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Martin SchrimpfView profile

Assistant Professor

• neuroAI
• computational neuroscience
• computer vision
• +5 more

Professor Martin Schrimpf is a Tenure Track Assistant Professor at EPFL, holding dual appointments in the School of Life Sciences (SV) and the School of Computer and Communication Sciences (IC). His research bridges computational neuroscience, deep learning, and cognitive science to model human natural intelligence in vision and language. He leads the NeuroAI Lab, focusing on aligning artificial neural networks with brain mechanisms and human behavior. Education: PhD in Brain and Cognitive Sciences from MIT (2017–2022), MSc in Software Engineering from TUM/LMU/UNA (2014–2017), and BSc in Information Systems from TUM (2011–2014). His work has been recognized with awards including the Neuro-Irv Open Science Prize, McGovern Fellowship, and Takeda AI+Health Fellowship. He co-founded Integreat, a social impact startup recognized with Google.org’s Impact Challenge and TUM’s Social Impact Award. Research interests include neuroAI, brain-like models, and clinical translation (e.g., visual prosthetics). He has published in top venues like Neuron, Nature Human Behavior, NeurIPS, and ICLR. Current projects involve developing topographic language models (TopoLM) and investigating causal language network interactions using LLMs. Teaching: Courses include Neuroscience Foundations for Engineers and Brain-like Computation and Intelligence . Supervised over 26 students, including PhD candidates Badr Alkhamissi, Ben Lönnqvist, and Yingtian Tang. Active in grants from SNSF, NeuroX, and EPFL’s AI Center. Labs/Teams: NeuroAI Lab at EPFL Neuro-X Institute. Future directions include advancing brain-inspired models for clinical applications and expanding interdisciplinary collaborations between neuroscience and AI.

Willemijn van DolenView profile

Professor

• Consumer Behavior
• Marketing Strategy
• Social Media Analysis
• +5 more

Prof. Willemijn van Dolen is a Professor in the Section of Marketing at the Faculty of Economics and Business, University of Amsterdam. Her research focuses on consumer behavior, marketing strategies in digital environments, and the psychological impacts of communication in service encounters. She has extensively studied topics such as visual influence in consumer decisions, corporate greenwashing detection, and the role of humor in service interactions. Her academic career includes notable contributions to understanding online consumer behavior through multimodal datasets and AI frameworks. She has published widely on social media analytics, CSR communication, and child helpline effectiveness, bridging psychological insights with practical marketing applications. Prof. van Dolen’s work emphasizes empirical investigations into customer engagement, ethical consumption, and the interplay between digital platforms and human decision-making. Her research demonstrates a consistent focus on real-world applications, from optimizing brand posts on Instagram to analyzing CEO communication during global crises. Her studies often employ interdisciplinary methods, combining marketing theory with data science and behavioral economics. Despite her prolific output (over 50+ publications), no specific scientific awards are highlighted in the provided texts.

Marcelo MattarView profile

Assistant Professor

• Computational Neuroscience
• Decision-Making
• Reinforcement Learning
• +3 more

Marcelo Mattar is an Assistant Professor of Psychology and Neural Science at New York University, leading the Mattar Lab. His research focuses on the neural computations underlying memory, decision-making, and reinforcement learning. He holds a Ph.D. in Psychology from the University of Pennsylvania and has held academic positions at NYU, UC San Diego, and postdoctoral roles at Princeton University and the University of Cambridge. His work bridges computational neuroscience and artificial intelligence, aiming to model how the brain uses internal models for planning and decision-making. Education: Ph.D. in Psychology (Computational and Cognitive Neuroscience), University of Pennsylvania, 2016 M.A. in Statistics, University of Pennsylvania, 2016 B.A. in Electronics Engineering, Instituto Tecnologico de Aeronautica, Brazil, 2010 Research Interests: The lab develops mathematical models of learning and decision-making, leveraging reinforcement learning, Bayesian statistics, and neural networks. Experiments involve human behavioral studies and neuroimaging, with collaborations in animal electrophysiology and computational psychiatry. Key Contributions: His work explores how episodic memory and hippocampal replay support flexible decision-making. Recent studies highlight parallels between human cognition and AI systems, such as language models' metacognitive abilities and brain-inspired algorithms. Awards: Newton International Fellowship, Royal Society (2018–2019) Lab Team: The lab includes postdocs, PhD students, and undergraduates from diverse fields like cognitive science, neuroscience, and computer science. Current members are listed on the lab's website. Lab Location: Meyer Hall, 6 Washington Place, New York, NY 10003.

