Statistics, Data Science, Machine Learning

Regression Analysis

Agent-Based Modeling (ABM)

Geographic Information Systems (GIS)

Natural Language Processing (NLP)

Julia, Python, and R

Data Science

Scientific Computing

Software Development


Computational Bioequivalence


Master Statistics with R

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
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Machine Learning

Build Intelligent Applications

In this Specialization, you’ll learn the fundamentals of one of the most exciting and high-demand fields in modern computer science. When you complete the five courses and Capstone Project, you’ll be prepared to analyze large and complex datasets from a variety of sources, make predictions from data, and create adaptable systems to solve real-world problems.

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

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Prepare for Advanced Computer Science Courses

This Specialization covers much of the material that first-year Computer Science students take at Rice University. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
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Data Science

This specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. The Specialization concludes with a Capstone project that allows you to apply the skills you’ve learned throughout the courses.
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Scientific Computing Intern

Center for Translation Medicine at the University of Maryland Baltimore

Sep 2018 – Present Baltimore, MD

As a scientific computing intern, I contribute to the data generation and processing tools for various modeling designs (e.g., PK/PD, PBPK, QSP) and for conducting bioequivalence studies. The code will be released in early 2019 as open-source. Projects are under the supervision of professor Vijay Ivaturi and Chris Rackauckas.

Supervisor: Vijay Ivaturi, PhD


Research Assistant


Jul 2018 – Present

I am a member of the team developing and maintaining the QuantEcon lectures for the Julia language and related code (e.g., QuantEcon.jl). The undergraduate and graduate level lectures cover various topics in quantitative economics. The code is available at the Github repository (open-source).

Supervisor: Jesse Perla, PhD


Graduate Fellow for the Data Science for the Public Good Program

Social and Decision Analytics Laboratory

May 2018 – Aug 2018 Arlington, VA

I led two research projects and consulted for a third one. My responsibilities included working with the sponsor and undergraduate students tasked to these projects.

Supervisor: Gizem Korkmaz, PhD


Research Consultant and Data Scientist


Sep 2016 – Sep 2018 Portland, OR

I assisted with analytical modeling and software integration. Some of my experiences include working in the team that developed the data validation and preprocessing for the analytic tools provided in the software. My work focused on the designed and implementation of the algorithms used in prediction and benchmarks for energy and water utility accounts (performed using R).

Supervisor: Hal Nelson, PhD


Research Assistant

The Center for Neuroeconomics Studies

Sep 2014 – May 2016 Claremont, CA

I performed various roles such as: recruiting participants, conducting design-stage research, piloting laboratory experiments, running experiments, and cleaning and analyzing data. The laboratory experiments included administrating drugs, collecting blood samples, eye-tracking, electroencephalogram (EEG), electrocardiogram (ECG), and standard experimental laboratory studies.

Supervisor: Paul J. Zak, PhD


Teaching Assistant

2016 Advanced Research Methods (Graduate), Michigan State University, for Dr. Lisa Cook, through the AEA Summer Program

2015 Principles of Microeconomics (Undergraduate), Johns Hopkins University, through the Center for Talented Youth

2014 Intermediate Microeconomics (Undergraduate), Southwestern University

2014 Principles of Economics (Undergraduate), Southwestern University


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