جست‌وجوی استاد و پژوهشگر

Yu Huangمشاهده پروفایل

استادیار

• Software Engineering
• Human Factors
• AI for Software Engineering
• +۷ مورد دیگر

Dr. Yu Huang is an Assistant Professor in the Department of Computer Science at Vanderbilt University's School of Engineering, with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her academic journey began with a BS in Aerospace Engineering from Harbin Institute of Technology in China (2011), followed by an MS in Computer Engineering from the University of Virginia (2015), and culminated with a PhD in Computer Science and Engineering from the University of Michigan in 2021 under Professor Westley Weimer. Dr. Huang's research bridges human cognition and machine intelligence to enhance software development. Her work spans software, hardware, AI, medical imaging (fMRI/fNIRS), eye tracking, and mobile sensing through collaborations with Security, Education, Psychology, and Neuroscience researchers. She leads the MIND Lab (Mixed INtelligence Development for programming lab), investigating programming expertise formation, code comprehension processes, cognitive error patterns, and diversity in programming communities. Her innovative approach combines empirical human studies with AI model development to create more effective programming tools. Her recent publications reveal a growing emphasis on leveraging human attention data to improve code language models, analyzing cognitive biases in security contexts, and examining social factors in technical communication. The research shows strong interdisciplinary connections between neuroscience, psychology, and software engineering, with increasing applications of LLMs in developer tooling. Dr. Huang's work consistently demonstrates how understanding human cognition can inform better AI systems for programming tasks. Dr. Huang has received numerous prestigious recognitions including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards (ICSE 2019, FSE 2023, ICSE 2024). Her lab has earned the Best Presentation Award at GI2024, while her students have received the Richard Bennett/Dorothy Danforth Compton Prize scholarship and the C. F. Chen Best Paper award. She actively mentors a diverse team of graduate students (Yifan Zhang, Zach Karas, Zihan Fang, Yueke Zhang, Jiahao Zhang) and undergraduate researchers, with many former students advancing to top institutions (Stanford, Harvard, Duke, UC Berkeley) and organizations (NASA JPL). Her research is supported by a 4-year NSF grant, GitHub Tech for Social Good funding, and the Provost's Faculty Immersion Vanderbilt Grant, enabling comprehensive studies of human-AI collaboration in software engineering. The MIND Lab maintains a strong collaborative culture, frequently working with Professor Kevin Leach's research group and organizing retreats to locations like Radnor State Park and the Great Smoky Mountains. This environment fosters innovation at the intersection of human cognition and software engineering while supporting the professional development of emerging researchers in the field.

Magnus Westerlundمشاهده پروفایل

مدرس ارشد

• Trustworthy AI
• Cybersecurity
• IT Security
• +۶ مورد دیگر

Magnus Westerlund is a Senior Lecturer in Information Technology and Director of the Laboratory for Trustworthy AI at Arcada University of Applied Sciences in Helsinki, Finland. His industry background spans telecom and information management, and he holds a doctoral degree in Information Systems from Åbo Akademi University. He actively contributes to the Z-Inspection® network, focusing on ethical AI implementation and governance. Westerlund’s research emphasizes trustworthy AI, cybersecurity, and distributed systems. Key areas include AI regulatory compliance (e.g., EU AI Act), healthcare AI applications, blockchain security, and IoT edge solutions. His work bridges academia and industry, such as the Valohai-CSC collaboration for machine learning infrastructure in Finnish academia. His publications highlight practical AI assessment methods, ethical AI integration, and decentralized technologies. Notable contributions include frameworks for sustainable AI development, privacy-preserving autonomous systems, and smart contract-based IoT security protocols. Westerlund also explores educational innovations, such as integrating large language models (LLMs) into coding education. His research consistently addresses real-world challenges like pandemic-era healthcare AI, edge computing for IoT, and cybersecurity in autonomous systems.

