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Ruben PortuguesView profile

Professor

• Neuroscience
• Systems Neuroscience
• Behavioral Neuroscience
• +7 more

Ruben Portugues is a Professor of Brain Circuit Function and Dysfunction at the Institute of Neuroscience, Technical University of Munich (TUM). He is a full member of the Graduate School of Systemic Neurosciences (GSN), an associate and advisory board member of the Munich Center for Neurosciences (MCN), and leads a research group focused on understanding the neural basis of behavior. His lab uses larval zebrafish as a model organism to investigate sensorimotor control, decision-making, and motor learning through whole-brain imaging and circuit analysis. His research interests lie at the intersection of systems neuroscience and behavior. He investigates how brain circuits process sensory information, integrate it with motor output, and enable adaptive and flexible behavior. Key areas include the function of the cerebellum, heading direction networks, sensorimotor transformations, and the neural mechanisms of decision-making. His lab employs cutting-edge techniques including custom-built microscopes, behavioral assays, and computational analysis. The recent publications and preprints from his lab demonstrate a strong trend in decoding distributed neural circuits underlying navigation and decision-making in zebrafish. There is a clear focus on identifying specific brain regions (e.g., interpeduncular nucleus, cerebellum) and cell types involved in processing visual, motor, and spatial information. The work increasingly emphasizes whole-brain functional imaging and the emergence of cognitive-like representations such as allocentric heading direction. FENS-Kavli Network of Excellence (FKNE) PhD Thesis Prize (awarded to student Luigi Petrucco) Ruben Portugues actively mentors PhD students, including current advisees Luigi Petrucco, Ot Prat, and Shuhong Huang, and has successfully graduated Dr. Elena Dragomir and Dr. Vilim Štih. His lab engages in extensive collaborations, hosts visiting researchers, participates in teaching (e.g., CSHL Imaging Course, Cajal Course), and secures resources for advanced research. The lab is known for building its own microscopes and software, fostering technical innovation. The Portugues Lab operates as a dynamic, interdisciplinary team that combines experimental neuroscience with computational and engineering approaches. They regularly hold retreats, participate in scientific events, and contribute to community initiatives like the Munich Brain Day. The lab is preparing to relocate to the Department of Neurobiology and Behavior at Cornell University, marking a new phase in its research trajectory.

Rishidev ChaudhuriView profile

Associate Professor

• Computational Neuroscience
• Neural Dynamics
• Cognitive Neuroscience
• +3 more

Rishidev Chaudhuri is an Associate Professor at the University of California, Davis in the Department of Neurobiology, Physiology and Behavior within the College of Biological Sciences. His research focuses on computational neuroscience and neural dynamics, employing mathematical models to investigate how neural circuits generate cognitive processes such as memory, perception, and decision-making. His work explores neural dynamics through models of memory systems, attentional mechanisms, and probabilistic inference. Recent publications highlight advances in understanding hippocampal memory scaffolds, parietal-frontal interactions, and neuromorphic computing inspired by brain architecture. Education: BA in Physics (Amherst College), PhD in Applied Mathematics (Yale University) Centers: Center for Neuroscience; affiliated with Applied Mathematics and Neuroscience Graduate Groups Scientific awards and honors are not explicitly mentioned in the provided materials.

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.

• Computational Linguistics
• Natural Language Processing
• Multilingual Models
• +7 more

