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AIML | ex-Lyft | ex-Uber
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Experienced in machine learning and deep learning. Enthusiastic about applying data science to solving different kinds of problems. Mathematics and computer science will change the world!
The Johns Hopkins University
Master of Science (MSc), Applied Mathematics
January 1, 2014 – January 1, 2016
Nanjing University
Bachelor of Science (BSc), Mathematics
January 1, 2010 – January 1, 2014
Netflix
Research Engineer
February 1, 2026 – Present
Uber
Machine Learning Engineer
November 1, 2025 – February 1, 2026
Lyft
Software Engineer, Machine Learning
July 1, 2019 – November 1, 2025
San Francisco, California
Tophatter
Machine Learning Engineer
April 1, 2018 – June 1, 2019
San Francisco, California
Xometry
Data Scientist
January 1, 2016 – April 1, 2018
Washington D.C. Metro Area
Johns Hopkins University
Research Assistant
September 1, 2015 – December 1, 2015
Baltimore, Maryland
Bosch China
Speech Recognition Intern
July 1, 2015 – August 1, 2015
Shanghai City, China
Albatross Global Solutions
Analytics Intern
June 1, 2015 – July 1, 2015
Shanghai City, China
Johns Hopkins University
Teaching Assistant
January 1, 2015 – May 1, 2015
Baltimore, Maryland
Vacant Housing Dynamics in Baltimore City Project
September 1, 2015 – Present
Working with City officials, our goal is to better understand the dynamics of vacant housing in Baltimore City, measure the impact of current interventions, and help to hone decision and policy making using statistical analyses of available data.
Getting And Cleaning Data
March 1, 2015 – Present
Collected, worked with, and cleaned a data set. Goal: To prepare tidy data that can be used for later analysis. Getting: 1. A tidy data set 2. A link to a Github repository with a script for performing the analysis 3. A code book that describes the variables, the data, and transformations and work that I performed to clean up the data Methods: 1. Merging the training and the test sets to create one data set.
Word Search Program
February 1, 2015 – Present
-A grid file contains an NxN grid of characters where N is at most 10. That is, there should be N lines containing N letters (any mix of upper or lower case), followed by an empty line. Anything after the empty line can be ignored. -There are 3 types of invalid grids that the program check for. In these cases, the program prints that the grid is invalid and exit: if two of the grid rows have differing numbers of characters if the number of rows doesn't match the number of columns (or) if the number of rows (and columns) is 0 or greater than 10 -The size of the 2D array variable will be set at compile time, and should be 10x10 -For each match of an input word to a word in the grid, the program should output a line of text containing the matched word, the row of the start of the word, the column of the start of the word, and the direction of the match: U for up, D for down, L for left, R for right. Number rows and columns starting with 0 in the output. -If there are no matches for a word, print a "Not Found" message. -There is no requirement regarding the order of the output lines (for example, if multiple copies of a word are present); however, the program should find all of them. -The program should be case insensitive. -Search words are separated by whitespace (any amount). The program continues looking for words until it reaches the end-of-input. -Words can appear in the grid forwards or backwards, horizontally or vertically, with the characters in the proper order in adjacent cells. Also remember that different words might share characters. Search for the word in all directions.
R Programming: Analyzing the data extracted from the Hospital Compare web site
May 1, 2014 – Present
-write an R function that is able to cache potentially time-consuming computations -take advantage of the scoping rules of the R language and how they can be manipulated to preserve state inside of an R object
Practical Analysis of Big Data: SQL Subject
March 1, 2014 – Present
Query the database and get the following: - the lengths of the (X,Y) vectors in descending order for all measurements (in the Data table) with X and Y closer than 0.1 - the histogram of Y if binned into intervals between integers - the largest Y value measured by each student - Implement a user-defined function that returns the 3 largest Y measurements by a given student (UserID) - Save a table that contains all users (UserID) as well as the instruments (InsID) that they used; and store the special value NULL for the InsID, if no measurements were done - Write a SQL query using a recursive Common Table Expression to calculate the first 5 factorials And explored: + the relation between X and Y +which instrument is more precise
Introduction to Big Data
Coursera
June 24, 2026 – Present
Big Data Integration and Processing
Coursera
June 24, 2026 – Present
SAS Certified Base Programmer for SAS 9
SAS
June 24, 2026 – Present
Regression Models
Coursera
June 24, 2026 – Present
R Programming
Coursera
June 24, 2026 – Present
Machine Learning With Big Data
Coursera
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Big Data Modeling and Management Systems
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera
June 24, 2026 – Present
American Mensa Member
American Mensa
June 24, 2026 – Present
Getting and Cleaning Data
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on data science and machine learning, aligning well with a data-driven culture. The diversity of projects, from academic research to industry applications in various domains (e-commerce, manufacturing, ride-sharing, streaming), suggests adaptability and a broad interest in applying analytical skills. The continuous pursuit of certifications in big data and machine learning indicates a proactive and growth-oriented mindset. However, the target role is 'Data Analyst' while the candidate's experience is heavily skewed towards 'Machine Learning Engineer' and 'Data Scientist', which might indicate a potential mismatch in the depth of ML/DS vs. pure data analysis focus, or a desire to transition. The projects are mostly personal or academic, with limited explicit mention of team collaboration or cross-functional work in the project descriptions themselves, though company roles imply it.
Soft Skills & Operational Fit
The candidate's project descriptions and work experience highlight a strong problem-solving aptitude, evidenced by building models from scratch, optimizing business metrics, and troubleshooting technical issues. The ability to explain statistical concepts to non-professional people suggests good communication skills. Experience in collaborative environments (e.g., 'Corporate with Senior Data Scientist', 'Corporated with CTO') indicates team-oriented operational fit. The 'Vacant Housing Dynamics' project shows an interest in applying data analysis to real-world societal problems.