YAN CHEN - People - Max Planck Institute for Software Systems

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B.E., Computer Science and Technology, Fuzhou University, China. June 2006. Professional Experience. • Software engine
YAN C HEN Google Inc. 1600 Amphitheatre Parkway Mountain View, CA 94043

%Phone: +1 (650) 253-6034 )Email: [email protected]

Research Interests • Self-adjusting computation, Program analysis, Dynamic and parallel computation, Big data systems • Formal methods, Model checking, Symbolic simulation

Education • Ph.D. student, Max Planck Institute for Software Systems, Germany Working on programming languages, and self-adjusting computation Adviser: Umut Acar • M.S., Computer Science, Portland State University, Portland, OR Thesis: Equivalence Checking for High-Level Synthesis Flow • B.E., Computer Science and Technology, Fuzhou University, China

2008 – Present

August 2008 June 2006

Professional Experience • Software engineer, Google Inc. • Intern, Microsoft Research Silicon Valley.

Oct 2015 – Present May 2012 – Aug 2012

Publications 1. Yan Chen, Umut A. Acar, and Kanat Tangwongsan. Functional Programming for Dynamic and Large Data with Self-Adjusting Computation. Proc. of ACM-SIGPLAN International Conference on Functional Programming (ICFP), 227–240. Sep 2014. Acceptance Ratio: 29% (28/97) 2. Yan Chen, Joshua Dunfield, Matthew A. Hammer, and Umut A. Acar. Implicit Self-Adjusting Computation for Purely Functional Program. Journal of Functional Programming (JFP), 24(1), 56–112. Jan 2014. 3. Umut A. Acar and Yan Chen. Streaming Big Data with Self-Adjusting Computation. Data Driven Functional Programming Workshop (DDFP), 15–18. Jan 2013. 4. Yan Chen, Joshua Dunfield, and Umut A. Acar. Type-Directed Automatic Incrementalization. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 299– 310. Jun 2012. Acceptance Ratio: 19% (48/255) 5. Matthew A. Hammer, Georg Neis, Yan Chen, and Umut A. Acar. Self-Adjusting Stack Machines. Proc. of ACM-SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), 753–772. Oct 2011. Acceptance Ratio: 37% (61/166) 6. Yan Chen, Joshua Dunfield, Matthew A. Hammer, and Umut A. Acar. Implicit Self-Adjusting Computation for Purely Functional Program. Proc. of ACM-SIGPLAN International Conference on Functional Programming (ICFP), 129–141. Sep 2011. Acceptance Ratio: 36% (33/92)

7. Matthew A. Hammer, Umut A. Acar, and Yan Chen. CEAL: A C-Based Language for SelfAdjusting Computation. Proc. of ACM-SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 25–37, Jun 2009. Acceptance Ratio: 20% (41/196) 8. Sandip Ray, Kecheng Hao, Yan Chen, Fei Xie, and Jin Yang. Formal Verification for HighAssurance Behavioral Synthesis. Proc. of 7th International Symposium on Automated Technology for Verification and Analysis (ATVA), 337–351. Oct 2009. Acceptance Ratio: 30% (26/84) 9. Yan Chen, Fei Xie, and Jin Yang. Optimizing Automatic Abstraction Refinement for GSTE. Proc. of 45th Design Automation Conference (DAC), 143–148, Jun 2008. Acceptance Ratio: 23% (147/639) 10. Yan Chen, Yujing He, Fei Xie, and Jin Yang. Automatic Abstraction Refinement for Generalized Symbolic Trajectory Evaluation. Proc. of 7th International Conference on Formal Methods in Computer-Aided Design (FMCAD) , 111–118, Nov 2007. Acceptance Ratio: 28% (23/80) 11. Yan Chen. An Efficient Search Algorithm for Partially Ordered Sets. Proc. of The IASTED International Conference on Advances in Computer Science and Technology (ACST), 91–94, Jan 2006.

