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Research Engineer @ Meta Previous CS PhD @Columbia, Masters from CMU LTI.
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Meta
Data Scientist
June 27, 2026 – Present
edu-sum
May 24, 2023 – August 14, 2023
Official Implementation of ACL 2023 Paper: "Generating EDU Extracts for Plan-Guided Summary Re-Ranking"
View Projectcalibrating-summaries
May 9, 2023 – August 14, 2023
This is the official PyTorch codebase for the ACL 2023 paper: "What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization".
View ProjectMEDIQA-Chat-2023-NUS-IDS
March 17, 2023 – March 30, 2023
MEDIQA-Chat-2023-NUS-IDS — GitHub repository
View ProjectLMC
November 20, 2019 – December 8, 2020
Jointly Learning Word and Metadata Embeddings: Latent Meaning Cells Applied to Clinical Acronym Expansion
View Projectparaphrase-detection
September 24, 2017 – September 30, 2017
Paraphrase Detection In PyTorch on Microsoft Research Paraphrase Corpus (MRPC)
View Projectbaseline-vqa
February 15, 2017 – March 31, 2017
Baseline VQA - BOW/LSTM + CNN with softmax prediction
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated in academic research, particularly in NLP and summarization. While this demonstrates deep technical expertise in a specific area, the diversity of projects outside of this niche is limited. The target role is 'Data Scientist' at Meta, which typically requires a broader range of data science skills beyond pure NLP research, including A/B testing, statistical modeling, and large-scale data manipulation. The current project portfolio aligns well with a research scientist role, but less so with a general data scientist role that might require more product-focused or business-oriented data analysis. The single listed experience at Meta as a Data Scientist starting in 2026 is a future entry and cannot be used for current assessment.
Soft Skills & Operational Fit
The candidate's project history suggests a strong inclination towards research and development in the data science domain. The descriptions are concise, indicating a focus on technical output. However, without specific soft skill assessments or detailed project narratives, it's difficult to fully assess operational fit beyond technical contributions.