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Jen J. Gong Curriculum Vitae

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Jen J. Gong Curriculum Vitae Jen J. Gong Curriculum Vitae Center for Clinical Informatics and Improvement Research (CLIIR) www.mit.edu/~jengong University of California, San Francisco [email protected] Education & Honors Ph.D. Massachusetts Institute of Technology 2014{2018 Electrical Engineering and Computer Science Thesis: Improving Clinical Decisions Using Correspondences Within and Across Electronic Health Records Advisor: Professor John Guttag S.M. Massachusetts Institute of Technology 2012{2014 Electrical Engineering and Computer Science Thesis: Improving Clinical Risk-Stratification Tools: Instance-Transfer for Selecting Relevant Training Data Advisor: Professor John Guttag A.B. Harvard College 2008-2012 Applied Mathematics, cum laude and High Honors Secondary in Statistics NSF Graduate Research Fellowship 2012 Research Experience Postdoctoral Scholar, University of California, San Francisco, July 2019{Present San Francisco, CA Center for Clinical Informatics and Improvement Research (CLIIR) Postdoctoral Scholar, University of California, Berkeley, Berkeley, CA July 2018{June 2019 Haas School of Business Research Assistant, Massachusetts Institute of Technology, Cambridge, MA Data-Driven Inference Group. Advisor: Professor John Guttag 2012{June 2018 Neuroscience Statistics Research Laboratory. Advisor: Professor Emery Brown 2010{2012 Research Assistant, Harvard Medical School, Boston, MA Summer 2009 Department of Systems Biology. Advisors: Professor Johan Paulsson, Dr. Andreas Hilfinger Industry Experience Data Science Intern, Grand Rounds, San Francisco, CA Summer 2017 Built neural network models and data processing pipelines for medical claims data. Software Engineering Intern, Google, Inc., Mountain View, CA Summer 2014 Trained and evaluated neural network models for learning representations of concepts from text. Jen J. Gong { Curriculum Vitae (Sept 2019) Page 1 of 3 Publications & Presentations Conferences • Learning Tasks for Multitask Learning: Heterogeneous Patient Populations in the ICU. 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2018). Poster presen- tation. Harini Suresh*, Jen J. Gong*, John V. Guttag. (*) equal contribution. • Learning to Summarize Electronic Health Records Using Cross-Modality Correspondences. Machine Learning for Healthcare (2018). Jen J. Gong and John V. Guttag. • Predicting Adverse Events Across Changing Electronic Health Record Systems. 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017). Oral presentation. Jen J. Gong*, Tristan Naumann*, Peter Szolovits, John V. Guttag. (*) equal contribution. • Towards an Automated Screening Tool for Developmental Speech and Language Impairments. Inter- speech (2016). Oral presentation. Jen J. Gong, Maryann Gong, Dina Levy-Lambert, Jordan R. Green, Tiffany P. Hogan, John V. Guttag. • Instance Weighting for Patient-Specific Risk Stratification Models. 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015). Oral presentation. Jen J. Gong, Thoralf M. Sundt, James D. Rawn, John V. Guttag. • Correcting for Serial Dependence in Studies of Respiratory Dynamics. IEEE Engineering in Biology and Medicine Conference (2011). Jen J. Gong, Kin Foon Kevin Wong, Joseph F. Cotten, Ken Solt, Emery N. Brown. Workshops • Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders. Digital acceptance to Machine Learning for Health Workshop at the Conference on Neural Information Processing Systems (NeurIPS), Poster presentation at Women in Machine Learning Workshop (2018) Jen J. Gong, Abigail Z. Jacobs, Toby E. Stuart, Mathijs de Vaan. • Multiple Instance Learning for ECG Risk Stratification. Poster presentation at the Machine Learning for Health Workshop at the Conference on Neural Infor- mation Processing Systems (NeurIPS) (2018). Divya Shanmugam, Davis Blalock, Jen J. Gong, John V. Guttag. • Comparing Clinical Data Modalities for Predicting Adverse Outcomes in the Intensive Care Unit. Poster presentation at the Women in Machine Learning Workshop (2017). Jen J. Gong and John V. Guttag. • Using Bag-of-Clinical-Events Features for Predicting Mortality in Low-Acuity Patients in the ICU. Poster presentation at the Workshop on Machine Learning in Healthcare at the Conference on Neural Information Processing Systems (NeurIPS) and the Women in Machine Learning Workshop (2015) Jen