Zongyi Li is a Research Fellow at Massachusetts Institute of Technology , hosted by Kaiming He. They are currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech (2019-2025), mentored by Anima Anandkumar and Andrew Stuart. Ph.D. candidate: Computing and Mathematical Sciences, Caltech (2019-2025) B.Sc. in Computer Science and Mathematics with a Jazz minor from Washington University in St. Louis (2015-2019) They focus on Neural Operators for learning solution operators in Partial Differential Equations (PDEs) , particularly in fluid mechanics and earth science . Their work models physical simulations with chaotic behaviors and complex geometries, showing applications in weather forecasting , carbon storage , and aerodynamics simulation . Publications emphasize resolution-invariant models , chaotic systems , and zero-shot super-resolution capabilities. Their research combines Fourier analysis , graph networks , and physics-informed loss functions to achieve state-of-the-art performance in PDE solving with up to 1000x speedup over traditional solvers. Fellowships: Kortschak Scholarship PIMCO Fellowship Amazon AI4Science Fellowship Nvidia Fellowship MIT Novo Nordisk AI Fellowship Code & Open-Source: Co-developer of the NeuralOperator library Implementations for Fourier Neural Operators , Graph Neural Operators , and Tensorized Neural Operators Media Recognition: Quanta Magazine MIT Tech Review NVIDIA Features Towards Data Science
Quanquan C. Liu is an Assistant Professor in the Department of Computer Science at Yale University, part of the School of Engineering & Applied Science. His research focuses on algorithms for large data, dynamic/distributed/parallel graph algorithms, and differential privacy. He holds a PhD from MIT's Theory Group, advised by Erik Demaine and Julian Shun, with postdoctoral experience at Northwestern University and MIT. Liu has authored over 50 publications in top venues like FOCS, SPAA, and STACS, and received a Best Paper Award at SPAA 2022. He advises a team of 12+ students, including PhD and undergraduate researchers. His service roles include PC membership for PPoPP, ESA, and SPAA, and coaching for the USA Computing Olympiad (USACO) and ICPC teams. Notable research contributions include advancements in parallel algorithms for graph problems and privacy-preserving techniques.
Yonatan Bisk is an Assistant Professor at Carnegie Mellon University within the Language Technologies Institute (with courtesy appointment in Robotics Institute). His research bridges Natural Language Processing , Robotics , and Embodied AI , focusing on language grounding, theory of mind, and multimodal interaction. Education : Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Postdoctoral Experience : USC ISI, University of Washington, Allen Institute for AI Industry Appointments : Microsoft Research, Meta AI His research emphasizes embodied language systems and social intelligence in AI . Recent projects include WebArena for autonomous agents, SOTOPIA for social reasoning, and HomeRobot for open-vocabulary manipulation. He leads the REAL Center (Robotics, Embodied AI, and Learning) to foster interdisciplinary collaboration. Key scientific awards include selection for the DARPA ISAT Study Group (2024). He teaches courses like "Talking to Robots" and "Multimodal Machine Learning" while serving as area chair/editor across NLP, Robotics, and ML communities.
Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.
Dinesh Jayaraman is an Assistant Professor at the University of Pennsylvania, with primary and secondary appointments in the Department of Computer and Information Science (CIS) and Electrical and Systems Engineering (ESE), respectively. He leads the Perception, Action, and Learning (PennPAL) Research Group at the GRASP Laboratory, focusing on interdisciplinary research at the intersection of robotics, machine learning, and computer vision. Research Interests: Robotics, computer vision, reinforcement learning, and autonomous systems. Recent Publications: His work explores vision-language models for robotic tool use, symmetry-based control acceleration, articulated object modeling, and in-context learning frameworks. Awards: Recipient of the 2022 NSF CAREER Award for innovative contributions to robotics and AI. Teaching: Co-teaching a robot-learning seminar (CIS 7000/ESE 6800) with Antonio Loquercio in Spring 2025. Students: Advising PhD candidates including Edward Hu, Arjun Krishna, and co-advised students with Osbert Bastani, Vijay Kumar, and Rajeev Alur.
