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Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.

• Biometrics
• Machine Learning
• Deep Learning
• +5 more

Rui Ning is an active Assistant Professor in the Department of Computer Science at Old Dominion University (ODU), within the Batten College of Engineering & Technology. His academic journey includes a B.S. in Computer Science & Engineering from Lanzhou University (China), an M.S. in Computer Science from the University of Louisiana at Lafayette, and a Ph.D. in Electrical & Computer Engineering from ODU. Dr. Ning's research focuses on cybersecurity, privacy-preserved AI, and secure AI systems, with particular emphasis on backdoor detection in neural networks, federated learning security, and privacy-preserving deep learning. His work bridges theoretical security mechanisms with practical implementations in real-world AI systems, addressing critical vulnerabilities in modern machine learning frameworks. Analysis of his publication trends reveals a strong focus on adversarial machine learning, with increasing attention to multimodal AI security since 2022. His research shows consistent growth in addressing sophisticated attack vectors while developing practical defense mechanisms applicable to industry settings. Notably, his work spans both theoretical contributions and practical implementations, often achieving high acceptance rates at top-tier conferences. Mark Weiser Best Paper Award, IEEE PERCOM, 2018 Best In-session Presentation Award, IEEE INFOCOM, 2019 NSF CRII Award, 2022 Ph.D. Researcher of the Year, ODU ECE, 2019 Dr. Ning actively mentors graduate students, currently supervising multiple Ph.D. candidates and an M.S. student at ODU. His grant portfolio demonstrates significant research impact, with over $1.5 million in funding as PI or Co-PI from sources including NSF, DoD, NSA, and industry partners like Interdigital. His research addresses critical challenges in AI security with practical applications for cybersecurity infrastructure. Dr. Ning also contributes substantially to academic service as a reviewer for top conferences and journals, and serves on program committees for major AI and security venues.

• Cybersecurity
• Privacy-preserved AI
• Secure AI systems
• +3 more

Anthony Clark is an Assistant Professor of Computer Science at Pomona College, where he has been teaching since 2020. Previously, he served as an Assistant Professor at Missouri State University from 2016 to 2020. He directs the ARCS (Autonomous Robotics and Complex Systems) Lab, which focuses on improving the robustness and adaptability of autonomous robots, particularly small-scale systems that can navigate unpredictable terrain and adapt to potential damage. Clark earned his Ph.D. in Computer Science from Michigan State University in 2016, where he worked under Dr. Philip K. McKinley, and his B.S. in Computer Engineering from Kansas State University, graduating magna cum laude. His research centers on making autonomous robots more robust and adaptive through optimization algorithms and multimodal systems. He specializes in evolutionary robotics, computer vision, neural networks, and simulation methods for developing control systems that leverage multiple locomotion mechanisms. His recent work demonstrates strong trends across several domains: developing hybrid locomotion systems (wheel/leg transformations), applying deep learning to terrain classification and pathfinding, using simulation environments for training, and exploring pretraining techniques for evolutionary robotics. His research shows a consistent focus on bridging simulation and real-world applications while addressing challenges in robot adaptability and robustness. Faculty Excellence in Teaching, Missouri State University (2018) Best Paper Award, Workshop on Evolutionary and Reinforcement Learning (2013) Best Paper Award, ALIFE Conference, Behavior and Intelligence Track (2012) Outstanding Reviewer, Elsevier (2018) Master Advisor Certification, Missouri State University (2017) Clark has advised numerous undergraduate and graduate students through the ARCS Lab, with current research involving projects like the Adabot (a robot with multiple locomotion mechanisms) and thermal semantic segmentation for aerial field robots. His teaching portfolio includes courses on data structures, algorithms, neural networks, computer systems, and mobile robotics. He has also served as a Visiting Associate at Caltech's ARC Lab from 2023-2024, working with Dr. Soon-Jo Chung. The ARCS Lab develops simulation environments, optimizes control systems, and fabricates physical robots. Current projects include the Adabot with its geared coaxial shaft mechanism for hybrid locomotion, thermal semantic segmentation using satellite data, and creating dynamic simulation environments with Unreal Engine 5. The lab emphasizes practical applications of theoretical research while training students in both hardware and software aspects of robotics.

