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).
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.
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.
Andrea Tagliasacchi is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), where he holds the Visual Computing Research Chair. He is also a part-time (20%) Staff Research Scientist at Google DeepMind in Toronto and holds an associate professor (status only) appointment in the Department of Computer Science at the University of Toronto. Education: PhD in Computing Science – Simon Fraser University (NSERC Alexander Graham Bell Fellow) Postdoctoral Research – École Polytechnique Fédérale de Lausanne (EPFL) MSc in Computer Science – Politecnico di Milano (Gold Medalist) His research lies at the intersection of computer vision, computer graphics, and machine learning, with a focus on 3D visual perception. Key areas include neural radiance fields (NeRF), 3D Gaussian splatting, inverse rendering, and geometric deep learning, with applications in robotics, augmented reality, and autonomous systems. His work emphasizes robust and efficient scene understanding and reconstruction from visual data. His recent publications, appearing in top venues like CVPR, SIGGRAPH, NeurIPS, and ECCV, demonstrate a strong emphasis on neural fields, 3D reconstruction, and generative modeling. Trends include improving rendering efficiency, enhancing robustness to noise and distractors, and enabling controllable and 3D-aware generation. His group has made significant contributions to Gaussian splatting, NeRF optimization, and diffusion-based 3D/4D synthesis. Scientific Awards: 2024 CVPR Best Paper Award (Honorable Mention) 2020 CVPR Best Student Paper Award 2015 SGP Best Paper Award NSERC Alexander Graham Bell Canada Graduate Scholarship MITACS Best Paper Award (SIGGRAPH Asia 2009) NSF Best Poster Award (SGP 2012) He has advised numerous PhD and MSc students, many of whom are now researchers at leading institutions and companies. His research has been supported through collaborations with Google, Intel, and academic partners. He serves the community as a Senior Area Chair for CVPR 2025, Associate Editor for IEEE TPAMI (2024–2026), Guest Editor for IEEE TPAMI on 3D GenAI, and Program Chair for 3DV 2024. He leads a vibrant research lab at SFU focused on pushing the boundaries of 3D scene understanding with machine learning.
Tushar Krishna is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology, with a courtesy appointment in the School of Computer Science. He earned his PhD in Electrical Engineering and Computer Science from MIT in 2014, an MSE in Electrical Engineering from Princeton University in 2009, and a B.Tech in Electrical Engineering from IIT Delhi in 2007. His research spans computer architecture, interconnection networks, networks-on-chip (NoC), and AI/ML accelerator systems, with a focus on optimizing data movement in modern computing platforms. His work is funded by NSF, DARPA, IARPA, SRC, Department of Energy, Intel, Google, Meta, Qualcomm, and TSMC. His papers have been cited over 17,000 times, with three receiving IEEE Micro's Top Picks recognition, one earning an honorable mention, and four winning best paper awards. Dr. Krishna leads the Synergy Lab at Georgia Tech and has developed several influential tools including ASTRA-sim for distributed AI/ML training, MAESTRO and SCALE-sim for accelerator design space exploration, and Garnet2.0 for NoC simulation. His recent work focuses on large language model acceleration, distributed training systems, and neuro-symbolic AI architectures. He has received numerous teaching and research awards including induction into the HPCA Hall of Fame (2022), the Class of 1940 Teaching Effectiveness Award (2018), and the Roger P. Webb Outstanding Mid-career Faculty Award (2024). HPCA Hall of Fame Inductee (2022) Roger P. Webb Outstanding Mid-career Faculty Award (2024) Richard M. Bass/Eta Kappa Nu Outstanding Junior Teacher Award (2023) Roger P. Webb Outstanding Junior Faculty Award (2021) Class of 1940 Course Survey Teaching Effectiveness Award (2018) Dr. Krishna currently serves as Associate Director for the Center for Research into Novel Computing Hierarchies (CRNCH) and co-chair of the Chakra Execution Traces and Benchmarks Working Group. He has held the ON Semiconductor (Endowed) Junior Professorship at Georgia Tech (2019-2021) and has been a visiting professor at MIT EECS, Harvard University CS, and a researcher at Intel's VSSAD group.
Andrew Zisserman is a Royal Society Research Professor at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). His research focuses on computer vision, artificial intelligence, and neural networks, with significant contributions to multimodal learning, video understanding, and 3D scene analysis. He leads projects exploring visual-language models, audio-visual synchronization, and clinical imaging applications. Key research areas include: Video analysis and temporal modeling Multimodal systems for sign language translation and action recognition 3D shape estimation and physical property inference Foundation models and cross-modal retrieval Recent work highlights: Developed Flamingo and Tapir models for video-language tasks Advancements in spinal MRI analysis and clinical imaging Leadership in EGO4D and VoxCeleb challenges Honors include Fellowship of the Royal Society (FRS) and the ISSLS Prize in Clinical Science 2023 for spinal analysis innovations. His lab collaborates globally, emphasizing real-world applications in healthcare and autonomous systems.
