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.
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
Roberto Martin-Martin is an Assistant Professor of Computer Science at the University of Texas at Austin, where he leads the Robot Interactive Intelligence (RobIN) Lab. His research bridges robotics, computer vision, and machine learning to enable robots to operate autonomously in human-centric environments like homes and offices. Previously, he was a Postdoctoral Scholar at the Stanford Vision and Learning Lab working with Fei-Fei Li and Silvio Savarese, and an AI Researcher at Salesforce AI. Education: Ph.D. and M.Sc. in Robotics from Technische Universität Berlin (TUB), advised by Professor Oliver Brock B.Sc. from Universidad Politécnica de Madrid Dr. Martin-Martin's research focuses on developing AI algorithms that combine reinforcement learning and imitation learning with advanced planning and control to address core challenges in robot perception. His work spans mobile and whole-body manipulation, dexterous and contact-rich interactions, and long-horizon tasks in unstructured environments. He takes inspiration from human cognition through psychology and cognitive science to develop solutions for skills ranging from simple pick-and-place operations to complex tasks like cooking and furniture assembly. His recent publications demonstrate a strong trend toward enabling robots to learn from human demonstrations, particularly through video, and to safely adapt these demonstrations to their own morphology. There's significant emphasis on mobile manipulation, bimanual tasks, and developing hardware that supports robust robot learning through trial and error. His work shows increasing integration of large language models and vision-language models to enhance robot understanding and task execution. Scientific Awards: RSS Pioneer (2020) Winner of Amazon Picking Challenge (2015) RSS Best Systems Paper Award (2016) ICRA Best Paper Award IROS Best Mechanism Award Amazon Faculty Award AAAI Young Faculty IJCAI Early Faculty Nominated for Best Paper at IROS (2014, 2017) Dr. Martin-Martin advises PhD students including Arpit Bahety, who is working on mobile manipulation and learning. He serves as Chair of the IEEE Technical Committee on Mobile Manipulation and is a co-founder of QueerInRobotics. His research is supported by industry partnerships and academic funding sources that enable his lab to develop both hardware and software innovations in robotics. He directs the Robot Interactive Intelligence (RobIN) Lab at UT Austin, which takes a holistic approach to robot intelligence, developing both the hardware (like the BaRiFlex gripper) and software frameworks necessary for robots to learn from interaction. The lab's research addresses the full pipeline from perception to action, with particular emphasis on learning from human demonstrations, safe exploration, and adapting to novel objects and environments.
Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.
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 .
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.
Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver. He holds a Ph.D. in Machine Learning and Statistics from MIT and a B.A.Sc. in Aerospace Engineering from the University of Toronto. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, and streaming data analysis. Campbell is a core contributor to probabilistic programming tools like Pigeons.jl and has developed influential methods for coreset-based Bayesian inference. Education : Ph.D. in Machine Learning and Statistics (2016), MIT M.S. in Aeronautics and Astronautics (2013), MIT B.A.Sc. in Aerospace Engineering (2011), University of Toronto Research Interests : His work emphasizes scalable Bayesian computation, including variational inference, MCMC optimization, and coresets. He develops algorithms that balance statistical accuracy with computational efficiency, particularly for large-scale datasets. His recent work explores adaptive samplers (e.g., AutoStep, autoMALA) and theoretical guarantees for coreset methods. Applications span astrophysics, materials science, and network analysis. Awards & Grants : NSERC Discovery Grant (2025) Blackwell-Rosenbluth Award (2021) PIMS Early Career Award (2023) Google Perception Academic Funding (2020) Advising & Labs : He supervises a team of PhD and M.Sc. students at UBC, focusing on Bayesian methodology and computational tools. His lab collaborates with institutions like SFU and MIT on projects involving distributed sampling and probabilistic modeling. He co-organizes workshops on Bayesian computation and serves on editorial boards for Bayesian Analysis and TMLR.
Hyesoon Kim is a Professor at the Georgia Institute of Technology , affiliated with the College of Computing and leading the HPArch research group . She co-directs the Center for Research into Novel Computing Hierarchies (CRNCH) . Her research focuses on Computer Architecture , GPU , Compilers and Runtime Systems , and Hardware Security , particularly for heterogeneous systems. Contact : hyesoon@cc.gatech.edu Location : 266 Ferst Drive, KACB 2344, Atlanta, GA Research Trends Her recent work spans RISC-V extensions for security, CUDA optimization on softcore GPUs, memory safety techniques, and energy-efficient deep learning architectures. Articles emphasize heterogeneous computing , GPU performance scaling, and IoT -oriented neural network methods. Open Source Projects She leads development of Macsim (heterogeneous architecture simulator) and Vortex (open-source GPU platform).
Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).
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 .
Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.
Simon King is a Professor of Speech Processing at the University of Edinburgh , affiliated with the School of Philosophy, Psychology and Language Sciences . He serves as Director of the Centre for Speech Technology Research (CSTR) and teaches courses like Speech Processing and Speech Synthesis , while directing the MSc in Speech and Language Processing . Research Interests His research focuses on: Developing new acoustic models (e.g., Linear Dynamical Models, factorial-HMMs) for speech recognition Advancing unit selection and HMM-based speech synthesis Integrating articulatory measurement data for enhanced modeling Exploring perceptual measures in synthesis criteria Building multilingual speech systems to identify universal speech building blocks Publication Trends Simon's recent work emphasizes deep learning (DNNs, LSTMs) in speech synthesis, multilingual frameworks , and articulatory-acoustic feature integration . His studies often bridge grapheme-based modeling , perceptual error reduction , and noise-robust synthesis . Scientific Awards EPSRC Advanced Research Fellowship (2005-2009) Students & Collaborations He has supervised numerous PhD students including Rasmus Dall, Tom Merritt, and Srikanth Ronanki. Current research fellows like Mirjam Wester and Zhizheng Wu contribute to projects such as Natural Speech Technology (NST) and Simple4All .
Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial & Operations Engineering from the University of Michigan (2020). Prior to ASU, he was a postdoctoral appointee at Argonne National Laboratory’s Mathematics and Computer Science Division. His research focuses on optimization methodologies for decentralized and stochastic decision-making distributed algorithms for machine learning and operations research applications in healthcare systems and energy grids Teaching responsibilities include courses on applied deterministic operations research (IEE 574), optimization (IEE 622), and research practicums (IEE 792). His work emphasizes computational challenges in decision-making under uncertainty, with recent projects addressing nurse staffing optimization, federated learning frameworks (e.g., APPFL/APPFLX), and resilient power grid systems. He has no recorded academic awards but actively contributes to open-source software and cross-disciplinary research collaborations. Research interests bridge theory and practice, targeting social goods through optimization techniques like distributionally robust optimization, federated learning, and heuristic algorithms for energy and healthcare systems.
April Yi Wang is a tenure-track Assistant Professor in the Department of Computer Science at ETH Zürich, where she directs the Programming, Education, and Computer-Human Interaction Lab (PEACH Lab). She is a core faculty member at the Institute for Intelligent Interactive Systems and associated with the ETH AI Center. Wang is also an active member of ETH HCI and Swiss CHI communities, contributing significantly to human-computer interaction and educational technology research. Dr. Wang's educational background includes: Ph.D. in Information Science from University of Michigan (2023), advised by Steve Oney and Christopher Brooks M.Sc. in Computer Science from Simon Fraser University (2018), advised by Parmit Chilana B.Eng in Computer Science from Zhejiang University (2016) Dr. Wang's research focuses on human-centered approaches to programming and data science. Her work reimagines programming as a form of literature that communicates with both machines and people, exploring creative representations like text, shapes, animations, and everyday objects. She investigates how to make programming more natural and intuitive through literate programming environments, with applications in professional and educational contexts. Her research spans human-computer interaction, educational technology, and AI-assisted programming tools. Analysis of Dr. Wang's recent publications reveals a strong focus on AI-enhanced educational tools, particularly for programming and data literacy. Her work increasingly integrates large language models to scaffold learning while maintaining user agency. There's a clear trajectory toward developing situated learning approaches that connect abstract concepts to real-world contexts through augmented reality and tangible interfaces. Her research bridges HCI, education, and AI to create more accessible and engaging technical learning experiences. Dr. Wang has received numerous prestigious awards including: 2023 Gary M. Olson Award and Honourable Mention Award at ACM CHI 2022 Rising Stars in EECS and Heidelberg Laureate Forum Young Researcher 2020 Best Short Paper Award at IEEE VL/HCC and Honourable Mention at ACM CHI 2019 Best Paper Award at ACM CSCW Dr. Wang actively mentors students through thesis projects at ETH Zürich, supervising numerous bachelor's and master's students on topics ranging from AI-assisted programming to data literacy tools. Her lab, PEACH Lab, has secured funding including the recent innovedum funding for the Coducate project. She serves on program committees for major conferences including CHI and UIST, and regularly reviews for top HCI and education journals. The PEACH Lab, directed by Dr. Wang, focuses on creating expressive, intelligent, and human-centered systems that make technical topics more accessible. The lab explores textual, visual, and embodied representations for programming, with emphasis on enhancing communication, collaboration, and learning. Current research directions include balancing automation with user agency, supporting diverse learning needs, and developing tools for interdisciplinary technical communication.
David Stillwell serves as Professor of Computational Social Science at Cambridge Judge Business School and Academic Director of The Psychometrics Centre, University of Cambridge. His research leverages big data to understand human psychology and behavior, with significant contributions in personality prediction from digital footprints and personalized advertising applications. His educational background includes: BSc in Psychology from the University of Nottingham (2007) MSc in Research Methods from the University of Nottingham (2008) PhD in Decision Making from the University of Nottingham (2012) Dr. Stillwell's research centers on computational social science and psychometrics, pioneering the myPersonality Facebook application that collected data from over 6 million users. His work demonstrates computers can predict personality as accurately as spouses, reveals psychological targeting's advertising effectiveness, and establishes links between personality-matched spending and life satisfaction. He also explores linguistic honesty markers through profanity analysis and personality-based dating patterns. His recent publications (2019-2025) reveal a strong trajectory toward AI evaluation using psychometric frameworks, particularly in medical and general-purpose AI assessment. Key themes include fairness metrics in language models, crisis emotional responses through social media, and computational personality recognition - demonstrating consistent innovation at the psychology-technology intersection. He has received significant recognition: Top 10 most influential papers of 2013 by Altmetric Nieman Journalism Lab highlight of 2013 Named in 'top 30 thinkers under 30' by Pacific Standard Magazine Dr. Stillwell maintains active industry engagement through consultancy with Barclays, Hilton, and Ubisoft on projects spanning computer-adaptive testing systems to interactive experiences like Predictive World for Watch Dogs 2. His policy impact is substantial, with citations by the European Data Protection Supervisor, World Bank, and multiple national governments, leading to speaking engagements at the European Parliament and Bank of England. As Academic Director of The Psychometrics Centre, he leads a global hub for advancing psychological assessment through innovative methods including Concerto open-source software, collaborating with organizations ranging from the European Commission to major corporations on psychometric applications in people analytics and digital behavior.
