Feng Yan

Feng Yan Email and Phone Number

Associate Professor @ University of Houston
Reno, NV, US
Feng Yan's Location
Reno, Nevada, United States, United States
Feng Yan's Contact Details

Feng Yan personal email

Feng Yan phone numbers

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/.

Feng Yan's Current Company Details
University of Houston

University Of Houston

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Associate Professor
Reno, NV, US
Feng Yan Work Experience Details
  • University Of Houston
    Associate Professor
    University Of Houston
    Reno, Nv, Us
  • University Of Houston
    Associate Professor
    University Of Houston Sep 2022 - Present
    Houston, Texas, United States
  • University Of Nevada, Reno
    Associate Professor
    University Of Nevada, Reno Jul 2022 - Aug 2022
    Reno, Nevada, United States
  • University Of Nevada, Reno
    Assistant Professor
    University Of Nevada, Reno Jul 2016 - Jun 2022
    Reno, Nv
  • College Of William And Mary
    Research Assistant
    College Of William And Mary Jun 2010 - Jun 2016
    Williamsburg, Va
    + 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.
  • Microsoft
    Research Intern
    Microsoft Jun 2014 - Aug 2015
    Redmond
    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).
  • Hp Labs
    Research Associate (Intern)
    Hp Labs Jun 2013 - May 2014
    Palo Alto
    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).
  • College Of William And Mary
    Teaching Assistant
    College Of William And Mary Aug 2009 - Apr 2010
    Lab Instructor: CSCI 141L Intro Computer Science Lab (Java Programming)

Feng Yan Skills

Big Data Analytics Data Analysis Cloud Computing Distributed Systems Deep Learning Mapreduce Hadoop Apache Spark Heterogeneous Computing Predictive Modeling Performance Engineering Performance Evaluation Of Systems Resource Management Data Science Machine Learning Data Mining Benchmarking Power Management Data Center High Performance Computing C++ C Matlab Perl Mathematical Modeling Modeling Storage Systems Java Sysstat Openmp Simulations Latex Sql Spec Linux

Feng Yan Education Details

Frequently Asked Questions about Feng Yan

What company does Feng Yan work for?

Feng Yan works for University Of Houston

What is Feng Yan's role at the current company?

Feng Yan's current role is Associate Professor.

What is Feng Yan's email address?

Feng Yan's email address is fy****@****unr.edu

What is Feng Yan's direct phone number?

Feng Yan's direct phone number is +175774*****

What schools did Feng Yan attend?

Feng Yan attended The College Of William And Mary, The College Of William And Mary, Northeastern University.

What are some of Feng Yan's interests?

Feng Yan has interest in Consistency, Priority Scheduling, Resources Allocation And Consolidation, Queuing Theory, Data Analysis, Resource Management, Power Management, Time Series Analysis And Prediction, Storage Systems, Deep Learning.

What skills is Feng Yan known for?

Feng Yan has skills like Big Data Analytics, Data Analysis, Cloud Computing, Distributed Systems, Deep Learning, Mapreduce, Hadoop, Apache Spark, Heterogeneous Computing, Predictive Modeling, Performance Engineering, Performance Evaluation Of Systems.

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