• Autonomous Robotics
• Evolutionary Robotics
• Computer Vision
• +7 more

Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.

• Human Factors Engineering
• Neuroergonomics
• Robotic Surgery
• +3 more

Richard Naud is an Assistant Professor in the Department of Cellular and Molecular Medicine at the Faculty of Medicine, University of Ottawa, with a cross-appointment in Physics at the Faculty of Science. He holds dual research positions at the Center for Neural Dynamics (CND) and Brain and Mind Research Institute (BMRI), focusing on computational approaches to decode neural signaling mechanisms and develop brain-machine interfaces. His educational background includes: PhD in Neuroscience from École Polytechnique Fédérale de Lausanne (EPFL), 2011 MSc in Physics from McGill University, 2006 BSc in Physics from McGill University, 2004 Dr. Naud's research investigates how neurons encode information through spikes, bursts, and silences using mathematical models and statistical analysis of electrophysiology data. His lab develops computational protocols for synaptic dynamics analysis, studies dendritic computation in neurological diseases, and creates neuromorphic algorithms for spiking neural networks. Current work emphasizes serotonin system dynamics, burst coding mechanisms, and neural network simulations for demyelinating conditions. Analysis of his recent publications reveals three dominant research vectors: (1) Burst coding as an independent information channel beyond firing rates, (2) Serotonin-mediated value coding in decision systems, and (3) Neuromorphic implementation of biologically plausible learning rules. His work bridges theoretical neuroscience with clinical applications in stroke recovery and neurological disorders. Dr. Naud leads the Neural Coding Lab, which actively recruits postdoctoral fellows, graduate students, and undergraduates for projects in neural coding theory, computational psychiatry, and neuromorphic engineering. The lab maintains collaborations with experimental neuroscience groups for model validation and develops open-source tools like SRPlasticity for synaptic dynamics analysis.

• Computational Neuroscience
• Neural Coding
• Statistical Methods in Neuroscience
• +5 more

Rianne de Heide is an Assistant Professor in the Statistics group (STAT) within the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. She maintains collaborative arrangements with LUXs Data Science in Leiden, CWI, and VU Mathematics in Amsterdam as a guest researcher while working partly remotely during her family's relocation. Her academic journey includes a previous position as Assistant Professor at Vrije Universiteit Amsterdam. PhD Dissertation: 'Bayesian Learning: Challenges, Limitations and Pragmatics' (2020) MSc Thesis: 'The Safe-Bayesian Lasso' (2016) De Heide's research spans multiple interconnected domains within statistics and machine learning, with particular emphasis on developing mathematically rigorous frameworks that remain accessible to diverse audiences. Her work bridges theoretical foundations with practical applications, focusing on hypothesis testing with e-values, Bayesian learning methodologies, and best-arm identification problems in multi-armed bandit settings. She demonstrates exceptional interdisciplinary range, connecting statistical theory with philosophical inquiry and even theological discussions as evidenced by her publications on biblical authorship verification and mathematical beauty. Analysis of her publication trajectory reveals a clear evolution toward developing anytime-valid statistical methods, particularly through e-values and e-processes for multiple testing scenarios. Her recent work shows increasing focus on foundational questions in statistical inference while maintaining strong connections to practical machine learning applications. The 2024 'Safe Testing' paper in the Journal of the Royal Statistical Society represents a significant contribution that generated a formal discussion meeting. VENI project 'E-values for Multiple Testing' NWO M2 grant of €742,708 with Jelle Goeman (funding 2 PhD students and a scientific programmer) 2025 Bernoulli Society New Researcher Award De Heide actively supervises research through her VENI project and the NWO M2 grant, while also contributing to broader academic service through the 'Kindness and Excellence in Academia' initiative she co-founded. This initiative addresses critical cultural issues in academic environments through opinion pieces, resources, and community building around compassionate academic practices. She has organized specialized events like the E-Day meet-up for e-value researchers at CWI in Amsterdam, demonstrating leadership in her niche research community. Her research activities are centered around the Statistics group at the University of Twente, with significant external collaborations through the E-mailing list for e-value researchers and partnerships with institutions including CWI, VU Amsterdam, and Leiden's LUXs Data Science. The interdisciplinary nature of her work creates connections across mathematics, computer science, philosophy, and even religious studies.

