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Feng Yan Email & Phone Number
Associate Professor
at
University of Houston
1 work email found
@uh.edu
2 phones found area 757
LinkedIn matched
✓ Verified August 2026
4 data sources
Profile completeness 100%
Who is Feng Yan? Overview
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Quick answer
Feng Yan is listed as Associate Professor at University of Houston, based in Reno, Nevada, United States. AeroLeads shows a work email signal at uh.edu, phone signal with area code 757, and a matched LinkedIn profile for Feng Yan.
Feng Yan previously worked as Associate Professor at University Of Nevada, Reno and Assistant Professor at University Of Nevada, Reno. Feng Yan holds Doctor Of Philosophy (Ph.D.), Computer Science from The College Of William And Mary.
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Email format at University of Houston
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{first_initial}{last}@uh.edu
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AeroLeads found 1 current-domain work email signal for Feng Yan. Compare company email patterns before reaching out.
Profile bio
About Feng Yan
Dr. Feng Yan is a tenured Associate Professor of Computer Science and Associate Professor of Electrical and Computer Engineering at University of Houston, and the director of the Intelligent Data and Systems Lab (IDS Lab). Dr. Yan’s research bridges the fields of big data, AI, and systems. Some of his recently focused research topics include large language models (LLM), large-scale distributed deep learning, machine learning as a service (MLaaS), federated learning, AutoML, serverless computing, and broad topics in cloud and high performance computing (HPC). Dr. Yan is also dedicated to interdisciplinary research and has established fruitful collaborations with domain experts in areas such as health, physics, geography, material science, mechanical engineering, civil engineering, and innovated big data and AI-driven approaches for these domains. Dr. Yan closely collaborates with industry partners (such as Microsoft AI & Research, IBM Research, Google Brain, Meta AI, Bell Labs, Amazon AI Lab, HP Labs, NetApp ATG) to solve challenging yet impactful problems.Dr. Yan has led and participated in several projects that attracted more than $4M external funding, including $2.6M as PI/Site-PI. Dr. Yan and his team are actively publishing at the most prestigious venues in AI/machine learning areas (such as NIPS/NeurIPS, ICLR, KDD, AAAI, etc.) and computer system areas (such as SOSP, SC, HPDC, USENIX ATC, EuroSys, FAST, VLDB, etc.).Dr. Yan has advised (advising) 14 PhD students, 7 MS students, 21 undergraduate students, and 3 K-12 students. Dr. Yan's students have been recruited by top industry research labs such as Microsoft Research, IBM Research, Amazon Web Services, and national labs such as Argonne National Laboratory and Oak Ridge National Laboratory. Dr. Yan and his students are the recipients of the Best Student Paper Award of IEEE CLOUD 2018, the Best Paper Award of CLOUD 2019, and the Best Student Paper Award of ITNG 2021. Dr. Yan is the recipient of the NSF CAREER Award, the NSF CRII Award, the Outstanding Service Award of IEEE ACSOS, the Regents' Rising Researcher Award, and the CSE Best Researcher Award. Dr. Yan serves as Social Media Chair of ACM SIGMETRICS. For more information, please visit Dr. Yan’s homepage: www.cs.uh.edu/~fyan/.
Listed skills include Big Data Analytics, Data Analysis, Cloud Computing, Distributed Systems, and 31 others.
