledge assembly in humans and artificial systems, the impact of alpha frequency on visual perception, and the neurochemical effects of ethanol on subcortical brain regions. While no specific awards are listed, her contributions reflect interdisciplinary innovation at the intersection of cognitive science and computational neuroscience. Office hours are held weekly on Tuesdays from 4:30-5:30pm and Wednesdays from 2:00-4:00pm in Swan Hall #223. She can be reached via email at nelli@oxy.edu .

• Neuromorphic Engineering
• Computational Neuroscience
• Brain-Inspired Computing
• +2 more

Alessio Franci is Lecturer in Electrical Engineering and Computer Science at the University of Liege, where he co-founded the ULiege Neuroengineering Lab. His research bridges mathematics, neuroscience and engineering through brain-inspired computing paradigms. Funded by a WEL-T Starting Grant, his work develops neuromorphic controllers and biomorphic systems for robotics and intelligent sensing. Research explores: neurocomputational principles of decision-making; neuromodulation in adaptive systems; and degenerate coding in neural circuits. Publications apply nonlinear dynamics to opinion formation, neural coding, and neuromorphic hardware design, demonstrating consistent innovation in theoretical frameworks. Honors include the WEL-T Starting Grant supporting his investigations into neuronal degeneracy and physiological mechanisms. Current work emphasizes real-world applications in robotics through spiking neural controllers and embodied neuromodulation strategies.

Wolfgang MaassView profile

Professor

• Neural Networks
• Machine Learning
• Computational Neuroscience
• +3 more

Wolfgang Maass is a Professor at the Institute of Machine Learning and Neural Computation at Technische Universität Graz . His research focuses on computational neuroscience, neuromorphic engineering, and machine learning, with an emphasis on understanding how biological neural networks process information and inspire artificial systems. He has contributed extensively to the development of spiking neural networks, synaptic plasticity models, and neuromorphic hardware. Education: Dipl.-Ing. (Diploma in Engineering), Dr.rer.nat. (PhD in Natural Sciences) Affiliations: Institute of Machine Learning and Neural Computation, TU Graz Key research interests include: Neural network models for cognitive functions Biologically plausible learning algorithms Neuromorphic computing and hardware implementations Computational principles of cortical microcircuits Recent work highlights the integration of biological principles into artificial intelligence, including studies on synaptic plasticity (BTSP), spiking neural networks for image recognition, and energy-efficient neuromorphic systems. His research bridges theoretical neuroscience and applied machine learning, with a focus on systems that emulate brain-like functionality. He has presented at major conferences such as the International Convention on the Mathematics of Neuroscience and AI and has authored over 200 publications. His work has been recognized for its interdisciplinary impact, advancing both neuroscience and AI fields.

• Machine Learning
• Deep Learning
• Signal and Image Processing
• +5 more

BOHI Amine is a Lecturer-Researcher at CESI , affiliated with the Engineering and Numerical Tools research team. He holds a PhD in Computer Science from the University of Toulon (2013–2017), focusing on Fourier descriptors inspired by the human visual cortex for maritime surveillance. His academic background includes a Research Master in Computer Science (University of Fez, Morocco, 2009–2011) and a Bachelor's in Mathematics and Computer Science (University of Fez, 2006–2009). Research Interests: Machine Learning, Deep Learning, Signal and Image Processing, 3D Shape Analysis, and applications in Computer Vision and Digital Health. Current projects include developing emotional interaction solutions for neurodegenerative patients (collaboration with VyV3 Bourgogne) and multimodal emotion recognition systems. Education Activities: Teaches Computer Science and Engineering Sciences at various levels, including Integrated Preparatory Cycle, Engineering Cycle, and Specialized Bachelor's Degree. Specializes in Algorithms, Programming, Operations Research, and Web Development. Advising & Collaborations: Supervised over 10 research internships across institutions like the University of Limoges, Marrakech, and Aix-Marseille University. Notable projects include AI-driven facial emotion recognition, multimodal pain assessment, and EEG/ECG monitoring systems on Raspberry Pi. Collaborates with institutions such as the Timone Neuroscience Institute and Institut Fresnel. Publications: Authored 5 peer-reviewed journal articles and 9 conference papers (2017–2024). Key contributions include deep learning approaches for emotion recognition in elderly patients, medical imaging segmentation, and biomechanical modeling of cortical folding patterns.