J. Gong, Mohammed Saeed, John V. Guttag. • Using Instance-Transfer to Improve Risk-Stratification Models for Cardiac Surgery. Oral presentation at the Women in Machine Learning Workshop (2014). Jen J. Gong, Thoralf M. Sundt, James D. Rawn, Robert Neely, John V. Guttag. Jen J. Gong { Curriculum Vitae (Sept 2019) Page 2 of 3 • Risk Stratification of Isolated Aortic Valve Replacements: Considering the Utility of Similar Cardiac Surgeries as Training Data. Poster presentation at Workshop on Machine Learning for Clinical Data Analysis and Healthcare at the Conference on Neural Information Processing Systems (NeurIPS) (2013). Jen J. Gong and John V. Guttag arXiv • Visualizing Patient Timelines in the Intensive Care Unit. Dina Levy-Lambert, Jen J. Gong, Tristan Naumann, Tom J. Pollard, John V. Guttag. https://arxiv.org/abs/1806.00397 Journals • Assessing the Effects of Pharmacological Agents on Respiratory Dynamics Using Time-Series Modeling. IEEE Transactions on Biomedical Engineering, 60(4): 1118-1125. (2012). Kin Foon Kevin Wong, Jen J. Gong, Joseph F. Cotten, Ken Solt, Emery N. Brown. Teaching Experience Guest Lecturer, 6.03 Introduction to EECS by Medical Technology Spring 2017 Gave lectures introducing supervised and unsupervised machine learning approaches in an introductory undergraduate course. Teaching Assistant Spring 2015 6.0001 Introduction to Computer Science and Programming in Python 6.0002 Introduction to Computational Thinking and Data Science Mentorship Supervised two undergraduate research projects Sept. 2014 - June 2016 Supervised Master of Engineering student Jan. 2017 - Feb. 2018 Service Technical Reviewer Machine Learning for Healthcare Conference 2016, 2017, 2018, 2019 American Medical Informatics Association (AMIA) Informatics Summit 2019 American Medical Informatics Association (AMIA) Joint Summits 2017 AAAI: Association for the Advancement of Artificial Intelligence 2016 Women in Machine Learning Workshop 2016, 2017, 2018 NeurIPS Workshop on Machine Learning for Health 2016, 2017, 2018 Community Member of the EECS Resources for Easing Friction and Stress (REFS) Feb. 2015{Sept. 2017 Co-chair of Graduate Women @ MIT Mentorship Program Committee Sept. 2015{Sept. 2017 Committee on MIT Graduate Admissions in EECS 2016 Reviewed Ph.D. student applications for Machine Learning and Artificial Intelligence in MIT EECS. Jen J. Gong { Curriculum Vitae (Sept 2019) Page 3 of 3.
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    Bibliography [Abelson85] Harold Abelson, Gerald Jay Sussman, and Julie Sussman, Structure and Interpretation of Computer Programs, MIT Press, Cambridge, MA, 1985. [Aho86] Alfred Aho, Ravi Sethi, and Jeffrey Ullman, Compilers: Principles, Tech- niques, and Tools, Addison-Wesley, Reading, MA, 1986. [Alagic78] Suad Alagic and Michael Arbib, The Design of Well-Structured and Correct Programs, Springer-Verlag, New York, 1978. [Allison86] Lloyd Allison, A Practical Introduction to Denotational Semantics, Cam- bridge University Press, Cambridge, UK, 1986. [Anderson76] E. R. Anderson, F. C. Belz, and E. K. Blum, “SEMANOL (73) A Metalanguage for Programming the Semantics of Programming Languages”, Acta Informatica, 6, 1976, pp. 109–131. [Astesiano91] Egidio Astesiano, “Inductive and Operational Semantics”, In Formal De- scription of Programming Concepts, edited by Erich Neuhold, et al, Springer- Verlag, Berlin, 1991. [Backhouse79] Roland Backhouse, Syntax of Programming Languages: Theory and Prac- tice, Prentice Hall International, Hemel Hempstead, UK, 1979. [Backhouse86] Roland Backhouse, Program Construction and Verification, Prentice Hall International, Englewood Cliffs, NJ, 1986. [Backus78] John Backus, “Can Programming Be Liberated from the von Neumann Style? A functional Style and Its Algebra of Programs”, Communications of the ACM, 21.8, August 1978, pp. 613–641. [Barendregt84] H. P. Barendregt, The Lambda Calculus, Its Syntax and Semantics, North- Holland, Amsterdam, 1984. [Bennett90] Jeremy Bennett, Introduction to Compiling Techniques, McGraw-Hill, New York, 1990. 611 612 BIBLIOGRAPHY [Bergstra89] J. Bergstra, J. Heering, and Paul Klint, Algebraic Specification, Addison- Wesley, Reading, MA, 1989. [Bird76] Richard Bird, Programs and Machines, Wiley, New York, 1976. [Bochman76] Gregor Bochman, “Semantic Evaluation from Left to Right”, Communica- tions of the ACM, 19.2, February 1976, pp.
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