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8 roles
Feng Yan work experience
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Associate Professor
Current
Sep 2022 - Present
Associate Professor
Jul 2022 - Aug 2022
Assistant Professor
University Of Nevada, Reno
Jul 2016 - Jun 2022
Research Assistant
+ NSF: SHF-Small: Robust Methodologies for Effective Data Center ManagementLead student in designing and implementing:• An agile priority scheduling middleware that is built upon nice and ionice and provides performance isolation by adjusting relative priority between tasks based on the instantaneous resource requirements and priorities of applications.• A workload isolation tool for large-scale tiered storage systems that has an autonomic learning engine to predict the intensity of user workload and proactively warms up the fast tier with user working set to minimize the performance impact due to interleaving with system work. The above work has been implemented and evaluated in the testbeds in College of William and Mary and EMC.+ NSF: Interleaving Workloads with Performance Guarantees on Storage Clusters• For automating storage cluster consolidation: developed a performance tool to estimate beforehand the benefits and overheads of each consolidation options to help make intelligent and automatic consolidation decisions. Also developed a copy synchronization framework with performance guarantees to minimize the overhead during consolidation process.• For efficient data movement: developed a fast eventual consistency framework with performance guarantees in distributed storage systems.• For practical power savings: developed a performance, power and reliability framework for storage systems.The above work has been implemented and evaluated in the testbeds in College of William and Mary and EMC.+ NSF: Effective Resource Allocation under Temporal Dependence • Investigated the scheduling behaviors under temporal dependent workloads and developed simulators for evaluating the performance impact of temporal dependence under different scheduling policies and the impact of using different scheduling policies in tandem queuing system.
Jun 2010 - Jun 2016
Research Intern
Lead student in designing and implementing:• A novel performance tool for scalability estimation of distributed deep learning systems. • A scalability optimizer that efficient searches and finds the optimal configuration for distributed deep learning system in terms of minimizing training time and maximizing system throughput.• A distributed deep learning serving system and a performance tool for it.• A combined training and serving platform for distributed deep learning.The above work has been implemented and evaluated in a state-of-the-art deep learning cluster in Microsoft Research. In addition, one related US patent application has been filed (in collaboration with Dr. Yuxiong He, Dr. Olatunji Ruwase, and Dr. Trishul Chilimbi).
Jun 2014 - Aug 2015
Research Associate (Intern)
Lead student in:• Investigating the benefits of using heterogeneous multi-core processors, heterogeneous storage device for improving the performance of MapReduce processing.• Benchmarking the networking performance of Hadoop.• Developing a novel scheduling framework DyScale that exploits the capabilities offered by heterogeneous multi-core processors for achieving different performance objectives in MapReduce Processing.The above work has been verified in HP cluster and implemented and evaluated in the SimMR Hadoop simulator. In addition, one related US patent application has been filed (in collaboration with Dr. Lucy Cherkasova).
Jun 2013 - May 2014
Teaching Assistant
Lab Instructor: CSCI 141L Intro Computer Science Lab (Java Programming)
Aug 2009 - Apr 2010
3 education records
Feng Yan education
Doctor Of Philosophy (Ph.D.), Computer Science
Master Of Science, Computer Science
Bachelor Of Science (B.S.), Computer Science
FAQ
Frequently asked questions about Feng Yan
Quick answers generated from the profile data available on this page.
What company does Feng Yan work for?
Feng Yan works for University of Houston.
What is Feng Yan's role at University of Houston?
Feng Yan is listed as Associate Professor at University of Houston.
What is Feng Yan's email address?
AeroLeads has found 1 work email signal at @uh.edu for Feng Yan at University of Houston.
What is Feng Yan's phone number?
AeroLeads has found 2 phone signal(s) with area code 757 for Feng Yan at University of Houston.
Where is Feng Yan based?
Feng Yan is based in Reno, Nevada, United States while working with University of Houston.
What companies has Feng Yan worked for?
Feng Yan has worked for University Of Houston, University Of Nevada, Reno, College Of William And Mary, Microsoft, and Hp Labs.
How can I contact Feng Yan?
You can use AeroLeads to view verified contact signals for Feng Yan at University of Houston, including work email, phone, and LinkedIn data when available.
What schools did Feng Yan attend?
Feng Yan holds Doctor Of Philosophy (Ph.D.), Computer Science from The College Of William And Mary.
What skills is Feng Yan known for?
Feng Yan is listed with skills including Big Data Analytics, Data Analysis, Cloud Computing, Distributed Systems, Deep Learning, Mapreduce, Hadoop, and Apache Spark.
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