Professor Christian Bizer is a leading figure in web-based systems and data integration at the University of Mannheim , where he chairs Information Systems V: Web-based Systems . His research focuses on integrating data from multiple sources using large language models and LLM-based agents, with applications in product data extraction and DBpedia knowledge graph construction. He co-founded the DBpedia project and initiated the WebDataCommons initiative. Current research areas: Entity matching, schema matching, table annotation, information extraction, data discovery Key projects: WebMall benchmark, WInte.r integration framework, Schema.org analysis His work applies to e-commerce data integration and knowledge graph construction, with empirical studies on schema.org adoption. He supervises PhD students including Alexander Brinkmann and Ralph Peeters. Scientific Awards: Best Paper at iiWAS 2024 SWSA Ten-Year Award at ISWC 2019 Yahoo FREP Award 2015 Semantic Web Challenge winners Teaching includes courses on web data integration, web mining, large language models, and data mining for master's programs. He leads the DWS PhD colloquium and team projects on LLM agents for data integration.
Nadia Polikarpova is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego . She earned her PhD from ETH Zurich in 2014 under Bertrand Meyer , followed by postdoctoral research at MIT CSAIL with Armando Solar-Lezama . Her academic contributions have been recognized with prestigious awards including the 2020 Sloan Fellowship , 2020 Intel Rising Stars Award , and 2020 NSF CAREER Award . Polikarpova's research focuses on program synthesis , program verification , and type systems . She leads the Programming Systems group at UCSD and contributes to the IFIP Working Group 2.8 on Functional Programming since 2022. Her work spans foundational research and practical tools, including projects like Synquid , SuSLik , and Laurel that combine formal methods with machine learning for code generation. Her recent publications in venues like OOPSLA , NeurIPS , and ICFP reveal trends in AI-assisted programming , live programming environments , and formal verification . She has advised numerous PhD and Master’s students including Shraddha Barke , Zheng Guo , and Tristan Knoth , many of whom have moved to prominent academic and industry positions. Notable artifacts from her lab include tools like ColDeco for spreadsheet inspection and Superfusion for eliminating intermediate data structures. 2020 : Sloan Fellow 2020 : Intel Rising Stars Award 2020 : NSF CAREER Award 2021 : Distinguished Paper at POPL 2023 : Distinguished Artifact at PLDI 2023 : Distinguished Paper at OOPSLA Polikarpova actively contributes to academic service, serving on program committees for PLDI , POPL , and OOPSLA , and co-chairing the OOPSLA Review Committee in 2023. She has delivered keynotes at APLAS'20 and PLDI'24 , emphasizing the integration of large language models with formal methods.
Prof. Eleni Chatzi is a Full Professor and Chair of Structural Mechanics at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. She holds a PhD from Columbia University (2010) and has held roles from Assistant to Full Professor at ETH since 2010. Her research focuses on intelligent structural monitoring and data-driven asset management, emphasizing nonlinear dynamics and sensor integration. Affiliations : Institute of Structural Engineering, European Academy of Wind Energy (EAWE President), Swiss Community for Computational Methods (SWICCOMAS Chair) Research interests include Structural Health Monitoring (SHM), system identification, and advanced simulation tools. She pioneered work on data-driven diagnostics and self-aware infrastructure, supported by grants like the ERC Starting Grant (2015). Awards include the 2020 Walter L. Huber Prize and 2024 SHM Person of the Year Award. Her work spans wind energy infrastructure, metamaterials for vibration control, and AI-driven structural analytics. Over 600 publications and 200k+ citations highlight her impact. She teaches computational science and structural dynamics in ETH's programs and collaborates globally on sustainable infrastructure projects.
