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).
Byron Wallace is the Sy and Laurie Sternberg Interdisciplinary Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as Associate Dean for Research and Director of the BS in Data Science Program. His research focuses on Natural Language Processing and Machine Learning applications in healthcare. Education Details of formal education are not explicitly provided in the available text, though he holds a PhD from Tufts University (mentioned in thesis award context). Research Interests His work centers on developing NLP and ML models for health applications, with particular emphasis on: Biomedical evidence synthesis automation Electronic Health Record processing Model interpretability and trustworthiness Human-in-the-loop systems Learning with limited supervision Research Trends Recent publications demonstrate strong focus on large language model applications in healthcare, including factuality evaluation for medical summarization, evidence extraction from clinical trials, and interpretable risk prediction models. Notable contributions include work on GPT-3 applications in medical evidence synthesis and neural methods for EHR analysis. Scientific Awards ACL Outstanding Paper Award (2022) ICLR Spotlight (top 5% acceptance) (2024) Best Student-led Paper Award at AMIA 2021 NSF CAREER Award (2018-2023) Advising and Grants Currently advises 4 PhD students and has mentored numerous others. Major funding includes: NSF CAREER Award ($500K+) NIH R01 grant for EHR summarization NSF Medium grant for healthcare summarization Support from Army Research Office, Amazon, and Seton Hospital Labs and Teams Leads the Evidence Inference project team working on automated biomedical evidence synthesis. Collaborates with Brigham and Women's Hospital, Mass General Hospital, and Reboot Rx for clinical translation of research.
Jiaxuan You is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, leading the U Lab focused on achieving Artificial General Intelligence (AGI) in digital environments. His research spans graph neural networks (GNNs), relational data, foundation models, and machine learning systems. PhD and MS in Computer Science from Stanford University (2021) Developed GraphGym and PyTorch Geometric (PyG) for graph learning Core member at Kumo AI (2021-2023) His research explores: Graph-enhanced LLMs: Integrating relational structures into foundation models AGI Development: Self-optimizing AI agents and tool utilization ML Systems: Scalable architectures and redundancy-free computation Interdisciplinary Applications: Financial networks, crop yield prediction, and metro systems Recent publications focus on temporal reasoning, multi-agent dynamics, and hybrid architectures for LLMs. He actively develops open-source tools like DBGYM and GraphRouter. Scientific recognition includes: JPMC PhD Fellowship Baidu Scholarship Best Student Paper at AAAI 2017 World Bank Big Data Innovation Challenge winner He mentors PhD and intern students, emphasizing machine learning systems expertise. His lab collaborates on AGI workshops (e.g., ICLR 2024) and industry projects.
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 .
Kaki Ryan is a Teaching Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. She holds a Ph.D. (2025), M.S. (2021), and B.S. (2020) from UNC, with a minor in AAAD. Her research focuses on hardware security, symbolic execution, and computer science education. Education Ph.D., 2025, UNC-Chapel Hill M.S., 2021, UNC-Chapel Hill B.S., 2020, UNC-Chapel Hill (Computer Science and Mathematics) Ryan's research centers on hardware security, particularly using symbolic execution to identify bugs and vulnerabilities in hardware designs. She also emphasizes broadening participation in computing and improving pedagogical strategies in computer science education. Her recent publications (2023-2025) explore symbolic execution techniques for hardware security verification, including query caching, information flow analysis, and path explosion mitigation. These works span conferences like ASPLOS, VTS, FMCAD, and workshops such as HASP. Scientific Awards John M. Glotzer Graduate Teaching Assistant Award (2020-2021) Tanner Award for Excellence in Undergraduate Teaching (2022) As an educator, Ryan has served as Instructor of Record for COMP 435 (Computer Security Concepts) and COMP 311 (Computer Organization) in Fall 2025. She has extensive teaching experience as a Graduate Teaching Assistant, Head Teaching Assistant, and Undergraduate Teaching Assistant for courses like COMP 210, COMP 110, and COMP 435, with a focus on data structures, security concepts, and programming fundamentals.