• Machine Learning
• Statistics
• Hypothesis Testing
• +7 more

Luis Miguel Bergasa is a Full Professor at the Department of Electronics, University of Alcalá (UAH), with a career spanning over 25 years. He leads the RobeSafe Lab (since 2010) and serves as Director of Digital Transformation at UAH (since 2022). His academic roles include heading the Department of Electronics (2004–2010) and coordinating multiple educational programs. He teaches Perception Systems (Master in Industrial Engineering) Intelligent Control Systems (Computer Science) Computer Vision (Computer Science) His research focuses on Perception Systems for Intelligent Vehicles , emphasizing driver behavior analysis, scene understanding, and sensor fusion via deep learning. He has authored over 300 papers and holds 9 patents. Notable recognitions include being ranked #81 in Computer Science (Spain) by Research.com (2025) and receiving 30+ awards in Robotics/Automotive fields. Recent publications highlight advancements in Transformer-based driver action recognition (2025) Infrastructure-vehicle cooperative frameworks (2025) 3D semantic segmentation for autonomous perception (2024) Simulation-to-reality gap bridging (2024) Scientific Leadership Senior Editor, IEEE Transactions on ITS (2025) International Program Committee roles in 15+ conferences (2024–2025) Co-founder of Vision Safety Technologies Ltd (2009–2016) He supervises 9 active PhD students and has advised 14 former PhD candidates. His group collaborates with institutions in Germany, USA, China, and Spain, including KIT, UC Berkeley, and Northwestern Polytechnic University.

• Perception Systems
• Autonomous Vehicles
• Deep Learning
• +4 more

Silvia Cascianelli is an AI and Computer Vision Researcher at the University of Modena and Reggio Emilia (UNIMORE). She actively contributes to the computer vision and document analysis communities through research, conference organization, and academic mentorship. She serves as Area Chair for major computer vision conferences including CVPR2025, BMVC2025, and ECCV2024, demonstrating her standing in the field. Her research focuses on several key areas within computer vision and document analysis: Image Generation : Developing efficient and lightweight methods for image generation with desired characteristics, particularly using diffusion models Handwriting Imitation : Creating algorithms for generating images of text with specific content and handwriting styles, along with evaluation methods Document Understanding : Extracting information from 2D and 3D document images, ranging from modern documents to historical artifacts like carbonized Roman papyri Dr. Cascianelli's work shows a clear progression toward more sophisticated generative models and evaluation frameworks, with recent publications focusing on diffusion models for handwritten text generation, efficient token reduction for multimodal tasks, and innovative approaches to historical document analysis. Her research bridges theoretical advancements with practical applications across diverse document types. Her scientific contributions have been recognized through invitations to serve as Area Chair for top-tier computer vision conferences (CVPR, ECCV, BMVC) and opportunities to organize specialized workshops including VisionDocs at ICCV, AI4DH at ECCV, and ADAPDA at ICDAR. Area Chair at CVPR2025 Area Chair at BMVC2025 Area Chair at ECCV2024 Organizer of VisionDocs Workshop at ICCV2025 Organizer of AI for Digital Humanities Workshop at ECCV2024 Organizer of ADAPDA Workshop at ICDAR2024 Dr. Cascianelli actively mentors the next generation of researchers: Vittorio Pippi - PhD Student at UniMoRe (National PhD program in AI) Fabio Quattrini - PhD Student at UniMoRe (ICT program) Carmine Zaccagnino - Research Intern at UniMoRe (formerly MSc student) Kostantina Nikolaidou - PhD Student at Luleå University of Technology Pau Torras Coloma - PhD Student at Computer Vision Center, Universitat Autònoma de Barcelona Bram Vanherle - CV Engineer at Colruyt Group Smart Innovation (formerly PhD student) She is actively involved in several research initiatives including the AI Governance Lab where she serves as a lecturer, and collaborates with institutions worldwide. Her current projects focus on advancing diffusion models for image generation, improving handwritten text recognition systems, and developing novel methods for document understanding across historical and contemporary contexts.

