Find Academic Supervisors

Risto MiikkulainenView profile

Professor

• Neuroevolution
• Evolutionary Computation
• Deep Learning
• +7 more

Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.

H. Jerry QiView profile

Professor

• Soft Active Materials
• 4D Printing
• 3D Printing Technologies
• +5 more

H. Jerry Qi is a Professor in the Department of Mechanical Engineering at the Georgia Institute of Technology. He specializes in finite deformation multiphysics modeling of soft active materials, with a focus on shape memory polymers, 4D printing, and material recycling. His research integrates experimental and computational approaches to advance additive manufacturing technologies. Education: Sc.D., Massachusetts Institute of Technology, 2003 Ph.D., Tsinghua University, China, 1999 B.S., Tsinghua University, China, 1994 Research Interests: Dr. Qi's work spans 4D printing of active materials, mechanics in 3D printing, and sustainable polymer processing. His group develops hybrid printing methods and recyclable thermosetting polymers, collaborating with institutions like SUTD and AFRL. Key areas include smart material design, photomechanical experiments, and finite element modeling. Scientific Awards: ASME Fellow (2015) Woodruff Faculty Fellow (2015) J. T. Oden Faculty Fellowship (2012) NSF Career Award (2007) Advising & Grants: Dr. Qi actively seeks undergraduate, PhD, and postdoc researchers. His projects are funded by NSF, AFOSR, and industry partnerships. He leads a research group focused on advancing active materials and sustainable manufacturing. Labs & Teams: His lab integrates computational modeling, experimental mechanics, and additive manufacturing to create innovative materials and structures for applications in aerospace, biomedical, and environmental engineering.

Brad KnoxView profile

Associate Professor

• Reinforcement Learning
• Human-Computer Interaction
• Artificial Intelligence
• +4 more

Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).

Athina E MarkakiView profile

Professor

• Tissue Engineering
• Biomaterials
• Regenerative Medicine
• +3 more

Professor Athina E Markaki serves as Professor of Materials & Biomedical Engineering in the Department of Engineering at the University of Cambridge, leading research in advanced biomaterials and tissue engineering solutions for regenerative medicine with emphasis on vascularization and tubular scaffold development for human conduit replacement. Her academic credentials include a Diploma in Metallurgical Engineering (8.6/10) from the National Technical University of Athens and a PhD in Materials Science from the University of Cambridge. Markaki's research program centers on vascularisation techniques for clinically relevant tissue dimensions and tubular scaffolds to replace diseased or damaged human conduits, integrating biomaterials science with regenerative medicine principles. Key applications span liver tissue engineering, neural crest-derived stem cell differentiation, and vascular graft development, with strong translational focus on orthopaedic and cardiovascular medical devices. Analysis of her recent publications reveals dominant trends in biomimetic scaffold design, particularly collagen-based tubular structures and hydrogel systems for vascularized tissue constructs. Her work demonstrates interdisciplinary convergence of AI-driven retinal assessment, glioblastoma modeling, and self-healing cementitious materials, with consistent emphasis on clinically applicable regenerative solutions for liver, bone, and neural tissues. Her distinguished scientific contributions are recognized by major awards: Rosetrees Trust 2017 Interdisciplinary Award European Research Council (ERC) Starting Grant (2010) Advanced EPSRC Fellowship (2005) De Montfort Award at SET for Britain National Event (2004) Young Scientist Prize 2003 (5th Euromech Solid Mechanics Conference) Multiple academic excellence awards from Greek foundations Markaki directs a well-funded research program including ERC and EPSRC grants, mentoring graduate students in tissue engineering while teaching core engineering curricula covering plastic deformation, fracture mechanics, and medical materials design. Her group maintains strong industry and clinical partnerships to advance regenerative technologies. Her laboratory, accessible via http://www-memti.eng.cam.ac.uk/, specializes in vascularized tissue constructs and tubular scaffolds using laser-based manufacturing, biomimetic design, and hydrogel engineering to address critical challenges in tissue replacement and disease modeling.

