Education
- Ph.D. in Computer Science, Northwestern University (Evanston, IL), Jul 2020 – May 2026 (expected).
- M.S. in Analytics, Georgetown University (Washington, DC), Aug 2018 – May 2020.
- B.S. in Biochemistry, University of Washington (Seattle, WA), Aug 2014 – Dec 2017.
Work Experience
- Jun 2025 – Aug 2025: Data Scientist Intern, iSoftStone Inc
- Designed a multimodal indoor localization framework integrating Wi-Fi RSSI fingerprinting with Fine Time Measurement (FTM) range estimation, using probabilistic sensor fusion (Bayesian filtering) alongside a graph neural network-based correction model to improve positioning accuracy.
- Developed a data-driven, site-independent positioning model that leverages FTM’s resilience to multipath interference and utilized contrastive signal embedding to reduce reliance on dense fingerprint calibration, enabling robust cross-site generalization.
- Built an experimental testbed and benchmarking suite for repeatable wireless positioning trials, enabling rigorous evaluation of indoor localization algorithms.
- Jun 2024 – Aug 2024: Machine Learning Engineer Intern, AstraZeneca
- Developed a domain-specific Retrieval-Augmented Generation (RAG) pipeline by integrating a FAISS-based dense retriever with a fine-tuned LLaMA-2 generator; created a custom Q&A dataset from internal medical documents via dual-encoder ranking and GPT-4 validation to fine-tune the system for contextually accurate, factually grounded answers.
- Optimized persona-aware prompting by dynamically adjusting role/context embeddings (injecting learned “role vectors”) to bias the model’s internal activations.
- Implemented hierarchical chunking strategies to maintain semantic coherence across long documents.
- Aug 2018 – Sep 2019: Innovation Data Analyst, Office of Diversity & Inclusion
- Optimized SQL queries for efficient demographic data collection and analysis, improving the tracking of medical student diversity and performance.
- Conducted statistical analyses on intervention outcomes using t-tests and bootstrapping, providing actionable insights for program improvements.
- Performed NLP-based sentiment analysis on survey responses to gauge participant feedback and identify sentiment trends.
- Created interactive Tableau dashboards to visualize key metrics and presented findings to stakeholders, facilitating informed decision-making.
- Mar 2018 – Aug 2018: Data Analyst Intern, iSoftStone Inc
- Built and maintained Power BI dashboards to track Microsoft CELA team KPIs, enabling leadership to monitor performance indicators at a glance.
- Developed ETL pipelines using Power Query and DAX to automate data integration from multiple sources, significantly reducing manual data processing time and effort.
- Automated data collection and analysis tasks with Python and R, reducing manual effort and substantially increasing reporting efficiency.
- Presented monthly analytics reports to senior stakeholders, translating data insights into strategic recommendations that informed high-level decision-making.
Technical Skills
- Programming: Python, R, SQL, Java, C++, MATLAB, Arduino
- Frameworks & ML Libraries: PyTorch, TensorFlow (Lite), DeepSpeed, HuggingFace, scikit-learn, Pandas, NumPy
- Machine Learning: Large Language Models (OpenAI GPT-4, LangChain), Neural Architecture Search (NAS), TinyML, Computer Vision, Natural Language Processing
- Data Tools: Power BI, Tableau, Azure ML Studio, Azure Cognitive Services, Hadoop/Spark
- Statistical Analysis: Hypothesis Testing, Time Series Analysis, Experimental Design, Crowdsourcing Design