Santosh S. Vempalaمشاهده پروفایل

استاد

• Algorithms
• Convex Geometry
• High-Dimensional Sampling
• +۴ مورد دیگر

Santosh S. Vempala is the Frederick P. Storey II Chair and Professor of Computer Science at Georgia Institute of Technology's College of Computing with joint appointments in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and the School of Mathematics. He teaches courses including CS6150: Computing for Good (C4G) and CS6550/CS8803DAA: Continuous Algorithms: Optimization and Sampling. His research spans multiple interconnected domains: Algorithmic convex geometry and high-dimensional sampling Continuous optimization methods Computational models of brain function Randomized algorithms with applications to machine learning Vempala's recent publications reveal a strong focus on developing efficient algorithms for high-dimensional problems, particularly logconcave sampling and convex body integration. His work bridges theoretical computer science with practical applications in optimization and neuroscience, with increasing attention to the intersection of theoretical frameworks and brain computation models through his collaboration with Christos Papadimitriou. He leads the Computing for Good (C4G) initiative which applies computational approaches to social challenges, including projects like Safe and Easy Passwords!, LifeNet, C4G BLIS, and Shelter-to-Home that address problems in resource-constrained settings. Vempala currently advises PhD students Xinyuan Cao, Mirabel Reid, Max Dabagia, and Yunbum Kook, and has authored influential books including 'Spectral Algorithms' and 'The Random Projection Method' that have shaped research in algorithmic convex geometry. His tutorials at major conferences, including STOC 2015 on 'Sampling and Volume Computation in High Dimension' and FOCS 2020 on 'Computation in the Brain,' demonstrate his leadership in connecting theoretical computer science with broader scientific challenges.

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۵ مورد دیگر

Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.

Cuiyun Gaoمشاهده پروفایل

استاد

• Software Repository Mining
• Natural Language Processing
• Code Analysis
• +۵ مورد دیگر

Cuiyun Gao is a Full Professor and PhD Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. She has established herself as a prominent researcher in the intersection of artificial intelligence and software engineering. Her educational background includes a PhD from the Chinese University of Hong Kong (completed in 2018), followed by postdoctoral work at CUHK and a Research Fellowship at Nanyang Technological University. She also had a visiting period at University College London supervised by Prof. Mark Harman and Prof. Federica Sarro. Dr. Gao's research primarily focuses on Software Repository Mining, Natural Language Processing, Code Analysis, Large Language Models, Source Code Understanding, User Review Analysis, Vulnerability Detection, and Mobile Advertising Analysis . Her work bridges the gap between traditional software engineering practices and modern AI techniques, particularly in the context of code intelligence and software maintenance. Her recent publications (2024-2025) demonstrate a strong emphasis on Large Language Models for code-related tasks, including code generation, optimization, vulnerability detection, and software engineering applications. Her research shows a clear trend toward addressing practical challenges in integrating LLMs into the software development lifecycle while maintaining code quality and security. Scientific Awards: Distinguished Paper Award at ASE 2023 Best Paper Award of the Track at ICSE 2024 Distinguished Paper Award at ICSE 2024 Dr. Gao actively supervises multiple PhD and Master's students, contributing to the next generation of software engineering researchers. She has served on numerous conference committees including FSE, ISSTA, ICSE, ASE, and SANER. Her research has received significant attention in the software engineering community, with multiple papers published in top-tier venues like FSE, ICSE, ASE, and TSE. Her lab appears to be actively engaged in both theoretical research and practical applications, particularly in the context of WeChat and other industry collaborations, demonstrating strong industry-academia connections.

Chang Xuمشاهده پروفایل

استاد

• Big data software engineering
• Intelligent software testing and analysis
• Adaptive and autonomous software systems
• +۳ مورد دیگر