Emine AyazView profile

Professor

• Electrical Engineering
• Mechanical Fault Detection
• Signal Processing
• +12 more

Emine Ayaz is a Professor at Istanbul Technical University's Department of Electrical Engineering. Her research spans fault detection in electric motors, signal processing, and nuclear power plant monitoring, with recent work integrating deep learning (e.g., dual RNN architectures) and medical applications (e.g., parasitology, plant-based wound healing). Key Collaborations : International partnerships in motor diagnostics and nuclear engineering. Projects : Led grants on high-voltage training and predictive maintenance for TEİAŞ and industrial processes. Research Trends : Recent publications emphasize neural networks for motor fault classification, coherence analysis for insulation diagnostics, and interdisciplinary work in plant biotechnology and parasitology. Labs & Teams : Involved in projects analyzing vibration signals, wavelet transforms, and sensor fusion for industrial and nuclear systems.

James S. DuncanView profile

Professor

• Biomedical Image Processing
• Quantitative Image Analysis
• Cardiac Function Analysis
• +4 more

James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering at Yale University, with additional appointments in Electrical & Computer Engineering and Radiology & Biomedical Imaging. His research focuses on biomedical image processing, quantitative image analysis using geometrical models, and applications in cardiac function and neuro-structure analysis. He has pioneered image-guided interventions and developed computational frameworks for medical imaging challenges. He holds a Ph.D. from the University of Southern California. His work integrates AI, deep learning, and statistical decision-making to advance medical imaging technologies. Notable contributions include advancements in 3D image segmentation, deformable models, and MRI-based tumor response assessment. Dr. Duncan has received prestigious awards, including IEEE Fellow (2001) and induction into the American Institute for Medical and Biological Engineering (2000). His recent research spans AI-driven hemodynamics modeling, trustworthy healthcare AI guidelines, and molecular MRI innovations in immunotherapy monitoring. He collaborates across disciplines to address challenges in cardiovascular, neuroimaging, and oncological applications.

Andrea FaliniView profile

Professor

• Advanced MR Techniques
• Neuro-oncology
• Degenerative Diseases
• +3 more

Andrea Falini is a Full Professor of Neuroradiology at the Vita-Salute San Raffaele University (School of Medicine). He holds leadership roles including Head of the Neuroradiology Department at the Scientific Institute S. Raffaele Hospital and Head of the Advanced Diagnostic in Neuro-oncology Unit. His career spans clinical, academic, and administrative roles since 1991, with extensive experience in neuroradiology, neuro-oncology, and neuroimaging research. Education: MD (1986, University of Milan), PhD in Neurological Sciences (1989), and specialized training in Neurology and Radiology at leading institutions, including a visiting scholar position at UCSF (1996). Research interests focus on advanced MRI techniques, neuro-oncology, neurodegenerative diseases, and cognitive neuroscience. His work emphasizes functional MRI, diffusion tensor imaging, and clinical applications in glioma management and neurodegenerative disorders. Awarded the 1989 'De Visart' Prize and grants supporting his international collaborations. He has authored 218+ peer-reviewed articles, with an H-index of 40+, and actively contributes to academic committees and international conferences. Leadership roles include Vice-Director of the Neuroscience Division (2014–present) and coordination of the BraiMap Research Program. His academic teaching spans neuroradiology, biomedical engineering, and cognitive neuroscience courses.

Xin LuView profile

Associate Professor

• Cancer Biology
• Tumor Microenvironment
• Tumor Immunology and Immunotherapy
• +12 more

Xin Lu is the John M. and Mary Jo Boler Collegiate Associate Professor in the Department of Biological Sciences at the University of Notre Dame. She is a full member of the Harper Cancer Research Institute (HCRI), the Boler-Parseghian Center for Rare and Neglected Diseases (CRND), and the Tumor Microenvironment and Metastasis Program at the Indiana University Simon Comprehensive Cancer Center. Her research spans tumor immunology, immunotherapy, metastasis, and multi-omics, with a focus on prostate, breast, and rare cancers. Ph.D. in Molecular Biology, Princeton University (2004–2010) B.S. in Biological Sciences, Tsinghua University, China (2000–2004) Postdoctoral Fellow, Dana-Farber Cancer Institute and M.D. Anderson Cancer Center (2010–2016) Assistant to Associate Professor, University of Notre Dame (2017–Present) Dr. Lu's research investigates the molecular and cellular mechanisms of tumor-immune crosstalk, particularly the role of myeloid-derived suppressor cells (MDSCs) and neutrophils in promoting immunotherapy resistance. Her lab uses genetically engineered mouse models, functional genomics, single-cell and spatial transcriptomics, and high-throughput screening to uncover novel therapeutic targets. A major focus is on how cancer-cell-intrinsic oncogenic signaling shapes the immunosuppressive tumor microenvironment. Her recent publications reveal mechanisms such as Acod1-mediated ferroptosis resistance in neutrophils, Pygo2-driven immunosuppression in prostate cancer, and the efficacy of ketogenic diets in overcoming checkpoint blockade resistance. These studies span multiple disciplines including cancer biology, immunology, metabolism, epigenetics, and bioengineering, reflecting a highly integrative approach to immuno-oncology. Jane Coffin Childs Postdoctoral Fellow John M. and Mary Jo Boler Faculty Appointment Cluster Chair, Cellular & Molecular Biology, IBMS PhD Program Junior Chair, Boler-Parseghian Center for Rare and Neglected Diseases Dr. Lu mentors a diverse team of graduate students, postdoctoral fellows, and undergraduates. Her lab is actively recruiting and has secured funding from federal agencies and private foundations. She collaborates with chemists, bioengineers, and bioinformaticians to develop novel therapeutics, including antibody-drug conjugates, CAR-NK cells, and small-molecule inhibitors. Her lab also develops innovative platforms like mini-tumor chips for immunotherapy evaluation. Her lab maintains affiliations with multiple interdisciplinary centers including the Warren Family Center for Drug Discovery, Eck Institute for Global Health, and Berthiaume Institute for Precision Health, underscoring her collaborative and translational research vision.

