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Devesh Tiwari

409 Dana
360 Huntington Avenue
Boston, MA 02115


Devesh's research focus revolves around designing sustainable, resilient, and scalable systems with special emphasis on understanding and exploiting cross-layer interactions. His research interest also involves applying high performance computing and data analytics expertise to emerging inter-disciplinary research domains. His research publications have received best paper award nominations at conferences including Supercomputing (SC), Dependable Systems and Networks (DSN), and Parallel & Distributed Processing Symposium (IPDPS). His work has appeared in various conferences such as USENIX FAST, SC, DSN, HPCA, MICRO, IPDPS, and have been covered by the news media including Slashdot and HPCWire. 

Before joining Northeastern, Devesh was a staff scientist at the Oak Ridge National Laboratory, a flagship multiprogram science and technology national laboratory of the United States Department of Energy (DOE). Devesh earned his Ph.D. in Electrical and Computer Engineering from North Carolina State University. Before that, he obtained his B.S. degree in Computer Science and Engineering from Indian Institute of Technology (IIT) Kanpur in India.

Research & Scholarship Interests

Sustainable and resilient systems; machine learning and big data analytics; high performance data-intensive computing for inter-disciplinary domains
Affiliated With

Department Research Areas

Selected Publications

  • [SC 2016] "Granularity and the Cost of Error Recovery in Resilient AMR Scientific Applications", Anshu Dubey, Hajime Fujita, Daniel Graves, Andrew Chien, Devesh Tiwari, Pro- ceedings of the 29th IEEE/ACM Int’l Conference on High Performance Computing, Networking, Storage and Analysis (SC), Salt Lake City, Utah, November 2016.
  • [MICR0 2016] "Low-Cost Soft Error Resilience with Unified Data Verification and Fine-Grained Recovery for Acoustic Sensor Based Detection", Qingrui Liu, Changhee Jung, Dongyoon Lee, Devesh Tiwari, Proceedings of 49th IEEE/ACM International Symposium on Microarchitecture (MICRO), Taipei, Taiwan, October 2016.
  • [SC 2015] "A Practical Approach to Reconciling Availability, Performance, and Capacity in Provisioning Extreme-scale Storage Systems", Lipeng Wan, Feiyi Wang, Sarp Oral, Devesh Tiwari, Sudharshan Vazhkudai, and Qing Cao, Proceedings of the 28th IEEE/ACM Int’l Conference on High Performance Computing, Networking, Storage and Analysis (SC), Austin, Texas, November 2015.
  • [HPCA 2015] "Understanding GPU Errors on Large-scale HPC Systems and the Implications for System Design and Operation", Devesh Tiwari, Saurabh Gupta, Jim Rogers, Don Maxwell, Paolo Rech, Sudharshan Vazhkudai, Daniel Oliveira, Dave Londo, Nathan Debardeleben, Philippe Navaux, Luigi Carro, Arthur S. Bland, Proceedings of the 21st International Symposium on High-Performance Computer Architecture (HPCA), February 2015.
  • [DSN 2014] "Lazy Checkpointing: Exploiting Temporal Locality in Failures to Mitigate Checkpointing Overheads on Extreme-Scale Systems", Devesh Tiwari, Saurabh Gupta and Sudharshan Vazhkudai, Proceedings of the 44th Annual IEEE/IFIP Int’l Conference on Dependable Systems and Networks (DSN 2014), Atlanta, Georgia, USA, June 2014.
  • [USENIX FAST 2013] "Active Flash: Towards Energy-Efficient, In-Situ Data Analytics on Extreme-Scale Machine", Devesh Tiwari, Simona Boboila, Sudharshan Vazhkudai, Youngjae Kim, Xiasong Ma, Peter Desnoyers and Yan Solihin, Proceedings of the 11th USENIX Conference on File and Storage Technologies (FAST ’13), California, USA, February 2013.

Related News

September 20, 2017

ECE Assistant Professor Desesh Tiwari was selected to be the Program Area Vice Chair for the 32nd IEEE International Parallel and Distributed Processing Symposium which is one of the leading conferences in high performance parallel and distributed computing.

January 20, 2017

Devesh Tiwari joins the Electrical & Computer Engineering department in January 2017 as an Assistant Professor.

January 13, 2017

ECE Assistant Professor Devesh Tiwari is researching methods to improve supercomputing by making large-scale, data-intensive computing more efficient, more reliable, and more sustainable so innovation can flourish.