Mariya TonevaView profile

Researcher

• Machine Learning
• Natural Language Processing
• Neuroscience
• +5 more

Mariya Toneva is a tenure-track faculty member at the Max Planck Institute for Software Systems , conducting groundbreaking research at the intersection of Machine Learning , Natural Language Processing , and Neuroscience . She leads the Bridging AI and Neuroscience (BrAIN) group , focusing on computational models that align AI systems with human brain processes. Her work aims to enhance both AI capabilities and neuroscience understanding through this cross-disciplinary approach. Actively recruiting postdocs, PhDs, and research interns in areas like code/text representation, brain-AI alignment, and neuroimaging data analysis Collaborator on NIH-funded projects using fMRI and neuropixel data Research Themes : Her group explores neural mechanisms of language processing, event segmentation in narratives, memory reactivation via music, and effective human-AI collaboration frameworks. Key methods include LLM analysis, cross-modal similarity metrics, and naturalistic task-based fMRI studies. Key Publications (2024-2025): Brain-tuned speech models (INTERSPEECH 2025) Cognitive event boundaries in LLMs (Behavioral Research Methods 2025) Music-induced memory reactivation (biorxiv 2024) LLM-brain alignment reasons (EMNLP 2024) Advising : Mentors PhD candidates Omer Moussa (speech processing), Camila Kolling (representational similarity), and Gabriele Merlin (LLM alignment). Collaborates with institutions like MIT, NYU, and ETH Zurich.

David KlindtView profile

Assistant Professor

• NeuroAI
• Computational Neuroscience
• Sensory Processing
• +3 more

David Klindt is Assistant Professor at Cold Spring Harbor Laboratory, leading research at the intersection of biological systems and artificial intelligence. His lab investigates how brains process sensory information and generalize knowledge across contexts, studying neural representations to inspire robust AI models. Research combines computational neuroscience and machine learning to develop algorithms mimicking biological learning efficiency. Current projects examine latent computing in biological neural networks through dynamical systems frameworks, sparse coding principles in neural representations, and geometric organization in visual processing. His group develops methods for mechanistic interpretability, self-supervised learning identifiability, and compute-efficient inference. Recent publications analyze toroidal representations in grid cells, retinal feature detection, and Cryo-EM structure disentanglement. Dr. Klindt's work has been recognized through publications in Nature Communications, eLife, and NeurIPS. Before joining CSHL, he was a Machine Learning Research Scientist at Meta Reality Labs and postdoctoral researcher at Stanford University and NTNU. He holds a Ph.D. in Computational Neuroscience and Machine Learning from the University of Tübingen.

Alexei KoulakovView profile

Professor

• Neuroscience
• Theoretical Neurobiology
• Applied Mathematics
• +5 more

Alexei Koulakov is a Professor at Cold Spring Harbor Laboratory (CSHL) and the Charles Robertson Professor of Neuroscience. His research focuses on applying mathematical and computational approaches to unravel the principles of brain organization, particularly in sensory systems like olfaction and vision. Koulakov's work explores how neural circuits form during development, the role of genetic and experiential factors, and the evolutionary basis of brain architecture. Education: PhD in Physics from the University of Minnesota (1998). Key Research Areas: Olfactory system development, neural network modeling, and AI inspired by biological computation. Koulakov's recent publications emphasize cross-disciplinary integration of neuroscience and AI, including NeuroAI initiatives and DeepNose models predicting olfactory percepts. His team investigates how innate abilities are encoded genomically and how experience shapes neural networks. Scientific contributions include studies on primacy coding in olfaction, stochastic learning mechanisms , and high-throughput neural mapping . Awards include the Charles Robertson Professorship , reflecting his leadership in theoretical neuroscience. Koulakov collaborates extensively, with notable work on genomic bottlenecks , odor mixture interactions , and neural integrator models . His lab at CSHL is at the forefront of NeuroAI research, leveraging brain circuit insights to advance artificial intelligence.

