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• Quantitative Research, trading. • Work experience with Large Language Models (LLM), Natural Language Processing (NLP), Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), and related state-of-the-art (SOTA) solutions and business applications.
Cornell University
Master's degree, Applied Statistics
January 1, 2017 – January 1, 2018
UCLA
Bachelor of Science - BS, Applied Mathematics, Statistics
January 1, 2013 – June 1, 2017
Two Sigma
Firefighter
January 1, 2026 – Present
New York, United States
Citadel
Machine Learning Engineer
May 1, 2020 – January 1, 2025
Chicago, Illinois, United States
KPMG US
NLP Data Scientist
July 1, 2018 – May 1, 2020
Greater Chicago Area
Nations Info Corp
NLP / Machine Learning Intern
November 1, 2017 – May 1, 2018
Ithaca, New York Area
Baidu, Inc.
Machine Learning Intern
June 1, 2016 – September 1, 2016
Beijing City, China
Veritable Screening
Marketing Analytics Intern
June 1, 2015 – September 1, 2015
Greater Los Angeles Area
Hangzhou Thinktown School
Teaching Assistant
September 1, 2014 – October 1, 2014
Hangzhou, Zhejiang, China
NLP - Name Entity Recognition (NER) with HMM
August 1, 2017 – December 1, 2017
1. Trained HMM model by calculating emission probability (bigram + trigram) and transition probability. 2. Performed Name Entity Recognition (NER) using the HMM model obtained above.
NLP - Question Answer (QA) System
August 1, 2017 – December 1, 2017
1. Prioritizing key sentences: calculated the cosine similarity between embedded words generated by Google word2vec and the question.
NLP - Sentiment Classification
August 1, 2017 – December 1, 2017
1. (Model 1) Designed an interpolated n-gram model to perform sentiment classification of data from Rotton Tomato and generated random sentences. 2. (Model 2) Performed word embedding using word2vec, Glove, Tf-Idf. 3. (Model 3) Trained model with SVM, Naive Bayes and Neural Network using the embedded words obtained above and used cross-validation to test model accuracy.
Generative AI with Large Language Models
Coursera
June 24, 2026 – Present
XCS234 - Reinforcement Learning
Stanford Online
June 24, 2026 – Present
XCS224U - Natural Language Understanding
Stanford Online
June 24, 2026 – Present
XCS224N - Natural Language Processing with Deep Learning
Stanford Online
June 24, 2026 – Present
Learn the Command Line Course
Codecademy
June 24, 2026 – Present
Learn Bash Scripting Course
Codecademy
June 24, 2026 – Present
DeepLearning.AI TensorFlow Developer
DeepLearning.AI
June 24, 2026 – Present
Sequences, Time Series and Prediction
DeepLearning.AI
June 24, 2026 – Present
Natural Language Processing in TensorFlow
DeepLearning.AI
June 24, 2026 – Present
Convolutional Neural Networks in TensorFlow
DeepLearning.AI
June 24, 2026 – Present
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
DeepLearning.AI
June 24, 2026 – Present
Deep Learning Fundation
Udacity
June 24, 2026 – Present
Sequence Models
DeepLearning.AI
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong background in research and development within Machine Learning and NLP, which aligns with innovative and technically driven environments. The project diversity, while focused on NLP, shows depth in a specific area. However, the target role is 'Big Data Engineer', and the resume lacks explicit experience with core big data technologies (e.g., Hadoop, Spark, Kafka, distributed systems, data warehousing, ETL pipelines). This creates a potential gap in cultural fit for a pure Big Data Engineering role, as the candidate's expertise leans heavily towards ML/NLP model development rather than large-scale data infrastructure and processing.
Soft Skills & Operational Fit
The candidate's resume highlights leadership in research and independent ownership of product life-cycles, suggesting strong initiative and problem-solving skills. The description of sharing research results indicates good communication and collaboration potential. However, without specific psychometric test results or interview data, a comprehensive assessment of soft skills and operational fit is limited.