Chang Xu is a Professor and Ph.D. supervisor at Nanjing University, affiliated with the State Key Laboratory for Novel Software Technology, School of Computer Science, and Institute of Computer Software (ICS). He has been a full-time faculty member since 2010, when he joined as an associate professor and was later promoted to full professor in 2015. Education: Ph.D. from The Hong Kong University of Science and Technology (HKUST) in 2008 (advisor: Prof. S.C. Cheung) M.Eng. from Institute of Software, Chinese Academy of Sciences (ISCAS) in 2003 B.Eng. from University of Science and Technology of China (USTC) in 2000 Research Interests: Professor Xu's research focuses on big data software engineering, intelligent software testing and analysis, and adaptive and autonomous software systems. His recent work centers on constructing and providing runtime support for intelligent software in open environments, with emphasis on inconsistency detection and resolution for environments, and quality assurance for adaptive, concurrent, learning-based, smartphone-based, and spreadsheet-based applications. His work bridges theoretical foundations with practical applications in software engineering, particularly in program analysis, software testing, and self-adaptive systems. Scientific Awards: ACM SIGSOFT Distinguished Paper Award from ICSE 2025 Best Student Paper Award from EUROSYS 2025 ACM Distinguished Member in 2024 Best Paper Award from SOSP 2023 Best Paper Candidate from ISSRE 2022 Yangtze River Scholar by the Ministry of Education in 2021 Multiple ACM SIGSOFT Distinguished Paper Awards from conferences including ASE, ICSE National Science and Technology Progress Award (Second Class) in 2011 Academic Service and Advising: Professor Xu has served on numerous program committees for top software engineering conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He is an editorial board member for several journals including Journal of Computer Science and Technology and Frontiers of Computer Science. He has supervised numerous Ph.D. and MSc students, with research topics spanning program analysis, software testing, self-adaptive systems, and more. His students have gone on to successful careers in both academia and industry. Research Groups: Professor Xu is associated with the SPAR research group at Nanjing University and the CASTLE research group at HKUST, focusing on software analysis, reliability, and testing.

Courtney N. Reedمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Music Technology
• Digital Instrument Design
• +۵ مورد دیگر

Courtney N. Reed is a Lecturer in Digital Technologies at Loughborough University London, where she joined in November 2023. She maintains a dual role as a visiting research fellow at the Max Planck Institute for Informatics. Her academic journey includes a BMus in Electronic Production and Design from Berklee College of Music (2016), followed by an MSc (2018) and PhD (2023) in Computer Science from Queen Mary University of London. Prior to her current position, she completed postdoctoral research at both the Max Planck Institute for Informatics and King's College London. Bachelor of Music: Electronic Production and Design, Berklee College of Music (2016) Master of Science: Computer Science, Queen Mary University of London (2018) Doctor of Philosophy: Computer Science, Queen Mary University of London (2023) Dr. Reed's research explores the entangled relationships between humans, bodies, instruments, and technology in music interaction, with particular focus on vocal electromyography (VoxEMG) and the vocalist-voice relationship. Her work incorporates feminist and post-human theories to examine sociopolitical contexts within arts technology, aiming to design for creativity while acknowledging individual, messy bodies in artistic practice. She has developed an open-source platform for vocal electromyography to investigate how biosignal feedback changes understanding and perception of the body in vocal performance. Her interdisciplinary approach bridges music technology, human-computer interaction, and embodied interaction studies. Analysis of Dr. Reed's recent publications (2023-2025) reveals a strong thematic focus on embodied interaction in music technology, with particular emphasis on vocal performance, biosignal feedback, and the philosophical underpinnings of digital instrument design. Her work consistently integrates theoretical frameworks like Karen Barad's agential realism with practical applications in digital musical instruments. Key trends include the exploration of ambiguity in data representation, the sociocultural dimensions of timbre in instrument design, and the development of novel methodologies for understanding embodied musical experiences through micro-phenomenology and ethnographic approaches. ACM SIGCHI Outstanding Dissertation Award (2024) for her thesis 'Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback' Best Newcomer Award at Loughborough University London's Community Awards Celebration (2024) Dr. Reed actively contributes to the academic community through conference organization and leadership roles. She serves as Member-at-Large on the NIME Board, previously chaired papers for NIME 2024, and co-organized the IBM SkillsBuild Sprint at Loughborough London. She has also chaired sessions at the ACM TEI Conference and co-chaired the Student Design Competition. Her collaborative work spans multiple institutions and includes significant contributions to interdisciplinary projects that bridge music, technology, and human experience. She has been instrumental in developing the senSInt research group and the RaveNET wearable network project. Dr. Reed leads the senSInt research group which focuses on sensorimotor interaction in music and performance contexts. The group develops innovative technologies including the VoxEMG platform for vocal electromyography, the Bones anti-corset for vocal performance, and the RaveNET network of wearable biosensing nodes. These projects explore the intersection of biosignals, embodied interaction, and musical expression, creating novel frameworks for understanding how technology mediates human creativity and performance. The group frequently collaborates with musicians, technologists, and theorists to develop and test these systems in real-world performance contexts.

