Neeraja J. Yadwadkar
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I recently graduated with a PhD in Computer Science from University of California, Berkeley. At Berkeley, I was part of the AMP Lab and then the RISE Lab; my PhD advisors were Prof. Randy Katz, and Prof. Joseph E. Gonzalez. My PhD dissertation looked at Machine Learning for Automatic Resource Management in the Datacenter and the Cloud. I am now a post-doctoral researcher in the Computer Science Department at Stanford University, working with Profs. Christos Kozyrakis and Balaji Prabhakar.

I am interested in Systems and Machine Learning. 

Email: 

Publications
  • Context: The Missing Piece in the Machine Learning Lifecycle
    Rolando Garcia, Vikram Sreekanti, Neeraja J. Yadwadkar, Daniel Crankshaw, Joseph E. Gonzalez, Joseph M. Hellerstein
    Workshop on Common Model Infrastructure at KDD, 2018
    [pdf]    
  • Selecting the Best VM across Multiple Public Clouds: A Data-Driven Performance Modeling Approach
    Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, Burton Smith, and Randy Katz
    ACM Symposium on Cloud Computing (SoCC), 2017
    [pdf]     [demo]
  • Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling
    Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, and Randy Katz
    Journal of Machine Learning Research (JMLR), 2016
    [pdf]
  • Faster Jobs in Distributed Data Processing using Multi-Task Learning
    Neeraja J. Yadwadkar, Bharath Hariharan, Joseph E. Gonzalez, and Randy Katz
    SIAM International Conference on Data Mining (SDM), 2015
    [pdf]     [video]
  • Wrangler: Predictable and Faster Jobs using Fewer Resources
    Neeraja J. Yadwadkar, Ganesh Ananthanarayanan, and Randy Katz
    ACM Symposium on Cloud Computing (SoCC), 2014
    [pdf]     [slides]
  • Discovery of Application Workloads from Network File Traces
    Neeraja J. Yadwadkar, Chiranjib Bhattacharyya, K. Gopinath, Thirumale Niranjan, and Sai Susarla
    Usenix Conference on File and Storage Technologies (FAST), 2010
    [pdf]     [video]
Teaching
  • CS162: Operating Systems and Systems Programming, Fall 2017 (ongoing) 
    Graduate Student Instructor with Prof. Ion Stoica 
    Also, taught a couple of lectures (and it was a lot of fun!):
    • Caching (Finished), Demand Paging  [slides:  pptx  pdf]
    • General I/O [slides:  pptx  pdf]
  • CS162: Operating Systems and Systems Programming, Spring 2013 
    Graduate Student Instructor with Prof. Anthony Joseph 
Talks
  • Machine Learning for Resource Management in the Datacenter and the Cloud 
    Lawrence Berkeley National Lab, Berkeley, CA, January 2018 
  • Machine Learning for Resource Management in the Datacenter and the Cloud 
    Platforms Lab, Stanford, CA, November 2017 
  • Machine Learning for Resource Management in the Datacenter and the Cloud 
    Microsoft Research, Redmond, WA, November 2017 
  • Selecting the Best VM across Multiple Public Clouds: A Data-Driven Performance Modeling Approach 
    ACM Symposium on Cloud Computing (SoCC), Santa Clara, CA, September 2017 
  • Selecting the Best VM across Multiple Public Clouds using PARIS: A Data-Driven Performance Modeling Approach 
    RISELab/VMware Day, Berkeley, CA, May 2017 
  • Selecting the Best VM across Multiple Public Clouds using PARIS: A Data-Driven Performance Modeling Approach 
    Google, Mountain View, CA, May 2017 
  • Data-Driven Modeling for Cloud-Hosted Systems' Management and Optimization 
    Smule, San Francisco, CA, Jan 2017 
  • Let your Workloads Choose your VMs in the Cloud using PARIS 
    RISELab Winter Retreat, Berkeley, CA, Jan 2017 
  • Data-Driven Modeling for Cloud Management and Optimization 
    Splunk, San Francisco, CA, July 2016 
  • Data-Driven Modeling for System Management and Optimization 
    SAP Dublin, CA, June 2016 
  • PARIS: Model Based Performance Estimation Across the Cloud 
    AMPLab Summer Retreat June 2016 
  • Managing Sample Bias in a Model-Based Cluster Resource Manager 
    AMPLab Summer Retreat June 2016 
  • The Judgement of PARIS: Performance-Aware Resource Inference System  
    Microsoft Research, Redmond, Intern Talk, August 2015 and AMPLab Winter Retreat, January 2016 
  • Faster Jobs in Distributed Processing Systems using Machine Learning 
    Department Seminar, Department of Computer Science and Automation (CSA), Indian Institute of Science (IISc), May 2015 
  • Faster Jobs in Distributed Data Processing using Multi-Task Learning 
    SIAM International Conference on Data Mining (SDM), April 2015 
  • Wrangler: Predictable and Faster Jobs using Fewer Resources 
    ACM Symposium on Cloud Computing (SoCC), November 2014 
  • Wrangler: A Machine Learning Approach for Straggler Avoidance 
    AMPLab Summer Retreat, May 2014 and AMPLab All Hands 2014  
  • Zone Localization Methods and Services 
    Software Defined Buildings (SDB) Winter Retreat, Jan 2014 
  • Discovery of Application Workloads from Network File Traces 
    Usenix Conference on File and Storage Technologies (FAST) Feb 2010 and Riverbed Technology, Feb 2010