I am Jing Ma :)

Boston University, Boston, MA

• Ph.D., Department of Physics

• 2015 - 2022

• Master of Arts, Department of Physics

• 2015 - 2019

Peking University, Beijing, China

• Bachelor of Science, School of Physics

• 2011 - 2015

• Bachelor of Economics, National School of Development

• 2013 - 2015

Software Engineer
Security and Privacy
Google Cloud Platform (GCP)

• C++, Python3, Go, Java, GoogleSQL

• Maintaining the administrative access control core storage (ACS) system, that enforces security constraints on Googlers' access to customer data, based on data sensitivity categories and product security levels; developing features that support more GoogleSQL keywords, new data sensitivity categories, and additional compliance requirements, which allows more product teams to achieve data security compliance with unified and robust tools.

• Led design discussions and development of an alternative data protection strategy, that allows products with lower security risks to meet Department of Defense (DoD) Impact Level 4 and 5 (IL4 and IL5) requirements with significantly reduced efforts. Certain workflows achieved as much as 90% reduced effort estimates with this alternative approach. Also conducted reviews with Google's chief information security officer (CISO) and achieved agreements on data freshness SLOs.

• Migrated and extended our administrative access control systems to trusted partner cloud (TPC) universes, that address strict sovereignty requirements in Europe and APAC by providing isolated cloud instances, and ensuring that data is stored and handled locally.

Software Engineer
Offboard Algorithm

• C++14, Python3, PostgreSQL, ROS, Docker, AWS

• Maintained the simulation platform that allows for the creation and editing of scenarios, and provides performance evaluations; actively discussed with our client teams, and delivered timely and reliable updates to the platform.

• Maintained databases of simulation scenarios and task results, and extended their structures to allow for more flexibility in testing cases design, and to deliver user-friendly performance results.

• Independently designed and developed the pipeline to regenerate datasets synchronously and reproducibly, through ROS requests and responses; deployed to production, the new pipeline has proven to significantly reduce in overhead, run comparably to or even faster than the asynchronized version, on average, and also fixed the frame loss issue.

Software Engineer Internship
Simulation Platform

• C++14, Python3

• Maintained the backend of the simulation platform (e.g., unified the standard of frame alignments, redesigned the frame logic to ensure consistency in results replay), and added new functionalities to support internal and external needs.

• Refactored the metrics system for the simulation platform, decoupled the evaluation process from running, and thus enabled the possibility to design future-dependent as well as history-dependent evaluators.

Research
Critical Phenomena in Epidemic Models

• C++, Python3

• Mapped the SIR (Susceptible-Infected-Recovered) epidemic model as a percolation process embedded in networks.

• Studied critical phenomena and power-law behaviors of the SIR model, especially for multi-community networks, in the framework of generating functions.

• Implemented Monte Carlo simulations for large-size systems, and studied its difference from the theory due to finite size effects.

Research
Neuronal Spike Data Analysis

• Python3, PyTorch, Scikit-learn

• Cleaned and standardized large-scale neuronal spike data from rats, and selected features using various dimension reduction methods.

• Implemented LSTM models and trained on historical data, to predict future motions of the same rats, and achieved 70 - 80% explained variance.

• Proposed similarity metrics to match features, and to transfer learning between rats.

Project
Sharing Bike Usage Prediction

• R

• Cleaned and normalized weather data, and removed auto-correlation using the generalized least squares (GLS) technique.

• Implemented and compared different linear models based on adjusted R-squared, AIC, AICc, and BIC scores.

• Predicted the sharing bike usage for the following year, with correlation higher than 90%.

Teaching Fellow
Elementary Physics I, II, General Physics I, II

• Led lab and discussion sections for undergraduate level physics classes, i.e., PY105, 106 (Elementary Physics I, II), PY211, 212 (General Physics I, II), including both face-to-face classes and Zoom classes.

• Was also responsible for preparing materials/equipments, making up questions for exams, and grading reports/quizzes/exams/etc.

Publication(s)

• J. Ma, X. Meng, and L. A. Braunstein, Peak fraction of infected in epidemic spreading for multi-community networks , J. Complex Netw. 10(3), cnac021 (2022)

• J. Ma, L. D. Valdez, and L. A. Braunstein, Role of bridge nodes in epidemic spreading: Different regimes and crossovers , Phys. Rev. E 102(3), 032308 (2020)

• W. Jiang et al., Determination of the cluster-decay branching ratio from a near-threshold molecular state in ¹⁰Be , Phys. Rev. C 101(3), 031304 (2020)

• W. Jiang et al., High-lying excited states in ¹⁰Be from the ⁹Be(⁹Be, ¹⁰Be)⁸Be reaction , SCI CHINA PHYS MECH 60(6), 1-5 (2017)

• J. Li et al., Selective decay from a candidate of the σ-bond linear-chain state in ¹⁴C , Phys. Rev. C 95(2), 021303 (2017)

• Z. Y. Tian et al., Cluster decay of the high-lying excited states in ¹⁴C , Chin. Phys. C 40(11), 111001 (2016)

• S. Chen, Q. Li, J. Ma, H. Kong, Y. Ye, J. Gao, and Y. Jiang, Simulation of a small muon tomography station system based on RPCs , J. Instrum. 9(10), C10022 (2014)

Award(s)

• The 2019 - 2020 Outstanding Teaching Fellow in the Department of Physics (recognized by the Graduate School of Arts & Sciences, Boston University)

Coding

C++ (fluent), Python3 (fluent), Go, SQL, Java, Fortran, R, MATLAB, JavaScript, etc.

Tool(s)

Linux, Git, Tmux, Vim, machine learning libraries, Docker, AWS, etc.

Package(s)

Numpy, Scipy, Pandas, Scikit-learn, PyTorch, etc.

Course(s)

Computational Physics, Linear Models, Learning from Data, Statistical Machine Learning, etc.

Sudoku

Classical Music

Bridge