Kevin BaumView profile

Researcher

• Explainable AI
• AI Ethics
• AI Fairness
• +4 more

Kevin Baum is a computer scientist currently serving as the deputy head of the Neuro-Mechanistic Modelling (NMM) department at the German Research Center for Artificial Intelligence (DFKI) since January 2023, and head of the Centre for European Research in Trusted AI (CERTAIN) at DFKI since December 2023. Based at the Saarland Informatics Campus in Saarbrücken, Germany, he completed his doctorate in philosophy in March 2024, combining technical expertise with philosophical depth. His work bridges computer science with ethics, focusing on making AI systems transparent and accountable to human users. Dr. Baum's research program centers on interdisciplinary questions concerning the explainability and transparency of AI systems. His work spans multiple significant projects including the Explainable Intelligent System (EIS) initiative and project E7 of the Transregional Collaborative Research Centre 248 "Foundations of Perspicuous Software Systems" (CPEC). He has developed frameworks for understanding stakeholder perspectives on explainable AI and investigated how different information types about automated systems affect user perceptions of fairness and justice. His approach consistently combines theoretical foundations with practical implementations across diverse contexts. Analysis of his publication trends reveals a clear trajectory from theoretical foundations in machine ethics toward practical implementations of explainability requirements in real-world contexts. His work demonstrates increasing focus on human oversight effectiveness, fairness monitoring, and ethical considerations across various AI applications. He has made significant contributions to both academic discourse and practical AI development guidelines, with publications spanning computer science, philosophy, psychology, and human-computer interaction venues. Award for Ethics for Nerds lecture series As a research leader, Dr. Baum contributes to shaping AI development practices through his departmental leadership and interdisciplinary collaborations. His current work with CERTAIN focuses on establishing European research standards for trusted AI development and deployment, emphasizing the practical implementation of ethical requirements in AI systems. He maintains active collaborations across multiple institutions and disciplines, reflecting his commitment to bridging technical and philosophical considerations in AI development. At DFKI, he leads research that combines neuroscientific insights with AI development to create more interpretable systems. The NMM department focuses on both theoretical research on explainable AI foundations and practical applications in various domains, with particular attention to how different stakeholders understand and require explanations from AI systems.

