Neeraja J. Yadwadkar
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I am a sixth year PhD student in the Computer Science Department at University of California, Berkeley. I am part of the AMP Lab and now the RISE Lab; I am advised by Prof. Randy Katz, and more recently also co-advised by Prof. Joseph E. Gonzalez.

I am interested in Systems and Machine Learning. 

Email: 

Publications
  • 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!):
  • CS162: Operating Systems and Systems Programming, Spring 2013 
    Graduate Student Instructor with Prof. Anthony Joseph 
Talks
  • 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