Angelina WangView profile

Assistant Professor

• Machine Learning Fairness
• Algorithmic Bias
• Responsible AI
• +3 more

Angelina Wang is an incoming Assistant Professor at Cornell Tech and the Department of Information Science at Cornell University, starting Fall 2025. Her research focuses on responsible AI, particularly machine learning fairness and algorithmic bias. She holds a Ph.D. in Computer Science from Princeton University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Current postdoctoral work at Stanford’s HAI and RegLab explores sociotechnical challenges in AI deployment. Her research addresses fairness evaluation in generative AI, societal impacts of AI systems, and ethical trade-offs in algorithm design. Notable awards include the NSF GRFP, Siebel Scholarship, and Microsoft AI & Society Fellowship. Her work bridges technical and social dimensions of AI, emphasizing human-centered evaluation and interdisciplinary collaboration. Recent publications span medical AI applications (e.g., Alzheimer’s subphenotypes, corticosteroid treatment efficacy) and foundational fairness research. She advocates for proactive ethical considerations in technical work, citing examples like surveillance risks in facial recognition and dataset biases in computer vision. Angelina advises prospective PhD students in Cornell’s Information Science program and collaborates on projects like SciDaSynth for scientific knowledge synthesis. Her advocacy includes challenging fairness impossibility theorems and promoting algorithmic pluralism in auditing practices.

• NeuroAI
• Computational Neuroscience
• Artificial Intelligence
• +4 more

Shahab Bakhtiari is an Adjunct Professor in the Department of Psychology at the University of Montreal's Faculty of Arts and Sciences. His research focuses on NeuroAI, exploring the intersection of neuroscience and artificial intelligence, particularly in visual perception and learning mechanisms in biological systems and artificial neural networks. He holds a PhD in Neuroscience from McGill University and conducted postdoctoral research at Mila, Quebec AI Institute. Education: Bachelor's and Master's in Electrical Engineering, University of Tehran PhD in Neuroscience, McGill University His research interests include computational neuroscience, machine learning, visual system modeling, and energy-efficient predictive coding. He teaches courses on AI, cognitive neuroscience, and deep learning applications in psychology. Key grants include a CRSNG grant (2023–2029) for comparative visual system studies and the UNIQUE strategic initiative (2022–2029), co-led by 50+ researchers. He has supervised one Master's student, Hamza Abdelhedi, on AI-human face recognition comparisons. His work bridges AI and biological systems, leveraging neuroimaging and deep learning to model brain dynamics and improve AI's biological plausibility.

Sara SollaView profile

Professor

• Statistical Mechanics
• Neural Networks
• Complex Systems
• +3 more

Sara Solla is a Professor in the Department of Physics and Astronomy at Northwestern University, with a joint appointment in Physiology. She holds a PhD from the University of Washington (1982). Her research focuses on applying statistical mechanics to complex systems, particularly neural networks, exploring topics like associative memory, supervised learning, and neural dynamics. Solla's work bridges theoretical physics and neuroscience, with contributions to computational models of neural computation and brain-machine interfaces. Research Interests : Solla investigates neural networks through the lens of statistical mechanics, examining how systems like spin-glass models can describe associative memory and generalization in adaptive systems. She has contributed to understanding neural network dynamics, spiking neuron behavior, and the role of heterogeneity in network computations. Her recent work explores low-dimensional neural manifolds for motor control, brain-computer interface development, and neuro-inspired artificial intelligence. Key Contributions : Her research spans theoretical frameworks for neural learning algorithms, applications in motor control decoding, and interdisciplinary approaches combining neuroscience with robotics. She has advanced methods for analyzing multi-electrode neural recordings and contributed to understanding tactile perception and somatosensory processing. Labs & Affiliations : Solla is affiliated with Northwestern's interdisciplinary centers, including the Center for Interdisciplinary Research and Exploration in Astrophysics (CIERA), the Center for Network Dynamics (CND), and the Center for Applied Physics and Superconducting Technologies (CAPST). These collaborations highlight her role in fostering cross-disciplinary research in physics, neuroscience, and engineering.

