I'm a PhD candidate in computational chemistry. I enjoy playing around with data and machine learning. Welcome and learn more about my journey!

Jianing Lu

Computational Chemistry PhD Candidate

917-593-3713

Research Assistant

New York University

I worked in a computational chemistry lab and attempting to integrate machine learning methods and molecular modeling for drug discovery:

Developed a scoring function to predict interaction between protein and small molecule using XGBoost (published).
Proposed a protocol to combine graph neural network and transfer learning for learning atom representation and predicting molecular energy (published)

Fellow

Insight Data Science

Built Pursearch (http://pursearch.com/), an image based handbag searching tool, to help users identify brands and style names of handbags seen on social media data and recommend similar handbags at various price points.
Scraped thousands of branded handbag images from the Google search results and retailers’ official websites. Detected and identified handbags from images using CNN models built by PyTorch and recommended handbags based on cosine similarity of handbag vectors.
Created a web-app using Flask and deployed it on AWS to enable user upload images and get results in several seconds.

Data Science for All: Women's Summit

Created visualizations for NYC airline delays and presented results as a member (80/900+) selected by Correlation- One to provide travel suggestions for users
Combined several datasets, conducted exploratory data analysis and tested hypotheses for airline delay patterns at different NYC airports and times using Pandas, Pingouin, Matplotlib, and Seaborn.

Member

Teaching Assistant

New York University

I was a teaching assistant in several courses including General Chemistry I & II, Physical Chemistry and Quantum Mechanics.

Supervised students in lab
Prepared and taught recitations for small groups of students focused on specific assignments
Mentored students through office hours and one-on-one communication

Python - Advanced (>4 years)

Machine Learning

R - Advanced (1 year)

Deep Learning