Zhi-Ping FengView profile

Researcher

• Bioinformatics
• Genomics
• Proteomics
• +3 more

Dr. Zhi-Ping Feng is a Bioinformatician at the John Curtin School of Medical Research (JCSMR), Australian National University (ANU). Her research focuses on integrating omics data with protein structure-function relationships to study interactions between macromolecules. She has expertise in analyzing genomic and transcriptomic data (e.g., RNA-Seq, ChIP-Seq) and protein structure determination via nuclear magnetic resonance (NMR) spectroscopy. Previously, she held a Senior Research Fellow position at the Walter and Eliza Hall Institute (WEHI) from 2009, working on quality control in omics research and genomic data analysis. Her postdoctoral work at WEHI (2002–2005) involved structural biology of malaria-related proteins, supported by an Australian Postdoctoral Fellowship. She holds a PhD in protein bioinformatics from China and a physics background from Peking University. Education: PhD in Protein Bioinformatics (China) Bachelor’s in Physics, Peking University Research Interests: Her work bridges computational biology and structural biology, with emphasis on: Intrinsically unstructured proteins (IUPs) and their applications in malaria proteomics Omics data integration for disease modeling (e.g., cancer, diabetes, neurodegeneration) Protein-protein interaction networks and structural bioinformatics Publications: Recent work spans cancer immunotherapy, miRNA regulation in retinal degeneration, and T cell biology, with contributions to understanding Wnt signaling in joint replacement complications and genetic fusions in pediatric brain tumors. Awards: Australian Postdoctoral Fellowship (2005) Grants/Teams: Currently affiliated with ANU Bioinformatics Consultancy, supporting translational medical research in immunology, cancer, and genomics. Labs/Teams: Collaborates with the JCSMR’s multidisciplinary teams focusing on biomedical informatics and translational research.

• Molecular Biology
• Biochemistry
• Cell Biology
• +5 more

Dr. Maruf Ali is an Associate Professor in Molecular and Structural Biology at Imperial College London's Department of Life Sciences, part of the Faculty of Natural Sciences. He holds affiliations with the Centre for Structural Biology, Electron Microscopy Centre, and Membrane Biology. His research focuses on understanding molecular mechanisms of protein quality control during cellular stress, particularly in cancer and neurodegenerative diseases. Research interests include the Unfolded Protein Response (UPR), ER stress signaling pathways, and therapeutic interventions targeting these mechanisms. His multidisciplinary approach combines structural biology, biochemistry, and cellular techniques to study proteins and complexes involved in ER stress responses. Notable affiliations include the Membrane Biology and Molecular Mechanisms of Disease groups. His work explores how the UPR is activated and propagated, with implications for developing novel treatments for diseases linked to protein misfolding. Recent studies emphasize drug repurposing strategies targeting RSK4 to combat chemoresistance in cancers like lung and bladder cancer, as well as identifying key regulators of ER stress pathways such as IRE1 and PERK. Laboratory activities are centered at the Ali Lab (http://www.imperial.ac.uk/ali-lab), where research integrates structural insights with functional studies to advance understanding of cellular stress responses and their therapeutic applications.

• Structural Biology
• Biophysics
• Notch Signaling
• +7 more

Wendy R Gordon serves as an Associate Professor in the Structural Biology and Biophysics department at the University of Minnesota Medical School. Her research program bridges structural biology, biophysics, and translational medicine with significant funding from NIH sources including the National Cancer Institute and National Institute of General Medical Sciences. Dr. Gordon leads multiple active research projects focused on mechanobiology, protein engineering, and cancer therapeutics. Dr. Gordon's research interests center on understanding molecular mechanisms of cell surface receptors, particularly the Notch signaling pathway, and developing innovative tools for studying cellular mechanics. Her work spans structural biology approaches to characterize protein-DNA interactions, mechanotransduction mechanisms, and receptor regulation. She has developed novel technologies including HUH-Tags for protein-DNA labeling and tension sensors for measuring cellular forces. Her research has significant implications for cancer immunotherapy, particularly in engineering protein modulators of Notch activation for CAR T-cell therapy. Analysis of Dr. Gordon's recent publications reveals a strong focus on three interconnected research themes: 1) molecular mechanisms of Notch signaling and receptor regulation, 2) development of innovative tools for protein-DNA conjugation and cellular mechanotype measurement, and 3) applications of these technologies to cancer biology and immunotherapy. Her work demonstrates increasing integration of structural biology with functional cellular assays and therapeutic applications, particularly in tumor microenvironment and immunotherapy contexts. Dr. Gordon currently leads or collaborates on seven major research projects totaling millions in NIH funding. Her active grants include: LUMICKS C-trap for Mechanistic Studies of Biological Systems (as PI), Engineering Protein Modulators of Notch Activation for CAR T-cell immunotherapy (as CoI), Viral vector technology for cell type specific gene delivery (as CoI), Decoding mechanotransduction mechanisms of cell-surface receptors (as PI), and the Center for Multiparametric Imaging of Tumor Immune Microenvironments (as CoI). These projects reflect her expertise spanning structural biology, mechanobiology, and translational applications. Dr. Gordon's laboratory operates at the intersection of structural biology and cellular mechanobiology, with strong collaborations across the University of Minnesota campus including with cancer researchers, bioengineers, and clinicians. Her team develops and applies cutting-edge technologies including single-molecule techniques, protein engineering approaches, and high-throughput cellular mechanotype measurement systems to address fundamental questions in receptor biology with therapeutic implications.