Malvina Nissim is a leading researcher in computational linguistics and NLP at the University of Groningen's Department of Artificial Intelligence, with a focus on multilingual modeling, bias mitigation, and human evaluation frameworks. Key Contributions : Developed CALAMITA (Italian LLM benchmark), IT5 models for Italian language processing, and ReproHum framework for NLP evaluation reproducibility Research Pillars : Multilingual reasoning consistency, perspective-based text analysis, and figurative language modeling Her work spans activation steering techniques, cross-lingual transfer learning, and the creation of specialized language resources like the EurekaRebus dataset and MAGPIE idiom corpus. She pioneered methods for gender bias measurement in BERT and developed the SocioFillmore tool for perspective visualization. Recent publications explore model uncertainty as MCQ difficulty proxy, Italian headline generation benchmarks, and multilingual multi-figurative language detection. She actively participates in teaching initiatives like the "NLP with Bracelets" workshop for Italian high school students. Scientific Awards : ACL Best Paper Award (2025) EMNLP Outstanding Reviewer (2023) EVALITA Leadership Recognition (2024) She advises PhD students in model bias analysis and has contributed to the development of the Dutch Abusive Language Corpus (DALC) and the ReproNLP reproducibility framework. Her collaborations span institutions in Italy, Netherlands, and international NLP communities.

Yu MengView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Data Mining
• +3 more

Yu Meng is an Assistant Professor in the Department of Computer Science at the University of Virginia (UVA), part of the School of Engineering and Applied Science. He joined UVA in 2024 as a tenure-track faculty member. His research focuses on machine learning, natural language processing (NLP), and data mining, with recent emphasis on large language models (LLMs), alignment, reliability, and ethical AI development. Educated at the University of Illinois Urbana-Champaign (UIUC), Meng earned his Ph.D. in 2023 under advisor Jiawei Han. His doctoral thesis, Efficient and Effective Learning of Text Representations , received the ACM SIGKDD 2024 Dissertation Award. He also held a visiting researcher position at Princeton University under Danqi Chen and was a Google PhD Fellow. His work has been recognized with awards including the Superalignment Fast Grant from OpenAI and notable publications at venues like NeurIPS, ICLR, and ACL. Meng’s research explores topics such as preference optimization (SimPO), retrieval-augmented generation (InstructRAG), and zero-shot learning. He actively serves on program committees for top conferences (ICLR, ICML, NeurIPS) and as an action editor for Transactions of Machine Learning Research (TMLR) . He teaches graduate-level courses on NLP, emphasizing cutting-edge LLM topics like architecture design, instruction tuning, and ethical considerations. Key achievements include contributions to LLM alignment via retrieval optimization, efficient pretraining techniques, and foundational work on weakly supervised learning. His research bridges theory and practice, addressing both technical challenges and societal impacts of AI systems.

Steve LuckView profile

Professor

• Attention
• Working Memory
• Cognitive Neuroscience
• +3 more

Steve Luck is a Distinguished Professor at the University of California, Davis, holding appointments in the Department of Psychology and the Center for Mind and Brain (CMB). He served as CMB Director from 2009–2019 and is affiliated with the UC Davis MIND Institute and the Center for Neuroscience. His research focuses on attention, working memory, and cognitive dysfunction in psychiatric disorders (e.g., schizophrenia), employing ERP recordings, eye tracking, and behavioral methods. He is a leading developer of ERP methodologies, including the ERPLAB Toolbox and global ERP Boot Camp workshops. Education: Ph.D., Neurosciences, UC San Diego, 1993 M.S., Neurosciences, UC San Diego, 1989 B.A., Psychology, Reed College, 1986 Research Interests: Dr. Luck explores mechanisms of cognitive control, with a focus on working memory's role in guiding attention. His lab investigates ERP correlates of attentional deficits in schizophrenia and develops standardized ERP protocols. Recent work emphasizes multivariate decoding of EEG signals and transdiagnostic neurocognitive biomarkers. Awards: Troland Award (2001) APA Distinguished Scientific Award (1998) McGuigan Young Investigator Prize (2004) Elected Fellow, Society of Experimental Psychologists and AAAS Teaching & Leadership: Professor Luck pioneered hybrid course formats in Cognitive Science and teaches advanced topics in perception and cognitive neuroscience. He co-founded the UC Davis Cognitive Science major and advocates for innovative undergraduate education models. Labs & Collaborations: The Luck Lab integrates clinical and basic research, collaborating globally on ERP method development and schizophrenia biomarker studies. Key projects include ERP Core resources and the CNTRACS consortium for neurocognitive reliability studies.