Roderick MelnikView profile

Professor

• Mathematical Modelling
• Coupled Systems
• Multiscale Phenomena
• +4 more

Dr. Roderick Melnik is a Professor and Tier 1 Canada Research Chair in Mathematical Modelling at Wilfrid Laurier University, Canada. He previously held positions at the University of Southern Denmark. His research focuses on coupled systems, multiscale phenomena, and their applications in biomedicine, nanotechnology, and environmental sustainability. He develops advanced mathematical models and computational tools to address interdisciplinary challenges, particularly in neurodegenerative diseases, nonlocal dynamics, and stochastic processes. Melnik leads the M3AI® Lab, collaborating globally on projects ranging from brain network models to energy-saving technologies. His work emphasizes the development of constructive mathematical procedures for solving complex coupled processes, including partial differential equations and stochastic systems. Research Interests : His expertise spans coupled multiscale phenomena, nonlocal and far-from-equilibrium dynamics, stochastic networks, and environmentally friendly technologies. He applies these methods to neurodegenerative diseases (e.g., Alzheimer’s and Parkinson’s), nanoscale systems, and sustainable energy solutions. Recent trends in his articles highlight advancements in Bayesian physics-informed neural networks, astrocyte roles in disease progression, and data-driven brain network analysis. Awards : Tier 1 Canada Research Chair (2011–present) Advising & Grants : He advises students like PhD candidate Hina Shaheen and collaborates with researchers globally. His grants support projects such as predictive multiscale materials design and interdisciplinary bioscience alliances. Labs/Teams : Head of the M3AI® Lab, focusing on coupled systems and complex networks. The lab hosts events like the XI International Conference on Coupled Problems in Science and Engineering.

• Computational Neuroscience
• Theoretical Neuroscience
• Multisensory Integration
• +9 more

Wenhao Zhang is an Assistant Professor in the Lyda Hill Department of Bioinformatics at UT Southwestern Medical Center, with a secondary appointment in the Peter O’Donnell Jr. Brain Institute. He holds the Lupe Murchison Foundation Scholar in Medical Research award. His research bridges computational neuroscience and biomedicine, focusing on neural circuits' role in information processing and developing brain-inspired algorithms. He collaborates with experimental neuroscientists, psychologists, and computer scientists to translate theoretical insights into practical applications. Education: B.E. in Biomedical Engineering from Shanghai Jiao Tong University (2009), Ph.D. in Theoretical Neuroscience from the Institute of Neuroscience, Chinese Academy of Sciences (2016). Postdoctoral training includes the University of Chicago (2020–2021), University of Pittsburgh (2018–2020), Carnegie Mellon University (2016–2017), and Hong Kong University of Science and Technology (2015–2016). Research interests center on biologically plausible normative theories for information processing in neural circuits and artificial systems. Key areas include Bayesian inference, multisensory integration, neural circuit dynamics, and the development of brain-inspired machine learning algorithms. His work employs methods like nonlinear dynamics, Bayesian inference, and information theory to study how perception and decision-making emerge from neural circuits. Recent publications emphasize theoretical frameworks for neural computation, such as motion planning circuits using Lie group theory, inhibitory interneurons' role in Bayesian sampling efficiency, and grid cells' spatial representation mechanisms. His lab also explores place cell dynamics and recurrent network models for equivariant representations. Scientific Award: Lupe Murchison Foundation Scholar in Medical Research Zhang advises PhD students including Armand Rathgeb, Zimei Chen, Yi Ren, and Eryn Sale, as well as visiting scholars like Junfeng Zuo and Xinruo Yang. His lab collaborates with institutions such as UCLA, Peking University, and the University of Rochester. Funding includes travel grants for students, e.g., Eryn Sale’s 2024 Cosyne award. He directs the Computational Neuroscience Lab (CNL) at UT Southwestern, which combines normative theories with biologically detailed models to study neural circuit principles underlying cognition. The lab actively pursues interdisciplinary research, using techniques from mathematics and computer science to inform experimental neuroscience and vice versa.