Abdullah Mueenمشاهده پروفایل

استاد

• Temporal Data Mining
• Blockchain Data Mining
• Social Media Mining
• +۵ مورد دیگر

Abdullah Mueen is a Professor and Associate Chair in the Department of Computer Science at the University of New Mexico (UNM), where he has been since 2013. Previously, he worked as a Scientist in the Cloud and Information Sciences Lab at Microsoft Corporation. Research Interests : His work focuses on Temporal Data Mining , with emphasis on efficiency , interactivity , and interpretability . Key areas include Blockchain Data Mining (e.g., BitLink for Bitcoin cluster analysis), Seismic Data Mining (e.g., PAW for aftershock detection), and Social Media Mining (e.g., DeBot for Twitter bot detection). Article Trends : His recent publications span four domains: Seismology : Algorithms for earthquake data analysis (e.g., focal depth inference, aftershock classification). Blockchain : Temporal linkage of Bitcoin addresses (BitLink) and cryptocurrency fraud detection. Traffic Safety : Multi-LiDAR data fusion for real-time road safety monitoring. Time Series Methods : Innovations like MASS similarity search and DAMP anomaly detection for massive datasets. Scientific Awards : ACM SIGKDD Test-of-Time Award (2022) UNM Provost Research Leader Award UNM School of Engineering Junior Faculty Research Excellence Award KDD 2012 Doctoral Dissertation Contest Runner-Up KDD 2012 Best Paper Award Advising and Grants : He has mentored 11 PhD students now employed at institutions like Microsoft, Meta, and Lawrence Livermore National Lab. His research is funded by NSF , NIH , DARPA , AFRL , NEC , Exxon , Microsoft , and LANL .

Michael Qizhe Shiehمشاهده پروفایل

استادیار

• Large Language Models
• Deep Learning
• Natural Language Processing
• +۴ مورد دیگر

Michael Qizhe Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the Tree and Rock AI Lab (TRAIL). He holds a PhD and Master's from Carnegie Mellon University (Machine Learning and Language Technologies) and a Bachelor's from Shanghai Jiao Tong University's ACM Class. His research focuses on Large Language Models, Deep Learning, and Natural Language Processing, with notable contributions to semi-supervised learning techniques like Noisy Student and UDA, and the RACE benchmark for reading comprehension. Education: PhD in Machine Learning, Carnegie Mellon University (2020) Master's in Language Technologies, Carnegie Mellon University (2018) Bachelor's in Computer Science, Shanghai Jiao Tong University (2016) His research explores robustness, safety, and scalability of AI systems. He has served as Area Chair for top conferences like NeurIPS, ICML, and ICLR. Current research directions include adversarial robustness, LLM self-evaluation, and alignment mechanisms. His lab, TRAIL, emphasizes foundational AI research. Selected contributions include: Developing UDA and Noisy Student techniques for semi-supervised learning Creating the RACE benchmark for exam-based reading comprehension Advancing methods for LLM safety and adversarial defense Prospective students are encouraged to apply to NUS's PhD program for collaborative research opportunities.

• Computational Social Science
• Text-as-data methods
• Natural Language Processing
• +۷ مورد دیگر

Kenneth BENOIT is the Dean and Full-time Professor of Computational Social Science at the School of Social Sciences, Singapore Management University (SMU). Previously, he served as Director of the Data Science Institute at the London School of Economics (LSE) from 2020 to 2024. He holds a PhD in Government from Harvard University, specializing in statistical methodology. His research focuses on computational methods for analyzing textual data, particularly political texts and social media. Key areas include text-as-data techniques, natural language processing, and the application of large language models in social sciences. He has pioneered methods combining machine learning with crowd-sourced coding to improve the accuracy of political text analysis. Ken’s work emphasizes the analysis of big data, electoral systems, and comparative party competition, with notable contributions to the European Parliament and policy positioning studies. His expertise extends to software development, including R packages like quanteda and spacyr , which are widely used in text analysis. His articles and publications span methodological innovations, policy analysis, and interdisciplinary applications. Notable projects include scaling political party positions and examining the role of AI in public policy. He is actively involved in academic leadership, having served on editorial boards and organized collaborative research initiatives like the CIVICA research hackathon. Beyond SMU, he maintains professional profiles on LinkedIn and GitHub , reflecting his commitment to open-source tools and scholarly collaboration.

