Portfolio

Consulting Projects

  • I built an automated sales pipeline proof of concept for a client. Performed data cleaning and analysis for auto dealers data all across China, with millions of data points. Estimated customer return dates.
  • Deep learning in industrial defect detection: Researched, created data collection pipeline (industrial cameras and lighting setup), collected and labeled data, trained and tuned a CNN and delivered a proof of concept to a client. System performance was on par with human quality assurance employees (97% accuracy).
  • Performed two AI product feasibility analyses: read and prepared papers summaries, compiled state of the art results on the topics of 3D pose estimation with deep learning and emotion recognition in voice data using deep learning. As a result, one of the clients proceeded to develop a solution for voice emotion recognition.

Deep Learning

  1. Eye tracking system. Built a web app for data collection and labeling, collected data, and then trained a deep learning model that predicts which part of laptop screen a user is looking at. My team productized this technology at a Beijing Startup Weekend in May 2019 and got third place. [Python, fastai, Flask]
  2. Leafy greens classifier. I often feel confused when shopping for leafy greens in China. This is why I duilt a classifier to tell them apart. You can try the deployed model here. [Python, fastai, Docker]

Finance

  1. Stock portfolio optimization using SVM. I developed a novel method for selecting and optimizing low-risk stock portfolios using support vector machines. The model outperformed SP500 benchmark by 15% in terms of return per unit of risk [Python, scikit-learn, CVXOPT]
  2. Sentiment analysis of Twitter feed to predict stock market movements. I analyzed 200 gigabytes of Twitter feed data to extract sentiments of individual tweets. Then I built a sentiment index that was predictive of stock market movements [Python, R]

Autonomous Driving

  1. Lane detection using computer vision techniques [Python, OpenCV]
  2. Traffic sign classification using deep learning [Python, Tensorflow, OpenCV]
  3. Behavioral cloning using deep learning [Python, Keras, OpenCV]
  4. Advanced lane detection with camera calibration. In addition road curvature and vehicle offset relative to the center of line are calculated [Python, OpenCV]
  5. Computer vision-based real-time vehicle detection system [Python, scikit-learn, OpenCV]

Medicine

  1. Predictive modeling in breast cancer diagnostics using supervised machine learning techniques [Python, scikit-learn]

Deep Learning: Non-Technical Writing

  1. Why Swift may be the next big thing in deep learning - an overview of advantages of Swift in deep learning applications (also published on Medium).
  2. CapsNet Series in four parts (1, 2, 3, 4) — a popular introduction of the novel neural network architecture. This series was originally published on Medium (1, 2, 3, 4), where it got a lot of attention.
  3. Stochastic Weight Averaging — a new way to get state of the art results in deep learning.
  4. Deep Learning Vision for Non-Vision Tasks — three case studies about creative application of deep learning vision models to non-vision tasks
  5. Key Deep Learning Architectures for Visual Object Recognition — a series of posts about the most important CNN architectures.