I am an Assistant Professor in the Computer Science Department at University of Wisconsin, Madison. My research interests are in designing systems and algorithms for large scale data analysis and machine learning. My dissertation research looked at abstractions that make it easier to express new machine learning algorithms and systems that can improve their performance.
Before coming to Madison, I was a post-doctoral researcher in the Systems Research Group at Microsoft Research in Redmond. Previously, I completed my PhD from UC Berkeley where I was advised by Ion Stoica and Mike Franklin. I also have a Masters from University of Illinois at Urbana-Champaign and worked in the Systems Research Group, with Prof. Roy Campbell.
CS 744 Big Data Systems: Fall 2018
CS 537 Intro to OS: Spring 2019
Anand Padmanabha Iyer, Zaoxing Liu and Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica ASAP: Fast, Approximate Pattern Mining at Scale - OSDI 2018
Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik, Shivaram Venkataraman, Paramvir Bahl, and Matthai Philipose, Phillip B. Gibbons, Onur Mutlu Focus: Querying Large Video Datasets with Low Latency and Low Cost - OSDI 2018
Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanam Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems - VLDB 2018
Anand Iyer, Aurojit Panda, Shivaram Venkatraman, Mosharaf Chowdhury, Aditya Akella, Scott Shenker, Ion Stoica Bridging the GAP: Towards Approximate Graph Analytics - GRADES-NDA 2018.
Anand Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica Towards Fast and Scalable Graph Pattern Mining - HotCloud 2018
Shivaram Venkataraman System Design for Large Scale Machine Learning - PhD Dissertation
Shivaram Venkataraman, Aurojit Panda, Kay Ousterhout, Michael Armbrust, Ali Ghodsi, Michael J. Franklin, Benjamin Recht, Ion Stoica Drizzle: Fast and Adaptable Stream Processing at Scale - SOSP 2017
Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, Benjamin Recht Occupy the Cloud: Distributed Computing for the 99% - SoCC 2017 - arxiv version
Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics - ICDE 2017 arxiv version
Omid Alipourfard, Jianshu Chen, Hongqiang Liu, Shivaram Venkataraman, Minlan Yu, Ming Zhang Cherry Pick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics - NSDI 2017
Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez Hemingway: Modeling Distributed Optimization Algorithms - Learning Systems Workshop, NIPS 2016
Matei Zaharia, Reynold S. Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J. Franklin, Ali Ghodsi, Joseph Gonzalez, Scott Shenker, Ion Stoica Apache Spark: A Unified Engine for Big Data Processing - CACM Contributed Article, Nov 2016
Shivaram Venkataraman, Zongheng Yang, Michael J Franklin, Ben Recht, Ion Stoica Ernest: Efficient Performance Prediction for Large Scale Advanced Analytics - NSDI 2016
Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, Xiangrui Meng, Reynold Xin, Ali Ghodsi, Michael Franklin, Ion Stoica, Matei Zaharia SparkR: Scaling R Programs with Spark - SIGMOD 2016
Reza Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Sparks, Aaron Staple, Matei Zaharia Matrix Computations and Optimization in Apache Spark - KDD 2016. Best Paper runner-up, Applied Data Science Track.
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Ben Recht Large Scale Kernel Learning using Block Coordinate Descent - arxiv preprint
Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar MLlib: Machine Learning in Apache Spark - JMLR 17(34):1–7, 2016
Shivaram Venkataraman, Aurojit Panda, Ganesh Ananthanarayanan, Michael Franklin, Ion Stoica The Power of Choice in Data-Aware Cluster Scheduling - OSDI 2014
Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica Quantifying eventual consistency with PBS - CACM Research Highlight August 2014
Kay Ousterhout, Aurojit Panda, Joshua Rosen, Shivaram Venkataraman, Reynold Xin, Sylvia Ratnasamy, Scott Shenker, Ion Stoica The Case for Tiny Tasks in Compute Clusters - HotOS 2013
Shivaram Venkataraman, Erik Bodzsar, Indrajit Roy, Alvin AuYoung, and Robert S. Schreiber Presto: Distributed Machine Learning and Graph Processing with Sparse Matrices - Eurosys 2013
Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica PBS at Work: Advancing Data Management with Consistency Metrics. - Demo at SIGMOD 2013
Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, and Randy Katz Cake: Enabling High-level SLOs on Shared Storage Systems - SoCC 2012
Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, and Randy Katz Sweet Storage SLOs with Frosting - HotCloud 2012
Shivaram Venkataraman, Indrajit Roy, Alvin AuYoung, and Robert S. Schreiber Using R for Iterative and Incremental Processing - HotCloud 2012
Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica Quantifying Eventual Consistency with PBS - VLDB Journal Special Edition - Best of VLDB 2012
Peter Bailis, Shivaram Venkataraman, Michael Franklin, Joseph M. Hellerstein, and Ion Stoica Probabilistically Bounded Staleness for Practical Partial Quorums - VLDB 2012
Storage system design for non-volatile byte-addressable memory using consistent and durable data structures - Masters Thesis, University of Illinois, Urbana-Champaign 2011
Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy Campbell Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory - FAST 2011
Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy Campbell Redesigning Data Structures for Non-Volatile Byte-Addressable Memory - Non-Volatile Memories Workshop 2011
Reza Farivar, Harshit Kharbanda, Shivaram Venkataraman, Roy Campbell An Algorithm for Fast Edit Distance Computation on GPUs - IEEE Innovative Parallel Computing (InPar) 2012
Abhishek Verma, Shivaram Venkataraman, Matthew Caesar, and Roy H. Campell Scalable Storage for Data-intensive Computing - Handbook of Data-Intensive Computing, Springer Science, 2011.
Ellick Chan, Shivaram Venkataraman, Nadia Tkach, Kevin Larson, Alejandro Gutierrez and Roy H. Campbell Characterizing Data Structures for Volatile Forensics - Workshop on Systematic Approaches to Digital Forensic Engineering (SADFE), 2011
Elllick Chan, Shivaram Venkataraman, Francis David, Amey Chaugule, Roy Campbell Forenscope: A Framework for Live Forensics - ACSAC 2010
Abhishek Verma, Xavier Llora, Shivaram Venkataraman, David Goldberg and Roy Campbell Scaling eCGA Model Building via Data Intensive Computing - IEEE Congress on Evolutionary Computation, CEC 2010