Eleven years ago, I developed my first computer program, a basic calculator, in C# and I was hooked
right away. Over the years, I worked as a Software Engineer for two years and published an iOS app.
My undergraduate Statistics minor changed the way I think about data and since then, I have enjoyed
analyzing how my personal data (such as sleep, nutrition, and running pace) interact with one
another. I started learning about Machine Learning soon after as it gave me a unique opportunity to
apply my statistics knowledge in combination with my software engineering skills. To learn more
about this field, I studied Machine Learning in the Master of Engineering program at UC
Berkeley.
During my summer 2019 internship at an early-stage startup, I learned about and used new techniques
of modeling and data collection to build on my existing machine learning knowledge. My tasks
involved training image classification models with TensorFlow to predict a hospital bed’s
patient-occupancy score and collecting training data by scraping images and converting YouTube
videos using a video annotation tool that I built with OpenCV.
At UC Berkeley, my team researched traffic light coordination using deep reinforcement learning
with an open-source traffic control framework called Flow. Our work, in improving Flow over the past
year, has allowed me to apply the new skills that I learned in technical courses such as Machine
Learning Systems and Deep Reinforcement Learning.
In 2017, I published an iOS app called Drinks Tracker on the App Store. I created an algorithm that
calculates the user’s BAC level based on multiple metrics, which the app used to notify the user
about their estimated Blood Alcohol Content (BAC) level. The app was installed by over 3.9k users
and got 104k impressions when Apple removed this category of apps from the App Store.
My current fitness goal is to complete a 7.5 mile run in an hour. Currently, I am two minutes away
from making it!
In my spare time, I enjoy watching movies and TV, especially science fiction classics such as Star
Trek and Doctor Who. 🖖
Currently, I want to work as a Machine Learning Engineer, in the Bay
Area, to make better use of all the data that we generate every day.
Researched traffic light coordination using Deep Reinforcement Learning
with Prof. Alexandre Bayen
Fall 2019
Worked in a team to improve an open-source traffic control framework called Flow
Updated Flow to use AIMSUN’s (traffic simulation software) improved API methods
Trained with PPO and real-world peak-hour traffic data from Arcadia, CA
Optimized the time offsets of five arterial traffic lights to increase average traffic speed
Researched how to reduce mixed-autonomy traffic congestion using Deep Reinforcement Learning
with my Capstone team and Prof. Alexandre Bayen
2018 – 2019
Optimized the behavior of autonomous vehicles to increase average traffic speed in a city-scale scenario
Worked in a team to use and improve an open-source traffic control framework called Flow that
simulates traffic with SUMO and trains with Ray
In 2017, I published an iOS app called Drinks Tracker on the App Store. I created an algorithm that
calculates the user’s BAC level based on multiple metrics, which the app used to notify the user about
their estimated Blood Alcohol Content (BAC) level. The app was installed by over 3.9k users and got 104k
impressions when Apple removed this category of apps from the App Store in 2018.
This project was uploaded to GitHub in 2019.
Completed the Machine Learning by Stanford course
Taught by Andrew Ng, Co-founder, Coursera and Adjunct Professor, Stanford University
Spring 2017
Analyzed speech during video calls to automatically create meetings in Google Calendar
Worked with the IBM BlueMix, Google Cloud Speech, and Google Calendar APIs
Used Beautiful Soup 4 and scikit-learn to implement several clustering and classification algorithms
such as DBSCAN, Hierarchical Clustering, Naïve-Bayesian, and K-Nearest Neighbors
Calculated Jaccard Similarity scores and Mean Squared Error to accurately create association rules
Won Best Lifesaving Hack at Qualcomm’s intern hackathon for an Augmented
Reality app
Summer 2015, Qualcomm
Created an Android app to visualize sound to assist the hearing impaired
Machine Learning Engineering Intern
LookDeep Health
Summer 2019, Oakland, CA
Trained image classification models to predict a hospital bed’s patient-occupancy score
Built my model’s training pipeline in TensorFlow 2 and learned to use DVC, an open-source model
versioning system
Collected training data by scraping images and converting YouTube videos using a video
annotation tool that I built with OpenCV. Also used and contributed to Scalabel, an open-source
annotation tool
Generated 40k randomized synthetic training images after learning to use Blender
Improved my model using computer vision techniques with transfer learning on InceptionV3 and
ResNet-152
Assisted another intern in developing the end-to-end system from reading images off the camera
to serving predictions using TensorFlow Serving and displaying the output inference in the browser
Wrote Java code while updating 7 legacy webapps, used by 15k+ administrators for library management
(books, media, etc.), to use jQuery, PostgreSQL, and Apache Tomcat
Enhanced OAuth authentication services used by OCLC’s enterprise partners to support mobile apps
Supported a $3M+ contract by writing utilities to easily internationalize a webapp in 20+
languages
Optimized database handling and functionality for compliance with GDPR requirements
Built and deployed our webapps to several QA and Production environments
Improved my internal webapp from 2014 and created new features to automate tasks for other team
members
Automated tasks using C# to internalize SQL scripts into an ASP.NET application
Overhauled an internal webapp used to manage a portal which simplified doctor-patient communication
Resolved and implemented requested user stories to increase operational productivity
Made extensive use of ASP.NET, SQL, HTML5, and JavaScript while collaborating with other team
members using Team Foundation Server in Visual Studio
Assisted Computer Science professors, faculty, and students with their technical queries and issues
Replied to any queries sent to help@cse.ohio-state.edu diligently and in a timely manner
Printed posters for graduate students
Ensured that all the CSE printers (~20) are working at all times with no paper and ink issues
Gained experience programming in work environment
Primarily programmed in ASP.NET and C#
Created websites for inputting and recalling user details
Worked in Microsoft Access to create databases to store user details
