I love collecting and analyzing how all my personal data (including fitness, nutrition, sleep, location history, and more) interact with one another. To that end, my personal goal is to improve everyone’s daily lives by combining all their data and helping them make sense of it. To pursue this goal further, I am studying Computer Science (with a focus on Machine Learning) in the Master of Engineering program at UC Berkeley.
Over Summer 2019, I interned at a startup as a Machine Learning Engineering Intern and trained image classification models in TensorFlow to predict a hospital bed’s patient-occupancy score. I collected training data by scraping images and converting YouTube videos using my OpenCV-based video annotation tool. I also generated 40k randomized synthetic training images after learning to use Blender. I improved my model using computer vision techniques with transfer learning on InceptionV3.
The MEng program focuses extensively on technical leadership in the industry and so, I am taking courses about Machine Learning, Deep RL, and ML Systems. In Fall 2019, I am researching traffic light coordination using Deep RL.
In 2017, I published an iOS app called Drinks Tracker on the App Store based on an observation that I made a couple of years ago during my undergraduate years at The Ohio State University. In early 2018, I was working on integrating drink suggestions using machine learning in my app (to automatically suggest drinks to users based on their preferences and drink history) 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. 🖖
After I graduate in December 2019, I want to work as a Machine Learning Engineer, ideally in the Bay Area, to make better use of all the data that we generate every day.
Worked in a team to use and improve an open-source traffic control framework called Flow that works with SUMO (traffic simulation software) and RLlib (reinforcement learning Library) App uses an algorithm to notify the user about their estimated Blood Alcohol Content (BAC) level
Created a custom algorithm that calculates the user’s BAC level based on multiple metrics
Installed by 3.9k+ users with 104k+ impressions 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 Created an Android app to visualize sound to assist the hearing impaired
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 my OpenCV-based video annotation tool. 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
Replied to any queries sent to firstname.lastname@example.org 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
Drinks Tracker allows you to search a list of nearly 4,000 drinks and easily add drinks to track your Blood Alcohol Content (BAC) percentage.
Installed by 3.9k users with 104k impressions
Not currently available
- Search and easily add drinks. Favorite your frequent drinks for easy access!
- Track your BAC using the Today View widget and the Apple Watch app. Complications and Dynamic Notifications are coming soon!
- Easily add custom drinks and favorite them in a few taps.
- Get notifications when your BAC is at 0.08% and 0% and a reminder to add your next drink.
- Customize your profile to get an estimated BAC tailored to you!
- 3D Touch the app icon to quickly add a drink and to see your BAC in the widget.
- Suggest drinks so that they can be added to the list!
I am interested in Machine Learning Engineering positions starting January 2020 after I graduate from UC Berkeley in December.