Paul Gölz is an Assistant Professor at Cornell University's School of Operations Research and Information Engineering (ORIE), with affiliations in Computer Science. His research focuses on computational social choice, algorithmic fairness, and AI ethics, addressing topics like democratic innovation, fair resource allocation, and AI systems for diverse users. Education: Undergraduate studies at Saarland University (Germany), PhD in Computer Science from Carnegie Mellon University, followed by postdoctoral research at Harvard University, UC Berkeley, and the Simons Laufer Mathematical Sciences Institute. His work has been recognized with awards including the JPMorgan Chase AI Research Fellowship (2021) and honorable mentions for prestigious dissertation awards (Dantzig and ACM SIGecom). Research Highlights : Developed Panelot, a tool for selecting citizens' assemblies using state-of-the-art algorithms. His work on fair refugee resettlement and apportionment methods has been featured in Operations Research and Nature . Recent projects include AI alignment distortion analysis and generative social choice mechanisms. Teaching : Teaches Optimized Democracy (Spring 2025) and Mathematical Programming (Fall 2024). Supervises PhD students in ORIE, focusing on interdisciplinary applications of optimization and social choice. Awards & Grants : Received a $40K Structural Democracy Fellowship (Crankstart) and an OpenAI grant for Democratic Inputs to AI. Active in policy briefs (e.g., mini-public selection strategies). Labs/Tools : Co-developed Panelot.org , a nonprofit platform for fair citizen assembly selection. Active in open-source tool development for social choice applications.
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.
Angjoo Kanazawa is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. She leads the Kanazawa AI Research (KAIR) lab under the Berkeley Artificial Intelligence Research (BAIR) umbrella and serves on the advisory board of Wonder Dynamics. Her research focuses on the intersection of computer vision, computer graphics, and machine learning, with a particular emphasis on 4D reconstruction of dynamic scenes, neural radiance fields (NeRF), and systems that model human-environment interactions from 2D visual data. Education : Ph.D., Computer Science (2017), University of Maryland, College Park BA, Mathematics and Computer Science (2012), New York University (NYU) Her work aims to build systems that can capture, perceive, and understand complex 3D/4D worlds from photographs and videos, enabling applications in scene reconstruction, motion analysis, and generative modeling. She has pioneered techniques for scaling NeRFs across GPUs (NeRF-XL), developing open-source tools like nerfstudio and gsplat. Her recent publications focus on topics like self-occluded avatar recovery (SOAR), decentralized diffusion models, and 4D reconstruction of articulated objects for robotics. Kanazawa's research has been recognized with prestigious awards including the IEEE CS TCPAMI Young Researcher Award (2024) , Sloan Research Fellowship (2023) , and Google Faculty Research Award (2021) . Her lab has trained numerous students who now hold positions at leading institutions and companies like Anthropic, Meta Reality Labs, and Luma AI. Key Scientific Awards : IEEE CS TCPAMI Young Researcher Award (2024) Sloan Research Fellow (2023) Hellman Fellow (2022) Bakar Fellows Spark Award (2022) Google Faculty Research Award (2021) Her KAIR lab collaborates extensively with industry partners and academic institutions, including the Max Planck Institute and Google Research. She has served as an advisor for PhD students and postdocs who now lead teams at UC Berkeley, MIT, Stanford, and Luma AI, while her teaching includes graduate courses like CS 280A (Computer Vision) and CS 294-173 (Learning for 3D Vision).
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.
Prof. Dr. Thomas Brinkhoff is Chair of the Institute Board and Chair of Geoinformatics at Oldenburg University of Applied Sciences. He leads the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and contributes to institutions like the Association for the Promotion of Geoinformatics in Northern Germany (GiN e.V.) and the Oldenburg Research and Development Institute for Computer Science (OFFIS e.V.). Education: Diploma in Computer Science (Informatik), Universität Bremen (1990) Doctorate in Computer Science (Dr. rer. nat.), Ludwig Maximilian University of Munich (1994) Brinkhoff's research spans geodatabase systems, spatiotemporal data processing, geosensor analytics, and location-based services. His work addresses Volunteered Geographic Information (VGI), web-based geospatial visualization, and mobile data integration, with applications in traffic management and forensic science. Recent projects include ProSaDi (Digital Provenance and Collection Research) and contributions to the Laboratory for optical 3D metrology . He has served on program committees for ACM SIGSPATIAL (2002-2019), AGILE conferences (2010-2025), and editorial boards of journals like GeoInformatica and TGIS. Notable Lectures: 2024: Forensic applications of tachograph data 2023: Geoinformatics in homicide investigations 2022: Spatiotemporal analysis for sustainability projects 2015: Open geodata standards at FOSSGIS 2014: Mobile sensor data processing