Danai Koutra is an Associate Professor in Computer Science and Engineering at the University of Michigan, Ann Arbor, and an Amazon Scholar. Her research focuses on large-scale graph mining, graph neural networks, and interpretable machine learning methods for understanding complex networks. Key roles include leading the GEMS Lab and contributing to projects like DeltaCon (graph similarity) and VoG (graph summarization). She holds a PhD from Carnegie Mellon University and has authored over 80 publications in top venues like KDD, SDM, and NeurIPS. Educations: PhD in Computer Science, Carnegie Mellon University (2015) MS in Computer Science, Carnegie Mellon University (2015) Diploma in Electrical & Computer Engineering, National Technical University of Athens (2010) Research Interests: Her work spans graph mining, anomaly detection, knowledge graph completion, and applications in neuroscience, healthcare, and social networks. Recent projects include MAGNET (multi-agent graph networks) and GT2VEC (multimodal graph-text encoders). Grants & Awards: Recipient of the 2025 PECASE award, NSF CAREER Award (2019), and the 2016 ACM SIGKDD Dissertation Award. Active in organizing conferences like KDD and ECML/PKDD. Labs & Teams: Directs the GEMS Lab, collaborating on projects like FIDDLE (clinical data preprocessing) and SpecGreedy (dense subgraph detection). Engages in interdisciplinary efforts, including M-DICE (urban mobility analysis with Detroit).
Heather Battey is a Professor in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. Her work bridges foundational statistical theory with practical scientific applications, focusing on parametrization effects, sparsity, and high-dimensional inference. Education PhD, University of Cambridge (2008-2011) Research Interests Battey's research examines how model structure and parametrization influence inferential procedures, particularly in high-dimensional settings. She investigates the equivalence between sparsity and reparametrization, and challenges traditional Fisherian statistical abstractions through modern practices. Her publications reveal a pattern of innovation in high-dimensional regression, covariance matrix analysis, and statistical methodology for complex data. Collaborations span disciplines including machine learning, economics, and biomedical research. Scientific Awards Fellow of the Institute of Mathematical Statistics (2023) EPSRC Early Career Research Fellowship (2020-2026) EPSRC Postdoctoral Research Fellowship (2017-2020) Advising and Grants Battey supervises PhD students Charlotte Edgar, Jakub Rybak, and Rebecca Lewis, with informal guidance to Henrique Hoeltgebaum. Over 15 pre-doctoral researchers have been mentored in topics ranging from support vector machines to spatial point processes. Current funding includes an EPSRC grant for theoretical foundations of inference with nuisance parameters and prior support for covariance matrix inference.
Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.
Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .
Professor Ernest Foo is a distinguished academic at Griffith University's School of Information and Communication Technology, specializing in cybersecurity with a focus on industrial control systems and cryptographic protocols. With over 15 years of experience in computer networking, he has established himself as a leading expert in SCADA security and smart grid cybersecurity. His research has significant practical applications in critical infrastructure protection, and he has developed hands-on security training programs that have trained professionals from major Australian utilities and government agencies. Professor Foo's educational background includes: Bachelor of Engineering with Honours in Electronic and Computer Engineering from University of Queensland Doctor of Philosophy from Queensland University of Technology Professor Foo's research interests center around secure cryptographic protocols with specific applications in industrial control system security and cyber physical systems. His work spans SCADA security, smart grid protection, wireless sensor network security, and post-quantum cryptography. He has made significant contributions to understanding vulnerabilities in industrial protocols like Modbus and DNP3, and has pioneered the application of process mining and data mining techniques for attack detection in critical infrastructure systems. His research bridges theoretical security concepts with practical implementations in real-world industrial environments. Professor Foo's recent publications demonstrate a clear trajectory toward increasingly sophisticated security frameworks for critical infrastructure. His work shows a progression from foundational SCADA security research to advanced applications of artificial intelligence, machine learning, and formal methods in cybersecurity. A notable trend is the integration of zero trust principles with industrial control systems, alongside growing emphasis on post-quantum cryptographic solutions. His publications span high-impact journals and conferences in cybersecurity, with increasing focus on anomaly detection in cyber-physical systems and the application of graph-based machine learning techniques to network security challenges. Professor Foo's notable scientific achievements include: Best paper award at the 2nd International Cyber Resilience Conference for "Gap analysis of Intrusion Detection in Smart Grids" Professor Foo has secured significant research funding including an ARC Linkage grant with Powerlink Queensland focused on cyber security for electricity sub-stations. He currently leads multiple research projects including Westpac Micro-Credentials in Financial Crime Investigation, Digital Banking Micro-credentials with ANZ, and research on quantum-safe cryptography. As a dedicated educator, he serves as Program Director for multiple cybersecurity programs including the Master of Cyber Security, and has supervised numerous doctoral and masters students. His Cyber Security: Industrial Control System course, conducted annually from 2013-2018, featured innovative hands-on training with real-world participants from major Australian utilities. Professor Foo has been instrumental in establishing the SCADA security research laboratory with multiple vendor system miniatures running industrial PLCs. His work bridges theoretical security concepts with practical implementations, making significant contributions to the security of industrial control systems and critical infrastructure worldwide.
Paolo Gardoni is the Alfredo H. Ang Family Professor and an Excellence Faculty Scholar in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, with additional professorial appointments in Industrial & Enterprise Systems Engineering and Biomedical & Translational Sciences. He also serves as Director of the MAE Center and Editor-in-Chief of Reliability Engineering & System Safety. Education Ph.D. in Civil Engineering, University of California, Berkeley (2002) M.A. in Statistics, University of California, Berkeley (2001) M.Eng. in Structural Engineering, University of Tokyo (1997) Laurea (BS+MS equivalent) in Structural Engineering, Politecnico di Milano (1997) Research Interests Gardoni’s scholarship integrates probabilistic methods with large-scale infrastructure systems to advance reliability, risk, and life-cycle analysis. His work quantifies the performance of deteriorating systems under natural and anthropogenic hazards, models societal impacts of disasters, and develops decision frameworks for sustainable and resilient infrastructure. He also examines ethical, social, and legal dimensions of risk, and investigates optimal strategies for hazard mitigation, disaster recovery, and climate adaptation. Across more than 250 refereed journal papers, he has advanced sub-fields ranging from probabilistic mechanics and earthquake engineering to catastrophe bond pricing and engineering ethics, leveraging tools such as stochastic differential equations, Bayesian networks, and physics-informed machine learning. Awards & Honors Alfredo Ang Award on Risk Analysis and Management of Civil Infrastructure (ASCE, 2021) Best Paper Awards in ASCE Journal of Sustainable Water in the Built Environment (2019) and Geotechnical Research (2018 Telford Premium Prize) Fellowships and named professorships: Alfredo H. Ang Family Professor, Excellence Faculty Scholar, and courtesy or honorary professorships at Tsinghua, IIT Guwahati, Tongji, Jianghan, and Loughborough universities. Research Leadership & Funding Gardoni has secured over $58 million in research funding from NSF, DHS, NIST, USAID, Qatar National Research Fund, and other agencies. He directs the MAE Center—formerly an NSF Engineering Research Center—focused on multi-hazard engineering approaches, and is Editor-in-Chief of Reliability Engineering & System Safety (Elsevier, IF 9.4). He founded and formerly led the journal Sustainable and Resilient Infrastructure (Taylor & Francis) and serves on editorial boards of nine additional journals. Advising & Mentorship He has graduated 27 PhD and 35 Master’s students, many of whom now hold faculty positions worldwide. His group maintains an active pipeline of doctoral and post-doctoral researchers working on resilience analytics, infrastructure monitoring, and risk-informed decision-making. Laboratories & Collaborations He leads the MAE Center and is affiliated with the Critical Infrastructure Resilience Institute (CIRI) and the Biomedical and Translational Sciences group. International collaborations span the UK (Loughborough), India (IIT Guwahati), and China (Tsinghua, Tongji, Jianghan), fostering cross-disciplinary research in reliability and resilience engineering.
Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence
