جست‌وجوی استاد و پژوهشگر

Stefano Ermonمشاهده پروفایل

دانشیار

• Machine Learning
• Generative AI
• Computational Sustainability
• +۷ مورد دیگر

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

Joseph A. Novemberمشاهده پروفایل

دانشیار

• History of Science and Technology
• Biomedical Computing
• History of Medicine
• +۳ مورد دیگر

Joseph A. November is an Associate Professor in the Department of History at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. His research focuses on the history of biomedical computing, distributed computing, and the intersection of technology and medicine. He holds a Ph.D. from Princeton University (2006), an M.A. from the University of Chicago (2002), and a B.A. from Hamilton College (1997). His work includes the award-winning book Biomedical Computing: Digitizing Life in the United States (2012), which explores the co-development of biomedicine and computing technologies. Current projects include Revolutions@home , examining distributed computing in protein folding research, and a biography of computing pioneer Robert S. Ledley. He has received grants from the NSF, NIH, and the Charles Babbage Institute. Teaching interests span the history of science and technology, including courses on the history of medicine, digital humanities, and the role of games in historical education. He actively contributes to professional organizations like SHOT and the History of Science Society. Awards include the Computer History Museum Prize (2013) and the National Institutes of Health DeWitt Stetten Fellowship (2007-2008). His research bridges historical analysis with contemporary issues in technology and biomedical ethics.

Christian Theobaltمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۵ مورد دیگر

Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays

Hutan Ashrafianمشاهده پروفایل

استاد

• Artificial Intelligence in Medicine
• Metabolic Surgery
• Health Policy
• +۴ مورد دیگر

Professor Hutan Ashrafian is a clinician-scientist and surgeon at the University of Leeds Business School, holding dual roles as Professor of Research Impact and Senior Research Fellow at Imperial College London. His expertise spans AI in healthcare, metabolic surgery, and ancient history. He pioneered STARD-AI and QUADAS-AI guidelines for AI diagnostics, collaborated on AI-driven breast cancer screening with Google and NHS, and led global health policy initiatives. Ashrafian’s work bridges medicine, history, and philosophy, including his contributions to understanding historical figures’ medical conditions, such as King Tutankhamun’s epilepsy. He has authored over 550 publications, 12 books, and holds a PhD in computational physiology and an MBA with distinction. Awards include the Hunterian Prize and Wellcome Trust Fellowship. His research also explores AI ethics, quantum physics paradoxes, and art-based medical diagnostics. Education: PhD in Computational Physiology (Imperial College London) MBA (Warwick Business School) MD (University College London) Bachelor of Science in Immunology (University College London) Research Interests: Ashrafian’s work spans: AI in Medicine: Diagnostic algorithms, guideline development (STARD-AI/QUADAS-AI), and ethical frameworks. Metabolic Surgery: Bariatric interventions, gut microbiome, and obesity treatments. Ancient History: Medical analyses of historical figures (e.g., Tutankhamun, Julius Caesar) and art-based pathology identification. Philosophy: AI rights (AIONAI law), temporal paradoxes, and the Simulation Argument. Awards: Royal College of Surgeons Arris and Gale Lectureship Hunterian Prize Wellcome Trust Research Fellowship Advising & Innovation: Supervised >50 PhD students, co-founded Oxford Medical Products (weight loss tech), and serves as CSO at Flagship Pioneering’s Preemptive Health division. He advises on NHS digital transformation and global health policy. Labs/Teams: Leads AI initiatives at Imperial’s Institute of Global Health Innovation and collaborates with Google, NICE, and international institutions on health tech solutions.

• Speech and Audio Processing
• Behavioral Signal Processing
• Real-Time MRI of Speech Production
• +۴ مورد دیگر

Shrikanth (Shri) Narayanan is a University Professor and holder of the Niki and Max Nikias Chair in Engineering at the University of Southern California (USC), serving as the inaugural Vice President for Presidential Initiatives. He leads the Signal Analysis and Interpretation Lab (SAIL) and holds joint appointments in Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head and Neck Surgery. His research focuses on speech and audio processing, behavioral signal processing, and real-time MRI of speech production, with applications in healthcare, education, and technology. Education: B.E. in Electrical Engineering from College of Engineering, Guindy (Chennai, India, 1988); M.S., Engineer, and Ph.D. in Electrical Engineering from UCLA (1990, 1992, 1995). Research interests span computational linguistics, machine learning, and multimodal human behavior analysis. He pioneered technologies for speech biomarkers in mental health, real-time MRI of speech production, and wearable sensor systems for longitudinal health studies. His work in speech emotion recognition, forensic interviews, and clinical applications has been recognized through over 40 awards, including the IEEE Flanagan Award and ISCA Medal. He has published extensively in journals like Proceedings of the IEEE , Journal of the Acoustical Society of America , and PLOS One . Key Grants: NSF CAREER, Okawa Research, IBM Faculty, Google/Amazon awards. Labs/Teams: Signal Analysis & Interpretation Lab (SAIL), USC Information Sciences Institute (ISI), Google Visiting Faculty Researcher.

