Zhangyang (Atlas) Wang​

Multiple Ph.D. Positions at CSE@TAMU

 

I am always looking for self-motivated students (with financial aid) who are interested in pursuing Ph.D. degrees in machine learning (not limited to deep learning), computer vision, image/video processing, and novel applications such as smart city and healthcare. Also, self-funded visiting students/scholars are welcome to apply.


About the PI
Dr. Zhangyang (Atlas) Wang is an Assistant Professor of the Computer Science and Engineering (CSE) Department, at the Texas A&M University (TAMU), since August 2017. During 2012-2016, he was a Ph.D. student in the Electrical and Computer Engineering (ECE) Department, at the University of Illinois at Urbana-Champaign (UIUC), working with Professor Thomas S. Huang. Prior to that, he obtained the B.E. degree at the University of Science and Technology of China (USTC), in 2012.  He was a research scientist in the University of Washington (spring and summer 2017), and a former research intern with Microsoft Research (summer 2015), Adobe Research (summer 2014), and US Army Research Lab (summer 2013).


Dr. Wang's research has been addressing machine learning, computer vision, and their interdisciplinary applications, using advanced deep learning and optimization techniques. He has published over 60 papers in top-tier venues in the fields of machine learning (NIPS, ICML), computer vision (CVPR, ICCV, ECCV, BMVC, IEEE TIP), artificial intelligence (AAAI, IJCAI), and general data science (KDD, ACM MM, etc.). He has published several books and chapters, has been granted 3 patents, and has received over 20 research awards and scholarships (including winning two recent CVPR'18 and ECCV'18 challenges).


Dr. Wang regularly serves as tutorial speakers, guest editors, area chairs, session chairs, TPC members, and workshop organizers at leading CV/ML/AI conferences and journals. His research has been covered by worldwide media, including BBC, Fortune, International Business Times, TAMU news, and UIUC news & alumni magazine. His research has been commercialized into some most profitable products such as Adobe Photoshop. His group widely collaborates with federal government and leading industrial research labs, through various sponsored projects and student internships. His research has been extensively funded (as PI or co-lead) by NSF, DARPA, seven industrial grants, plus a TAMU X-grant.



About CSE@TAMU
Texas A&M University (TAMU) is conveniently located in a quadrangle formed by Dallas, Houston, San Antonio and Austin. TAMU has been most renowned for its world-class College of Engineering (ranked 11th by US News 2017). As its part, the Department of Computer Science and Engineering has top-ranked Computer Science and Computer Engineering programs.


The department has faculty with a number of national distinctions, including several ACM/IEEE/AAAS/SIAM Fellows, ACM Distinguished Scientists and Engineers. It also has an extremely fast-growing research profile in AI, machine learning and computer vision. According to csrank.org, in the 2017-2018 year, CSE@TAMU is ranked 23th nation-wide in all CS research areas, and is ranked 22th in the specific field of AI. More information about the department is available at http://www.cse.tamu.edu .



How to Apply
Students from Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or other related disciplines are encouraged to apply. The candidate should have a strong passion in pursuing high-quality research, and should have the ambition to become a world-class researcher upon graduation from the Ph.D. program. A strong candidate is expected to possess an exceptional algorithmic and theoretical background, to have good analytical and programming skills, and to communicate and present well.

If you are interested in the positions, please first contact the PI at: 
atlaswang@tamu.edu. Please attach your CV, a sample publication or project report (if any), and everything else that you believe will help your application. Please apply to our graduate program, and mention your intention to work with me in your statement (NOTE: mentioning me in your personal statement will not automatically remind me to review your package. You have to email me first to state your interests in joining us. I do not review nor consider any application without informing me via email in advance).