Jiahao Chen

Vice President, Research Lead

JPMorgan AI Research

About me

Jiahao Chen is a Vice President and Research Lead at JPMorgan AI Research in New York, with research focusing on explainability and fairness in machine learning, as well as semantic knowledge management. He was previously a Senior Manager of Data Science at Capital One focusing on machine learning research for credit analytics and retail operations.

When still in academia, Jiahao was a Research Scientist at MIT CSAIL where he co-founded and led the Julia Lab, focusing on applications of the Julia programming language to data science, scientific computing, and machine learning. Jiahao has organized JuliaCon, the Julia conference, for the years 2014-2016, as well as organized workshops at NeurIPS, SIAM CSE, and the American Chemical Society National Meetings. Jiahao has authored over 120 packages for numerical computation, data science and machine learning for the Julia programming language, in addition to numerous contributions to the base language itself.

Interests

fair & trustworthy artificial intelligence
the Julia programming language

Education

PhD in Chemical Physics / Computational Science & Engineering, 2009

University of Illinois at Urbana-Champaign
MS in Applied Mathematics, 2008

University of Illinois at Urbana-Champaign
BS in Chemistry, 2002

University of Illinois at Urbana-Champaign

Experience

Vice President, Research Lead

JPMorgan AI Research

2019-03 – Present New York

Research Lead spearheading projects in data management, fairness in machine learning, and explainable AI.

Senior Manager, Data Science

Capital One

2017-03 – 2019-02 New York

ELI5: I led a team studying how we can use machine learning fairly, to improve customer service and experience, and change banking for good.

Technical Lead for Banking on Explainable AI ResearchGroup within Card Machine Learning. Compliance analytics for fair lending, natural language processing for customer service analytics, customer segmentation.

Research Scientist

Julia Labs, MIT CSAIL

2013-09 – 2017-01 Cambridge, Massachusetts

ELI5: I started and ran a research lab to prove that the Julia programming language was useful for big data and data science work.

Started and managed the Julia Lab together with Professor Alan Edelman, providing the main academic funding responsible for the development, growth and adoption of the Julia programming language. Applied Julia to problems in high performance computing, computational genomics, and statistical computing. The lab comprised 16 students and postdocs at its peak.

Visiting Scholar

Ritsumeikan University

2013-06 – 2013-09 Kusatsu City, Shiga Prefecture, Japan

ELI5: I coded up a model for studying how drug molecules dissolve in water, and shipped it in commercial software.

Productionized and shipped the 1D- and 3D-RISM (Reduced Interaction Site Model) codes that are now available in Accelrys Discovery Studio.

Postdoctoral associate

MIT Chemistry

2009-06 – 2013-05 Cambridge, Massachusetts

ELI5: I made new computer models of how molecules trap light and turn them into electricity. I used these models to study new materials used for OLEDs and solar cells.

Computational chemistry research on organic semiconductors, using new techniques of random matrix theory blended with new molecular models for describing atomic charge excitations and transfer.

Member of Technical Staff

DSO National Laboratories

2004-05 – 2002-10 Singapore

ELI5: I made new materials that would distort light and/or blow up, and shoot them with lasers to see how they would react.

Synthesized and characterized novel materials for nonlinear optics and energetic materials (explosives), with organic and inorganic chemical synthesis techniques and nonlinear laser spectroscopy.

Projects

Julia

I am a core contributor to the Julia programming language. In addition to starting and running the Julia Lab at MIT CSAIL from …

Recent & Upcoming Talks

Fair and explainable AI/ML for financial services

The financial services industry has many needs for fairness and explainability in artificial intelligence and machine learning, which …

2019-11-07 12:00 — 13:00 MIT CSAIL

Jiahao Chen

PDF

Sponsor Address: J.P.Morgan Chase & Co.

2019-07-24 09:30 — 09:45 University of Maryland School of Nursing

Jiahao Chen

Project Video

DCGSAC Careers in Chemistry Symposium

2019-06-28 14:00 — 15:00 University of Illinois at Urbana-Champaign

Jiahao Chen

Fairness under unawareness: assessing disparity when protected class is unobserved

2019-01-29 14:00 Atlanta, Georgia

Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell

PDF Video

Robustly Benchmarking Julia in Noisy Environments

We propose a benchmarking strategy that is robust in the presence of timer error, OS jitter and other environmental fluctuations, and …

2018-11-07 15:30 — 16:30 Boston Park Plaza Hotel

Jiahao Chen

Project Slides

See all talks

Featured Publications

Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell

2019-01-31 FAT‘19 Proceedings of the Conference on Fairness, Accountability, and Transparency*

Fairness under unawareness: assessing disparity when protected class is unobserved

Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to impute these missing labels for compliance assessments. Empirically, these methods are observed to exaggerate disparities, but the reason why is unknown. In this paper, we decompose the biases in estimating outcome disparity via threshold-based imputation into multiple interpretable bias sources, allowing us to explain when over- or underestimation occurs. We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership. Finally, we illustrate our results with numerical simulations and a public dataset of mortgage applications, using geolocation as a proxy for race. We confirm that the bias of threshold-based imputation is generally upward, but its magnitude varies strongly with the threshold chosen. Our new weighted estimator tends to have a negative bias that is much simpler to analyze and reason about.

PDF Slides Video DOI

Jiahao Chen

2018-10-06 FATREC’18 Proceedings of the Second Workshop on Responsible Recommendation

Fair lending needs explainable models for responsible recommendation

The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.

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