Arturo DezaView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +5 more

Arturo Deza is an Assistant Professor of Computer Science at Universidad de Ingeniería y Tecnología (UTEC) in Lima, Peru, and CEO of Artificio, a research-driven company advancing autonomous driving technology. His academic work bridges neuroscience, machine learning, and computer vision, focusing on adversarial robustness, foveated systems, and human-machine perception. He holds a PhD in Dynamical Neuroscience from UCSB and a B.S. in Mechatronics Engineering from Universidad Nacional de Ingenieria. His research has been recognized with awards like the EB-1A Green Card for Extraordinary Ability and the Harvard Brain Initiative Travel Award. Deza's interdisciplinary approach includes contributions to image virality analysis, peripheral representation modeling, and bio-inspired AI. His recent talks and publications address topics ranging from autonomous driving challenges to neuro-symbolic AI integration. Education : PhD in Dynamical Neuroscience, UCSB (Vision and Image Understanding Lab) B.S. in Mechatronics Engineering, Universidad Nacional de Ingenieria Research Interests : Adversarial Robustness in Deep Learning Foveated Vision Systems Human-Machine Perception Autonomous Driving Technology Bio-inspired AI Models Awards : EB-1A Green Card for Extraordinary Ability in the Sciences Harvard Brain Initiative Young Scientist Travel Award NVIDIA Best Poster Award (2015) Professional Roles : Assistant Professor, UTEC (since Aug 2023) CEO & Co-Founder, Artificio (Lima-based autonomous driving tech) His recent work emphasizes practical AI applications, including the Robusto-1 dataset for autonomous driving evaluation and studies on adversarial robustness in perception systems. Deza also actively contributes to academic reviewing (ICLR, NeurIPS, CVPR) and organizes workshops like the Shared Visual Representations in Human and Machine Intelligence (SVRHM) at NeurIPS.

Cristina SavinView profile

Associate Professor

• Probabilistic modeling
• Artificial neural networks
• Time series models
• +3 more

Cristina Savin is the Director of Graduate Studies for the PhD Program/Admissions and an Associate Professor of Neural Science and Data Science. Her research explores the intersection of computational neuroscience and machine learning, focusing on adaptive computation mechanisms in biological systems. Roles: Dir. Graduate Studies for PhD Program/Admissions Departments: Neural Science and Data Science Research Interests: Probabilistic modeling, Artificial neural networks, Time series models, Representation learning, Metalearning, and Applications in Neuroscience. Her work investigates context-dependent information processing, neural substrates of probabilistic computation, and neuroAI integration. Awards: Google Faculty Research Award (2020)

Mariya TonevaView profile

Professor

• Computational Neuroscience
• Natural Language Processing
• Machine Learning
• +2 more

Mariya Toneva is a Tenure-track Professor (W2) at the Max Planck Institute for Software Systems in Saarbrücken, Germany, where she leads the Bridging AI and Neuroscience (BrAIN) group. Her research integrates machine learning, natural language processing, and neuroscience to develop computational models of language processing in the brain and improve NLP systems through brain alignment. Her research vision focuses on bidirectional benefits: 1) Using brain data to develop better language models that align with human cognition, and 2) Using insights from language models to understand human language processing. Current projects investigate how episodic memory can enhance long-term LLM agents and how speech models can better reflect neural processing hierarchies. Toneva's recent publications demonstrate significant contributions in brain-LLM alignment, with work appearing at ICLR, CogSci, TMLR, EMNLP, and INTERSPEECH. Her research group systematically examines why language models align with brain recordings and develops methods to improve this alignment through brain-tuning techniques. She actively seeks PhD students and postdoctoral researchers for projects involving neuropixel data analysis of speech processing and multimodal transformer architectures. Toneva regularly presents keynotes at major conferences (e.g., ELLISxUniReps Speaker Series) and participates in workshops on NeuroAI and cognitive modeling. Her interdisciplinary approach combines techniques from machine learning, natural language processing, and cognitive neuroscience to address fundamental questions about language representation in both artificial and biological neural systems.