Caglar Gulcehre is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) and Principal Investigator of the CLAIRE (Caglar Gulcehre Laboratory of Artificial Intelligence Research) lab. Previously, he worked as a Staff Research Scientist at Google DeepMind, Microsoft Research, and IBM Research. His research focuses on reinforcement learning , foundation models , LLM alignment , and sequence modeling . Current Position : Assistant Professor, EPFL Lab : CLAIRE Lab Previous Roles : Staff Research Scientist at DeepMind, MSR, IBM Research His work spans reinforcement learning , deep learning , and neural architecture design , with a focus on safety , trustworthy AI , and real-world applications . He has published in top venues including Nature , NeurIPS , ICML , and JMLR . Scientific contributions include: Best paper award at NeurIPS Nonconvex Optimization workshop Honorable mention for best paper at ICML 2019 Co-organizer of seven workshops at NeurIPS, ICML, and ICLR He supervises PhD students in areas related to AI for algorithm discovery , neural architectures , and foundation models , including: Skander Moalla Justin Samuel Deschenaux Liangze Jiang Xiuying Wei Yitao Xu
Claire Célérier is an Associate Professor of Finance at the Rotman School of Management, University of Toronto, where she holds the Canada Research Chair in Household Finance. Her academic career spans prestigious institutions including the University of Toronto (where she advanced from Assistant to Associate Professor between 2016-2022) and the University of Zurich (2014-2016). She maintains an affiliation with the People's History Lab and actively contributes to academic discourse through conference organization including 'Race, Equity and Public Policy' (2023) and 'Banks, States and the People' (2024). Dr. Célérier's research explores how finance can benefit households through multiple lenses including behavioral economics, asset pricing, and financial history. Her work investigates innovation in finance, diversity and inclusion impacts, and historical perspectives on financial systems. She has developed significant expertise in household finance, banking, and the intersection of finance with social issues, particularly racial disparities in financial markets as demonstrated in her award-winning research on the Freedman's Savings Bank. Her scholarly output shows a clear trajectory from foundational work on security design and investor behavior toward increasingly socially relevant research on financial inclusion, racial equity, and household financial decision-making. Recent publications demonstrate her growing focus on historical contexts of financial systems and their contemporary implications, particularly regarding marginalized communities. Canada Research Chair in Household Finance (2024) Bank of Canada Governor's Award (2022) University of Toronto Top 5% Merit Award (2021) Petro Canada Young Innovators Award Program (2021) Multiple Rotman School of Management Teaching Awards (2019-2021) Ieke van den Burg Prize for Research on Systemic Risk (2015) Dr. Célérier has successfully secured substantial research funding including Insight Development Grants (CAD 54,500), Connaught Research Grant (CAD 10,000), and the Marianne and Marcus Wallenberg Foundation Grant (EUR 400,000). She mentors several PhD students including Andrada Bilan (2020), Alexey Vasilenko (2023), and Purnoor Tak (Expected 2026), and has supervised numerous master's theses at both University of Toronto and University of Zurich. Her service contributions include Rotman School of Management MBA program committee, executive committee, seminar coordination, and faculty recruiting committee.
Dr. Alex Bigazzi is an Associate Professor and Associate Head (Undergraduate Program) in the Department of Civil Engineering at the University of British Columbia (UBC), with a joint appointment in the School of Community and Regional Planning. He leads the REACT Lab, focusing on active transportation, micromobility, and sustainable infrastructure design. His research emphasizes the intersection of physics, physiology, and behavior in active travel, particularly for pedestrians and cyclists, and addresses emissions reduction and equitable urban mobility. Bigazzi holds a B.M. in Jazz Saxophone from the University of Miami and a Ph.D. in Civil Engineering from Portland State University. His career includes academic and practical contributions, such as founding the REACT Lab and leading projects on e-bike incentives, air pollution exposure, and infrastructure design. His work has earned awards like the 2024 Wall Legacy Award for inclusive transport design and the 2023 Climate Action Award. He teaches courses on pedestrian/bicycle facility design and capstone projects, mentoring students through UBC’s Civil Engineering programs.
Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.
Kenneth P. Birman is the N. Rama Rao Professor of Computer Science at Cornell University, where he has had a long and impactful career in distributed systems, cloud computing, and AI/ML infrastructure. He is known for foundational contributions to reliable and scalable distributed systems, and for leading high-impact projects such as Cascade, Vortex, and Derecho. He is also the author of a widely used textbook on reliable distributed systems and has founded multiple companies based on his research. Education: Ph.D. in Computer Science, University of California, Berkeley M.S. in Computer Science, University of California, Berkeley B.A. in Computer Science, Columbia University Research Interests: Professor Birman's research focuses on building reliable, secure, and scalable distributed systems . His current emphasis is on AI and ML infrastructure , particularly in reducing latency and improving performance through hardware acceleration, RDMA-based communication, and edge computing. He explores how to eliminate data movement bottlenecks in AI pipelines and how to support real-time, mission-critical applications in domains like healthcare, smart grids, and industrial IoT. His work spans systems programming, cloud computing, fault tolerance, and formal verification . He has designed systems that have been deployed in high-stakes environments such as the New York Stock Exchange, the Swiss Exchange, and the French Air Traffic Control system. Scientific Awards: ACM Fellow (1999) IEEE Fellow (2014) IEEE Tsutomu Kanai Award for innovations in distributed computing Teaching and Mentorship: Professor Birman teaches two courses in the fall semester: CS4414: Systems Programming and CS5416: Cloud and ML Systems Programming . He has advised numerous Ph.D. and M.S. students, including Alicia Yang, Tiancheng Yuan, Yifan Wang, Weijia Song, Edward Tremel, Sagar Jha, Jonathan Behrens, and Mae Milano. He has announced that Fall 2025 will be his last semester teaching, and he is no longer recruiting new students, though he will continue supervising current ones. Labs and Projects: He leads the Derecho Project and the Cascade/Vortex Project , both focused on high-performance distributed systems. These projects are collaborative efforts with students and industry partners, and the software is released under open-source licenses. He also maintains strong ties with Cornell's systems group and collaborates with faculty across CS, ECE, IS, and the Cornell Tech NYC campus.
