My Google Scholar

Patents

1. Optimizing Deep Neural Network Models Based on Sparsification and Quantization, with Bita Darvish Rouhani, Eric Chung, Doug Burger.

2. Determining Shared Exponent Values for Shared Exponent Floating Point Data Types, with Bita Darvish Rouhani, Marius Stan, Mathew Hall, Preyas Shah, Ankit More, Eric Chung, Doug Burger.

3. Sparsity and quantization for deep neural networks, with Bita Darvish Rouhani, Doug Burger, Ming Liu, Eric Chung, Ritchie Zhao.

4. Hierarchical and shared exponent floating data types, with Bita Darvish Rouhani, Venmugil Elango, Jeremy Fowers, Ming Liu, Jinwen Xi, Doug Burger, Eric Chung.

5. Sparsifying narrow data formats for neural networks, with Bita Darvish Rouhani, Venmugil Elango, Eric Chung, Doug Burger, Mattheus Heddes, Nishit Shah, Ankit More.

Conference Papers

1. R. Shafipour, D. Harrison, M. Horton, J. Marker, H. Bedayat, S. Mehta, M. Rastegari, M. Najibi, S. Naderiparizi, "SeedLM: Compressing LLM Weights into Seeds of Pseudo-random Generators," Proc. of Int. Conf. on Learning Representations (ICLR), Singapore, April 24-28, 2025.

2. B. Rouhani, R. Zhao, V. Elango, R. Shafipour, M. Hall, M. Mesmakhosroshahi, A. More, L. Melnick, M. Golub, G. Varatkar, L. Shao, G. Kolhe, D. Melts, J. Klar, R. L'Heureux, M. Perry, D. Burger, E. Chung, Z. Deng, S. Naghshineh, J. Park, M. Naumov, "With shared microexponents, a little shifting goes a long way," Proc. of 50th Annual Intl. Symp. on Computer Architecture, Orlando, FL, June 17-21, 2023.

3. M. Rahmani, R. Shafipour and P. Li, "Non-Local Feature Aggregation on Graphs via Latent Fixed Data Structures," Proc. of 55th Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, November 1-3, 2021.

4. R. Shafipour and G. Mateos, "Online proximal gradient for learning graphs from streaming signals," in Proc. of European Signal Process. Conf., Amsterdam, Netherlands, August 24-28, 2020.

5. Y. Li, R. Shafipour, G. Mateos, and Z. Zhang, "Supervised graph representation learning for modeling the relationship between structural and functional brain connectivity," Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Barcelona, Spain, May 4-8, 2020.

6. R. Shafipour and G. Mateos, "Online network topology inference with partial connectivity information," Proc. IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Process., Guadeloupe, West Indies, December 15-18, 2019.

7. Y. Li, R. Shafipour, G. Mateos, and Z. Zhang, "Mapping brain structural connectivities to functional networks via graph encoder-decoder with interpretable latent embeddings," Proc. of IEEE Global Conf. on Signal and Information Processing, Shaw Centre, Ottawa, Canada, Nov. 11-14, 2019.

8. R. Shafipour, A. Hashemi, G. Mateos, and H. Vikalo, "Online topology inference from streaming stationary graph signals," Proc. of IEEE Data Science Workshop, Minneapolis, MN, June 2-5, 2019.

9. R. Shafipour, A. Khodabakhsh and G. Mateos, "A windowed digraph Fourier transform," Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Brighton, UK, May 12-17, 2019.

10. A. Hashemi, R. Shafipour, H. Vikalo and G. Mateos, "A novel scheme for support identification and iterative sampling of bandlimited graph signals," Proc. of IEEE Global Conf. on Signal and Information Processing, Anaheim, California, Nov. 26-28, 2018.

11. R. Shafipour and G. Mateos, "Spread and sparse: Learning interpretable transforms for bandlimited signals on directed graphs," Proc. of 52nd Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, Oct. 28-31, 2018.

12. C. Ye, R. Shafipour and G. Mateos, "Blind identification of invertible graph filters with multiple sparse inputs," Proc. of European Signal Processing Conference, Rome, Italy, Sep. 3-7, 2018.

13. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, "Directed network topology inference via graph filter identification," Proc. of IEEE Data Science Workshop, Lausanne, Switzerland, Jun. 4-6, 2018.

14. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, "Identifying undirected network structure via semidefinite relaxation," Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Calgary, Canada, Apr. 15-20, 2018.

15. R. Shafipour, A. Khodabakhsh, G. Mateos and E. Nikolova, "Digraph Fourier transform via spectral dispersion minimization," Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Calgary, Canada, Apr. 15-20, 2018. Best student paper award (top 5 among 2830 submissions)

16. A. Hashemi, R. Shafipour, H. Vikalo and G. Mateos, "Sampling and reconstruction of graph signals via weak submodularity and semidefinite relaxation," Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, Calgary, Canada, Apr. 15-20, 2018.

17. R. Shafipour, A. Khodabakhsh, G. Mateos and E. Nikolova, "A digraph Fourier transform with spread frequency components," Proc. of IEEE Global Conf. on Signal and Information Processing, Montreal, Canada, Nov. 14-16, 2017.

18. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, "Network topology inference from non-stationary graph signals," Proc. of Intl. Conf. on Acoustics, Speech and Signal Processing, New Orleans, LA, Mar. 5-9, 2017.

Journal Papers

1. A. Hashemi, R. Shafipour, H. Vikalo, and G. Mateos, "Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals," Signal Processing, vol. 195, June 2022.

2. R. Shafipour, S. Segarra, A. G. Marques and G. Mateos, "Identifying the topology of undirected networks from diffused non-stationary graph signals," IEEE Open Journal of Signal Processing, vol. 2, pp. 171-189, Apr. 2021.

3. R. Shafipour and G. Mateos, "Online topology inference from streaming stationary graph signals with partial connectivity information," Algorithms, vol. 13, no. 9, Sep. 2020.

4. R. Shafipour, A. Khodabakhsh, G. Mateos and E. Nikolova, "A directed graph Fourier transform with spread frequency components," IEEE Transactions on Signal Processing, vol. 67, no. 4, pp. 946-960, Feb. 2019.

5. R. Shafipour, R. A. Baten, Md. K. Hasan, G. Ghoshal, G. Mateos and M. E. Hoque, "Buildup of speaking skills in an online learning community: A network-analytic exploration," Palgrave Communications (Nature Humanities & Social Sciences Communications), vol. 4, June 2018.

Preprints

1. B. Rouhani, R. Zhao, A. More, M. Hall, A. Khodamoradi, S. Deng, D. Choudhary, M. Cornea, E. Dellinger, K. Denolf, S. Dusan, V. Elango, M. Golub, A. Heinecke, P. James-Roxby, D. Jani, G. Kolhe, M. Langhammer, A. Li, L. Melnick, M. Mesmakhosroshahi, A. Rodriguez, M. Schulte, R. Shafipour, L. Shao, M. Siu, P. Dubey, P. Micikevicius, M. Naumov, C. Verilli, R. Wittig, E. Chung, "Microscaling data formats for deep learning", Oct. 2023.

PhD Thesis

• R. Shafipour, Learning Representations for Signal and Data Processing on Directed Graphs. PhD thesis, University of Rochester, March 2020.