Key Skills
- Languages & Frameworks: Python, SQL, PySpark, Scala, C++, Shell, LaTeX
- ML & Optimization: Deep Learning, Elasticity Modeling, Reinforcement Learning, Mixed-Integer Programming,
TensorFlow, PyTorch, scikit-learn, TensorFlow Lattice, Pyomo
- GenAI & Multimodal: OpenAI APIs, Prompt Engineering, RAG, Persona Modeling, Embeddings
- Data & Tools: Databricks, AWS, Jupyter/IPython, Pandas, NumPy, SciPy, HTML/CSS, Airflow
- Soft Skills: Cross-functional leadership, Technical mentorship, Roadmapping, Product-algorithm alignment
Awards & Recognition
-
Expedia GenAI Hackathon – 2nd Place Winner
-
Achieved 2nd place among 30+ teams in the Expedia Astronaut Expedition 2025 hackathon.
-
Received $2000 team award for developing a traveler-facing GenAI product prototype.
-
Proposal influenced internal exploration of product innovation strategies.
-
Expedia Travel Award
-
Recognized for outstanding performance in A/B testing that achieved $21M GBV uplift per year.
-
Received $1000 per person award for exceptional contribution to business outcomes.
-
Demonstrated significant impact on company revenue through data-driven optimization.
Projects
-
Expedia GenAI Hackathon – 2nd Place Winner
Expedia Astronaut Expedition 2025
-
Developed a traveler-facing GenAI product prototype enhancing the vacation rental experience.
-
Leveraged OpenAI models and prompt engineering to summarize the conversation between host and traveler, feed the hosts with how they could improve via
personalize recommendations to emotional engagement.
-
Recognized among top 2 of 30+ teams; proposal influenced internal exploration of product innovation strategies.
-
Traveler Persona & Avatar Personalization Engine
Expedia Brainwave AI Hackathon, 2025
-
Designed a system using LLMs (OpenAI) to summarize traveler behavior into personas for personalization.
-
Integrated semantic filtering and RAG techniques to retrieve context from conversational and booking data.
-
Enabled downstream badge recommendation and ranking refinement based on persona-matching.
-
Doordash Estimated Delivery Time:
-
A machine learning project that estimates the Doordash delivery time including:
- Case study and business understanding
- Data preprocessing and analysis
- Machine learning predictions with linear, Lasso, random forest and XGBoost including hyperparameter tuning
-
Q-Chem:
-
Interface between Q-Chem and Psi4:
-
This mini program automates the creation of input files for Psi4 program based on the Q-Chem files. After the Psi4 output is generated, it could
also help organize the output and analyze the matrices.
-
Personal Website:
-
The personal website is generated with full stack coding with HTML and CSS/SCSS skills acquired. It can also be used as a template.
-
Lloyd Fisher Jr., Ricardo Javier Vázquez, Madeleine Howell, Angelar K. Muthike,
Meghan E. Orr, Hanjie Jiang,
Betsy Dodgen, Dong Ryun Lee, Jun Yeob Lee, Paul M. Zimmerman, and Theodore Goodson III
Investigation of Thermally Activated Delayed Fluorescence in Donor–Acceptor Organic
Emitters with Time-Resolved Absorption Spectroscopy
-
Bushra Alam, Hanjie Jiang,
Paul M. Zimmerman, and John M. Herbert,
State-specific Solvation for Restricted Active Space Spin-Flip (RAS-SF) Wave Functions based on the Polarizable Continuum Formalism
In preparation
-
Hanjie Jiang, Duy-Khoi Dang, Soumi Tribedi, and Paul M. Zimmerman
Optimized Slater-Type Basis Sets for Correlation-Consistent Calculations
In preparation