Anupam Joshiمشاهده پروفایل

استاد

• Networked Computing
• Intelligent Systems
• Cybersecurity
• +۶ مورد دیگر

Anupam Joshi is the Acting Dean of the College of Information Technology and Engineering and Oros Family Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He also directs UMBC’s Center for Cybersecurity and leads the National Cybersecurity FFRDC for the University System of Maryland. His research focuses on networked computing, AI-driven cybersecurity, privacy-preserving technologies, and policy-driven security frameworks. He holds a Ph.D. in Computer Science from Purdue University (1993), an M.S. (1991), and a B.Tech in Electrical Engineering from the Indian Institute of Technology, Delhi (1989). Dr. Joshi’s work spans over 400 publications with 32,650+ citations (h-index 92) and nine patents. His grants include funding from NSF, DARPA, NASA, NIST, and industry partners like IBM and Northrop Grumman. Key contributions include developing CAPD frameworks for IoT security, FABULA for automated threat intelligence, and KiNETGAN for intrusion detection through synthetic data. He is an IEEE Fellow and pioneer in applying AI to secure critical infrastructure, smart grids, and healthcare systems. His research trends emphasize AI-empowered cybersecurity, privacy compliance in data sharing (e.g., agriculture, healthcare), and mitigating attacks on smart systems. Notable projects include combating fake cybersecurity reports using provenance analysis, securing EV charging infrastructure, and enhancing smart farming resilience through policy-driven access control. Awards: IEEE Fellow Grants: Over $30M from NSF, DoD, NASA, and industry collaborations Labs/Teams: Director of UMBC Center for Cybersecurity, Cybersecurity Knowledge Graph initiatives Future work includes advancing neurosymbolic AI for cybersecurity, semantic data extraction from scientific literature, and AI ethics in healthcare applications.

Jacco van Ossenbruggenمشاهده پروفایل

استاد

• Cultural Heritage
• Digital Libraries
• Ontology Engineering
• +۷ مورد دیگر

Prof. Jacco van Ossenbruggen is a Full Professor in Intelligent Information Systems at Vrije Universiteit Amsterdam (VU), affiliated with the Network Institute. He serves on the Management Board of ODISSEI, a national research infrastructure for social sciences and economics. His academic background includes a PhD in Computer Science (2001) from VU’s Faculty of Science, focusing on hypermedia processing. Research Interests: His work centers on cultural AI, FAIR data principles, ontology engineering, and semantic web technologies. Key areas include inclusive cultural heritage metadata, bias mitigation in AI systems, and knowledge discovery via linked data. Recent projects involve leveraging large language models (LLMs) for metadata enrichment and ontology construction. Key Contributions: He leads initiatives like the Cultural AI Lab, exploring AI applications for cultural heritage. His research bridges technical innovations (e.g., semantic integration of restricted-access data) with societal impacts (e.g., ethical AI frameworks for public-sector applications). Developed frameworks for evaluating entity alignment in knowledge graphs Pioneered FAIR-aligned data management plans for scientific communities Designed tools like Alter Heritage for collaborative metadata curation Grants & Projects: Principal Investigator of the ODISSEI Portal project (2020–2024), advancing open data infrastructures. Active in funding initiatives promoting reproducible research and ethical data practices. Labs/Teams: Cultural AI Lab at VU, focusing on AI-driven solutions for cultural heritage preservation and accessibility.

