Experience

Harvard University

• United States
• Higher Education
• 700 & Above Employee

• Ph.D. Candidate

  • Aug 2022 - Present

  Research interests: human-computer interaction, machine learning, natural language processing Research interests: human-computer interaction, machine learning, natural language processing



Lyft

• United States
• Ground Passenger Transportation
• 700 & Above Employee

• Data Scientist Intern

  • Jun 2020 - Jul 2020

  - Estimated the opportunity and risk of Lyft Family (a new feature of Lyft) by exploring existing rides and payment data - Identified all household users of Lyft and analyzed their unique characteristics in terms of engagement, experience, etc. - Clustered family riders and rides and recommended incentive products targeting each segment of users and use cases - Estimated the opportunity and risk of Lyft Family (a new feature of Lyft) by exploring existing rides and payment data - Identified all household users of Lyft and analyzed their unique characteristics in terms of engagement, experience, etc. - Clustered family riders and rides and recommended incentive products targeting each segment of users and use cases



Cornell Engineering

• United States
• Education Administration Programs
• 100 - 200 Employee

• Teaching Assistant

  • Jan 2020 - May 2020

  CS 3410: Computer System Organization and Programming

• Teaching Assistant

  • Aug 2019 - Dec 2019

  CS 4780: Machine Learning for Intelligent Systems

• Teaching Assistant

  • Aug 2018 - Aug 2019

  CS 2110: Object-Oriented Programming and Data Structures (with Java)



Cornell CIS (Computing and Information Science)

• Ithaca, New York Area

• Research Assistant

  • Jan 2019 - May 2020

  - Co-author: JN Yan, Z Gu, H Lin, J Rzeszotarski. “Enhancing Sensemaking in Machine Learning Fairness Assessments”. CHI 2020. - Developed an interactive bias exploration dashboard with Flask and Bokeh, helping data analysts reason about the source of bias in their classifiers through feature engineering and shortening their average decision-making time by 22.5 s. - Re-formulated the open information extraction problem as a sequence-to-sequence transduction task and trained a Transformer… Show more - Co-author: JN Yan, Z Gu, H Lin, J Rzeszotarski. “Enhancing Sensemaking in Machine Learning Fairness Assessments”. CHI 2020. - Developed an interactive bias exploration dashboard with Flask and Bokeh, helping data analysts reason about the source of bias in their classifiers through feature engineering and shortening their average decision-making time by 22.5 s. - Re-formulated the open information extraction problem as a sequence-to-sequence transduction task and trained a Transformer encoder-decoder model with PyTorch to extract relational triples from sentences. - Evaluated the model and showed it was on par with the state-of-the-art, but without dependencies on other NLP tools. Show less - Co-author: JN Yan, Z Gu, H Lin, J Rzeszotarski. “Enhancing Sensemaking in Machine Learning Fairness Assessments”. CHI 2020. - Developed an interactive bias exploration dashboard with Flask and Bokeh, helping data analysts reason about the source of bias in their classifiers through feature engineering and shortening their average decision-making time by 22.5 s. - Re-formulated the open information extraction problem as a sequence-to-sequence transduction task and trained a Transformer… Show more - Co-author: JN Yan, Z Gu, H Lin, J Rzeszotarski. “Enhancing Sensemaking in Machine Learning Fairness Assessments”. CHI 2020. - Developed an interactive bias exploration dashboard with Flask and Bokeh, helping data analysts reason about the source of bias in their classifiers through feature engineering and shortening their average decision-making time by 22.5 s. - Re-formulated the open information extraction problem as a sequence-to-sequence transduction task and trained a Transformer encoder-decoder model with PyTorch to extract relational triples from sentences. - Evaluated the model and showed it was on par with the state-of-the-art, but without dependencies on other NLP tools. Show less



Cornell Data Science

• United States
• Information Services
• 1 - 100 Employee

• Project Lead

  • Feb 2018 - Dec 2019

  - Led sub-team of 4 in knowledge graph construction from raw text, using a pipelined process of coreference resolution and relation tuple integration - Modeled a virtual traffic network using directed graphs and collected data by running simulations - Improved the network by optimizing traffic objectives with respect to network parameters under various constraints - Led sub-team of 4 in knowledge graph construction from raw text, using a pipelined process of coreference resolution and relation tuple integration - Modeled a virtual traffic network using directed graphs and collected data by running simulations - Improved the network by optimizing traffic objectives with respect to network parameters under various constraints