Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.
Rebecca Herissone is Professor of Musicology at the University of Manchester and a leading scholar in early modern English music. She co-edits the peer-reviewed journal Music & Letters and serves on editorial boards for the Purcell Society, Musica Britannica, and the Complete Works of John Eccles. Her research focuses on seventeenth-century creativity, material culture, and the ontological dimensions of music notation. Key research areas: Early Modern Music, Creativity, Source Study, Notation, Reception Major awards: Diana McVeagh Prize (2015), Westrup Prize (2007) Her recent work includes digital humanities projects on music preservation and critical editions of Purcell's operas. She has pioneered interdisciplinary approaches connecting musicology with drama, art, and literature, and currently leads research on Purcell's posthumous reception in the eighteenth and nineteenth centuries. Her teaching spans music historiography, performance practices, and advanced source analysis.
Erik Ketzan is a Lecturer in Digital Humanities and Cultural Computation at King's College London. He holds a PhD in English/Digital Humanities from Birkbeck, University of London, and has held postdoctoral roles at the University of Cologne and Trinity College Dublin, where he coordinated the MA in Digital Humanities and Culture. His research focuses on computational literary studies, legal/ethical issues in digital research, and corpus stylistics. He has published widely on topics such as Thomas Pynchon’s computational stylistics, legal frameworks for digital humanities research, and gender bias in historical texts. His work includes developing tools like Coleto for textual variant analysis and co-authoring the book *Thomas Pynchon and the Digital Humanities* (2022). Ketzan actively engages in policy advocacy, advising on copyright and data protection in DH, and has contributed to projects like CLARIN and the Leibniz Institute for the German Language. Education: PhD in English/Digital Humanities, Birkbeck, University of London Postdoctoral Researcher, University of Cologne (EncycNet Project) Postdoctoral Research and Teaching Fellow, Trinity College Dublin Research Interests: Computational approaches to literary analysis, legal challenges in data-driven research, corpus stylistics, and gender studies in historical corpora. Teaching: Courses include Python for the humanities, computational literary studies, and legal issues in digital humanities.
Lutz Schubert is a researcher at the Institute of Computer Science , University of Cologne. He focuses on efficient distributed and parallel execution environments for heterogeneous systems, notably the MyThOS operating system in collaboration with Brandenburg University of Technology, Cottbus-Senftenberg. His interdisciplinary work bridges computer science and digital archaeology , addressing modeling of non-deterministic events from sparse excavation data and exploring human behavior constraints in archaeological contexts. Research Interests Design of modular, scalable operating systems for distributed systems Optimization of execution environments for heterogeneous hardware Application of complex systems modeling to digital archaeology Statistical and probabilistic methods for archaeological interpretation Autonomic resource distribution and adaptation in computing Publication Trends : His work spans operating systems , parallel computing , and digital humanities , with recent emphasis on probabilistic reasoning in archaeology and adaptive OS design for multicore architectures. Labs & Collaborations : He collaborates with Brandenburg University of Technology on MyThOS and leads research in computational archaeology as chair of Computer Applications and Quantitative Methods in Archaeology (CAA) , Germany.
Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
Steven Jackson is a Professor of Information Science and Science and Technology Studies at Cornell University's Bowers College of Computing and Information Science, with additional graduate field appointments in Communication and Public Affairs. Since July 2023, he has served as Vice-Provost for Academic Innovation at Cornell, and previously held leadership positions as Chair of Information Science and Dean of William Keeton House. His work bridges academic research with significant institutional leadership in higher education innovation. Dr. Jackson's research focuses on critical intersections of technology, society, and the environment, with three primary strands: technology ethics, law and policy; maintenance, repair and sustainability; and technology, inequality and global development. His work draws from American pragmatism, political economy, and science and technology studies, integrating methods from sociology, anthropology, philosophy, and media studies. He is particularly interested in how computing practices meet social and material worlds, with implications for infrastructure, collaboration, sustainability, and cultural practice. His recent publications reveal a strong emphasis on repair as both theoretical concept and practical methodology, exploring 'ordinary hope' as a framework for technological engagement and examining unmaking as a necessary counterpart to design. The research spans diverse geographical contexts from the global North to South, addressing caste dynamics in computing, digital authoritarianism, and infrastructure challenges in developing contexts. Dr. Jackson has received significant recognition including an NSF CAREER award and more than twenty paper awards at leading computing and information science venues. His research has been supported by major organizations including the U.S. National Science Foundation, Social Science Research Council, Ford Foundation, Sloan Foundation, World Bank, Intel Research, Atkinson Center for Sustainability, and the Canadian Social Science and Humanities Research Council. As an advisor, Dr. Jackson has mentored numerous graduate students who have gone on to prominent positions at institutions worldwide. He currently advises Jen Liu, Amy Cheatle, Daniel Mwesigwa, Johan Michalove, and Ritik Batra, while his former students include Palashi Vaghela (Assistant Professor at Simon Fraser University), Samir Passi (Responsible AI Researcher at Microsoft), Maggie Jack (Assistant Professor at NYU), and Ranjit Singh (Senior Researcher at Data and Society). Dr. Jackson convenes the Computing On Earth Lab, an experimental collaboration bringing together social scientists, humanists, artists, and engineers to rethink the material and planetary foundations of computing. The lab reflects his commitment to interdisciplinary approaches that address computing's environmental impacts and sustainability challenges.
