Multiple PhD, Master, Intern, Visiting Student Positions at KAUST

Di Wang is currently an Assistant Professor in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at the King Abdullah University of Science and Technology (KAUST). He is always looking for PhD students, internship and visiting students (all are fully funded) start from either Spring or Fall.

Di Wang obtained his PhD degree in Computer Science and Engineering at the State University of New York (SUNY) at Buffalo. During his PhD studies, he has been invited as a visiting student to the University of California, Berkeley, Harvard University, and Boston University. During the last 3 years, he was the first author for more than twenty four papers, include several JMLR, ML, ICML, NeurIPS, AAAI, IJCAI, ALT. He has collaborated with people from several top universities such as UVA, University of Toronto, Cornell, University of Boston, SUNY at Buffalo, MIT.

His interested areas include Machine Learning, Statistical Estimation, Fairness, Privacy and Security, see his personal website for details. If you are interested in working or have common interests with him, feel free to send him ( ) your CV and transcripts. You can discuss with him whether there are some suitable projects for you.

Requirements: Minimum GPA is 3.5/4 or 83/100, TOFEL IBT 79 or IELTS 6.0. Students should be self-motivated and have critical thinking, also they should be proficient in either programming or statistics or mathematics. Students from all related background such as CS, Mathematics, Statistics, EE etc. are invited to apply. Di Wang has different topics for students with different background. Publication is preferred but not required.

More information can be found at (PhD), (Master), (intern and visiting students).

In 2022, he is looking for students who are interested in the following areas:

  1. Theory: Differential privacy, Robust Estimation, Quantum Statistical Learning (Students with Mathematics or Statistics major are highly welcome to apply)

  2. Trustworthy Machine Learning: System, Interpretable ML, Fairness in Machine Learning, Machine Unlearning (Students will all related background are welcome to apply)

  3. Privacy-preserving Machine Learning : (Students with Security or Crypto background are highly welcome to apply)

In 2021, he is looking for students who are interested in the following areas:

  1. Private Data Analytics: Differential privacy, Privacy-preserving Machine Learning and Privacy-attack in Machine Learning

  2. Trustworthy Machine Learning: Fairness in Machine Learning, Robust Statistics/Estimation and Causal Inference

  3. Learning Theory: High Dimensional Statistics, Quantum Statistical Learning