Aishwarya Agrawal is an Assistant Professor at Université de Montréal in the Department of Computer Science and Operations Research (DIRO), affiliated with Mila – Quebec Institute of Artificial Intelligence and a Canada CIFAR AI Chair. She also serves as a research scientist at Google DeepMind, spending one day weekly there. Education: B.E. in Electrical Engineering (IIT Gandhinagar, 2014), Ph.D. in Computer Science (Georgia Tech, 2019). Her research focuses on multimodal learning , deep learning , natural language processing , and computer vision , particularly in developing AI systems that 'see' and 'communicate' effectively. Grants & Awards: Canada CIFAR AI Chair, 2020 Sigma Xi Best PhD Thesis Award, NVIDIA Fellowship (2018–2019), and multiple fellowships from Google and Facebook. She leads projects like Advancing Multimodal Vision-Language Learning (CRSNG-funded) and StarDoc: Document Structure Extraction (MITACS). Research Contributions: Pioneered benchmarks like CulturalVQA and UI-Vision , and frameworks such as PROGRESS for efficient VLM training. Her work emphasizes cross-modal alignment, robust evaluation, and cultural understanding in AI systems. Labs/Teams: Active in Mila’s core academic group and collaborates with Google DeepMind on multimodal and vision-language research. Supervises a dynamic team of PhD and master’s students in Montreal.
Suhas Diggavi is a Professor in the Department of Electrical and Computer Engineering at the University of California, Los Angeles, within the Henry Samueli School of Engineering and Applied Science. His primary research area is Signals and Systems, with a strong focus on information theory and its interdisciplinary applications. His research interests span Information Theory , Machine Learning , Differential Privacy , Federated Learning , Cyber-Physical Systems , and Bio-informatics . He investigates fundamental limits and practical algorithms for secure, efficient, and robust data processing in distributed and networked environments. The recent publications highlight a strong trend in privacy-preserving machine learning, particularly in the shuffled model of differential privacy , communication-efficient distributed SGD , and robust optimization . His work bridges theoretical information-theoretic foundations with real-world applications in federated learning, wireless networks, and genomic data analysis. Notable scientific awards include: Guggenheim Foundation Fellow (2021) ACM CCS Best Paper Award (2021) IEEE Fellow (2013) IEEE Donald G. Fink Prize Paper Award (2006) Multiple Google, Amazon, and Facebook Research Awards Suhas Diggavi actively advises graduate students and leads a research group focused on learning, information, and optimization. His work is supported by major industry grants and collaborations, particularly in privacy and distributed learning. He has made significant contributions to information-theoretic models in bio-sequencing and wireless security. He leads the LIOS (Learning, Information, Optimization, and Stochastic Systems) research group at UCLA, where his team develops theoretical frameworks and practical algorithms for next-generation data-driven systems.
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.
Dr. Jiamin Li is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), Shenzhen, China. She holds a Ph.D. and B.Eng. in Electrical and Computer Engineering from the National University of Singapore (NUS). Her research focuses on biomedical integrated circuits, energy harvesting, and wireless body-area networks. Education: Ph.D. and B.Eng. in Electrical and Computer Engineering from NUS Employment: Associate Professor (2024–Present), Assistant Professor (2022–2024) at SUSTech; Research Fellow at NUS Dr. Li's research addresses wireless body area powering , low-power biosignal interfaces , and intelligent biosignal processors . Her work leverages microsystem design for applications in biomedical engineering , particularly in energy-autonomous medical devices and human body-coupled communication . Her recent publications highlight advancements in concurrent power/data transmission , subharmonic pulse injection , and triboelectric energy harvesting for biomedical systems. Key venues include IEEE JSSC , Nature Electronics , and conferences like ISSCC and BioCAS. Awards: IEEE SSCS Predoctoral Achievement Award, ISSCC Best Demo Award, ASSCC SDC Best Design Award Committees: IEEE SSCS Women-in-Circuit (WiC) Committee, ISSCC Student Research Preview Committee Opportunities: Positions available for Ph.D., Master's, Postdoctoral Fellows, and Visiting Scholars
