Xin Luna Dong is affiliated with Facebook in the United States and has contributed extensively to research in computer science, with a focus on artificial intelligence and related subfields. Their work encompasses a variety of areas including topic modeling, advanced graph neural networks, natural language processing techniques, and recommender systems.
Their publication record includes a significant number of contributions across different venues, with notable frequent publication outlets such as:
Some recent papers authored or co-authored by Xin Luna Dong include:
The scientist's research topics focus on several core areas:
Co-authorship collaborations are an important aspect of their work. Frequent co-authors include:
The scientist has also authored a book titled An Introduction to Machine Learning in Quantitative Finance published by Advanced Textbooks in Mathematics in 2020, which has accumulated several citations.
Xun Luna Dong's contributions span over 60 publications in computer science, with a prominent emphasis on artificial intelligence, molecular biology, information systems, computer vision and pattern recognition, and surgery.
In recognition of professional achievements, they were awarded the ACM Distinguished Member title in 2018.
Xin Dong;Evgeniy Gabrilovich;Geremy Heitz;Wilko Horn
Xin Dong;Alon Halevy;Jayant Madhavan;Ema Nemes
Xin Luna Dong;Divesh Srivastava
Xin Dong;Alon Halevy;Jayant Madhavan
Xin Luna Dong;Laure Berti-Equille;Divesh Srivastava
Xin Luna Dong;Alon Halevy;Cong Yu
Anish Das Sarma;Xin Dong;Alon Halevy
Xian Li;Xin Luna Dong;Kenneth Lyons;Weiyi Meng
Xin Luna Dong;Laure Berti-Equille;Divesh Srivastava
Xin Luna Dong;Felix Naumann
Igor Tatarinov;Zachary Ives;Jayant Madhavan;Alon Halevy
Xin Dong;Alon Y. Halevy
Xin Dong;Alon Halevy
Xin Luna Dong;Barna Saha;Divesh Srivastava
Xin Luna Dong;Evgeniy Gabrilovich;Geremy Heitz;Wilko Horn
Xin Luna Dong;Evgeniy Gabrilovich;Kevin Murphy;Van Dang
Anja Gruenheid;Xin Luna Dong;Divesh Srivastava
Pei Li;Xin Luna Dong;Andrea Maurino;Divesh Srivastava
Xin Luna Dong;Laure Berti-Equille;Yifan Hu;Divesh Srivastava
Ravali Pochampally;Anish Das Sarma;Xin Luna Dong;Alexandra Meliou
Guineng Zheng;Subhabrata Mukherjee;Xin Luna Dong;Feifei Li
Pei Li;Xin Luna Dong;Andrea Maurino;Divesh Srivastava
Yuhan Cai;Xin Luna Dong;Alon Halevy;Jing Michelle Liu
Xin Luna Dong;Theodoros Rekatsinas
Xin Luna Dong;Theodoros Rekatsinas
Namyong Park;Andrey Kan;Xin Luna Dong;Tong Zhao
Xiaolan Wang;Xin Luna Dong;Alexandra Meliou
Wenfei Fan;Zhe Fan;Chao Tian;Xin Luna Dong
Theodoros Rekatsinas;Xin Luna Dong;Divesh Srivastava
If you think any of the details on this page are incorrect, let us know.
A background in computer science opens doors to many online degrees and career routes in the USA. If you’re considering specialties, fields like online electrical engineering courses USA are increasingly popular, blending software and hardware knowledge for high-tech careers.
For those seeking quick entry into the workforce or to boost credentials, quick certifications that pay well offer practical skills and strong salary potential—ideal for tech roles that don’t require a full degree.
Time-conscious professionals can benefit from the shortest online masters degree programs, which provide advanced knowledge without long-term commitment. These programs are designed to help you upskill rapidly and stay competitive.
Unsure which path offers the greatest career payoff? Explore which master’s degree is most in demand in USA to find programs with strong job outlooks, top salaries, and growth prospects.
Shanghai Jiao Tong University
University of Dayton
Jiangsu University
Delft University of Technology
Leibniz Institute for the Social Sciences
The University of Texas Southwestern Medical Center
University of Sheffield
Monash University
University of Rochester Medical Center
Tokyo Medical and Dental University
Goddard Space Flight Center
Dartmouth College
Foundation for Research and Technology Hellas
University of Münster
Norwegian University of Science and Technology
MRC Laboratory of Molecular Biology
