Neena Imam, Ph.D.

Peter O’Donnell Jr. Director of the O’Donnell Data Science and Research Computing Institute
Adjunct Professor of Computer Science (by Courtesy)
University Distinguished Professor

Department	CS
Email	nimam@smu.edu
Office Location	Ford Hall for Research and Innovation, Suite 106
Phone	(214) 768-6793
Website	View personal website

Connect

Neena Imam is the inaugural Director of the O’Donnell Data Science and Research Computing Institute (ODSRCI) at SMU, a role central to advancing the university’s commitment to data-driven education and next-generation computational research. Prior to joining SMU, she was the Director of Strategic Researcher Engagement at NVIDIA Corporation, where she partnered with academic researchers to accelerate GPU-enabled applications in AI and machine learning. Earlier, Neena served as a Distinguished Scientist and Director of Research Collaboration in the Computing and Computational Sciences Directorate at Oak Ridge National Laboratory (ORNL). Her work at ORNL focused on high-performance computing, emerging microelectronics, and post-Moore’s Law architectures. She has authored and co-authored many scientific publications, has been invited to speak and serve on panels at major conferences, and remains active in professional organizations supporting research and education in HPC and AI.

Neena earned her Doctoral degree in Electrical Engineering from the Georgia Institute of Technology, following Master’s and Bachelor’s degrees in the same field from Case Western Reserve University and the California Institute of Technology. She also served as a Science and Technology Fellow for Senator Lamar Alexander in Washington, D.C. from 2010 to 2012.

Education

Ph.D. in Electrical Engineering, Georgia Institute of Technology
M.S. in Electrical Engineering, Case Western Reserve University
B.Sc. in Electrical Engineering, California Institute of Technology

Research

AI-Focused High Performance Computing
Federated Learning
Resource Efficient AI\
Post-Moore Computing Technology

Publications

F. Wang, H. S. Oral, S. Sen, and N. Imam, “Learning from five-year resource-utilization data of Titan system,” in Proc. IEEE Int. Conf. Cluster Computing (CLUSTER), Albuquerque, NM, USA, Sep. 2019.
N. Bates, C. H. Hsu, N. Imam, T. Wilde, and D. Sartor, "Re-examining HPC energy efficiency dashboard elements," in Proc. IEEE Int. Parallel and Distributed Processing Symp. Workshops (IPDPSW), Chicago, IL, USA, May 2016, pp. 1106–1109.
S. Sen, N. Imam, and C. H. Hsu, “Quality assessment of GPU power profiling mechanisms,” in Proc. IEEE Int. Parallel and Distributed Processing Symp. Workshops (IPDPSW), Vancouver, BC, Canada, May 2018.
R. Bridges, N. Imam, and T. Mintz, "Understanding GPU power: A survey of profiling, modeling, and simulation methods," ACM Comput. Surveys, vol. 49, no. 3, Sep. 2016, Art. no. 41. doi:10.1145/2962131.
N. Imam and T. Bednarz, “Modeling the impact of network link dropout on robust federated learning in distributed compute environments,” in Proc. IEEE Int. Syst. Conf. (SysCon), Halifax, NS, Canada, 2026, pp. 1–8, doi: 10.1109/SysCon66367.2026.11503495.
N. S. V. Rao, N. Imam, Z. Liu, R. Kettimuthu, and I. Foster, “Machine learning methods for connection RTT and loss rate estimation using MPI measurements under random losses,” in Machine Learning for Networking: 2nd IFIP TC6 Int. Conf. (MLN 2019), Revised Selected Papers, Lecture Notes in Computer Science, vol. 12081. Cham, Switzerland: Springer, 2020, pp. 154–174.
S. Sen and N. Imam, “Machine learning based design space exploration for hybrid main-memory design,” in Proc. Int. Symp. Memory Systems (MEMSYS), Washington, DC, USA, Sep. 30–Oct. 3, 2019, pp. 480–489.
S. M. Hasan, D. Schmidt, R. Kannan, and N. Imam, “A scalable graph analytics framework for programming with big data in R (pbdR),” in Proc. IEEE Int. Conf. Big Data (Big Data), Los
A. Passian and N. Imam, “Nanosystems, edge computing, and the next generation computing systems,” Sensors, vol. 19, no. 18, Art. no. 4048, Sep. 2019, doi: 10.3390/s19184048.
A. Miloshevsky, N. Nair, N. Imam, et al., “High-Tc superconducting memory cell,” Journal of Superconductivity and Novel Magnetism, vol. 35, pp. 373–382, 2022.
N. Nair, A. Jafari-Salim, A. D’Addario, N. Imam, and Y. Braiman, “Experimental demonstration of Josephson cryogenic memory cell based on coupled Josephson junction arrays,” Superconductor Science and Technology, vol. 32, no. 11, Nov. 2019, doi: 10.1088/1361-6668/ab416a.

Honors and Awards

Best Paper Award for “Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses,” IFIP International Conference on Machine Learning for Networking, December 2019.
Best Paper Award for “Experimental Analysis of File Transfer Rates over Wide-Area Dedicated Connections,” 18th IEEE International Conference on High Performance Computing and Communications (HPCC), December 2016.
Patent: Y. Y. Braiman, N. Imam, and B. Neschke, “Memory Cell Comprising Coupled Josephson Junctions,” U.S. Patent No. 10,516,089, Issued: December 24, 2019.