Takuya Ooura is an Assistant Professor at the Research Institute for Mathematical Sciences (RIMS) at Kyoto University, specializing in numerical analysis and mathematical software development. His work bridges theoretical mathematics with practical applications in scientific computing and software development. Dr. Ooura earned his educational credentials through a rigorous academic path: he graduated from Hokuriku High School in 1987; completed his undergraduate studies at Nagoya University's School of Science in 1992; earned his Master of Engineering from the Department of Applied Physics at the University of Tokyo in 1994; and completed his PhD (Engineering) from the same department in 1997. His academic journey continued with a Research Fellowship from the Japan Society for the Promotion of Science (1997) followed by a position as Research Associate at RIMS, Kyoto University (2000). Dr. Ooura's research focuses on numerical integration algorithms, particularly his groundbreaking double exponential formula for Fourier-type integrals, which has been incorporated into Mathematica's NIntegrate function. He has also developed a high-speed FFT library that's utilized in Google Chrome browser (visible in chrome://credits). His work on continuous Euler transformation for accelerating convergence of slowly decaying integrals represents significant innovation in numerical analysis. His research spans both theoretical development and practical implementation of mathematical algorithms with real-world applications. His publication record demonstrates consistent contributions to numerical analysis, with particular emphasis on quadrature methods, integral transforms, and high-precision computation. His work shows a clear progression from theoretical foundations to practical implementations, with several algorithms achieving widespread adoption in commercial and open-source software. Paper prize awarded by JSIAM (2000) for 'A continuous Euler transformation and its application to Fourier transforms of slowly decaying functions' Paper prize awarded by JSIAM (2001) for 'Improvement of the PI Calculation Algorithm and Implementation of Fast Multiple-Precision Computation' Paper prize awarded by JSIAM (2005) for 'An Improved Convergence Test for the Double Exponential Formula' Japan Society for Industrial and Applied Mathematics 4th Achievement Award (2014) for 'Pioneering and practical development of the double exponential numerical integration method' Dr. Ooura has developed several widely used mathematical software packages including the double exponential integral formula, Clenshaw-Curtis numerical integration rule, and a general-purpose FFT library. His FFT package is particularly notable for its speed and accuracy, with benchmark tests showing superior performance compared to other implementations. His software has been incorporated into major projects including Google Chrome and SETI@home, demonstrating the practical impact of his theoretical work. His future research directions include further development of numerical computation libraries and applying his methods to various computational problems.
Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Ian Goldberg is a Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. He holds the Canada Research Chair in Privacy Enhancing Technologies and leads the Cryptography, Security, and Privacy (CrySP) group . He is affiliated with the Cybersecurity and Privacy Institute and the Institute for Quantum Computing . Research Focus: Security, privacy, and privacy-enhancing technologies (PETs) for the Internet, including Off-the-Record (OTR) Messaging , censorship resistance , private information retrieval , and zero-knowledge proofs . Recent Publications: His work addresses metadata protection , deniable authentication , and secure multi-party computation , with applications to anonymous communication and censorship resistance. Scientific Awards: Canada Research Chair in Privacy Enhancing Technologies Students: He advises current and former PhD/Master’s students in projects like Lox , PRSONA , Walkie-Talkie , and SkypeMorph . Notable advisees include Sajin Sasy (Privacy-Preserving Communications), Justin Tracey (PETs Development), and Tao Wang (Website Fingerprinting Defenses). Labs & Teams: Goldberg is a core member of the CrySP group and contributes to the Tor Project and Off-the-Record Messaging open-source initiatives. His research bridges theoretical and applied cryptography, with deployments in real-world systems like Tor and PIR protocols.
Oliver Kroemer is an Associate Professor at Carnegie Mellon University's Robotics Institute (RI), affiliated with the Intelligent Autonomous Manipulation (IAM) Lab. His research focuses on enabling robots to learn versatile manipulation skills through lifelong frameworks, with applications in elder care, environmental maintenance, and hazardous operations. Developed methods for robot learning via physical interaction and reinforcement learning Created representations for contact states and motor primitives to improve skill generalization Research Interests: Spanning robot learning, tactile sensing, force-velocity control, and lifelong skill acquisition. Projects include Agile and Dynamic Interactions for Mobile Manipulation and Integrated Planning and Learning (Pillar project). Scientific Awards: Finalist, Georges Giralt Ph.D. Award (2015) Education: Masters & Bachelors in Engineering, University of Cambridge (2008) Ph.D., Technische Universitaet Darmstadt (2014) Students & Affiliates: Current PhD: Mark Lee, Sarvesh Patil, Saumya Saxena, Yunus Seker, Zilin Si Past PhD: Alex LaGrassa, Tabitha Lee, Qiao Liang, Shivam Vats, Kevin Zhang
