projects
- Deep generative model with constraints for peptide structural design (Jan.2019 - now)
- Developed a deep generative model for novel dextrorotary helical conformations to facilitate the peptide drug design.
- Developed and implemented various MCMC searches to optimize the synthetic data with optimized pharmetutical properties. Published the work as first author in NeuIPS Workshop (MLSB) 2021. Link for my talk.
- Cell detection application for pathological image analysis (Jan.2019 - April.2020)
- Designed and developed customized object detection algorithms to detect brain cells and mitosis cells on pathology segmented images and whole slide images by using deep Convolutional Neural Network.
- Designed and developed Ki-67 cell segmentation and detection algorithm based on cellpose and image classification.
- Recommendation system for prediction of user purchase behavior (Jan. 2019 - Mar.2019)
- Classified data as train sets, validation sets and test sets. Constructed features using Pandas. Dealt with positive and negative sample imbalances using k-means and subsample.
- Used the Gradient Boosting Decision Tree to predict user purchase behavior through model training, parameter tuning and performance evaluation using F1-Score. Ranked 4th in 135 submission teams. Kaggle leaderboard link.
- Data Mining for Twitter Unstructured Data (Sep.2018 - Dec.2018)
- Mining interesting information from twitter tweets (JSON). Operated complex and unstructured data using MongoDB. Used MapReduce to process and summarize information. Conducted ElasticSearch. Visualized the information using Kibana.