• Computer Vision
• Document Analysis
• Handwritten Text Recognition
• +4 more

Carlo D'Eramo is Professor and Head of the Reinforcement Learning and Computational Decision-Making professorship at the Center for Artificial Intelligence and Data Science (CAIDAS) of University of Würzburg. He additionally serves as an independent group leader of hessian.AI, focusing on developing lightweight methods to obtain adaptive autonomous agents that can handle real-world complexity. His academic journey includes: B.Sc. in Computer Engineering from Politecnico di Milano (2011) M.Sc. in Computer Engineering from Politecnico di Milano (2015) Double degree in Computer Science from University of Illinois at Chicago (2015) Ph.D. in Information Technology from Politecnico di Milano (2019) Postdoctoral research at TU Darmstadt's Intelligent Autonomous Systems group (2019-2022) D'Eramo leads the LiteRL research group investigating how agents can efficiently acquire expert skills accounting for real-world complexity. His research spans multiple reinforcement learning domains including multi-task, curriculum, adversarial, options, and multi-agent RL. His work bridges theoretical advances with practical applications, particularly in robotics and decision-making systems. His recent publications (2023-2025) demonstrate significant contributions across exploration strategies, neural network architectures, multi-agent coordination, and physics-informed machine learning. His work frequently appears in top-tier venues including TMLR, RLJ, IEEE PAMI, ICML, and ICLR, with multiple papers receiving spotlight or oral presentation designations. Professional activities include: Senior area chair for RLC Area chair for AAAI, ACML, AISTATS, NeurIPS, and ICLR Reviewer for DFG and ERC proposals Creator of MushroomRL reinforcement learning framework D'Eramo actively contributes to academic community service while mentoring researchers in his group. His work on lightweight methods aims to make reinforcement learning more practical for real-world applications across various domains.

• Reinforcement Learning
• Computational Decision-Making
• Artificial Intelligence
• +5 more

Dr. Pei Huang is a Senior Lecturer in Energy Engineering at Dalarna University, Sweden, working within the Department of Information and Technology. His academic career focuses on multidisciplinary research at the intersection of energy systems, electromobility, and sustainable urban development, with significant contributions to both teaching and research in renewable energy and energy efficiency. Dr. Huang received his Ph.D. from the City University of Hong Kong in 2017. His educational background has provided a strong foundation for his current research in energy systems and sustainable technologies, bridging engineering principles with practical applications in the energy transition. Dr. Huang's research interests span several critical areas in modern energy systems. He specializes in peer-to-peer energy sharing, urban energy systems, and electromobility, with particular focus on electric vehicles as mobile power sources. His work also encompasses positive energy districts, district heating systems, building energy efficiency, and HVAC systems. A distinctive aspect of his research involves applying machine learning to address uncertainty in energy systems, creating more resilient and adaptive solutions for the energy transition. His multidisciplinary approach connects energy engineering with computer science, urban planning, and sustainability science. Analysis of Dr. Huang's recent publications reveals a strong emphasis on integrating electric vehicles into energy systems as flexible resources. His work demonstrates how vehicle-to-grid technology can enhance grid resilience and enable community energy sharing through innovative solutions like the Electric Vehicle based virtual Electricity Network (EVEN). There's also a notable focus on applying artificial intelligence to optimize energy systems, particularly in data-scarce scenarios where he combines clustering analysis and transfer learning. His research bridges the gap between theoretical models and practical implementation, with several studies based on real-world data from Sweden, demonstrating immediate relevance to current energy challenges. Dr. Huang has been highly successful in securing research funding, with approximately SEK 10 million secured for projects at Dalarna University. His current research portfolio includes: PI for a 2023-2026 Energy Agency project on enhancing grid resilience through electric vehicle-based virtual electricity networks (SEK 2.64 million) PI for a 2023-2026 FORMAS project on photovoltaic and electric vehicle utilization (3.75 million SEK, with a competitive success rate of 13.8%) Co-PI and national coordinator for a 2023-2026 CETPartnership project on thermal energy storage in district heating (2.32 million Euro) Co-PI for a 2024-2026 Swedish Energy Agency project on electric vehicles for frequency regulation (3.25 million SEK) Dr. Huang serves on the editorial board of the journal Buildings and has published extensively, with 49 journal articles, 1 book, 5 book chapters, and 19 conference papers to his name. His research has active participation in IEA tasks, demonstrating international recognition of his expertise. In addition to his primary energy research, Dr. Huang has made significant contributions to neuroscience, particularly in Parkinson's disease diagnostics and treatment, showing the breadth of his interdisciplinary approach.

