Minjia Zhang

Microsoft Research at Redmond,
Building 99,
14820 NE 36th St,
Redmond, WA 98052
E-mail: minjiaz@microsoft.com

 

I graduated as a Ph.D. student from the CSE department of Ohio State University in 2016 and joined Microsoft Research at Redmond. You can find my Microsoft page here. While I was in OSU, I was a member of the Programming Languages and Software Systems (PLaSS) group. I worked with Prof. Michael D. Bond, focusing on building efficient and scalable runtime systems that have nice properties-- like strong semantics-- for parallel programs. 

Check out my CV here.

Interests: 

    Concurrency and Parallelism, Efficient Runtime Systems, Distributed Systems.

 Conferences: (Google Scholar)

  • ISMM 2017        Minjia Zhang, Swarnendu Biswas, Michael D. Bond,  "Avoiding Consistency Exceptions Under Strong Memory Consistency Models" [pdf]
  • CC 2017              Swarnendu Biswas, Man Cao, Minjia Zhang, Michael D. Bond, and Benjamin P. Wood,  "Lightweight Data Race Detection for Production Runs" [pdf]
  • CC 2016             Minjia Zhang, Swarnendu Biswas, Michael D. Bond, "Relaxed Dependence Tracking for Parallel Runtime Support" [pdf]
  • PPoPP 2016       Man Cao, Minjia Zhang, Aritra Sengupta, and Michael Bond,  "Drinking from Both Glasses: Combining Pessimistic and Optimistic Tracking of Cross-Thread Dependences" [pdf]
  • OOPSLA 2015   Swarnendu Biswas, Minjia Zhang, Michael D. Bond, and Brandon Lucia, "Valor: Efficient, Software-Only Region Conflict Exceptions"(Distinguished Artifact Award) [pdf]
  • PPoPP 2015       Minjia Zhang, Jipeng Huang, Man Cao, and Michael D. Bond, "Low-Overhead Software Transactional Memory with Progress Guarantees and Strong Semantics"  [pdf]
  • ASPLOS 2015    Aritra Sengupta, Swarnendu Biswas, Minjia Zhang, Michael D. Bond, and Milind Kulkarni, "Hybrid Static-Dynamic Analysis for Statically Bounded Region Serializability"  [pdf]
  • WoDet 2014       Man Cao, Minjia Zhang, and Michael D. Bond, Drinking from Both Glasses: Adaptively Combining Pessimistic and Optimistic Synchronization for Efficient Parallel Runtime Support
  • OOPSLA 2013   Michael D. Bond, Milind Kulkarni, Man Cao, Minjia Zhang, Meisam Fathi Salmi, Swarnendu Biswas, Aritra Sengupta, and Jipeng Huang "Octet: Capturing and Controlling Cross-Thread Dependences Efficiently " [pdf]
  • ICPP 2011          J. Jose, H. Subramoni, M. Luo, M. Zhang, J. Huang, M. W. Rahman, N. S. Islam, X. Ouyang, S. Sur and D. K. Panda, "Memcached Design on High Performance RDMA Capable Interconnects" [pdf]
  • ICPADS 2010    Minjia Zhang,Hai Jin,Song Wu,Xuanhua Shi "VirtCFT: A Transparent VM-level Fault-Tolerant System for Virtual Clusters", International Conference on Parrallel and Distributed System [pdf]

Other publications:

  • Man Cao, Minjia Zhang, Aritra Sengupta, Swarnendu Biswas, and Michael D. Bond, “Hybridizing and Relaxing Dependence Tracking for Efficient Parallel Runtime Support”, In ACM Transactions on Parallel Computing (TOPC), August 2017.
  • Minjia Zhang, Efficient Runtime Support for Reliable and Scalable Parallelism, Ph. D. Dissertation, August 2016. [pdf][external]
  • Minjia Zhang, Swarnendu Biswas, and Michael D. Bond. All That Glitters Is Not Gold: Improving Availability and Practicality of Exception-Based Memory Models, Technical Report #OSU-CISRC-04/16-TR01, Computer Science & Engineering, Ohio State University, April 2016.
  • Minjia Zhang, Swarnendu Biswass, Michael D. Bond, Optimizing Parallel Runtime Support with Asynchronous Coordination, Technical Report #OSU-CISRC-11/15-TR23, Computer Science & Engineering, Ohio State University, Nov 2015. [external]
  • Minjia Zhang,  Jipeng. Huang, Man Cao, and Mike D. Bond. Larktm: Efficient, strongly atomic software transactional memory. Technical Report OSU-CISRC-11/12-TR17, Computer Science &
    Engineering, Ohio State University, 2012. [external]
  • Minjia Zhang, “SIRe: An Efficient Snapshot Isolation­based Memory Model for Detecting and Tolerating Region Conflicts”, SPLASH '15 Companion, October 25­30, 2015, Pittsburgh, PA, USA [external]

News:

  • Gave a talk on “DeepCPU: Deep Learning Serving Optimizations on CPUs” at Machine Learning, AI & Data Science Conference (MLADS), December 2017, Redmond, WA, USA
  • Graduated from the inaugural Microsoft AI School (40 selected students out of 1100+ applicants) (<4 % acceptance rate)  April 2017, Redmond, WA, USA
  • Won the Bronze Medal (3rd place) in SPLASH 2015 Student Research Competition, October 2015, Pittsburgh, PA, USA [external]
  • Won the OOPSLA’15 Distinguished Paper Award (2015) [external]
  • Won the OOPSLA'15 Distinguished Artifact Award  (2015) [external]
  • Received NSF Travel Grants to attend PPoPP’15, PACT’15, SPLASH’15.
  • My project LarkTM got the PPoPP'15 Artifact Evaluation Badge  (2015)
  • Won Silver medal in PLDI 2013 Student Research Competition, June 2013, Seattle, WA, USA [external]

Service

  • Primary Program Committee of Program Models, IPDPS 2018
  • Shadow Program Committee, ASPLOS 2018
  • Subreviewer, 24th IEEE International Conference on High Performance Computing, Data, and Analytics 2017
  • Reviewer, Journal of Computer Science
  • Reviewer, the 14th IEEE International Conference on Automatic Computing 2017
  • Reviewer, Journal of Concurrency and Computation: Practice and Experience 2017
  • Committee Member, PLDI 2017 Artifact Evaluation
  • Subreviewer, 7th Workshop on the Theory of Transactional Memory (WTTM 2016)
  • Committee Member, SPLASH 2015 Artifact Evaluation
  • Committee Member, PLDI 2015 Artifact Evaluation                    

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