About
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Hello there! I am a graduate of Carnegie Mellon University, where I obtained a Master's degree in Statistics as well as a Bachelor's degree in both Statistics and Computer Science. In my free time, I enjoy playing/following major US sports1 and eating good food2.
1specifically football and basketball (NFL, CFB, NBA, March Madness). 2A bold take on authenticity that has changed how I view food in general, courtesy of David Chang (Ugly Delicious, 2018). |
Experience
I've been fortunate enough to have the opportunity to work on some intellectually engaging projects and teams in industries ranging from big tech to sports and energy trading. Some recent notables:
- Software Development Engineer, Amazon (Fall 2020-pres.)
- Quantitative Developer - Data Science, SESCO Enterprises (Summer 2020)
- SDE Intern, Amazon Lab126 (Summer 2019)
- SDE Intern, Amazon AI (Summer 2018)
- Researcher, Pittsburgh Penguins - affiliated with Carnegie Mellon (Winter/Spring 2018)
[Click here to expand/hide details.]
(in detail):
- During the summer of 2020, I worked at SESCO Enterprises in the Greater Pittsburgh area, where I used PyTorch to implement deep learning models for wholesale energy trading while also creating software tools and infrastructure to better analyze energy futures as a member of the Data Science team.
- The previous two summers I had the opportunity to work on two different teams at Amazon:
- During the summer of 2018, I interned on the AI Labs team within Amazon Web Services in New York City. During my time on the AI Labs team, I implemented, tested, and benchmarked additions to first-party machine learning algorithms for Amazon Sagemaker using Python, R, and the Apache MXNet library.
- During the summer of 2019, I interned on the Alexa Devices team as a part of Amazon Lab126 in Sunnyvale, Calif. There, I worked on core distributed systems development and large-scale data processing for the Alexa Multi-Room Music feature. During my time there, I optimized control servers by creating an event-based server system using the Libevent library in C++. This functionality manages audio distribution, inline controls, and visibility between multiple Alexa-enabled devices. Since then, my work has been deployed to millions of Amazon Echo devices across the world!
- Prior to that, I have been affiliated with the Pittsburgh Penguins as an undergraduate researcher at Carnegie Mellon University. Working with Director of Hockey Research Sam Ventura and three other peers, I conducted survival analysis research on hockey injuries and injury recurrence across 15 years of self-mined data using Python and R. Main findings can be found here. Our work has been presented at
- Carnegie Mellon Sports Analytics Conference (Fall 2018)
- Carnegie Mellon Meeting of the Minds Research Symposium (Spring 2018)
Coursework
In five years of college, I've completed courses in a fairly wide variety of subjects. [Click here to expand/hide a noteworthy subset of them.]
(* denotes courses I'd highly recommend taking as an undergraduate)
Computer Science:
- 15-381 Artificial Intelligence*
- 10-601 Machine Learning (Graduate)*
- 15-440 Distributed Systems
- 15-451 Algorithm Design and Analysis*
- 15-251 Great Theoretical Ideas in Computer Science*
- 15-210: Parallel Algorithms
- 15-213 Computer Systems
Statistics:
- 36-726 Statisics Capstone
- 36-610 Probability Modeling (Graduate)*
- 36-462/662 Data Mining*
- 36-490 Statistics Undergraduate Research*
- 36-226 Statistical Inference
- 36-401 Modern Regression
- 36-402 Advanced Data Analysis
- 36-617 Applied Linear Models (Graduate)
- 36-618 Time Series and Experimental Design (Graduate)
- 36-665 Survival Analysis (Graduate)
Other Noteworthy:
- 51-262: Communication Design Fundamentals
- 15-661: Interaction and Expression using the Pausch Bridge Lighting
- 09-108: Illusion and Magic of Food (Food Science Seminar)