Hello! I'm currently a data science teaching instructor at UC Berkeley. I graduated 2 years early from my Bachelor's in Computer Science (also at UC Berkeley) and finished my M.S. in Electrical Engineering and Computer Sciences (EECS) in 2018 as a UC Berkeley Graduate Opportunity Fellow, advised by Dean of Data Sciences David Culler.
My research focus is in Deep Learning for computing education at scale (JupyterHub, Kubernetes, Docker). I'd love to talk about data science pedagogy, scaling technical infrastructure for 40,000 student EdX MOOCs, NLP research, Deep Knowledge Tracing, identifying the best boba order, poker strategy, good books, great screenplays, and pretty much anything under the sun. Feel free to reach me using the buttons below.
Next steps? I will be joining Microsoft AI + Research full time after my summer as a data science lecturer. Thank you for taking time out of your day to find out what I do with mine!
Recent studies have shown that "zombie" virtual machines in hybrid/private clouds have been wasting millions of dollars worth of resources. The CSi2 algorithm is an ensemble machine learning algorithm to detect inactivity of VMs as well as suggest a course of action like termination or snapshot. It is projected to save IBM Research at least $3.2 million dollars with 95.12% recall and 88% F1 score (>> industry standard) and is being implemented into the Watson Services Platform. 2 patents have been filed and papers are in the process of being finished.
Collaborated with Neeraj Asthana, Sai Zheng, Ivan D'ell Era, Aman Chanana.
Knowledge Tracing is a body of learning science literature that seeks to model student knowledge acquisition through their interaction with coursework. This project uses a recurrent neural network (LSTM) to optimize prediction of student performance in large scale computer science classes.
Collaborated with Samuel Lau, Allen Guo, Madeline Wu, Wilton Wu, Professor Zachary Pardos, Professor David Culler.
Expanded on an MIT CSAIL paper by Shen et. al. to improve the accuracy of neural style transfer for unaligned text using author disambiguation algorithms.
Collaborated with Vasilis Oikonomou and Professor David Bamman.
A Distill-style (interactive visualization) literature review of neural networks for Natural Language Processing, specifically in the field of analyzing sentiment and emotion.
Collaborated with Stefan Palombo and Michael Brenndoerfer.
Exploring reward design for reinforcement learning through the framework of causality and fairness.
Worked on the CSi2 project as a Machine Learning Research Scientist intern on the Hybrid Cloud team. Presented an exit talk, filed 2 patents, and am currently working on a research paper.
Head GSI of Data 8 for 3 semesters, responsible for management of 1000+ undergraduates, 40 GSIs, 30 tutors, and 100+ lab assistants. Helped create data science curriculum material for the large lecture and domain-specific seminar courses. Also in charge of maintaining JupyterHub infrastructure for 1500+ active users (with Docker/Kubernetes/Google Compute Engine backend).
Interned at LinkedIn headquarters with the Growth Division's Search Engine Optimization (SEO) Team the summer before entering UC Berkeley. Worked on fullstack testing infrastructure for the public profile pages, as well as a minor Hadoop project; outside of assigned work, helped plan LinkedIn’s DevelopHER Hackathon and worked on several Market Research/User Experience Design initiatives.
Spent a summer learning the fundamentals of Computer Science, algorithms, and computational thinking at Google Headquarters in Mountain View, CA. Chosen as a Google Ambassador for Computer Science following the experience. Worked with Google, Salesforce, and AT&T to introduce coding to over 15,000 girls across California with the Made w/ Code Initiative.
President of Computer Science Honor Society (UPE)
Head Graduate Student Instructor of Foundations of Data Science
Advisor: Dean of Data Sciences, Freisen Professor in EECS, David Culler