• Electromobility
• Peer-to-peer energy sharing
• Urban energy systems
• +10 more

Souradeep Dutta is an Assistant Professor in the Department of Electrical and Computer Engineering within the Faculty of Applied Science at the University of British Columbia (UBC), joining in Fall 2024 after postdoctoral research at the PRECISE center, University of Pennsylvania. His academic credentials include a PhD in Electrical and Computer Engineering from the University of Colorado Boulder and a BE in Instrumentation and Electronics Engineering from Jadavpur University, India. Education: PhD, Electrical and Computer Engineering, University of Colorado Boulder BE, Instrumentation and Electronics Engineering, Jadavpur University, India Dr. Dutta's research centers on artificial intelligence with emphasis on reinforcement learning, cyber-physical systems, and formal methods. He investigates fundamental challenges in efficient data-driven learning and assurance techniques for learned models, targeting applications in robotics and medical devices. His work bridges theoretical guarantees with practical implementations for safe human-machine knowledge transfer. Analysis of his 15 most recent publications reveals dominant trends in robustness verification for learning-enabled systems, memory-based adaptation techniques, and distribution shift handling. His research consistently addresses safety-critical applications, particularly in medical diagnostics (e.g., ECG analysis, acne grading) and autonomous control systems, while maintaining strong theoretical foundations in formal methods. Awards: Recognition at top-tier conferences including ICLR, CORL, HSCC, ICCPS, ICAPS, NFM, L4DC, CHASE, and ADHS Dr. Dutta actively seeks graduate students for Fall 2025 and welcomes interdisciplinary collaborations. He serves on program committees for AAAI, ICCPS, ICML, and NeurIPS, and is available for undergraduate research supervision. His advising philosophy emphasizes safe and efficient transfer of human expertise to machine systems. He leads a research group at UBC focused on developing verifiable AI frameworks for cyber-physical applications, with current projects spanning medical device assurance and adaptive robotics control systems.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +4 more

Daniel Baum is a Research Professor and Head of the Visual Data Analysis research group at the Zuse Institute Berlin (ZIB), which is affiliated with Freie Universität Berlin. His work spans across scientific visualization, computational biology, and image analysis, with a particular focus on developing methods for analyzing complex biological structures and neural circuits. He is actively involved in multiple interdisciplinary research projects including HFSP Chitons, Geometric Learning for Single-Cell RNA Velocity Modeling, and RobustCircuit. Dr. Baum's research interests center on visual and data-centric computing approaches to solve complex problems in biology and medicine. His work bridges the gap between computational methods and biological applications, with significant contributions to cryo-electron tomography analysis, neural circuit mapping, and geometric morphometrics. He develops innovative algorithms for 3D reconstruction, image segmentation, and visualization of biological structures, from molecular to organismal scales. His publication record demonstrates consistent contributions to visualization techniques applied to biological problems, with recent work focusing on neural circuit analysis in zebrafish and Drosophila, biomechanical studies of animal structures, and advanced methods for analyzing ancient artifacts. The research shows a clear trajectory toward increasingly sophisticated multimodal data integration and machine learning approaches. Dr. Baum leads a productive research group with several key collaborators who frequently appear as co-authors on his publications, indicating a strong mentoring relationship. His projects involve substantial funding from various sources supporting interdisciplinary collaborations across biology, computer science, and engineering. His laboratory at ZIB focuses on visual data analysis for complex biological systems, with particular strength in developing computational methods for neuroscience applications and biomaterial analysis. The group maintains strong collaborations with multiple institutions working on cutting-edge imaging technologies and biological model systems.

