Lucas Who?

I am a recent graduate of the University of Southern California, where I earned a BS in Computer Science and an MS in E.E. While at USC, I worked at USC's Center for AI in Society (CAIS), and founded the Center for AI in Society's Student Branch (CAIS++). I currently work full-time as a Data Scientist at Palo Alto Networks, and lead the engineering efforts at Duet. My interests include machine learning, health & nutrition science, and the use of AI for social good.

Skills / Interests / Hobbies

  • Artificial Intelligence
  • Health & Fitness
  • Machine Learning & Data Science
  • Hiking
  • Representation Learning
  • Goldendoodles

Experience / Projects

Data Scientist | Palo Alto Networks

Sept. 2020 - Present: PAN-DB Data Science Team — Machine learning for phishing URL detection.

CTO | Duet

Jan. 2019 - Present: Duet is a tech nonprofit that enables donors to provide meaningful aid to vulnerable populations in a more dignified, efficient, and personal way.
More info at

Deep Learning for Land-Cover Classification

Fall 2019 - Spring 2020: Trained deep learning models to predict high-res land-cover labels from low-res satellite imagery (Arxiv). Won 1st place in the 2020 IEEE GRSS Land-Cover Competition.

Data Science Intern | Palo Alto Networks

Summer 2019: Implemented a production-ready, deep learning-based NLP model that detects and categorizes sensitive and/or confidential files (e.g. source code, financial records). Achieved over 99% accuracy on a test set of 5,000 documents.

Software Engineering Intern | Falkonry

Summer 2018: Built a semi-supervised convolutional autoencoder for image feature extraction, allowing images to be incorporated into Falkonry’s event-detection pipeline. Developed ready-to-use Jupyter notebooks for performing exploratory data analysis on time-series data.

Founder & President | CAIS++

Spring 2017 - Spring 2019: Empowering USC's top undergraduate ML talent to work on
AI-for-social-good projects that truly matter.
More info at

ML for Kawasaki Disease Diagnosis

Spring 2018 - Spring 2019: Developed an ML-based diagnostic tool for Kawasaki Disease, a rare (and often undiagnosed) heart disease that affects children all over the world.
GitHub repository link: here.

Representation Learning for Link Prediction in Social Networks

Fall 2017 - Fall 2018: Published a codebase for evaluating representation learning models for link prediction within online social networks. Over 200 stars on GitHub.
GitHub repository link: here.

Deep Learning for Cell Nucleus Segmentation

Spring 2018: Trained Mask-RCNN and U-Net models to detect and segment cell nuclei from microscopic cell images. Placed in Top 10% worldwide in Kaggle's 2018 Data Science Bowl.
Competition page here.

Summer Research | Tsinghua University

Summer 2017: Analyzed web-crawler data to create a recommender system for suggesting content delivery network (CDN) services to various websites with an AUROC of 0.89.
Research report here.

Contact Me

Looking to get in touch? Feel free to email me directly at