Harish Ravichandar is an Assistant Professor at the School of Interactive Computing , Georgia Institute of Technology, and a core faculty member of the Institute for Robotics and Intelligent Machines (IRIM) . He leads the Structured Techniques for Algorithmic Robotics (STAR) Lab , focusing on structured computational frameworks and learning algorithms with inductive biases to enhance robot efficiency, reliability, and self-sufficiency in human-robot collaboration and complex applications like dexterous manipulation and multi-agent coordination. His research bridges robot learning , human-robot interaction , and multi-agent systems , emphasizing stable, frugal, and safe skill acquisition from human demonstrations. Key themes include intention inference , trajectory optimization , and heterogeneous team coordination , often leveraging Koopman operators , hypernetworks , and graph-based methods . Scientific recognition includes the NSF CAREER Award , IEEE MRS Best Paper Award , and Georgia Tech’s College of Computing Outstanding Post-Doctoral Research Award . His work also received the ASME DSCC Best Student Paper Award and P&W Institute Graduate Fellowship . Harish’s educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Connecticut (2018) , an M.S. from the University of Florida (2014) , and a B.E. in Instrumentation and Control Engineering from Anna University (2012) . He previously held postdoctoral and research scientist roles at Georgia Tech before his current position.
W. Hong Yeo is a Professor in the Woodruff School of Mechanical Engineering and Program Faculty in Bioengineering at the Georgia Institute of Technology, where he also directs the WISH Center. He holds adjunct appointments in the Wallace H. Coulter Department of Biomedical Engineering. Previously, he was an Assistant Professor at Virginia Commonwealth University (2014-2016) and a postdoctoral fellow at the University of Illinois Urbana-Champaign's Beckman Institute. Dr. Yeo's research integrates nanomechanics, soft materials, and nano-microfabrication to develop bio-interfaced systems. Key areas include: Flexible Bioelectronics : Wearable/implantable sensors for health monitoring Human-Machine Interfaces : Neural prosthetics and soft robotics Translational Nanoengineering : Nanoparticle biosensing and diagnostics His publications (2023-2025) demonstrate strong focus on wireless health technologies, including multi-modal wearable systems, implantable sensors for cardiovascular/neurological monitoring, and AI-integrated diagnostics. Trends show increasing emphasis on closed-loop therapeutic systems and scalable manufacturing. Awards & Recognition : BMES Innovation and Career Development Award Virginia Commercialization Award Blavatnik Award Nominee NSF Summer Institute Fellowship Research funding sources include MEDARVA Foundation, NIH, DARPA, and industry partners like CooperVision. He leads the Center for Human-Centric Interfaces & Engineering , developing next-generation bio-interfaced systems.
Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science and an Adjunct Professor at the University of Montréal. She serves as a Visiting Faculty Researcher at Google, a Core Academic Member at MILA (Quebec Institute for Learning Algorithms), and holds a prestigious Canada CIFAR AI Chair. Farnadi co-directs McGill's Collaborative for AI & Society (McCAIS) and founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on advancing algorithmic fairness and responsible AI. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), with postdoctoral research at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). During her doctoral studies, she was a visiting scholar at UCLA, University of Washington, Tsinghua University, and Microsoft Research. Dr. Farnadi's research centers on developing mathematical tools and algorithms for fairness-aware machine learning systems. Her work addresses bias and discrimination in AI decision-making across critical domains including healthcare, criminal justice, financial services, and social media. She has pioneered approaches to ensure fairness in deep learning models, particularly in sequential decision-making under uncertainty. Her research bridges theoretical foundations with practical applications, examining how AI systems can be designed to promote equity while maintaining performance. Analysis of her recent publications reveals a strong focus on practical implementations of fairness mechanisms across diverse AI applications. Her work spans technical domains from generative models and large language models to recommender systems and healthcare optimization. A unifying theme is the development of mathematically rigorous frameworks that balance performance with fairness considerations, with increasing attention to cultural diversity in multilingual AI systems and privacy-preserving fairness approaches. Google Scholar Award (2021) Facebook Research Award (2021) Rising Stars in AI Ethics (2021) Google Award for Inclusion Research (2023) WAI Responsible AI Leader of the Year Finalist (2023) 100 Brilliant Women in AI Ethics (2023) Canada CIFAR AI Chair Dr. Farnadi advises numerous doctoral and master's students across McGill University, University of Montréal, and MILA, with research focusing on fairness, privacy, and responsible AI. Her EQUAL Lab brings together researchers from computer science, social sciences, and policy domains to address systemic challenges in AI ethics. She has secured significant research funding from Google and other major organizations to support her work on fairness-aware AI systems, with applications spanning healthcare, social media safety, and public policy. The EQUAL Lab serves as a hub for interdisciplinary research on algorithmic fairness, bringing together computer scientists, social scientists, and policy experts. The lab's work spans theoretical foundations of fairness metrics, practical implementations in real-world systems, and policy recommendations for responsible AI deployment. Current projects include developing frameworks for fair kidney exchange programs, mitigating cultural stereotypes in multilingual language models, and creating privacy-preserving approaches for detecting online harms while protecting user data.
