Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.
Colin Raffel , currently an Associate Professor at the University of Toronto and Associate Research Director at the Vector Institute , is a leading researcher in machine learning and natural language processing . His career spans roles at Hugging Face (Faculty Researcher), Google Brain (Senior Research Scientist), and UNC Chapel Hill (Assistant Professor). Education: PhD in Electrical Engineering (Columbia), MA in Music/Science (Stanford), BA in Mathematics (Oberlin) Key affiliations: Google Brain (2016-2020), Hugging Face (2021-present), Vector Institute (2023-present) His research focuses on language model development , attention mechanisms , efficient machine learning , and music information retrieval . Recent work explores model merging , parameter-efficient fine-tuning , and data-constrained language models . Teaching : Has instructed courses at University of Toronto and UNC Chapel Hill on Neural Networks , Deep Learning , and Information Theory . Academic service includes organizing ICLR workshops and serving as Senior Area Chair for NeurIPS and EMNLP . Notable awards : NSF CAREER (2022), Caspar Bowden Award (2023), NeurIPS Outstanding Paper (2023) Key contributions : Core developer of WT5 , Git-Theta , and mir_eval software
Adriana Kovashka is an Associate Professor at the University of Pittsburgh , affiliated with the School of Computing and Information and serving as Department Chair . Her academic journey began with BA degrees in Computer Science and Media Studies from Pomona College (2008) and a PhD in Computer Science from The University of Texas at Austin (2014). Joined Pitt’s faculty in January 2015 NSF CAREER awardee (2021) Google Faculty Research Award recipient Dr. Kovashka’s research spans Computer Vision , Machine Learning , and Natural Language Processing , focusing on visual rhetoric, weak multimodal supervision, and domain adaptation. She pioneered techniques for analyzing political imagery, developing robust object detection frameworks, and exploring the intersection of visual and textual persuasion through large-scale annotated datasets. Her recent work emphasizes geographic diversity in vision-language systems, audio-visual fusion for domain generalization, and shape-texture bias mitigation in CNNs. Key publications include groundbreaking studies on symbolic reasoning, multimodal dialogue systems, and ethical AI applications in education. Scientific honors include: NSF CRII Award (2016) NSF CAREER Award (2021) Pitt CRDF Award (2016, 2018) Best Paper at ECV Workshop (2021) Google Faculty Research Award (2016, 2018) Dr. Kovashka actively mentors students in multimodal learning projects and collaborates with interdisciplinary teams on NSF-funded initiatives. She co-organizes workshops like the first CVPR workshop on advertisement understanding and leads research groups exploring human-AI co-learning systems.
Wei-Lun (Harry) Chao is an Associate Professor in the Department of Computer Science and Engineering at the Ohio State University (OSU), College of Engineering. Promoted to this role in May 2025, he is also an Innovation Scholar and Distinguished Assistant Professor of Engineering Inclusive Excellence. His work spans machine learning, computer vision, and their applications in autonomous driving, healthcare, biology, and natural language processing. Research Focus: Machine learning with imperfect data, interpretable and personalized learning, robust perception for autonomous systems, and visual recognition in real-world scenarios. Awards: 2025 OSU Early Career Distinguished Scholar Award, CVPR Best Student Paper Award (2024), CSE Faculty Teaching Award (2024), Lumley Research Award (2023). Grants: Funded by NSF, NIH, ONR, Cisco, AWS, and Google. Notable Research Trends: The 15 most recent articles highlight his work on vision foundation models, federated learning, diffusion models for biological species generation, interpretable vision transformers, and robust perception systems for autonomous driving. Key subfields include sparse autoencoders, 3D object detection, semi-supervised learning, and anomaly detection in scientific domains. Scientific Awards: 2025 Early Career Distinguished Scholar Award (OSU) CVPR Best Student Paper Award (2024) CSE Faculty Teaching Award (2024) Lumley Research Award (2023) Mentoring & Grants: As an advisor for the OSU Buckeye AutoDrive Team and AI Club, he mentors graduate and undergraduate students. His research is supported by major grants from NSF, NIH, ONR, and industry partners like Cisco and Google.
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).
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.
Bahar Asgari is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park. She holds appointments in CS, UMIACS, and ECE, and directs the Computer Architecture and Systems Lab (CASL). Her research focuses on domain-specific architecture design, near memory processing, and reconfigurable computing. Before joining UMD, she worked at Google, contributing to system optimization and memory utilization strategies. Education: Ph.D., Georgia Institute of Technology, 2021. Research Interests: Developing energy-efficient architectures for sparse problems, scientific computing acceleration, FPGA optimizations, and reconfigurable hardware systems. Her work addresses challenges in memory efficiency, parallelization, and hardware-software co-design across domains like machine learning and IoT. Key Awards: 2024 UMD Excellence in Teaching Award 2023 DOE Early Career Award Advising & Grants: Supervises 9 graduate students and leads projects sponsored by NSF, DOE, and industry partnerships. Her lab explores systolic arrays, near-data processing, and fault-tolerant computing. Labs/Teams: Directs CASL, which builds next-generation computing systems through hardware innovation and algorithm-architecture co-design.
Virginia Smith is the Leonardo Associate Professor of Machine Learning at Carnegie Mellon University (CMU), with a courtesy appointment in the Department of Electrical and Computer Engineering. She holds a Ph.D. from UC Berkeley and undergraduate degrees from the University of Virginia. Her research focuses on developing efficient, privacy-preserving machine learning systems, particularly in federated learning, distributed optimization, and resource-constrained settings. Key areas include federated learning frameworks like CoCoA and Ditto, unlearning algorithms, and privacy amplification techniques. Smith's work bridges theory and practice, addressing challenges such as data heterogeneity, fairness, and robustness. She has led initiatives like the LEAF benchmark for federated learning and has contributed to open-source tools for scalable ML systems. Her awards include the Sloan Research Fellowship, Samsung AI Researcher of the Year (2023), and an NSF CAREER Award. She is actively involved in curriculum development, teaching courses on federated learning and large-scale ML at CMU. Her research group explores cutting-edge topics like federated optimization with sparse communication, unlearning in LLMs, and secure ML pipelines. Collaborations span academia and industry, with applications in healthcare, IoT systems, and AI safety. Smith serves as Program Chair for ICML 2025 and frequently contributes to workshops on federated learning and trustworthy AI.
Vijay Raghunathan is a Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. His work focuses on hardware and software architectures for embedded systems, wireless sensors for IoT, and wearable/implantable electronics with emphasis on low power design, energy harvesting, emerging memory technologies, and secure system design. Academic Rank: Professor Department: Electrical and Computer Engineering University: Purdue University Research Focus: Energy-efficient embedded systems, IoT, wearable devices His research explores low power design at both board-level and system-on-chip scales, micro-scale energy harvesting , and reliable/secure system design for medical devices. Recent work focuses on Processing-in-Sensor/Memory for battery-free AIoT devices, state space models for neural processing units, and security coprocessor integration in autonomous systems. Analysis of his publications reveals trends in energy-efficient neural network acceleration , approximate computing for edge inference , and compute-in-memory architectures . Key subfields include collaborative edge-cloud partitioning , sparse DNN accelerators , and security frameworks for medical devices. Vijay's work also addresses energy-accuracy tradeoffs in multimodal cognitive systems and intermittent computing using non-volatile memory technologies. His contributions span from microcontroller energy management to security protocols for implantable electronics.
University of Illinois Urbana-ChampaignUnited States
Rakesh Kumar is a Professor and John Bardeen Faculty Scholar in the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign. His work focuses on computer architecture, system-level design automation, and low-power computing. PhD in Computer Engineering from University of California, San Diego BS in Electrical Engineering from IIT Kharagpur His research spans all layers of the computing stack, with key contributions to flexible computer systems , waferscale computing , error-resilient architectures , and approximate computing . He has pioneered work on voltage-reliability tradeoffs and peak power management techniques. Recent publications highlight trends in space microdatacenters , printed microprocessors , and neural graph accelerators . His work on plastic chips was recognized as one of the three biggest semiconductor headlines of 2022 by IEEE Spectrum. IEEE Fellow (2024) ISCA Influential Paper Award MICRO Test-of-Time Award ICCAD Ten Year Retrospective Most Influential Paper Award Best Paper Awards at CASES, SELSE, HPCA He has received teaching accolades including the Stanley H. Pierce Faculty Award and Ronald W. Pratt Outstanding Teaching Award . His research group explores hardware-software co-design for emerging applications in AI, IoT, and sustainable computing.
Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.
Christopher Kanan is a tenured Associate Professor of Computer Science at the University of Rochester, leading the AI Initiative within the Hajim School of Engineering & Applied Sciences. He holds secondary appointments in Brain and Cognitive Sciences, the Goergen Institute for Data Science and AI (GIDS-AI), and the Center for Visual Science. His research focuses on deep learning systems for artificial general intelligence (AGI), including continual learning, medical computer vision, and visual question answering. Previously, he was an Associate Professor at RIT’s Carlson Center for Imaging Science and a leader at Paige.AI, contributing to the FDA-cleared Paige Prostate system. Kanan earned his PhD from UC San Diego, completed postdoctoral work at Caltech, and worked at NASA JPL. Education: PhD in Computer Science, UC San Diego MS in Computer Science, University of Southern California Bachelor’s in Philosophy and Computer Science, Oklahoma State University Research Interests: Kanan’s work spans foundational AI capabilities like continual learning, medical imaging (pathology and radiology), multi-modal reasoning, and cognitive science-inspired models. His lab develops bias-robust AI systems and applies deep learning to healthcare and fusion research. Articles Trends: His recent work emphasizes out-of-distribution generalization, foundation models in pathology, and stability in continual learning. Key themes include AI applications in healthcare, model robustness, and neuroscience-inspired algorithms. Awards: NSF CAREER Award Senior Member, AAAI and IEEE DoE and NSF grants totaling $5M+ DARPA/ARL awards Advising & Grants: Mentored over 10 PhD students, including Robik Shrestha and Usman Mahmood. Secured grants for AI in nuclear fusion and medical imaging. Led RIT’s Center for Human-aware AI (CHAI) as Associate Director. Labs & Teams: Heads the University of Rochester AI Initiative, collaborates with Paige.AI, and leads teams advancing AI in pathology and robotics. His lab’s KLab (klab.cis.rit.edu) focuses on vision and learning systems.
Fabian Suchanek is a full professor at Institut Polytechnique de Paris, specifically affiliated with Télécom Paris. He leads research in the Data, Intelligence, and Graphs (DIG) team within the Computer Science department. His academic career focuses on bridging artificial intelligence with structured knowledge representations. Suchanek's research interests span artificial intelligence, knowledge bases, and natural language processing, with particular emphasis on knowledge graph construction , rule mining , knowledge-based language models , and explainable AI . His work demonstrates how structured knowledge can enhance machine learning systems, particularly large language models, by providing factual grounding and interpretability. The research group he leads develops practical systems that address real-world knowledge management challenges. His recent publications showcase a strong trajectory in knowledge-intensive AI, with notable contributions to knowledge graph completion, rule mining techniques, and neural approaches to knowledge base validation. The research demonstrates increasing integration between symbolic and neural approaches to AI. Best Student Paper Award at KR 2024 for work on contextual reasoning Best Demo Award of IJCAI 2024 for rule mining in knowledge graphs French Open Research Award for the YAGO project Best Paper Award of ESWC 2021 for Neural Knowledge Base Repairs Suchanek has secured significant research funding, evidenced by his active recruitment of PhD students for knowledge-based language model research. He has held visiting positions, including at Nanyang Technological University (June-September 2023), and is recognized internationally through keynote invitations such as the Singapore ACM SIGKDD Symposium 2023. He has deliberately stepped back from administrative duties at Institut Polytechnique de Paris to focus on research. His laboratory maintains strong industry connections through open-source software projects including the YAGO knowledge base, AMIE for rule mining, STACI for explainable AI, and several other tools that have become standard in knowledge representation research.
Massachusetts Institute of TechnologyUnited States
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
Olga Saukh is an Associate Professor at the Institute of Technical Informatics, Graz University of Technology (TU Graz), and a Faculty member at the Complexity Science Hub Vienna (CSH). She leads the Embedded Learning and Sensing Systems research group, which operates across both institutions, focusing on the design and deployment of efficient AI-based systems on edge and mobile platforms. Her work bridges deep learning and embedded systems, with applications in environmental monitoring, precision agriculture, and digital health. Ph.D. in Computer Science, University of Bonn (2009) Habilitation in Embedded Systems, TU Graz (2020) Postdoctoral Training, ETH Zurich (2010–2016) B.Sc. in Applied Mathematics, Taras Shevchenko National University of Kyiv (2002) M.Sc. in Applied Computer Science, University of Freiburg (2004) Her research centers on efficient machine learning, particularly model optimization, neural network pruning, and contrastive learning for resource-constrained devices. She is deeply engaged in solving real-world challenges in IoT, sensor networks, and cyber-physical systems. Her work emphasizes data privacy, sustainability, and practical deployment of AI at the edge. The 15 most recent publications highlight a strong trend in efficient deep learning, including model compression, pruning, and transfer learning, applied to diverse domains such as environmental sensing (air quality, pollution tracking), digital agriculture (cattle farming), and embedded AI (sensor calibration, on-demand sensing). Her work frequently appears in top-tier venues like NeurIPS, ICLR, and IEEE/ACM IPSN, reflecting her leadership at the intersection of machine learning and embedded systems. Scientific awards include: CONET Ph.D. Academic Award (2010) Multiple Best Paper Awards at IEEE PerCom, ACM/IEEE IPSN, IEEE ICPADS, IEEE SECON, and UrbCom Spotlight and Oral presentations at ICML and CoLLAs workshops Ph.D. scholarship from IPVS, University of Stuttgart (2004–2005) Prizes in Ukrainian national mathematics competitions (1996–1998) Olga Saukh actively serves on program committees of leading international conferences in machine learning and embedded systems. She has advised multiple students and leads a collaborative research group spanning TU Graz and CSH Vienna. Her group develops practical AI systems for real-world deployment, with a focus on sustainability and privacy. She co-organizes the public EfficientML reading group and has secured recognition through numerous grants and awards. Her future work continues to explore the theoretical and practical challenges of deploying efficient, trustworthy AI in mobile and embedded environments. Her research group, Embedded Learning and Sensing Systems, operates jointly between TU Graz and CSH Vienna, fostering interdisciplinary collaboration across institutions. The team develops AI solutions for edge computing, sensor networks, and cyber-physical systems, with a strong emphasis on environmental sustainability and data privacy. Members work on joint challenges using advanced collaboration tools, reflecting the distributed nature of modern academic research.