Jason OstanekView profile

Assistant Professor

• Energy Storage
• Turbulence
• Heat Transfer
• +5 more

Jason Ostanek is an Assistant Professor at Purdue University's School of Engineering Technology and Environmental and Ecological Engineering. He directs the Applied Thermofluids Laboratory and Powertrain Technology Laboratory, focusing on battery safety and thermal management systems. Ph.D. in Mechanical Engineering from Penn State M.S. in Mechanical Engineering from Penn State B.S. in Mechanical Engineering from Virginia Tech His research explores energy storage systems, thermal runaway phenomena, heat transfer mechanisms in Li-ion batteries, fluid dynamics, and internal combustion engine thermal management. He has developed analytical models for battery degradation, thermal abuse simulations, and innovative cooling strategies for large-scale energy systems. Key publication trends show expertise in: Li-ion battery thermal runaway modeling Heat transfer in confined geometries Thermal management for energy storage systems Renewable energy forecasting Computational fluid dynamics applications Scientific awards include: 2020 Purdue Teaching Academy's Award for Exceptional Teaching and Instructional Support during the COVID-19 Pandemic 2020 SOET Outstanding Faculty in Engagement 2019 SOET Outstanding Faculty in Discovery 2015 NAVSEA Commander’s Award for Innovation 2013 ASME IGTI Young Engineer Travel Award 2007 DOD SMART Fellowship Recipient As director of Purdue's Applied Thermofluids Laboratory, he leads research on battery safety mechanisms, combustion dynamics, and thermal systems optimization. His work spans fundamental and applied research with industrial collaborators.

Charles RizzoView profile

Assistant Professor

• Neuromorphic Computing
• Event-Based Vision Processing
• Spiking Neural Networks
• +4 more

Charles Rizzo is a Research Assistant Professor in the TENNLab neuromorphic computing group at the University of Tennessee, Knoxville, within the Department of Electrical Engineering and Computer Science. He earned his PhD in Computer Science (2024), MS (2021), and BS (2019) from the same institution. PhD in Computer Science, University of Tennessee, Knoxville (2024) MS in Computer Science, University of Tennessee, Knoxville (2021) BS in Computer Science, University of Tennessee, Knoxville (2019) His research focuses on neuromorphic computing, particularly for embedded applications involving event-based vision processing and machine learning with spiking neural networks. He has contributed to neuromorphic control systems, event camera data processing, and spiking network architectures. Recent publications emphasize neuromorphic hardware design (e.g., memristor-based synapses, RISP neuroprocessor), algorithm adaptation (DBSCAN clustering), and real-time applications in vision processing and control. Key subfields include event-based sensors, recurrent spiking networks, and low-power embedded systems. Charles is affiliated with the TENNLab neuromorphic computing group and supports course website development for EECS programs. His work bridges neuromorphic theory with practical implementations in embedded environments.

Pouya BashivanView profile

Assistant Professor

• Computational Neuroscience
• Machine Learning
• Artificial Neural Networks
• +6 more

Pouya Bashivan is an Assistant Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on developing computational models to explain and regulate neural responses during visual tasks requiring memory, combining machine learning, neuroscience, and cognitive science. Education : Ph.D. in Computer Engineering (2016), Postdocs in Machine Learning (2020) and Computational Neuroscience (2016-2020) His lab investigates: Topographical neural networks for visual cortex simulation Massively-multitask models for prefrontal cortex Saccade-driven visual exploration models Predictive hippocampus models for episodic memory Recent publications explore adversarial robustness, memory-augmented networks, and brain-state decoding. Current projects emphasize causal models, brain-AI alignment, and translating computational neuroscience into therapeutic applications. The lab is located in the McIntyre Medical Sciences Building, Room 1117, Montreal, Quebec.

• Cellular Neuroscience
• Systems Neuroscience
• Behavioral Neuroscience
• +3 more

Prof. Laura Busse is a Professor at Ludwig Maximilian University of Munich (LMU), leading the Research Group in the Department of Biology II, Division Neurobiology. She holds roles as a Regular Member of MCN, Full Member of GSN, and Deputy Head of the GSN Examination Board. Her research focuses on cellular and systems neuroscience, particularly investigating how contextual information influences visual perception through neural circuits in mice. Key areas include feedback mechanisms, behavioral state effects, and thalamocortical interactions. Her work employs advanced techniques like high-density extracellular recordings and optogenetics to study active behavior in rodents. Current students include Simon Renner, Gregory Born, and others. Recent research highlights include studies on corticothalamic feedback effects, thalamic spatial integration, and the role of pupil dynamics in neural activity. She leads the Vision Circuits Lab (https://visioncircuitslab.org), exploring how sensory inputs and brain states shape visual processing. Her articles reveal trends in understanding thalamocortical communication, adaptive sensory systems, and the biological basis of neural network models. She coordinates the SPP2411 project on cortico-subcortical loops, emphasizing interdisciplinary neuroscience.

• Cognitive Neuroscience
• Auditory Neuroscience
• Music Cognition
• +3 more

Robert J Zatorre is a cognitive neuroscientist at the Montreal Neurological Institute (MNI) of McGill University . He earned his PhD in experimental psychology from Brown University in 1981 under Peter Eimas and completed postdoctoral research at MNI with Brenda Milner. His work focuses on the neural mechanisms underlying auditory perception, music cognition, and brain plasticity, utilizing techniques such as fMRI and non-invasive brain stimulation. Key Research Areas : Neural substrates of auditory processing, dopamine’s role in musical reward, congenital amusia, and sensory-motor learning. Recent publications highlight his expertise in decoding brain activity related to developmental disorders, auditory perception, and the neural basis of musical reward. He actively contributes to journals like Frontiers in Neuroscience and Proceedings of the National Academy of Sciences as an editor and reviewer. Editorial Roles : Chief Editor for Auditory Cognitive Neuroscience, Frontiers in Neuroscience Guest Associate Editor for Frontiers in Systems Neuroscience and Frontiers in Human Neuroscience

Yuntian DengView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +3 more

Yuntian Deng is an Assistant Professor at the University of Waterloo and a Visiting Professor at NVIDIA. He holds affiliations with Harvard SEAS as an Associate and the Vector Institute as a Faculty Affiliate. He completed his PhD in Computer Science at Harvard under Professors Alexander Rush and Stuart Shieber, followed by a postdoc under Yejin Choi. His research focuses on Natural Language Processing and Machine Learning, with notable contributions in chatbot interaction analysis (WildChat), implicit reasoning models, and markup-to-image generation. He has developed influential tools like OpenNMT and WildVis, and his work has been featured in outlets like the Washington Post and used by OpenAI and Anthropic. Education: PhD in CS (Harvard), Postdoctoral Research (University of Washington). Key achievements include the ACM Gordon Bell Prize for GenSLMs, Best Demo Runner-up at ACL 2017, and Best Paper at DAC 2020. His research emphasizes scalable datasets, efficient reasoning techniques, and real-world applications of AI models. Research interests span NLP, machine learning algorithms, and their applications in areas like dialogue systems, generative models, and ethical AI evaluation. Notable projects include WildChat (1M ChatGPT interactions), implicit chain-of-thought reasoning, and neural steganography for text-based information hiding. His articles explore topics ranging from knowledge distillation to diffusion models, with a focus on bridging theoretical advancements and practical implementations. He actively collaborates with industry partners like NVIDIA and maintains open-source tools to advance AI research accessibility.

