About Me
M.S. in Statistics & Data Science at Baruch College (Zicklin) and prior studies in Industrial Engineering (Penn State) and Investment & Wealth Management (Imperial College London). Experience in transforming messy data into clean, scalable systems — anywhere from multi-leveled quant pipelines for researching equities to AI agents that automate decisioning processes. Proficient in Python, R, and SQL and exposure to machine learning, multivariate analysis, and fairness-sensitive modeling. I want to work on AI/ML engineering, data science, and applied AI research projects that bring intelligent systems and end-to-end real-world applicability together.
Experience
Research Foundation of CUNY
Research Assistant — Prof. Zeda Li
Nov 2025 – Present
- Design simulation frameworks to study subject-specific brain connectivity networks influenced by demographic and clinical covariates
- Implement and benchmark classical and covariate-aware network inference methods (Graphical Lasso, Bayesian graphical models, transfer-learning approaches) within a unified simulation framework
- Evaluate model performance using precision–recall, F1 score, and scalability under controlled heterogeneity and noise conditions
- Contribute to methodological research in multivariate signal modeling and covariate-assisted spectral analysis
BCITS Pvt Ltd
Data Analyst Intern
Sept 2022 – July 2023
- Performed large-scale data cleansing and preprocessing across billing, IoT, and transaction datasets (50K+ records)
- Built predictive models to detect billing anomalies and reduce revenue leakage
- Contributed to improving billing accuracy by 15% and customer satisfaction by 20% through data-driven analysis
- Developed KPI dashboards using R and Excel for operational monitoring
- Collaborated with engineering teams to validate data integrity and improve reporting workflows
Education
Zicklin School of Business, NY
MS in Statistics
May 2026
- GPA (current): 3.952
- Relevant coursework: Regression Analysis, Statistical Inference, Multivariate Methods, Machine Learning, Data Mining, Advanced Data Analysis, Probability
Imperial College Business School, London
MS in Investment & Wealth Management
Sept 2021 – Sept 2022
- Honors: Merit Classification
- Project: Evaluated Microsoft’s acquisition of Pinterest using valuation models
Pennsylvania State University
BS in Industrial Engineering
Aug 2016 – May 2020
- GPA: 3.83
- Dean’s List (All Semesters)
Certifications
- Python Bootcamp – Udemy: Mastered OOP and timestamp manipulation in Python
- Python for ML & Data Science – Udemy: Regression, NLP, and clustering with Pandas, Scikit-learn, and Seaborn
- Finance Data with Python & Pandas – Udemy: CAPM, Sharpe Ratio, and MPT for portfolio optimization
Skills
- Programming & Analysis: Python, R, SQL, Swift, SAS, Excel, pandas, NumPy
- Modeling & Optimization: Regression (Ridge, Lasso, Elastic Net), Machine Learning (scikit-learn, SVM, LDA, ensembles, LightGBM, XGBoost), Multivariate Analysis, Optuna, CVXPY
- Deep Learning: PyTorch
- LLMs & AI Agents: LangChain, LangGraph, Hugging Face, OpenAI APIs, FastAPI, Streamlit
- Financial & Quant Methods: Statistical & Financial Modeling, Time Series
- Visualization & Reporting: Matplotlib, Seaborn, ggplot2, Quarto, LaTeX (Overleaf)
- Tools & Platforms: Git/GitHub, Jupyter, Xcode, Bloomberg Terminal, Capital IQ, n8n