Yinkai Wang

Yinkai Wang

PhD Student, Tufts University

Yinkai Wang

Yinkai Wang is a PhD student at Tufts University. He works on Protein Structure Prediction, Molecule Generation, Named Entity Recognition, and Educational Data Mining. His advisor is Dr. Soha Hassoun. He worked with Dr. Antonios Anastasopoulos, Dr. Liang Zhao, Dr. Amarda Shehu. He participated in CCI as scholar with Dr. Daniel Barbará. He act as intern in Peking University VDIG lab with Dr. Yongtao Wang. Also, he works in JD.com on Text Generation with Dr. Lingfei Wu and Dr. Xiaojie Guo.

Drop me an email if you’d like to talk about/collaborate on any of the following research topics with me.

I am open to opportunities in paper review, tutorial, workshop organization in bioinformatics, data mining, machine learning, multilingual Natural Language Processing, CV, Deep Graph Learning related topics.

Check out my CV and a pdf version.

Research Interests

  • Molecule Generation
  • Molecule Design
  • AI for Science (Biology/Physics/etc)
  • Natural Language Processing
  • Deep Graph Learning
  • Machine Learning for Discovery

News!

  • 09/29/23 Paper titled “On Separate Normalization in Self-supervised Transformers” has been accepted by NrueIPS 2023.
  • 09/14/22 Paper titled “Multi-objective Deep Data Generation with Correlated Property Control” has been accepted by NeurIPS 2022.
  • 06/26/22 Paper titled “Property-Controllable Generation of Quaternary Ammounium Compounds” has been accepted by DLG-KDD 2022.
  • 06/11/22 I act as reviewer in BIOKDD 2022.
  • 04/04/22 I am happy to announce that I am accepted to Tufts University for PhD program in Computer Science with Dr. Soha Hassoun.
  • 03/16/22 Paper titled “Small Molecule Generation via Disentangled Representation Learning. “ has been accepted by Bioinformatics.
  • 02/17/22 Paper titled “ Dataset Geography: Mapping Language Data to Language Users.” has been accepted by ACL 2022.
  • 12/14/21 Start working with Dr. Lingfei Wu and Dr. Xiaojie Guo in JD.com.
  • 12/7/21 Paper titled “Graph-based Ensemble Machine Learning for Student Performance Prediction” has been accepted in AAAI(AI4EDU, DLG’22).
  • 11/26/21 I act as reviewer in AAAI-DLG’22.
  • 10/21/21 Paper titled “Deep Latent-Variable Models for Controllable Molecule Generation” has been accepted in BIBM 2021.
  • 10/18/21 Congrats to be selected to participate in CCI Scholars.
  • 08/07/21 Congrats to get top 20% on Kaggle competition ‘Google Smartphone Decimeter Challenge’.
  • 05/22/21 I start working with Prof. Antonios Anastasopoulos on multilingual Natural Language Processing.
  • 04/21/21 Paper titled “Ensemble Machine Learning System for Student Academic Performance Prediction” is accepted by W4U workshop @EDM 2021.
  • 03/31/21 Accepted to be a ByteDance Intern.