• Neuroscience
• Metabolism
• Behavioral Neuroscience
• +3 more

Dr. Paulius Viskaitis is a Researcher affiliated with the Rehabilitation Engineering Lab at ETH Zürich's Department of Health Sciences and Technology. His work focuses on the neurophysiological mechanisms underlying metabolic regulation, motor behavior, and neurological disorders. Key research areas include orexin neuron dynamics, hypothalamic circuitry, and their roles in movement, energy homeostasis, and epilepsy mitigation. His studies integrate experimental neuroscience with engineering approaches, exploring how neural networks encode metabolic states and behavioral outputs. Notable contributions include identifying orexin neurons' roles in blood glucose tracking and seizure suppression. Current research emphasizes translational applications in rehabilitation engineering and neurotherapeutic strategies. Dr. Viskaitis collaborates across disciplines, leveraging mouse models to investigate neural circuits governing feeding, locomotion, and decision-making. His findings bridge basic neuroscience with clinical applications, particularly in metabolic and motor disorders.

• Topology of Complex Systems
• Higher-order Networks
• Topological Neuroscience
• +7 more

Giovanni Petri is a Professor in the Network Science Institute at Northeastern University London, where he joined in June 2023. Previously, he held positions at CENTAI as a Principal Researcher and at IMT Lucca as a Guest Scholar, with earlier affiliations at ISI Foundation and Imperial College London. His educational background includes a PhD in Complex Networks from Imperial College London (2012), an MSc in Theoretical Physics from the University of Pisa (2008), and a BSc in Physics from the University of Pisa (2005). Petri's research spans the analysis of neuroimaging data and AI systems with topological techniques, the formalization of cognitive control models with tools of statistical mechanics and network theory, and the study of the predictability of socio-technical systems. His work in Topological Neuroscience explores brain architecture using algebraic topology, while his research in Cognitive Neuroscience focuses on neural mechanisms underlying human cognition. He is particularly known for his work on higher-order networks, using mathematical frameworks like hypergraphs and simplicial complexes to model systems with multi-way interactions. His recent publications (2023-2025) demonstrate a strong focus on higher-order network theory applied to neuroscience, with particular emphasis on topological approaches to brain connectivity, social contagion models, and the physics of complex systems. These works reveal consistent themes in understanding how multi-body interactions shape system dynamics across biological, social, and technological domains. European Research Council Consolidator Grant (RUNES: Reconstruction and unification of neural and ecological systems, 2024) As Principal Investigator of the NPLab, Petri advises numerous PhD and postdoctoral researchers including Marilyn Gatica, Andrea Santoro, and Simone Poetto. His RUNES project, funded by the ERC Consolidator Grant, represents a significant research initiative. The lab maintains active collaborations with CENTAI, Project CETI (Cetacean Translation Initiative), and various international institutions. The NPLab investigates the role of topology and geometry in the collective dynamics of complex systems, ranging from neuroscience to society, using statistical mechanics, algebraic topology, and innovative computational approaches. Current projects include Topological Neuroscience, Cognitive Neuroscience, Higher-order Networks, Project CETI, and RUNES.

Lee M. MillerView profile

Professor

• Neuroengineering
• Computational Neuroscience
• Auditory and Speech Processing
• +4 more

Lee M. Miller is a Professor and Vice Chair of Academic Affairs in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis, affiliated with the Center for Mind and Brain. His research focuses on neuroengineering, computational neuroscience, and neural mechanisms underlying attention, speech processing, and multisensory integration. Research interests include the development of neural prosthetics, decoding of neuromuscular signals for prosthetic control, and understanding how auditory and visual systems interact during speech perception and attentional processes. His work bridges clinical applications (e.g., cochlear implants) with fundamental neuroscience, leveraging tools like electrophysiological recordings, EEG/MEG, and advanced signal processing techniques. Recent publications highlight innovations in electromyographic speech neuroprosthetics, the topology of neuromuscular signals, and the neural basis of speech-in-noise processing. Miller’s studies emphasize translational potential, such as improving speech synthesis from brain signals and designing haptic feedback systems for motor coordination. His contributions have advanced understanding of neural mechanisms in sensory integration, auditory attention, and the impact of cognitive factors on perception. Miller maintains a lab dedicated to these interdisciplinary efforts, with a focus on both basic science and clinical applications.