• We always have openings for truly outstanding PhD students, starting at every year's spring and fall semesters (so no need to inquiry "whether you have PhD openings" by email). Our group is well funded, and research assistantships will be provided to PhD students by default.
• Interested candidates for PhD opportunities are strongly encouraged to contact Dr. Wang by email. Please attach your CV, and clearly discuss your research interest and experiences. We are usually not very interested in your GPA and standard test scores.
• Postdocs, and self-funded visitors are welcome to apply, and will be evaluated case by case. Due to the recent challenging situation of international travels, we are also open to remote collaborations, on certain basic research topics of common interest.
• We are always open to working with UT undergraduates and master students for research projects. Please contact Dr. Wang by email and specify your interest.

• Nothing is more important than a true enthusiasm and devotion to research. We do research only because we are really obsessed with and enjoy our research.
• Two other important things that we look for: (1) a truly deep understanding of your problem of interest - don’t naively plug and play "hot" tools; (2) a solid background and a true passion for mathematics. Our research heavily leverages matrix analysis, optimization, and statistical learning.
• We have a very flat management structure with minimal communication overhead (“Talk is cheap. Show me the code/math”). Every student works directly and closely with Dr. Wang. The group also benefits a lot from its highly interactive, intimate, and helpful culture.
• We provide strong (in fact, much stronger than you'll usually see) support to students for internship, visiting, and collaboration opportunities. You can check from existing group members' experiences.

Professor Zhangyang “Atlas” Wang [Google Scholar] is currently an Assistant Professor of Electrical and Computer Engineering at UT Austin. He was an Assistant Professor of Computer Science and Engineering, at the Texas A&M University, from 2017 to 2020. He received his Ph.D. degree in ECE from UIUC in 2016, advised by Professor Thomas S. Huang; and his B.E. degree in EEIS from USTC in 2012. Prof. Wang is broadly interested in the fields of machine learning, computer vision, optimization, and their interdisciplinary applications. His latest interests focus on automated machine learning (AutoML), learning-based optimization, machine learning robustness, and efficient deep learning. His research is gratefully supported by NSF, DARPA, ARL/ARO, as well as a few more industry and university grants. He has received many research awards and scholarships, including most recently an ARO Young Investigator award, an IBM faculty research award, an Amazon research award (AWS AI), an Adobe Data Science Research Award, a Young Faculty Fellow of TAMU, and four research competition prizes from CVPR/ICCV/ECCV.

A Public Ivy (referred to a public university that can provide an Ivy League collegiate experience), The University of Texas at Austin (UT Austin) is one of the most prestigious universities in the US, and the flagship in the state of Texas. The university is a world-leading research institution in the engineering, computer science, and physical science fields. As of August 2020, 12 Nobel Prize winners, two Turing Award winners, two Fields medalists and two Abel prize winners have been affiliated with the school as alumni, faculty members or researchers. The Cockrell School of Engineering is consistently a top-10-ranked engineering school in the US.

UT Austin is among the world’s topmost universities for artificial intelligence (AI) and machine learning (ML) research. The U.S. News and World Report 2021 ranked UT Austin as No. 5 in the nation’s best Artificial Intelligence programs. Recently, the National Science Foundation has selected UT Austin to lead the NSF AI Institute for Foundations of Machine Learning, bolstering the university’s existing strengths in this emerging field. UT Austin is poised to develop entirely new classes of algorithms that will lead to more sophisticated and beneficial AI technologies.

UT Austin has also established a campus-wide Machine Learning Laboratory, that will focus its initial research agenda on core algorithmic advances and a select set of applications that reflect the strengths and capabilities of UT Austin. The ML Laboratory brings together a community that includes linguists, ethicists, mathematicians, engineers, and computer scientists with plans to grow as machine learning takes on increasing importance across disciplines. Professor Wang is a core faculty member of the UT Machine Learning Laboratory.

The University of Texas Department of Electrical and Computer Engineering has been consistently ranked in the US top-10 over several decades. It is the largest department in the Cockrell School of Engineering with more than 2200 students and 70 faculty. Based on U.S. News and World Report 2020, UT’s graduate programs in Electrical Engineering and Computer Engineering are ranked no.8 and no.9 respectively. UT ECE’s current faculty members include 3 National Academy of Engineering (NAE) Members, 3 National Academy of Inventors (NAI) Members, 1 Emmy Award winner, 3 ACM Fellows, 32 IEEE Fellows, and 29 NSF Young Investigator/CAREER award winners.

Many people consider Austin "the No. 1 Best Place to Live in the US" - and VITA members personally agree. Austin was recently voted the No. 1 place to live in America for the third year in a row — based on affordability, job prospects and quality of life. It was named the fastest growing large city in the US. It ranked No. 4 of the best large cities for startups.
Austin is the state capital of Texas, an inland city bordering the Hill Country region. Austin is internationally known for its eclectic live-music scene centered around country, blues and rock. Its many parks and lakes are popular for hiking, biking, swimming and boating. Last but foremost, it is one of the greatest US cities for foodies and drinkers!