Resume

Education

August 2018 - May 2022

Cornell University

B.A., Computer Science (Magna Cum Laude), Mathematics

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August 2022-May 2023 

Carnegie Mellon University

MS, Machine Learning; Left to Pursue Ph.D.

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August 2023-May 2028

Georgia Institute of Technology                                                   

Ph.D., Computer Science with Professor Peng Chen and Professor Chao Zhang

Research Experience

Undergraduate Researcher (with Professor. Volodymyr Kuleshov) 09/2020~05/2023

• Conducted research in the area of aleatoric and epistemic uncertainty estimation for calibrated uncertainty prediction using quantile loss functions

• Demonstrated that by integrating quantile loss with autoregressive flows, we can remove the need for the

• Jacobian while retaining sampling and uncertainty estimation capabilities

• Extended research to a non-autoregressive flow architecture for faster training and sampling

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Natural Language Processing Research Intern at Cornell Tech

05/2019~08/2019

• Conducted NLP research under Professor Yoav Artzi (https://yoavartzi.com/) at Language in Context Lab

• Developed system for using collaborative interactions to study second language acquisition in goal-specified environment with Unity and Python

• Used NewsRoom(https://arxiv.org/abs/1804.11283) dataset to generate articles with multiple summary labels for parameter based summary generation with various Python architectures

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Statistics Research Intern at Hong Kong University of Science and Technology

06/2017~10/2017

• Research mathematics and statistics under Professor Yuan Yao(https://www.math.ust.hk/people/faculty/profile/yuany/)

• Completed a pairwise comparison machine learning algorithm from cancer cell and drug sensitivity data using R

  • Model placed third (highest rank was second) on Kaggle leaderboard among graduate and undergraduate students)

• Enhanced model from database on cancerrxgene.org and conducted detailed analysis

Clubs And Organizations

Intelligent Systems Team Lead at Cornell Data Science

09/2019~05/2022

• Snapbee, using Deep Learning to identify handwritten text and figures in general documents (specifically testing and modifying the DeepFigures framework to work for handwritten inputs)

• Pleio Project, using causal inference to obtain information about when and how to best remind patients to take their medicine

• Work on combining Bayesian Networks with Kernel Density Estimators to reduce the effects of high-dimensional data

• Using Reinforcement Learning to develop a Pokerbot agent

• Organized and planned team meetings, held reading groups, and oversaw project progress through biweekly standups

Industry Experience

Machine Learning Research Intern at CardioPhi

Milpitas, CA

03/2023~08/2023

• Constructed Transformer and ResNet based models to process ECGs for detection and prediction of heart arrhythmia

• Collaborated with front-end engineers to deliver information for an insightful UI

• Wrote technical portions of grants for NSF and NIH funding

• Worked on paper ECGBERT: Understanding the Hidden Language of ECGs

Software Engineering Intern at Adobe Inc.

San Jose, CA

05/2021~08/2021

• Improved cost and efficiency of a generative search algorithm by incorporating native speedups using C++ and lower-level code as part of an innovation team developing cutting-edge and currently realizable technology

• Cut out entirety of heavy cloud-computing cost while retaining similar time performance against cloud GPUs

Software Engineering Intern at Adobe Inc.

San Jose, CA

05/2020~08/2020

• Researched and developed a ML pipeline in Python for efficiently resolving problems and directing users to right resource

  • Boosted performance compared to pre-existing Elasticsearch approach from 30% to 90% accuracy, from a hand-generated validation dataset

• Deployed the internal tool through an online docker container and Amazon Lambda (serverless), then integrated with a slack bot as a slash command

Machine Learning Intern at Vital Scientist INC.

Santa Clara, CA

06/2018~07/2018

• Used detailed scrum and sprint to collaborate with the project team, conducted a presentation regarding our final product, a (locally hosted) website which recommended restaurants based on previous likes

• Extracted data with SparkSQL and analyzed a restaurant model based on Yelp dataset with Numpy, using stochastic processes to decrease calculation complexity of the collaborative filtering algorithm

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