Dr. Min Chi is a Professor in the Department of Computer Science at North Carolina State University, where she joined in 2013 as a Chancellor's Faculty Excellence Program cluster hire in the Digital Transformation of Education. Her academic journey includes a Ph.D. and M.S. in Intelligent Systems from the University of Pittsburgh and a B.E. in Information Science and Technology from Xi'an Jiaotong University, China. She completed postdoctoral fellowships at Carnegie Mellon University's Machine Learning Department and Stanford University's Human Sciences and Technologies Advanced Research Institute. Dr. Chi's research focuses on the development and empirical evaluation of cutting-edge Artificial Intelligence, Deep Learning, and Reinforcement Learning frameworks tailored for addressing human-centric challenges. Her work spans multiple domains including advanced learning technologies, AI and intelligent agents, data sciences and analytics, and human-computer interaction. She has made significant contributions to intelligent tutoring systems, healthcare applications, nuclear power systems, and humanitarian efforts such as food distribution and disaster relief. Her publication record demonstrates a strong focus on applying AI techniques to real-world educational challenges, with recent work examining metacognitive knowledge transfer, reinforcement learning for pedagogical policy induction, and deep learning approaches for proactive help in educational settings. Her research also extends to healthcare applications, food distribution systems, and other socially impactful domains. 10 Best Paper, Best Student Paper, and Outstanding Paper Awards Prestigious Alcoa Foundation Engineering Research Achievement Award NSF CAREER Award Dr. Chi leads multiple significant research projects funded by the National Science Foundation, National Institutes of Health, and the Department of Energy, with a total funding exceeding $7 million. Her work bridges theoretical advances in AI with practical applications that address critical societal challenges in education, healthcare, and humanitarian operations.
Miroslav Pajic serves as a Professor in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. He also holds joint appointments as Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science and Associate Professor of Computer Science. As Director of Master's Studies, he oversees the graduate program in Electrical and Computer Engineering and teaches numerous courses spanning embedded systems, cyber-physical systems design, and robotics. Education: Ph.D. in Electrical and Computer Engineering from University of Pennsylvania (2012) Miroslav Pajic's research focuses on the design and analysis of cyber-physical systems (CPS) with varying levels of autonomy and human interaction. His work spans the intersection of embedded systems, artificial intelligence, machine learning, control theory, formal methods, and robotics. He specializes in developing high-assurance autonomous systems with applications in robotics, automotive systems, and medical devices, with particular emphasis on CPS security and resilient autonomy. His research addresses fundamental challenges in creating systems that can operate reliably in uncertain environments while maintaining security against potential cyber attacks. Analysis of Pajic's recent publications reveals a strong interdisciplinary research program bridging theoretical foundations with practical applications. His work spans secure sensor fusion for distributed autonomy, medical applications of CPS (particularly deep brain stimulation for neurological disorders), and innovative sensing technologies for autonomous vehicles. A significant portion of his research addresses security challenges in cyber-physical systems, including stealthy GPS attacks on UAVs and methods for attack-resilient state estimation. His publications increasingly integrate machine learning techniques with traditional control theory to create more adaptive and robust autonomous systems. Pajic actively mentors graduate students and leads research groups focused on cyber-physical systems security and high-assurance autonomy. His research is supported by multiple grants, including the NSF AI Institute for Edge Computing (Athena), which he co-leads. He has received funding from various sources to support his work on secure and resilient cyber-physical systems, medical device security, and autonomous vehicle technologies. Pajic collaborates extensively with medical researchers on applications of cyber-physical systems in healthcare, particularly in deep brain stimulation for neurological disorders. His work bridges the gap between theoretical control systems and practical implementations in safety-critical domains, with a growing emphasis on translating research into real-world applications that improve system security and reliability.
