Yuanqi Du is a senior undergraduate student studying Computer Science at George Mason University. He has broad interests in machine learning and data mining. He worked on Outlier Detection, American Sign Language Recognition (Milimeter Wave Signals & Kinect), Protein Structure Prediction, Molecule Generation via Graph Neural Network, Deep Graph Learning and Medical Image Analysis. He worked with Prof. Liang Zhao, Prof. Amarda Shehu, Prof. Parth Pathak, Prof. Carlotta Domeniconi while he was at GMU. He worked in the MIRACLE Lab in Chinese Academy of Sciences, Institute of Computing Technology, with Prof. Hu Han and Prof. S. Kevin Zhou for a research intern from Aug to Dec 2020 on medical image analysis. He joined Microsoft Research Asia Machine Learning and Computational Biology group as a research intern in Nov 2020, working on Protein Structure Prediction. He is actively collaborateing with researchers in chemistry, biology, machine learning and data mining.
Drop me an email if you’d like to talk about/collaborate on any of the following research topics or other research areas with me. I currently live in Beijing, China, feel free to grab a coffee with me, too!
I am open to opportunities in paper review, tutorial, workshop organization in data mining, machine learning, bioinformatics, computational chemistry related topics (especially deep generative models, graph neural networks and AI for sciences).
- Deep Graph Learning
- Deep Generative Models
- Protein Structure Prediction
- Computational Biology/Chemistry
- Machine Learning for Sciences
- Application of Millimeter-wave Signals
- 2/21 Paper titled “Generative Adversarial Learning of Protein Tertiary Structures” accepted in Molecules [IF: 3.267], Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu*.
- 1/21 Paper titled “Interpretable Property Controlling Molecule Generation” accepted in Scientific Discovery with AI Workshop as an oral presentation, co-located with AAAI 2021.
- 1/21 Paper titled “Property Controllable Variational Autoencoder via Invertible Mutual Dependence”, Xiaojie Guo, Yuanqi Du, Liang Zhao*, accepted in ICLR 2021.
- 12/20 Serve as a Web Team member for KDD 2021!
- 12/20 Paper titled “Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models” conditionally accepted in IPCAI 2021
- 11/20 Accepted to be a Microsoft Learn Student Ambassador!
- 11/20 Accepted to AAAI 2021 Student Technical Volunteer Program!
- 11/20 I am glad to receive a NeurIPS 2020 Travel Award. Excited to attend NeurIPS 2020!
Preprints (under review)
- Disentangled Deep Generative Model for Spatial Networks, submitted to a major Data Mining conference, Xiaojie Guo*, Yuanqi Du*, Liang Zhao.
- Controllable Molecular Graph Generation via Monotonic Constraints, submitted to a major Data Mining conference, Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao.
- Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image, submitted to a major Medical Image Analysis conference, Yuanqi Du, Quan Quan, Han Hu, S. Kevin Zhou.
- CT Film Recovery via Disentangling Geometric Deformation and Photometric Degradation: Simulated Datasets and Deep Models, submitted to a major Medical Image Analysis conference, Quan Quan, Qiyuan Wang, Liu Li, Yuanqi Du, S. Kevin Zhou.