Anjali Sandip
- Teaching Assistant Professor, Mechanical Engineering
Contact Info
- Email: anjali.sandip@UND.edu
- Office: 701.777.6855
Office Address
Mechanical Engineering
Upson II Room 160E
243 Centennial Drive Stop 8359
Grand Forks, ND 58202-8359
Websites
Biography
Anjali Sandip is a Teaching Assistant Professor in the Mechanical Engineering Department at the University of North Dakota. Her primary research interest is computational mechanics and, more recently, high-performance computing and machine learning. Anjali’s PI-led research projects have received support from the National Science Foundation, the Department of Energy, the North Dakota NASA EPSCoR, and NVIDIA. Before her current position, she was a postdoctoral researcher at the University of Nebraska, where she developed patient-specific computational models of the interaction between the superficial femoral artery and a stent to treat peripheral artery disease. Anjali serves as a mentor for both undergraduate and graduate researchers. She is an active member of the Association of Computational Mechanics (USACM & IACM) and has delivered numerous presentations and published journal articles. Anjali has bachelor’s, master’s, and doctoral degrees in mechanical engineering.
Outside of work, she enjoys reading and all things outdoors.
ME 101 Introduction to Mechanical Engineering
ME 341 Thermodynamics
ME 323L Machine Component Design Laboratory
ME 529 Advanced Finite Element Methods
ME 590 Modeling of Glaciers and Ice Sheets
ME 590 Continuum Mechanics
ENGR 201 Statics
ENGR 340 Engineering Ethics
- Computational Mechanics
- High-Performance Computing
- Uncertainty Quantification
- Physics-Informed Machine Learning
National Science Foundation EPSCoR Research Fellow, 2024-25
Doctor of Philosophy, Mechanical Engineering, University of Kansas, Lawrence, USA
Master of Science, Mechanical Engineering, University of Kansas, Lawrence, USA
Bachelor of Engineering, Mechanical Engineering, Osmania University, Hyderabad, India.
I am actively seeking graduate students (Master's and Ph.D.) interested in working in the interdisciplinary fields of computational mechanics, applied mathematics, and scientific machine learning. If interested, please email me your CV and official transcripts with the subject line "Prospective graduate student".