Petri Mähönen is a Full Professor at the Department of Information and Communications Engineering , Aalto University. His research focuses on networked systems, machine learning applications in telecommunications, and smart grid technologies. University: Aalto University Department: Information and Communications Engineering Research Interests span networked systems, IoT security, UAV communication, and AI-driven network optimization. His work addresses predictive QoS in cellular-connected drones and generative adversarial networks for cybersecurity. Recent Publications include studies on GAN-based traffic augmentation, anomaly detection in mobile networks, and regulatory frameworks for data platforms. His articles reflect expertise in both theoretical and applied network science.
David A. Glinbizzi is a Lecturer in the Department of Law and Philosophy at the United States Military Academy (USMA), where he teaches courses in ethical reasoning and political philosophy. He also serves as a department academic counselor, honor representative, and Officer in Charge of the West Point Speech and Parliamentary Debate Team. Military Career: Began as enlisted infantryman (2011), commissioned as Artillery Officer (2015), held leadership roles in 10th Mountain Division, and transitioned to Adjutant General Corps (2019). Academic Roles: Teaching Assistant at University of Pennsylvania (2021), Graduate Associate at Perry World House (2022), and led Academic Individual Advanced Development (AIAD) internship (2024) focused on AI ethics. His research centers on just war theory , artificial intelligence ethics , and political philosophy , with practical applications in military and technological domains. Courses taught include: PHIL 1433 (TA): The Social Contract PY201: Philosophy and Ethical Reasoning PY189X (Course XO): Independent Study in Philosophy (AI Ethics) Recent work integrated institutions such as the Office of the Undersecretary of Defense for Policy (OUSD-P), DARPA, and the Army's AI Integration Center (AI2C) to provide cadets with interdisciplinary exposure to emerging technology ethics.
Professor Barney Tan serves as Professor of Information Systems and Senior Deputy Dean (Impact and Partnerships) at UNSW Business School. Previously, he held the position of Head of School (Information Systems and Technology Management) and was formerly Professor of Strategic Information Systems at The University of Sydney. Professor Tan completed his PhD in Information Systems at the National University of Singapore in 2012, following a Bachelor of Computing (Honors) in Information Systems from the same institution in 2004. His research focuses on strategic information systems, AI management and implementation, digital platforms and ecosystems, fintech, and information systems for sustainable development with particular emphasis on IT trends in the Asia-Pacific region including China and ASEAN countries. As an engaged researcher, Professor Tan is deeply passionate about translational research that contributes toward sustainable development and frequently provides advisory support to organizations he has studied, including national governments through his work with the United Nations International Think Tank for Landlocked Developing Countries. His research has been published in top-tier journals including MIS Quarterly, Information Systems Research, Journal of Association of Information Systems, Information Systems Journal, and The Journal of Strategic Information Systems. Senior Editor, Information Systems Journal (2022-Present) Senior Editor, Information Technology and People (2019-Present) Associate Editor, Internet Research (2020-Present) Associate Editor, Information and Management (2016-Present) Editorial Board Member, Information Systems Research (2022-Present) Editorial Board Member, IEEE Transactions on Engineering Management (2017-Present) Professor Tan has received numerous prestigious teaching and research awards throughout his distinguished career: 22 awards in 11 years at The University of Sydney SUPRA Teacher of the Year award for 2019 and 2020 SUPRA Supervisor of the Year award for 2019, 2020 and 2021 The University of Sydney Business School Dean's Award for Teaching in 2019 Multiple Dean's Citations for Teaching across various courses and semesters Professor Tan has successfully secured multiple competitive research grants: Hong Kong University Grants Committee General Research Fund (2020-2022): 'The Impact of Social Media on Digital Guanxi Formation in the Chinese Workplace', Co-Investigator, HKD$475,617 (A$88,286) The University of Sydney Business School Industry Partnership Grant (2017): 'International Think Tank for Landlocked Developing Countries Research Development Project', Chief Investigator, A$27,238 Zhejiang Gongshang University Strategic Research Grant (2016-2019): 'Emerging Digital Commerce Platforms: Development Patterns and Usage Behavior', Principal Investigator, CNY$300,000 (A$60,535) Multiple University of Sydney Bridging Support Grants (2015-2016) focused on digital commerce and Australian-China trade Professor Tan is frequently featured in mainstream media outlets including ABC News, Business Insider, Sydney Morning Herald, The Australian, News.com.au, The Australian Financial Review, CIO, and Bloomberg Businessweek, where he provides expert commentary on technology-related topics such as cryptocurrency, digital platforms, and China's tech landscape.
Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.
