Advising
--- "We are a team!" ---
PHILOSOPHY
As a faculty advisor, I always strive to create a welcoming and collaborative environment for students from all backgrounds, nationalities, and identities, and to provide the necessary support for students to achieve their academic and research goals. When looking for new additions to my research lab, I look for students who are interested in fluid dynamics, algorithm development, numerical analysis, computer science, applied mathematics, and scientific computing for aerospace engineering applications. Students can demonstrate their interests and qualifications through taking relevant courses, completing independent study projects, having previous research experiences, and/or doing internships. Students must also have the ability to think critically, learn new skills quickly, work independently and collaboratively, tolerate ambiguity, and stay focus and motivated when facing challenges. In short, students who enjoy solving hard problems and have excellent work ethics are usually good fits. Furthermore, while I enjoy being an academic, I do not expect my students to pursue academic careers, and I fully support my students' career decisions.
I structure my lab so that each student meets with me on a weekly basis. To be able to do so, I generally have 3-4 graduate students and 1-2 undergraduate students per academic year. The purpose of these weekly meetings is to discuss research and other academic/career-related topics. Students drive the meetings and may cancel if there are not any significant updates or challenges. Students need to bring a summary of their work to these weekly meetings. The summary does not have to be a polished document, but it must be comprehensible to others. The summary may consist of slides, handwritten notes, figures, codes, and/or anything else that will result in a meaningful discussion. We will not meet on university and federal holidays. Additional meetings may be requested if there are special needs. In addition to weekly, one-on-one meetings, graduate students must also attend group meetings. The purpose of these group meetings is to learn about ongoing research projects in our group, discuss software development, and/or review recent publications in the field. Doctoral students are expected to mentor at least one undergraduate student and serve as teaching assistants for two semesters during their study. I believe the ability to mentor and teach is important even if students are not interested in academic careers. From past experiences, I find that significant mentoring and teaching experiences strengthen students' understanding and communication skills. While I do not enforce specific work hours and locations for my students, I do expect my students to meet research expectations and perform well academically. Having said this, I strongly encourage students to spend some time at the office and learn from their labmates. Finally, when using lab resources, students must be respectful and mindful of others. Remember that we, together as a team, can advance the boundary of human knowledge and solve problems more efficiently.
Undergraduate
Expectations:
Have taken at least one programming course.
Familiarity with C/C++ in a UNIX environment is a plus, but not required.
Enjoyed and did well in ME 391/397 (Engineering Analysis).
Have taken at least one 300-level fluid course.
Previous research experience is not required.
Meetings:
One-on-one meeting: weekly, 30 minutes.
Group meeting: none.
How to join:
Email your resume and unofficial transcript to the faculty advisor. Include a description of your research interests and academic goals. If there are openings and you are a good fit, we will exchange emails and schedule an interview.
Graduate
Expectations:
Have completed their undergraduate studies. Masters are not required.
Complete the Ph.D. program in 5 years.
Publish at least 2 journal papers.
Present at conferences regularly (at least once a year after year 2).
Participate in professional development activities.
Meetings:
One-on-one meeting: weekly, 50 minutes.
Group meeting: twice a month, ~2 hours.
How to join:
Email your curriculum vitae, unofficial transcript, list of publications, GRE, and TOEFL. Include a description of your research interests and career goals. If there are openings and you are a good fit, we will exchange emails and schedule an interview.
COURSE REQUIREMENTS FOR DOCTORAL STUDENTS
All Ph.D. students must complete a minimum of 72 graduate semester credit hours beyond their B.S. degrees:
Courses in major: 21 credit hours minimum required
Department 600-level courses: 6 credit hours minimum required
MATH 400 or above (excluding MATH 400): 9 credit hours minimum required (3 credit hours minimum at 500-600 level)
Other coursework: 12 credit hours
Dissertation: 24 credit hours minimum required
Please check the department website for more details. Here are links to Graduate Program Forms and Graduate Student Handbooks.
For advising and degree audit purposes, all students must keep living documents of their course plans and bring them to the advising sessions. Courses are selected based on consultation with the faculty advisor in order to ensure students have sound backgrounds in fundamental aerospace/mechanical engineering and other fields related to their dissertation research. Some suggested courses are listed below.
Aerospace Engineering
AE 511: Advanced Fluid Dynamics
AE 512: Viscous Flow
AE 521: Aerodynamics of Compressible Fluids
AE 532: Introduction to Turbulence
AE 518: Computational Fluid Dynamics (CFD)
AE 569: Plasma Dynamics
AE 525: Hypersonic Flows
AE 655: Advanced Topics in CFD
ME 644: Theory of Turbulence
AE 681: Advanced Viscous Flow Theory
AE 595: Seminar
AE 600: Ph.D. Dissertation
Computer Science
COSC 505: Introduction to Programming for Scientists and Engineers
COSC 522: Machine Learning
ECE 517: Reinforcement Learning in Artificial Intelligence
COSC 462: Parallel Programming
COSC 581: Algorithms
COSC 594: Scientific Computing for Engineers
COSC 670: Advanced Topics in Scientific Computing
Mathematics
ME 529: Applications of Linear Algebra in Engineering Systems
ME 570: Numerical Methods for Engineers
ME 591: Advanced Engineering Analysis
MATH 471: Numerical Analysis
MATH 472: Numerical Algebra
MATH 535/536: Partial Diff. Equations I/II
MATH 571/572: Numerical Mathematics I/II
MATH 577: Optimization
MATH 578: Numerical Methods for Partial Differential Equations