• Scientific Visualization
• Computational Biology
• Image Analysis
• +6 more

Dr. Fatih Nayebi is a Faculty Lecturer in Information Systems at McGill University while also serving as Vice President of Data & AI at the ALDO Group. He bridges academic research with enterprise innovation, focusing on data science, machine learning, and AI systems. Academic Background: Ph.D. in Computer Engineering from École de technologie supérieure M.Sc. in Software Engineering from Boğaziçi University B.Sc. in Computer Engineering from Boğaziçi University Dr. Nayebi's research interests include: Information Systems Data Science Machine Learning Engineering & MLOps Deep Learning Agentic AI Human-Computer Interaction AI in Retail His recent publications focus on AI for retail, mathematical foundations of AI, information integrity in democratic systems, and best practices for technical documentation. Dr. Nayebi also teaches graduate courses at McGill University including: Enterprise Data Science Machine Learning in Production Introduction to AI and Deep Learning Applications and Architectures of Deep Learning Designing and Developing Agentic AI Systems As an active speaker and thought leader, Dr. Nayebi has participated in events such as: World Summit AI Americas RETHINK Retail NRF Nexus 2025 Supply Chain Research Forum JOPT2025 - Annual Conference of Optimization Days He is also the founder of Gradient Divergence, an advisory studio focused on advanced AI solutions for retail and consumer brands.

• Information Systems
• Data Science
• Machine Learning
• +5 more

Maryam Imani is an Associate Professor of Water Systems Engineering at Anglia Ruskin University's School of Engineering and the Built Environment. As a Chartered Civil Engineer (CEng) and Fellow of the Higher Education Academy (FHEA), she specializes in water infrastructure resilience, sustainable drainage systems (SuDS), and computational modeling techniques. BEng (Hons) Civil Engineering, 2001 MEng Water Systems Engineering, 2006 PhD Water Systems Engineering, University of Exeter, 2012 PG Cert in Learning and Teaching in Higher Education, Anglia Ruskin University, 2016 Her research focuses on resilience modeling for water infrastructure, machine learning applications in water systems, and climate adaptation strategies . She leads projects addressing urban wastewater resilience, SuDS implementation in developing countries, and interdependent infrastructure systems. Maryam's work demonstrates a strong emphasis on multi-objective optimization and decision support systems for sustainable water management. Her recent publications explore challenges in Brazil, India, and the UK, integrating climate projections with urban planning. Exeter Research Scholarship (ERS) Fellow of the Higher Education Academy (FHEA) She contributes to major projects like Safe&SuRe water management and RESoURce@Brandia , securing grants from UKRI-GCRF, NERC, and EPSRC. Maryam collaborates with institutions across the UK, Brazil, and the US, including the University of Utah.

• Water Infrastructure Resilience
• Sustainable Drainage Systems (SuDS)
• Computational Modelling
• +6 more

Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University and an adjunct Associate Professor at the University of Illinois at Urbana-Champaign. He leads Stanford Trustworthy AI Research (STAIR), working to develop the principles and practice of trustworthy machine learning with applications to neuroscience and healthcare. Koyejo holds affiliations with multiple Stanford institutes including SAIL, HAI, CRFM, AIMI, AI Safety, Machine Learning Group, and Bio-X. Koyejo completed his Ph.D. at the University of Texas at Austin followed by postdoctoral research at Stanford University. His research bridges theoretical machine learning with practical healthcare applications, focusing on developing robust and fair AI systems that can be trusted in critical domains. His work spans algorithmic fairness, robust distributed learning, metric elicitation, and applications to medical imaging and neuroscience. His recent publications demonstrate a strong focus on emerging challenges in AI including emergent abilities in large language models, fairness in medical AI, federated learning, and robustness against adversarial attacks. His work has increasingly addressed real-world healthcare challenges through deep learning applications to medical imaging, particularly chest radiographs for disease detection. Scientific Awards: NSF CAREER Award 2021 Skip Ellis Early Career Award Sloan Research Fellowship Frederick E. Terman Faculty Fellow (2022) Best Paper Award from UAI Kavli Fellowship IJCAI Early Career Spotlight Koyejo actively mentors a large research group with numerous PhD students and postdocs. His research has been supported by significant grants including NSF funding for projects like 'Fair Federated Representation Learning for Breast Cancer Risk Scoring.' He serves in leadership roles including as General Co-chair for NeurIPS 2022 and President of the Black in AI organization. His STAIR research group focuses on developing trustworthy AI principles and practices, with applications to healthcare and neuroimaging. The group collaborates extensively with healthcare institutions including OSF Healthcare and participates in major initiatives like the NIH-funded MIDRC and the NSF AI research institute AIFARMS.

• Trustworthy Machine Learning
• Algorithmic Fairness
• Robust Distributed Learning
• +5 more