Oya Beyanمشاهده پروفایل

استاد

• Biomedical Informatics
• Semantic Web Technologies
• Clinical Decision Support
• +۵ مورد دیگر

Prof. Dr. Oya Beyan is a Professor at the University of Cologne's Institute for Biomedical Informatics and a Core Scientist at the Center for Data and Simulation Science. Her research focuses on enabling FAIR (Findable, Accessible, Interoperable, Reusable) data management, distributed analytics on sensitive medical data, and data-driven innovations in healthcare. She leads projects like the PADME platform for federated machine learning and privacy-preserving analytics. Key areas include biomedical informatics, semantic web technologies, clinical decision support systems, and ethical challenges in data science. Research Interests: FAIR Data Principles & Infrastructure Privacy-Preserving Distributed Learning Explainable AI in Healthcare Semantic Interoperability Medical Data Integration Ethical & Social Implications of Data Use Notable Contributions: Development of the Personal Health Train framework for decentralized medical data analysis Leadership in EU-funded initiatives like NFDI4Health and Medical Informatics Collaborations Pioneering work on federated learning applications in oncology and rare disease research Lab & Affiliations: Prof. Beyan's work is anchored in the Institute for Biomedical Informatics and the Center for Data and Simulation Science, fostering interdisciplinary collaboration between computational science and medical research.

Francesco Fabianoمشاهده پروفایل

پژوهشگر ارشد

• Multi-agent systems
• Epistemology
• Autonomous planning
• +۷ مورد دیگر

Francesco Fabiano is a Research Fellow at the Department of Computer Science, University of Oxford, and an Affiliated Faculty Member at New Mexico State University. He previously served as Assistant Professor at New Mexico State University (2023–2024) and held Adjunct Professor roles at Saint Joseph’s University and University of Parma. His research spans neuro-symbolic AI, multi-agent systems, epistemology, and autonomous planning. Postdoctoral Research Associate at University of Oxford (2025–present) Ph.D. in Computer Science from University of Udine (2018–2021) Master’s Degree in Computer Science from New Mexico State University (2017–2018) Bachelor’s Degree in Computer Science from University of Parma (2013–2016) His work focuses on neuro-symbolic architectures that integrate formal logic with machine learning, particularly for multi-agent epistemic planning and ethical decision-making frameworks . He explores hybrid systems combining System-1/System-2 cognitive paradigms to enhance AI trustworthiness. Recent publications analyze knowledge representation in neuro-symbolic systems, the role of large language models in planning, and heuristic optimization in multi-agent epistemic solvers like EFP. His work also addresses ethical AI and explainable data-to-text frameworks . Best Ph.D. Thesis Award by GULP (2022) He collaborates with IBM Watson Research Lab on cognitive theory-inspired AI paradigms and contributes to open-source planning tools like EFP. His teaching experience includes courses in Applied Machine Learning , Automated Planning , and LaTeX programming .

Danilo Bzdokمشاهده پروفایل

دانشیار

• Computational Biology
• Deep Learning
• Large Language Models (LLM)
• +۵ مورد دیگر

Danilo Bzdok is an Associate Professor in the Department of Biomedical Engineering at McGill University’s Faculty of Medicine and a Canada CIFAR AI Chair at Mila – Quebec Artificial Intelligence Institute. He holds dual expertise in systems neuroscience and machine learning, with two doctoral degrees: one in neuroscience from Forschungszentrum Jülich (Germany) and another in computer science (machine learning statistics) from INRIA–Saclay and Neurospin (France). His research bridges computational neuroscience and AI, focusing on understanding human intelligence through neuroimaging and biomedical data. Education: PhDs in Neuroscience (Jülich) and Computer Science (INRIA/Neurospin). Postdoctoral training at Harvard Medical School. Current affiliations include McGill University and Mila. Research interests span computational biology, deep learning, LLMs, and their applications in neuroimaging, precision medicine, and neurodegenerative diseases. Over 150+ peer-reviewed publications, with recent work on LLMs in autism diagnostics, brain network modeling, and social neuroscience. Key Awards: Canada CIFAR AI Chair. Lab focuses on interdisciplinary projects like AI4Science, neuroimaging analysis, and AI ethics. Supervises a dynamic team of PhD/Master’s students and postdocs. Collaborations include clinical institutions and industry partners through Mila’s Applied Research programs.