Alane Suhr is an Assistant Professor at UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department and a member of the Berkeley Artificial Intelligence Research Lab (BAIR). Her research focuses on natural language processing (NLP), machine learning, and computer vision, emphasizing systems that interact with humans through language. She designs models and datasets for language grounding (e.g., NLVR) and develops algorithms for learning through interaction. Education: PhD in Computer Science, Cornell University (2022), advised by Yoav Artzi Bachelor's in Computer Science and Engineering, Ohio State University (2016), with a Linguistics minor Research Interests: Interactive systems for collaborative language use (e.g., CerealBar) Language grounding in multimodal contexts Embodied agents and reinforcement learning Generalization in NLP and the role of language in learning Key Contributions: Developed the NLVR dataset for visual reasoning with natural language Pioneered work on SWE-Gym for training software engineering agents Contributed to research on language models' sensitivity and commonsense reasoning Awards: ACM Doctoral Dissertation Award (2022) Outstanding Paper Awards at ACL 2023 and EMNLP 2021 Professional Activities: Organized workshops at ICML, NeurIPS, and ACL on topics like agents, generalization, and theory of mind Active in academic outreach and conference speaking (e.g., ICML, NeurIPS, CVPR) Labs/Teams: Berkeley Artificial Intelligence Research (BAIR) Lab SWE-Gym research group
Prof. Matthias Nießner is a Professor at the Technical University of Munich, leading the Visual Computing Lab. His research intersects computer graphics, vision, and AI, focusing on 3D reconstruction, semantic understanding, and AI-driven video synthesis. He holds a PhD from the University of Erlangen-Nuremberg (2013) and was a Visiting Assistant Professor at Stanford University (2013–2017). Notable awards include the ERC Starting Grant (2018), Nvidia Professorship Award, and Eurographics Young Researcher Award (2019). His work has been featured in mainstream media and led to startups like Synthesia Inc. Research spans Gaussian splatting, neural radiance fields, and generative AI for 3D avatars. Over 150 publications include SIGGRAPH, CVPR, and ECCV, with best paper awards. Projects like Face2Face and ScanNet have driven innovation in facial reenactment and 3D scene datasets. Education: PhD in Computer Science, University of Erlangen-Nuremberg (2013) Diploma in Computer Science, University of Erlangen-Nuremberg (2010) Research Interests: 3D digitization, neural rendering, generative AI, non-rigid reconstruction, and applications in AR/VR. Awards: ERC Starting Grant (2018) Nvidia Professorship Award (2018) Google Faculty Award (2018) SIGGRAPH Best Emerging Tech Award (2016) Grants: Over €1.5M from ERC and industry partnerships. Labs/Teams: Visual Computing Lab at TUM and Synthesia Inc. (co-founder). Key projects include ScanNet (large 3D indoor dataset), Face2Face (real-time facial reenactment), and Gaussian-based 3D avatars. Current work focuses on diffusion models, neural radiance fields, and AI-generated media detection.
Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .
Gary Fedder is the Howard M. Wilkoff Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with courtesy appointments in Biomedical Engineering, Mechanical Engineering, and Robotics. He serves as Faculty Director of the Manufacturing Futures Institute (MFI) and previously held roles such as Vice Provost for Research and Interim CEO of the Advanced Robotics for Manufacturing (ARM) Institute. Fedder’s research focuses on MEMS, advanced manufacturing, and implantable microsystems. He earned his B.S., M.S., and Ph.D. in EECS from MIT and UC Berkeley, respectively. Education: Ph.D., Electrical Engineering and Computer Science, UC Berkeley (1994) M.S., Electrical Engineering and Computer Science, MIT (1984) B.S., Electrical Engineering and Computer Science, MIT (1982) Research Interests: Microelectromechanical systems (MEMS), digital twins, aerosol jet printing, stretchable electronics, and manufacturing innovation. His work integrates MEMS with CMOS processes, emphasizing low-cost, high-performance systems. Key Contributions: Co-founded the ARM Institute; developed MEMS-based sensors and actuators; pioneered methods for manufacturing innovation through projects like America Makes. His research spans over 300 publications and 21 patents. Awards: IEEE Fellow (2007), Ross Tucker Award (1993), NSF CAREER Award (1996), and leadership roles in Manufacturing USA initiatives. Leadership & Outreach: Directed the Institute for Complex Engineered Systems and led national initiatives to advance U.S. manufacturing competitiveness. Active in editorial roles for journals like IoP Journal of Micromechanics .
