College of Engineering
Phone: 260.422.5561, ext. 3479
Office: Cunningham Business Center, Room 105D
MW: 1 – 5 pm
By appointment (Feel free to just drop by during office hours!)
- CPE1600: Advanced Computer Program for Engineers
- CPE3500: Computer Engineering I
- CPE4200: Reconfigurable Computing
- CPE4500: Computer Engineering II
- CPE4700: Computer Architecture
- CPE4990: An Introduction to Parallel Programming using OpenMP
Academic Advisor Roles
- Ph.D. in Electrical and Computer Engineering, Computer Architecture, Western Michigan University, Expected 2019
Advisor: Dr. Ikhlas Abdel-Qader
- M.Sc. in Electrical Engineering, Computer Engineering, California State University, Fullerton, 2015
Advisor: Dr. Mohinder Grewal
Research Advisors: Dr. Chandrasekhar Putcha and Dr. Hassan H. Hashemi
- B.Sc. in Electrical Engineering, Electronics, Islamic Azad University, Central Tehran Branch, 2003
Advisor: Dr. Arash Dana
- High School Diploma in Math and Physics, Tazkiyeh High School, Tehran, 1996
Mentor: Dr. Mohammad Reza Pournaki
- Assistant Professor of Computer Engineering, Indiana Institute of Technology, 2018 – Present
- Research Assistant, Western Michigan University, 2016 – 2018
- Research and Teaching Assistant, California State University, Fullerton, 2013 – 2015
- Electrical Engineer, Alghamis Food Industry Co. LTD, Tehran, Iran, 2010 – 2011
- Electrical Engineer, Delneshin Tea Co. LTD, Tehran, Iran, 2005 – 2010
- Network administrator, NAJA Organization, Tehran, Iran, 2003 – 2005
- Tai-Hsien Wu, Mohammadreza Khani, Lina Sawalha, James Springstead, John Kapenga, Dewei Qi, “A CUDA-Based Implementation of a Fluid-Solid Interaction Solver: The Immersed Boundary Lattice-Boltzmann Lattice-Spring Method,” Communications in Computational Physics (CiCP) Cambridge University Press, p1-32, 2017.
- Chandrasekhar Putcha, Brian W. Sloboda, and Mohammadreza Khani, “Development Application of Composite Indices (CI): An Emerging Method to the Disciplines of Engineering, Economics and Finance,” European Scientific Journal (ESJ), Vol. 12, No. 28, ISSN: 1857 – 7881, October 2016.
- Chandrasekhar Putcha S., Brian W. Sloboda, and Mohammadreza Khani, “A New Approach for a Forecasting Model in the Estimation of Social Security Benefits,” Journal of Applied Business and Economics, Vol. 18(2), 2016.
- Adam Tabba, Chandrasekhar Putcha, Brian Sloboda, Vineet Penumarthy, and Mohammadreza Khani, “Mathematical Analysis of Unemployment Benefits.” Global Conference on Business and Finance Proceedings, Vol. 11, No. 1, Pages 118-125, ISSN 2168-0612, Honolulu, Hawaii, January 4-7, 2016.
- Chandrasekhar Putcha, Brian Sloboda, and Mohammadreza Khani, “A New Approach for a Forecasting Model in the Estimation of Social Security Benefits.” The 35th International Symposium on Forecasting, ISF 2015 Proceedings, Page 70, ISSN 1997-4124, Riverside, CA, June 21-24, 2015.
- Chandrasekhar Putcha, Brian Sloboda, Vishwanath Putcha, Mohammadreza Khani, and Adam Tabba, “Financial Aspects of Determining Optimal Occupancy Factor for Hotels Based on Probabilistic Analysis.” Global Conference on Business and Finance Proceedings, Vol. 10, No. 1, Pages 39-46, ISSN 1941-9589, Las Vegas, Nevada, January 4-7, 2015.
- Chandrasekhar Putcha, S., Mina Khani, Mohammadreza Khani, and Paul Miller, “A Detailed Risk Analysis of Factors Contributing to Occurrence of Subdural Hematoma,” European Scientific Journal (ESJ), Vol.10, No. 18, ISSN: 1857 – 7881, June 2014.
My research interests span a broad variety of fields, including computer architecture, central processing unit (CPU) and graphics processing unit (GPU) microarchitecture; machine learning, risk analysis, estimation theory and their applications in various areas ranging from medical sciences to business and finance. More recently, I have been particularly interested in general-purpose GPU computing or GPGPU which is the use of a GPU to do general purpose scientific and engineering computing. Within this area I am investigating parallel applications specifically heat transfer and computational fluid dynamics problems, data structures and algorithms, scalability, parallel architectures, and programming machine learning algorithms for the GPU and specifically implementation of machine learning in microarchitecture of GPU.