Binxu WangView profile

Research Fellow

• Generative Models
• Neuroscience
• Machine Learning
• +3 more

Binxu Wang is an Inaugural NeuroAI Research Fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, starting in Fall 2023. He holds a Ph.D. in Neuroscience from Harvard Medical School, advised by Dr. Carlos Ponce, after completing his doctoral studies at Washington University in St. Louis. His research focuses on using optimization, generative models, and geometry to bridge visual information processing in the primate brain and deep neural networks. Key interests include analyzing diffusion models' spectral bias, the geometry of image manifolds, and neural dynamics in ventral stream alignment. He has received the prestigious NeuroAI Research Fellowship at the Kempner Institute. His work spans theoretical insights into neural representations, practical applications of generative models, and interdisciplinary approaches linking neuroscience and AI. Notable contributions include uncovering hidden linear structures in score-based models and exploring the geometry of natural image manifolds. Wang has presented his research at venues like the Cognitive & Neural Computation Lab (Yale), NAISYS2024, and the KITP DL2023 Program. His research emphasizes a closed-loop approach, integrating computational theory with experimental neuroscience to advance understanding of brain function.

Alpha RennerView profile

Researcher

• Neuromorphic Computing
• Computational Neuroscience
• Artificial Intelligence
• +3 more

Dr. Alpha Renner is a researcher at Forschungszentrum Jülich, specifically in the Neuromorphic Software Ecosystems group (PGI-15) within the Peter Grünberg Institute. He leads the Cognitive Localization and Mapping (CLAM) project, which is funded by the Volkswagenstiftung and is part of the Young Excellent Scientist Program (YESP). Dr. Renner's research sits at the intersection of neuromorphic computing, computational neuroscience, and artificial intelligence. His work investigates navigation and memory formation systems inspired by the entorhinal-hippocampal formation in the brain. The CLAM project aims to develop neuromorphic systems that leverage hyperdimensional computing to create cognitive maps, offering better generalization than current AI approaches that require massive training data. This brain-inspired approach enables more efficient continual learning and transfer learning capabilities. His research has significant implications for creating sustainable AI technologies that don't rely solely on ever-increasing computational resources. By grounding AI architectures in biologically inspired principles (NeuroAI), the CLAM project provides both a practical testbed for neuroscience theories and a path toward more efficient AI systems that leverage neuromorphic hardware features like massive parallelism, in-memory compute, and event-based processing. Volkswagenstiftung Grant for CLAM Project Dr. Renner mentors PhD student Sven Krauße and Master's student Erdi Kararmaz in his research group. He actively collaborates with external partners including Professor Melika Payvand from the University of Zurich and PostDoc Johannes Leugering from UCSD. He is scheduled to present his research at Science Online+ on January 22, 2026, and welcomes students interested in internships or final projects (minimum 3 months) in neuromorphic navigation research.

Naima Elosegui BorrasView profile

Researcher

• Information Geometry
• Statistical Physics
• NeuroAI
• +1 more

Naima Elosegui Borras is a Researcher at the Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). Her interdisciplinary work bridges machine learning foundations and neuroscience, focusing on theoretical frameworks for neural computation. Her educational background includes: MSc in Neural Systems and Computation (2023) from ETH Zürich and UZH Zürich (joint program) BSc in Neuroscience (Honours) (2020) from The University of Edinburgh Her core research integrates Information Geometry and Statistical Physics to analyze learning dynamics in neural systems, with specific investigations into criticality in reservoir computing, hippocampal plasticity modeling, and neuromorphic vestibular system implementations. This NeuroAI-focused work employs probabilistic machine learning to uncover universal principles across biological and artificial networks. Through BIFOLD, she contributes to foundational AI research while maintaining active open-source development on GitHub. Her 16 public repositories demonstrate technical proficiency in Jupyter Notebooks, Component Pascal, and neural network simulations, with recent contributions reflecting ongoing doctoral research activities. No scientific awards or grants are documented in available sources. As a doctoral researcher, she has no formal advisees but collaborates through GitHub projects visible to the research community.