Haijun Xia is an Assistant Professor at the University of California, San Diego (UCSD), where he directs the Foundation Interface Lab and contributes to the Cognitive Science and Design Lab. His work focuses on Human-Computer Interaction (HCI) and Human-AI Collaboration , emphasizing the development of dynamic, malleable interfaces that integrate human cognition with intelligent systems to enhance thinking and working paradigms. Education: Bachelor's, Tsinghua University Master's and PhD, University of Toronto His research spans generative AI , interface design , and creative collaboration , with applications in data visualization, programming, and scholarly work. He advocates for treating information as a malleable material that can be flexibly manipulated by users and AI agents. Recent publications highlight advancements in: 2025 CHI Conference - Generative user interfaces, malleable overview-detail designs, and compositional structures for human-AI co-creation 2024 CHI Conference - Structured design space exploration with LLMs, ASCII diagram analysis, and adaptive presentation systems 2023 CHI/UIST Conferences - Data particle visualization, contextual logging tools, and metaphor generation for science communication Scientific recognitions include: Multiple Best Paper Awards and Honorable Mentions at CHI and UIST Hellman Fellowship and grants from NSF , Microsoft , Google , Meta , Apple , and Adobe He actively recruits undergraduate and graduate interns and has an upcoming postdoc position in early 2025 for researchers interested in foundational interface development.
K. Michael Hays is the Eliot Noyes Professor of Architectural Theory at Harvard University's Graduate School of Design (GSD) and Co-Director of the Master in Design Studies program. He has been a faculty member since 1988, specializing in architectural history and theory. His research focuses on European modernism, critical theory, and contemporary architectural practice, with notable contributions to the field through writings on figures like Mies van der Rohe and Peter Eisenman. Education: He holds a Bachelor of Architecture (1976) from Georgia Institute of Technology, a Master of Architecture in Advanced Studies (1979) from MIT, and a PhD in History, Theory, and Criticism of Architecture and Art (1990) from MIT. Research Interests: Hays is renowned for founding the journal Assemblage , a pivotal forum for architectural theory. His work bridges historical analysis and contemporary practice, emphasizing theoretical frameworks for understanding modernism and its evolution. He has curated exhibitions at institutions like the Whitney Museum of American Art (as its first Adjunct Curator from 2000–2009). Publications: His authored and edited works include Architecture's Desire , Inscriptions: Architecture Before Speech , and critical studies on architects such as Buckminster Fuller and Diller + Scofidio. These publications explore themes of representation, modernist aesthetics, and interdisciplinary design. Awards and Roles: While no specific awards are listed, his academic leadership roles include Chair of the GSD PhD Committee (1995–2005) and Director of Advanced Independent Study Programs. He has also served on editorial boards for journals like Assemblage and Thresholds . Labs and Collaborations: Hays collaborates with academic and institutional partners, notably through curatorial projects and his role in shaping the GSD's theoretical curriculum. His influence extends to both pedagogical innovation and scholarly discourse in architectural education.
Mathieu Salzmann is a Senior Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) in the School of Computer and Communication Sciences (IC). He also holds a courtesy appointment with the EPFL College of Humanities and serves as Deputy Chief Data Scientist at the Swiss Data Science Center (SDSC). He has held concurrent roles in teaching units including SIN, SODH, and SSC, reflecting his interdisciplinary engagement. His research focuses on the intersection of machine learning and computer vision, particularly in deep learning for 2D and 3D visual scene understanding, efficient and robust models, domain adaptation, and interpretable AI. These interests are evident across his extensive publication record in top-tier venues. His recent publications (2023–2024) show a consistent trend in advancing deep learning methods for visual recognition, with strong representation at CVPR, ICCV, ECCV, ICML, ICLR, and NeurIPS. Topics include domain generalization, 3D understanding, model robustness, and multimodal learning, often with applications in real-world systems. His editorial roles as Associate Editor for IEEE TPAMI and Action Editor for TMLR further highlight his leadership in the field. Area Chair: ICML 2023, CVPR 2023, ICCV 2023, NeurIPS 2023, AAAI 2024, ECCV 2024 Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Action Editor: Transactions on Machine Learning Research (TMLR) Mathieu Salzmann has supervised numerous PhD students at EPFL, both current and past, including Bouquet Yann Yanis, Javed Saqib, Li Shuangqi, and others. He has also been involved in research grants and collaborative projects, such as his work with S. Süsstrunk and R. Baroni on comics reconfiguration. His part-time role as Senior GNC Engineer at ClearSpace (2020–2024) illustrates his applied research engagement in aerospace systems. He is actively involved in EPFL’s data science and AI research ecosystem through SDSC and multiple labs.
David A. Smith is an Associate Professor at the Khoury College of Computer Sciences, Northeastern University. His research focuses on Natural Language Processing (NLP) and computational linguistics, with applications in machine translation, information retrieval, digital humanities, and social sciences. He is a founding member of the NULab for Texts, Maps, and Networks, a research center focused on digital humanities and computational social sciences. Smith's work has been funded by grants from the Mellon Foundation, NEH, and IMLS, supporting projects such as the Viral Texts initiative analyzing 19th-century newspaper networks and the Oceanic Exchanges project tracking transnational information flows. He has contributed to advancements in OCR for historical texts, text reuse detection, and computational analysis of classical languages. He has advised numerous PhD students, including Shijia Liu, Si Wu, and Ryan Muther, and teaches courses like Natural Language Processing and Information Retrieval. His research has been featured in outlets like Wired and the Economist .
Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .
