• Ph.D., College of William & Mary

J Dr. Feng Yan is the director of the Intelligent Data and Systems Lab (IDS Lab) and Associate Professor of Computer Science at University of Houston (UH). He is Presidential Frontier Faculty (PFF) and is also affiliated with Electrical and Computer Engineering at UH. Dr. Yan’s research bridges the fields of big data, AI, and systems. The focus of his research is on developing methodologies and building systems that are automated, high-performing, efficient, robust, and user-centric. Some of his recent research topics include Large Language Model (LLM), large-scale ML, Machine Learning as a Service (MLaaS), federated learning, AutoML, serverless, and broad topics in cloud and HPC. Dr. Yan is also dedicated to interdisciplinary research and has established fruitful collaborations with domain experts in areas such as health, physics, material science, civil engineering, and innovated big data and AI-driven approaches for these domains. Dr. Yan and his team are actively publishing at the most prestigious venues in ML/AI area (such as NIPS/NeurIPS, ICLR, AAAI, KDD, WWW, etc.) and computer system area (such as SOSP, SC, HPDC, VLDB, USENIX ATC, EuroSys, FAST, HPCA, etc.). He is well ranked in Computer Science Rankings (CSRankings, https://csrankings.org/#/index?all&us). Almost all students in Dr. Yan’s lab get research internships and full-time job opportunities in top industry research labs (such as Microsoft Research, IBM Research, Bell Labs, Snowflake Research, Baidu Research USA), national labs (such as Argonne National Laboratory and Oak Ridge National Laboratory), and leading industry companies (such as Meta, Amazon).

• NSF CAREER Award,
• NSF CRII Award,
• Regents' Rising Researcher Award,
• Outstanding Service Award of IEEE ACSOS,
• UNR CSE Best Researcher Award,
• Best Student Paper Award of IEEE CLOUD 2018,
• Best Paper Award of CLOUD 2019,
• Best Student Paper Award of ITNG 2021

• Guanhua Wang*, Heyang Qin*, Sam Ade Jacobs, Xiaoxia Wu, Connor Holmes, Zhewei Yao, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He, ZeRO++: Extremely Efficient Collective Communication for Large Model Training, in Proceedings of the Twelfth International Conference on Learning Representations (ICLR 2024), Vienna Austria, May, 2024 (*Co-First Authors)
• Syed Zawad, Cheng Li, Zhewei Yao, Elton Zheng, Yuxiong He, Feng Yan, DySR: Adaptive Super-Resolution via Algorithm and System Co-design, in Proceedings of The Eleventh International Conference on Learning Representations (ICLR 2023), Kigali Rwanda, May, 2023
• Ahsan Ali, Riccardo Pinciroli, Feng Yan, Evgenia Smirni, Optimizing Inference Serving on Serverless Platforms, in Proceedings of the 48th International Conference on Very Large Data Bases (VLDB 2022), Sydney, Australia, September, 2022
• Heyang Qin, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement, in Proceedings of the Neural Information Processing Systems 2021 (NeurIPS 2021), Virtual, December,
• Ahsan Ali*, Riccardo Pinciroli*, Feng Yan, Evgenia Smirni, BATCH: Machine Learning Inference Serving on Serverless Platforms with Adaptive Batching, in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2020), Atlanta, GA, USA, Nov, 2020 (*Co-First Authors)