Kenji Kawaguchiمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

Niki Kilbertusمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Ethics in AI
• +۵ مورد دیگر

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

Silvia Lindtnerمشاهده پروفایل

دانشیار

• Science and Technology Studies (STS)
• Cultural Anthropology
• Feminist Studies
• +۷ مورد دیگر

Silvia Lindtner is an Associate Professor at the University of Michigan's School of Information and Director of the Center for Ethics, Society, and Computing (ESC). She holds courtesy appointments in the Penny W. Stamps School of Art and Design and the Digital Studies Institute. Her work focuses on the social and cultural study of technology in China and globally, with a focus on data-driven governance, AI ethics, and the intersection of technology and labor. Lindtner has conducted nearly two decades of fieldwork in China, examining innovation ecosystems, digital economies, and the affective dimensions of state control. She is the author of the award-winning *Prototype Nation: China and the Contested Promise of Innovation* (2020) and co-author of *Technoprecarious* (2020). Education: PhD in Information and Computer Sciences from University of California, Irvine; BS/MS in Media Technology and Design from University of Hagenberg, Austria. Research Interests: Science and Technology Studies (STS), China Studies, HCI, Critical Computing, AI Ethics, Data Governance, Labor Studies, and Transnational Digital Economies. She explores how technological systems shape governance, labor practices, and everyday life, particularly in contexts of global inequality and state surveillance. Awards: Joseph Levenson Prize (2021), Francis L.K. Hsu Book Prize (2021), ACM Distinguished Member (2022), and fellowships from the National Committee on US-China Relations (2021-2023) and China-US Scholars Program (2021-2022). Grants: Supported by NSF, IMLS, Intel Labs, Google Anita Borg, and Chinese National Natural Science Foundation. Leads interdisciplinary initiatives including Tech.Culture.Matters research group and Michigan Interactive and Social Computing (MISC) research group. Labs/Teams: Directs the Center for Ethics, Society, and Computing (ESC) and collaborates with the Lieberthal-Rogel Center for Chinese Studies. Current projects include studies on affective control in AI governance and transnational data practices in healthcare and labor.

Heng Huangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Big Data Computing
• +۱۱ مورد دیگر

Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Maryland, College Park . He earned his Ph.D. in Computer Science from Dartmouth College and holds prior degrees from Shanghai Jiao Tong University. His research focuses on advancing the foundations and applications of artificial intelligence, particularly in machine learning, data mining, natural language processing, computer vision, and biomedical informatics . His work integrates large-scale optimization, fairness, and robustness in deep learning systems. Heng Huang’s recent publications demonstrate a strong trend in large language models, federated learning, model watermarking, continual learning, and medical image analysis . His work appears consistently in top venues like NeurIPS, ICML, CVPR, ICLR, and MICCAI, reflecting a broad impact across theoretical and applied AI. He actively mentors students and postdocs, seeking highly motivated researchers in machine learning and related domains. His work has significant implications for healthcare, privacy, and trustworthy AI.

Adriana Kovashkaمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Adriana Kovashka is an Associate Professor at the University of Pittsburgh , affiliated with the School of Computing and Information and serving as Department Chair . Her academic journey began with BA degrees in Computer Science and Media Studies from Pomona College (2008) and a PhD in Computer Science from The University of Texas at Austin (2014). Joined Pitt’s faculty in January 2015 NSF CAREER awardee (2021) Google Faculty Research Award recipient Dr. Kovashka’s research spans Computer Vision , Machine Learning , and Natural Language Processing , focusing on visual rhetoric, weak multimodal supervision, and domain adaptation. She pioneered techniques for analyzing political imagery, developing robust object detection frameworks, and exploring the intersection of visual and textual persuasion through large-scale annotated datasets. Her recent work emphasizes geographic diversity in vision-language systems, audio-visual fusion for domain generalization, and shape-texture bias mitigation in CNNs. Key publications include groundbreaking studies on symbolic reasoning, multimodal dialogue systems, and ethical AI applications in education. Scientific honors include: NSF CRII Award (2016) NSF CAREER Award (2021) Pitt CRDF Award (2016, 2018) Best Paper at ECV Workshop (2021) Google Faculty Research Award (2016, 2018) Dr. Kovashka actively mentors students in multimodal learning projects and collaborates with interdisciplinary teams on NSF-funded initiatives. She co-organizes workshops like the first CVPR workshop on advertisement understanding and leads research groups exploring human-AI co-learning systems.

Daniel Epsteinمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Ubiquitous Computing
• Social Computing
• +۶ مورد دیگر

Daniel Epstein is an Associate Professor in the Department of Informatics at the University of California, Irvine (UCI), where he leads the Personal Informatics Everyday (PIE) Lab. He holds a courtesy appointment in the Department of Computer Science and is affiliated with the Connected Learning Lab, Institute for Future Health, and Accessibility Research Collective. Epstein earned his Ph.D. in Computer Science & Engineering from the University of Washington and a B.S. in Computer Science from the University of Virginia. His research focuses on how personal tracking technology can better serve diverse users' needs, emphasizing ethical design, health equity, and long-term user engagement. Notable areas include pregnancy tracking, wearable technology for children and ADHD management, and AI-driven health chatbots. Epstein has received grants from NSF, Snap Inc., and UCI programs, totaling over $650k in funding. Epstein's work bridges HCI and health informatics, with over 60 peer-reviewed publications. He advises six Ph.D. students and has mentored numerous undergraduates. Awards include the ACM Senior Member distinction (2025), CHI Best Paper (2023), and NSF CAREER Award (2023). Professional service includes program committees for CHI, CSCW, and UbiComp, plus leadership roles in workshops like the Workgroup on Interactive Systems in Healthcare (WISH). Beyond academia, Epstein enjoys hiking, collecting turtle figures, and advocating for accessible technology.

Tanzeem Choudhuryمشاهده پروفایل

استاد

• Ubiquitous Computing
• Mobile Sensing Systems
• Wearable Technology
• +۴ مورد دیگر

Tanzeem Choudhury is a Professor at the Jacobs Technion-Cornell Institute at Cornell Tech, specializing in Information Sciences. She co-founded HealthRhythms Inc. and directs the People-Aware Computing lab, focusing on machine learning for human activity and social network analysis. Her work pioneered the 'Reality Mining' field, leveraging mobile sensors to model social interactions. Choudhury holds a Ph.D. from MIT's Media Lab and B.S. in Electrical Engineering from the University of Rochester. Her research spans wearable tech, mental health interventions, and ubiquitous computing. Notable projects include affective touch wearables for anxiety reduction, stress detection systems (e.g., StressSense), and the BeWell app for well-being monitoring. Choudhury has been recognized as an ACM Fellow and SIGCHI Academy member, with impactful contributions to healthcare technology and sensor-based behavioral analysis. Current lab initiatives include developing closed-loop interventions for mental health, equity-driven sensing systems, and exploring AI integration in psychotherapy. She advises PhD students on topics like digital biomarkers, burnout detection in healthcare workers, and smartphone-based mental health assessment. Education: Ph.D. in Media Arts & Sciences (MIT), M.S. (MIT Media Lab), B.S. in Electrical Engineering (University of Rochester) Lab Team: Includes PhD candidates (Dan Adler, Yiran Zhao) and researchers focusing on health AI, wearable devices, and clinical applications. Major Awards: ACM Fellowship (2020), SIGCHI Academy (2018), Best Paper Awards at Ubicomp and MobileHCI conferences. Key Projects: OptoBeat (skin-tone calibrated blood oxygen sensor), EmotionCheck (anxiety intervention), and Affective Touch devices.

Hanjie Chenمشاهده پروفایل

استادیار

• Natural Language Processing
• Interpretable Machine Learning
• Trustworthy AI
• +۵ مورد دیگر

Hanjie Chen is an Assistant Professor in the Department of Computer Science at Rice University, affiliated with the Ken Kennedy Institute. She holds a Ph.D. from the University of Virginia and a Master's from the University of Science and Technology of China. Her research focuses on Natural Language Processing, Interpretable Machine Learning, and Trustworthy AI, emphasizing model explainability, alignment with human needs, and applications in healthcare, sports, and medicine. She has advised numerous students and led initiatives in AI ethics and education. Education: Ph.D. (Computer Science, UVA 2023), M.Sc. (USTC 2018), B.Sc. (Nanjing University of Aeronautics and Astronautics 2015). Awards include the Outstanding Doctoral Student Award (UVA 2023) and John A. Stankovic Research Award (UVA 2023). She has organized workshops like BlackboxNLP and served on program committees for ACL, NAACL, and EMNLP. Her recent work includes developing benchmarks like SPORTU for multimodal LLMs, evaluating medical question-answering systems, and advancing methods for robust rationale evaluation (RORA). She teaches courses on Natural Language Processing and Trustworthy NLP, emphasizing pedagogical innovation recognized by teaching awards at UVA. Research collaborations include internships at Microsoft Research, IBM, and the Allen Institute for AI. She mentors students in SURF programs and advocates for diversity in tech, serving as a mentor in UVA's CSGSG Council.

Bobak Mortazaviمشاهده پروفایل

دانشیار

• Medical analytics
• Machine learning
• Embedded systems design
• +۳ مورد دیگر

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.

Steven Jacksonمشاهده پروفایل

استاد

• Technology Ethics
• Infrastructure Studies
• Human-Computer Interaction
• +۷ مورد دیگر

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.