Vasileios Mavroeidis is an Associate Professor in Digital Security at the Department of Informatics, University of Oslo (UiO). He specializes in security automation and orchestration (SOAR) and cyber threat intelligence (CTI) representation, reasoning, and sharing. He actively contributes to European cybersecurity initiatives, including Horizon Europe, Connecting Europe Facility, and the European Defense Fund, and serves as the primary representative of UiO at the OASIS standards development organization since 2017. Role : Associate Professor Department : Digital Security (SEC), University of Oslo Standardization Involvement : Chairman of OASIS Threat Actor Context (TAC), Leading Contributor to CACAO and OpenC2 Projects : Concordia, CyberHunt, JCOP (Joint Cyber Security Operations Platform), Oslo Analytics, P4C (Partnership for Cybersecurity) His research focuses on cyber threat intelligence (CTI), exploring its taxonomies, sharing standards (STIX, CACAO), and ontologies, with contributions to the European Union Agency for Cybersecurity (ENISA) Cybersecurity Playbooks task force. He analyzes quantum computing's impact on cryptography, develops automated threat detection systems using machine learning (e.g., recurrent neural networks for malware-generated domains), and investigates privacy issues under GDPR. Recent publications highlight his work on LLMs for code stylometry , neurosymbolic AI for cyber defense , and knowledge management systems for CACAO playbooks . His articles span 2017–2025, emphasizing formal verification, biometric data protection, and incident response automation. He collaborates with organizations like OASIS (Threat Actor Context, CACAO, OpenC2) and FIRST (Traffic Light Protocol), and participates in European research projects. His work includes standardization efforts in cybersecurity playbooks , MITRE ATT&CK representation, and quantum-resistant cryptography .
Pekka Marttinen is a tenured Associate Professor of Machine Learning at Aalto University, Department of Computer Science, and leads the Machine Learning for Health (Aalto-ML4H) group within the Helsinki Institute for Information Technology HIIT. Education: M.Sc. in Applied Mathematics, University of Helsinki (2004) Ph.D. in Statistics, University of Helsinki (2008) Title of Docent in Information and Computer Science, Aalto University (2015) Research Focus: His methodological work spans large language models, reinforcement learning, deep learning, probabilistic machine learning, and causal inference . These techniques are applied to critical domains of healthcare, bioinformatics, statistical genetics, epidemiology, and personalized medicine . The group develops novel algorithms, theoretical guarantees, and open-source software that enable data-driven discovery and decision-making in medicine and biology. Publication Trends: Across 2022-2024 the lab has concentrated on (i) rigorous causal reasoning over temporal clinical data, (ii) principled uncertainty quantification in LLMs, (iii) representation learning for neural network comparison, and (iv) translational projects that turn raw EHRs into actionable clinical insights. Earlier work integrated high-dimensional genomics with metabolomics and mapped evolutionary forces in bacterial pathogens. Scientific Awards & Recognition: While no specific awards are listed, his sustained publication record in top-tier venues (NeurIPS, ICML, AISTATS, Nature Genetics, PLOS CB) and his role as responsible professor of the Machine-Learning, Data-Science and AI major signify significant peer recognition. Advising & Grants: Prof. Marttinen currently mentors 8 PhD students as primary supervisor and an additional 7 PhD students as co-supervisor. He has already graduated 8 PhDs since 2014. The group is supported through competitive funding including the Finnish Center for Artificial Intelligence (FCAI) doctoral program. Labs & Teams: He directs the Machine Learning for Health (Aalto-ML4H) research group, comprising postdocs Hans Moen, Ti John, Alexander Nikitin, Negar Safinianaini, Linli Zhang, Zhiyuan Li, and the above-mentioned PhD cohort.
Anders Krogh is a Professor at the Department of Computer Science, University of Copenhagen, and also holds a position at the Department of Public Health in the Section for Health Data Science and AI. He serves as the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. Previously, he was affiliated with the Department of Biology at the University of Copenhagen until 2020. Dr. Krogh earned his PhD in theoretical physics but transitioned into machine learning and bioinformatics during his doctoral studies. His research spans both theoretical foundations and practical applications in these fields. He is particularly renowned for his pioneering work on hidden Markov models for biological sequences, which has had significant impact in computational biology. In recent years, Krogh's research has focused on deep generative models applied to gene expression data and other biomedical applications. His work bridges computer science with healthcare, developing AI-driven approaches for precision medicine, cancer diagnostics, and analysis of complex biological systems. His current research integrates machine learning with quantum computing applications in biomolecular modeling. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence to healthcare challenges, particularly in rare diseases, cancer diagnostics, and personalized medicine. His work increasingly incorporates federated learning approaches to address privacy concerns while enabling collaborative research across institutions. There's also a growing emphasis on quantum computing applications in biomolecular modeling and drug discovery. As head of the Center for Health Data Science, Krogh leads interdisciplinary research efforts that bring together computer scientists, medical researchers, and clinicians. His team develops novel computational frameworks like MOSAIC for multimodal analysis of rare cancers and multiDGD for multi-omics data integration. These tools are designed to translate AI innovations into clinical practice while addressing the unique challenges of medical data.
Hedvig Kjellström is a Professor at the Division of Robotics, Perception and Learning within the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology. She holds significant affiliations with the Swedish e-Science Research Centre and the Max Planck Institute for Intelligent Systems in Germany. Her work spans multiple interdisciplinary domains and she serves as Editor-in-Chief for CVIU and was Program Chair for CVPR 2025. Her research centers on Computer Vision as a sub-field of AI, with three interconnected themes: Computational Aesthetics (exploring aesthetic aspects of human communicative behavior), Communicative Behavior (developing models of how humans and animals perceive and produce non-verbal communication), and Embodied Artificial Intelligence (creating methodologies for robots and autonomous agents to perceive the world through sensors, primarily vision). Her work has significant applications in medical diagnostics, animal welfare, human-robot interaction, and creative arts. Analysis of her recent publications reveals a strong trend toward multimodal AI systems that integrate vision, language, and action understanding. Her research increasingly focuses on animal-centered applications, particularly equine pain detection and behavior analysis, while maintaining strong foundations in human communication modeling, gesture recognition, and 3D reconstruction techniques. The interdisciplinary nature of her work bridges computer science with veterinary medicine, neuroscience, and performing arts. Hedvig Kjellström actively supervises numerous PhD and Master's students across various projects and maintains extensive collaborations with institutions including Karolinska Institutet, Swedish University of Agricultural Sciences, and international partners. Her research is supported by major funding bodies including WASP, VR, and SeRC. She leads or participates in several notable projects including OrchestrAI (communication between conductor and orchestra), ANITA (Animal Translator), MARTHA (3D horse motion analysis), and STING (synthesis and analysis with transducers and invertible neural generators). Her work with ACAI (Animal Centered Artificial Intelligence), which she co-founded and directs, demonstrates her commitment to applying AI for animal welfare.
Natasha Zhang Foutz is a Research Associate Professor of Commerce at the McIntire School of Commerce, University of Virginia . Her work bridges Artificial Intelligence , Marketing Analytics , and Consumer Behavior , with a focus on Digital Content and Location-Based Services . She teaches Marketing Analytics , Entertainment Marketing , and Marketing Models across undergraduate to PhD programs. Ph.D. in Marketing, Cornell University M.S. in Statistics & Marketing, Cornell University B.S. in Economics, Fudan University Her research investigates: AI-Powered Entertainment Marketing : Using machine learning to analyze consumer behavior in digital content. Privacy and Ethical Data Use : Studying consumer trade-offs between privacy and public good, especially during crises like the COVID-19 pandemic . Location Analytics : Leveraging mobile data for urban economics, real estate, and emergency response modeling (e.g., MobiRescue for disaster logistics). Prosocial Consumer Behavior : Exploring how social capital and diversity drive innovation and policy compliance. Recent publications highlight trends in Reinforcement Learning for crisis management, Privacy-Preserving AI , and Freemium Pricing Models in digital markets. Her awards include the 2025 UVA Outstanding Researcher Award and the 2018 Mallen Award for motion picture studies. Natasha serves as an Area Editor for multiple journals, emphasizing Data Science in marketing. She has advised numerous PhD students and collaborated on projects analyzing Big Data in consumer mobility and platform economics.
Esther Rolf is an Assistant Professor in the Department of Computer Science at the University of Colorado Boulder. Her research focuses on blending methodological and applied machine learning techniques to address social and environmental challenges, emphasizing usability, data-efficiency, and fairness. Her work includes developing algorithms for environmental monitoring with satellite imagery and advancing geospatial ML systems. Her research explores the multifaceted role of data representation in machine learning, particularly how spatial distribution and data acquisition impact model fairness and efficacy. Projects include formalizing representivity in training data and tackling evaluation challenges in geospatial ML applications. Recent publications highlight her expertise in satellite-based poverty mapping, multimodal data efficiency, and geospatial foundation models. She integrates specialized architectures and domain-specific benchmarks into her work. Best Paper Award, ICML (2018) Best Paper Award, NeurIPS Workshop on AI for Social Good (2019) NSF Graduate Research Fellowship Google Research Fellowship Esther's lab at CU Boulder recruits PhD students and postdocs interested in statistical/geospatial ML and context-driven research for real-world problems. She teaches graduate courses in machine learning and geospatial ML at CU Boulder.