Technical Reports 1. Matthew A. Hammer, Umut A. Acar, and Yan Chen. CEAL: A C-Based Language for SelfAdjusting Computation. Technical Report TTIC-TR-2009-2. Toyota Technological Institute at Chicago. May 2009. 2. Sandip Ray, Yan Chen, Fei Xie, and Jin Yang. Combining Theorem Proving and Model Checking for Certification of Behavioral Synthesis Flows. Technical Report TR-08-48, Department of Computer Science, University of Texas at Austin. Dec 2008.

Awards • Google GRAD CS Scholarship, 01/2010. • Silver Medal (12th Place), 30th ACM International Collegiate Programming Contest (ICPC). Beijing Site, 11/2005. • Third Prize, National Undergraduate Electronic Design Contest Specialized in Embedded System. 09/2004. • Second Prize (for three continuous years), National Olympiad in Informatics Provincial Contest. 1999 – 2001.

Professional Services • Program Committee for ACM SIGPLAN Workshop on ML. 2016. • Reviewer for The Mathematical Reviews, American Mathematical Society. • Reviewer for The Computer Journal, Oxford University Press. • Reviewer for the Science of Computer Programming, Elsevier. • Reviewer for ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL). 2015,2012. • Reviewer for ACM SIGPLAN International Conference on Functional Programming. 2013,2010. • Reviewer for ACM SIGPLAN Workshop on ML. 2009. • Reviewer for ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE). 2009, 2008. • Assistant Coach. ACM/ICPC World Final Team of Fuzhou University. 2006.

Presentations • “Implicit Self-Adjusting Computation: From Types to Incremental Systems”. IBM Thomas J. Watson Research Center. Lab Seminar. Yorktown Heights, NY. Nov 6, 2014. • “Functional Programming for Dynamic and Large Data with Self-Adjusting Computation”. ACM SIGPLAN International Conference on Functional Programming (ICFP). Gothenburg, Sweden. Sep 2, 2014. • “Implicit Self-Adjusting Computation: From Types to Incremental Systems”. Microsoft Research Cambridge. Lab Seminar. Cambridge, UK. Mar 20, 2014. • “Streaming Big Data with Self-Adjusting Computation”. Data Driven Functional Programming Workshop. Rome, Italy. Jan 22, 2013. • “Refactoring DryadLINQ using the Compiler Forest”. Microsoft Research, Silicon Valley. Lab Seminar. Mountain View, CA. Aug 16, 2012. • “Type-Directed Automatic Incrementalization”. Software Engineering Institute, Peking University. Department Seminar. Beijing, China. Jun 15, 2012. • “Type-Directed Automatic Incrementalization”. Baidu. Lab Seminar. Beijing, China. Jun 14, 2012. • “Type-Directed Automatic Incrementalization”. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). Beijing, China. Jun 12, 2012. • “Implicit Self-Adjusting Computation for Purely Functional Programs”. ACM SIGPLAN International Conference on Functional Programming (ICFP). Tokyo, Japan. Sep 19, 2011. • “Implementing Implicit Self-Adjusting Computation”. ACM SIGPLAN Workshop on ML. Tokyo, Japan. Sep 18, 2011. • “Optimizing Automatic Abstraction Refinement for GSTE”. Design Automation Conference (DAC). Anaheim, California. Jun 10, 2008. • “Optimizing Automatic Abstraction Refinement for GSTE”. Progress report to Strategic CAD Lab at Intel, Portland, Oregon. Apr 7, 2008. • “Proving Correctness of Computer Systems”. Toyota Technological Institute at Chicago, Chicago, Illinois. Apr 4, 2008. • “Automatic Abstraction Refinement for GSTE”. International Conference on Formal Methods in Computer-Aided Design (FMCAD). Austin, Taxes. Nov 13, 2007. • “Automatic Abstraction Refinement for GSTE”. Progress report to Strategic CAD Lab at Intel. Portland, Oregon. Jun 15, 2007.

Programming Skills H Highly-Proficient Languages: C/C++, Standard ML H Proficient Languages: Python, C#, Prolog, Pascal, SQL, ASP/PHP, LATEX