Xiaobo LiView profile

Professor

• Neuroscience
• Medical Imaging
• Machine Learning Applications in Neurology
• +3 more

Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.

Dimitrios AnastasiouView profile

Research Professor

• Cancer Metabolism
• Metabolic Reprogramming
• Biochemistry & Proteomics
• +3 more

Dimitrios (Dimitris) Anastasiou is a Senior Group Leader at The Francis Crick Institute in London, UK, specializing in cancer metabolism research. Previously, he served as a Group Leader at the Medical Research Council National Institute for Medical Research (NIMR) starting in 2012 before transitioning to the Crick Institute in 2015. His research career includes postdoctoral work and an Instructor position at Beth Israel Deaconess Medical Center and the Department of Systems Biology, Harvard Medical School under Lewis Cantley, where he focused on metabolic reprogramming in cancer. University College London, UK - BSc Molecular Biology (2001) University of Basel, Basel, Switzerland - PhD in Biochemistry (2006) Anastasiou's research centers on understanding how cancer cells generate energy and utilize nutrients differently from normal cells. His laboratory conducts detailed analyses of metabolic pathways in cancer, investigating how tumor cells rewire their metabolism to support rapid growth and evade the body's defenses. His work spans biochemistry, proteomics, computational systems biology, human physiology, and tumor biology, with particular emphasis on identifying metabolic vulnerabilities that could be targeted for cancer therapy. His innovative approaches include developing chemical 'sensors' to monitor metabolic changes in cancer cells over time as tumors develop. Analysis of his recent publications reveals a consistent focus on metabolic regulation in cancer, particularly regarding glycolysis, hypoxia response, amino acid metabolism, and nucleotide biosynthesis. His work frequently examines enzyme regulation (particularly PKM2), metabolic adaptation to environmental stressors, and the intersection between metabolism and signaling pathways. A notable trend is his exploration of how metabolic enzymes function beyond their traditional roles, influencing cellular signaling and gene expression in cancer contexts. Anastasiou has made significant contributions to understanding metabolic reprogramming in cancer, particularly regarding pyruvate kinase M2 regulation, hypoxia responses, and nutrient utilization in tumor microenvironments. His work bridges basic biochemical mechanisms with potential therapeutic applications, focusing on identifying metabolic vulnerabilities in cancer cells that could be exploited for treatment. As a Senior Group Leader at the Crick Institute, Anastasiou leads a research group investigating how metabolism contributes to disease, particularly cancer. His laboratory utilizes a range of techniques including metabolomics, bioinformatics, structural biology, and high-throughput screening to study metabolic pathways. The group's work aims to identify fundamental differences between metabolic pathways in tumors and healthy tissue to discover new therapeutic targets against cancer.

Yves BoubenecView profile

Associate Professor

• Auditory Neuroscience
• Perceptual Attention
• Sound Encoding
• +3 more

Dr. Yves Boubenec is an Associate Professor at École Normale Supérieure (ENS)-PSL University, Paris, France. He serves as Head of the LSP Neuro Platform and Director of Studies at the Department of Cognitive Studies. Academic Rank: Associate Professor Institution: ENS-PSL Departments: Cognitive Studies (ENS), LSP Neuro Platform Email: yves.boubenec@ens.psl.eu Research Focus: Boubenec investigates neural mechanisms of auditory perception and cognition using integrated methodologies spanning single-neuron electrophysiology to large-scale neuroimaging. His work reveals how context, learning, and multisensory interactions shape sound encoding in mammalian neocortex. Primary Research Themes Context-dependent auditory encoding Perceptual attention mechanisms Task-driven neural plasticity Self-supervised learning models Population-level cortical dynamics Human/ferret auditory comparisons Publication Trends: Recent work (2024-2025) examines speech production networks, premotor auditory categorization, and algebraic structures in sound learning. Earlier studies (2018-2022) focus on population gating, hierarchical auditory coding, and self-voice mechanisms. 2025 Self-voice frequency analysis Hierarchical ferret auditory cortex mapping Temporal window constraints 2024 Premotor category hemodynamics Self-supervised sound structures Human speech cortical encoding Methodological Expertise: Combines awake ferret functional UltraSound, Neuropixels recordings, and computational modeling to analyze neural representations across spatial scales. Specializes in translating animal model findings to human auditory processes.