--- "As a mentor and an advisor, I listen, inspire, guide, and work together as a team." ---
MENTORING PHILOSOPHY
When I take a student under my wing, I always start by inquiring about their backgrounds and listening to their aspirations. Based on the given information, I work with the student to create a network-based mentoring map, set SMART (specific, measurable, achievable, realistic, and timely) goals, inspire them through stories of different career paths from people in my network, and guide them through the first few steps before taking the coach position. This process is iterative since some students may not share everything at once. In the past, I have helped students overcome their financial burdens by contacting hiring managers and recommending them for part-time engineering positions. The idea is that they can meet their financial needs while building their resumes. I have also helped students develop better habits to excel academically, overcome imposter syndrome, and gain more confidence in public speaking. In addition to one-on-one mentoring, I create venues for group mentoring and peer advising. Finally, I assist students in building a robust support system, introduce them to people in my network, give them access to opportunities, and adopt a proactive approach to identify barriers, anticipate challenges, and develop a clear path forward.
RESEARCH ADVISING PHILOSOPHY
As a faculty advisor, I always strive to create a welcoming, collaborative, and supportive environment where students from all backgrounds can achieve their academic and research goals. When recruiting students to my research lab, I look for individuals interested in fluid dynamics, algorithm development, numerical analysis, applied mathematics, and scientific computing. I typically recruit students pursuing degrees in aerospace or mechanical engineering, although I may also co-advise computational and applied mathematics students through my joint assistant professorship with the Department of Mathematics. Students may demonstrate their interest and academic preparation through relevant coursework, research projects, internships, or independent study. I value students who think critically, learn quickly, work effectively both independently and collaboratively, and remain motivated when facing challenging problems. While I find an academic career deeply rewarding, I fully support my students in pursuing their career goals.
I structure my lab to provide regular mentorship through weekly one-on-one meetings with each student. I view these meetings as an opportunity for students to develop a research mindset by engaging with my approach to problem-solving and for me to better understand their progress and tailor my guidance to their needs. While I take a hands-on approach to advising, particularly early in a student's training, I expect students to grow into independent researchers by the time they graduate. To maintain this level of interaction, I generally advise 3-4 graduate students and 1-2 undergraduate students per academic year. The weekly meetings focus on research progress, professional development, and academic planning. 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 include slides, handwritten notes, figures, code, 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. Students also participate in group meetings that include discussions of ongoing projects, software development practices, and recent research publications. Doctoral students are encouraged to mentor junior students and gain teaching experience, as I believe mentoring and teaching strengthen technical understanding, communication skills, and leadership abilities regardless of career goals.
I view my research lab as a collaborative team built on collegiality, accountability, and mutual respect. All students have equitable access to opportunities and resources, and I work closely with them to identify challenges, provide support, and adjust my mentoring approach as needed. I also help students design their course plans so they can build strong foundations in their engineering discipline and related computational fields. Although I do not impose strict work schedules, I expect students to meet research expectations, maintain strong academic performance, and contribute positively to the lab environment. In the past, my students have received nationally competitive fellowships and internationally competitive awards, including the NSF Graduate Research Fellowship Program (NSF GRFP), John S. Foster Jr. Undergraduate Fellowship, and AIAA Best Student Paper Award in CFD. To further highlight my success in mentoring and advising student researchers, REU students hosted in my research lab contributed to two conference papers (2026 and 2027 AIAA SciTech Forums), including one as first author. Finally, here is a selection of written feedback from 2024-2026 IREAAMU NSF-REU & Collaborative REU students:
“... I also appreciate the weekly AAM [Advanced Air Mobility] meetings hosted by Dr. Sanjaya. It's clear that she cares about the REU students and their outcomes. Smaller focused groups like this are where the most value is created, ...”
“Thank you for everything you've done for us during this program. I appreciate all of the time and effort that you put in to help us get from day one to here ... I will never forget your dedication and passion for this program, and how amazing you made this summer for me. I hope we meet again at some point, in a way that I can prove how much this program changed my life.”
“Thank you for pushing me to be my best. I appreciate all the work you did for us this summer. It was an honor to work in this program.”
“I feel it was extremely well put together, and I got a lot out of the experience! It wasn't just research; it was also workshops, group events, networking, and tours. I think that made for a very meaningful and well-rounded experience.”
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.
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, Ph.D. Committee Form, Admission to Candidacy (Ph.D.), 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.
AE 541: Fluid Mechanics I
AE 512: Viscous Flow
AE 521: Aerodynamics of Compressible Fluids
AE 532: Intro to Turbulence
AE 518: Computational Fluid Dynamics (CFD)
AE 504: Intro to Uncertainty Quantification
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
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
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