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Massachusetts Institute of Technology
Data Scientist
June 25, 2026 – Present
OpenFace-3.0
September 24, 2023 – June 10, 2025
OpenFace 3.0 – open-source toolkit for facial landmark detection, action unit detection, eye-gaze estimation, and emotion recognition.
View ProjectPID
February 22, 2023 – October 28, 2024
[NeurIPS 2023, ICMI 2023] Quantifying & Modeling Multimodal Interactions
View Projectawesome-phd-advice
March 22, 2022 – July 10, 2024
Collection of advice for prospective and current PhD students
View ProjectHighMMT
February 16, 2022 – November 2, 2024
[TMLR 2022] High-Modality Multimodal Transformer
View ProjectMultiViz
August 18, 2021 – August 22, 2024
[ICLR 2023] MultiViz: Towards Visualizing and Understanding Multimodal Models
View ProjectMultiBench
March 5, 2021 – January 27, 2024
[NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning
View ProjectLG-FedAvg
December 5, 2019 – July 25, 2024
[NeurIPS 2019 FL workshop] Federated Learning with Local and Global Representations
View Projectawesome-multimodal-ml
May 27, 2019 – August 20, 2024
Reading list for research topics in multimodal machine learning
View ProjectMFN
December 21, 2018 – August 4, 2020
[AAAI 2018] Memory Fusion Network for Multi-view Sequential Learning
View ProjectCultural Fit Analysis
The candidate's project portfolio is heavily skewed towards academic research and personal projects, primarily in multimodal machine learning. While this demonstrates deep expertise in a niche area, the lack of diverse industry-oriented projects or team collaborations makes it difficult to fully assess cultural fit for a broader Data Scientist role in a typical corporate environment. The current experience at MIT as 'Data Scientist' is listed as current but with a future start date, making it difficult to assess its relevance. The candidate's experience level is listed as 0, which contradicts the depth of their research contributions, suggesting a potential mismatch in how 'experience level' is defined or captured.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong focus on research and academic contributions. While this suggests dedication and problem-solving abilities, there is insufficient data to assess specific soft skills like teamwork, leadership, or communication in a corporate setting. The operational fit for a pure industry Data Scientist role might require further evaluation beyond research contributions.