Paul Weng | Welcome

News:

Openings for postdocs, PhD, master, or research associates: If you're interested in my research, please send me an email in English with your CV.

Sep. 2025: one paper accepted in NeurIPS 2025
Aug. 2025: I will serve as an area chair for ICLR 2026
Aug. 2025: I will serve as an area chair for AAMAS 2026
July 2025: one paper accepted at ACM MM 2025
June 2025: I will serve as an area chair for AAAI 2026
Jun-Jul 2025: I'll be an invited professor at Université Paris Dauphine-PSL
May 2025: our survey paper on RLHF has been accepted in TMLR
May 2025: one paper accepted at ICML 2025
Apr. 2025: I'll be an invited speaker at ICASIS 2025
Mar. 2025: one paper accepted in ACM TELO
Jan. 2025: one paper accepted at ICLR 2025
Jan. 2025: We got awarded the Shuangchuang talent program of Kunshan
Dec. 2024: two papers accepted at AAAI 2025
Nov. 2024: I will serve as an area chair for ECAI 2025
Oct. 2024: one paper accepted in NCAA
Sep. 2024: one paper accepted in the journal track of ACML 2024
June 2024: one paper accepted at ICML 2024 Workshop MHFAIA
May 2024: I will serve as an area chair for AAAI 2025
May 2024: one paper accepted at ICML 2024
Apr. 2024: I will serve as a co-chair for MIWAI 2024
Apr. 2024: our survey paper on interpretable RL has been accepted in Machine Learning
Jan. 2024: one paper accepted at ICLR 2024
Jan. 2024: I have moved to DKU

Paul Weng (CV)

Associate Professor at Duke Kunshan University (DKU)

I am currently recruiting research assistants, master, PhD, and post-docs to work with me at DKU in Kunshan, China.

Note that the master and PhD students would be officially enrolled in another university. If you have any questions, please send me an email with your CV.

Contact: Duke Kunshan University
Office: IB 2032
8 Duke Avenue, Kunshan
Suzhou, 215316, P.R. China
Email: paul.weng [at] dukekunshan [dot] edu [dot] cn
Phone: +86-512-3665-7593; 昆山杜克大学
IB 2032室
江苏省昆山市
杜克大道8号
邮政编码: 215316
Keywords: Artificial intelligence; Algorithmic Decision theory; Markov decision processes/reinforcement learning; Qualitative/ordinal decision-making; Multiobjective/multicriteria decision-making; Preference learning/elicitation.