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Salman A. Avestimehr

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Salman Avestimehr
Born
Amir Salman Avestimehr
NationalityIranian
Alma mater
Known for
  • “An Approximation Approach to Network Information Theory,” [5]
Awards
Scientific career
Fields
Institutions
ThesisWireless network information flow: a deterministic approach (2008)
Doctoral advisorDavid Tse
Websiteviterbi.usc.edu/directory/faculty/Avestimehr/Salman

Salman A. Avestimehr is a Dean's professor at the Electrical & Computer Engineering and Computer Science Departments of University of Southern California, where he is the inaugural director of the USC-Amazon Center for Secure and Trusted Machine Learning[6] (Trusted AI) and the director of the Information Theory and Machine Learning (vITAL) research lab.[7] [8] He is also the CEO and Co-Founder of FedML.[9] Avestimehr's contributions in research and publications are in the areas of information theory, machine learning, large-scale distributed computing, and secure/private computing and learning. In particular, he is best known for deterministic approximation approaches to network information theory and coded computing.[10][11] He was a general co-chair of the 2020 International Symposium on Information Theory (ISIT), and is a Fellow of IEEE.[12][13] He is also co-authors of four books titled “An Approximation Approach to Network Information Theory”, “Multihop Wireless Networks: A Unified Approach to Relaying and Interference Management”, “Coded Computing”, and “Problem Solving Strategies for Elementary-School Math.”

Education

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Avestimehr completed his bachelor's degree in electrical engineering from Sharif University of Technology in 2003. He received his M.S. degree in 2005 in electrical engineering and computer science from University of California, Berkeley in 2005. Continuing his studies at UC Berkeley, he finished his Ph.D. in computer science in 2008; his doctoral adviser was David Tse.[14][15][16]

Career and research

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Avestimehr was a postdoctoral scholar at the Center for the Mathematics of Information (CMI) at Caltech in 2008. He served as an assistant professor at the school of electrical and computer engineering of Cornell University from 2009 to 2013. Avestimehr was promoted to a Dean's professorship in electrical and computer engineering at the University of Southern California, where he has taught since 2013. He is also the inaugural director of the USC-Amazon Center for Secure and Trusted Machine Learning. He has been a general co-chair of the 2020 International Symposium on Information Theory (ISIT). He has also been an associate editor for IEEE Transactions on Information Theory. Current research areas of Prof. Avestimehr include information theory, distributed computing, machine learning, and secure and private learning/computing.[17][11]

Awards and honors

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Bibliography

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  • “An Approximation Approach to Network Information Theory,” by A. S. Avestimehr, S. Diggavi, C. Tian and D. Tse, Foundations and Trends in Communications and Information Theory, 2015.[5]
  • “Multihop Wireless Networks: A Unified Approach to Relaying and Interference Management,” by I. Shomorony and A. S. Avestimehr, Foundations and Trends in Networking, 20114.[21]
  • “Coded Computing,” by S. Li and A. S. Avestimehr, Foundations and Trends in Communications and Information Theory, 2020.[22]
  • “Problem Solving Strategies for Elementary-School Math,” by K. Avestimehr and A. S. Avestimehr, Now Publishers, 2020.[23]

Selected publications

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  • C. He, M. Annavaram, and A. S. Avestimehr, “Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge,” in NeurIPS, 2020.[24]
  • C. He, et al, A. S. Avestimehr, “FedML: A research library and benchmark for federated machine learning”.[25]
  • Q. Yu, S. Li, N. Raviv, M. Mousavi Kalan, M. Soltanolkotabim and A. S. Avestimehr, “Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy,” in Proc. AISTATS 2019.[26]
  • M. Yu, Z. Lin, K. Narra, S. Li, Y. Li, N. S. Kim, A. Schwing, M. Annavaram, and A. S. Avestimehr, “GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training,” in NeurIPS,

2018.[27]

  • S. Li, M. A. Maddah-Ali, Q. Yu and A. S. Avestimehr, “A Fundamental Tradeoff Between Computation and Communication in Distributed Computing,” in IEEE Transactions on Information Theory, vol. 64, no. 1, pp. 109–128, Jan. 2018.[28]
  • S. Li, M. A. Maddah-Ali and A. S. Avestimehr, “A Scalable Framework for Wireless Distributed Computing,” in ACM/IEEE Transactions on Networking, vol. 25, no. 5, pp. 2643–2654, Oct. 2017.[29]
  • N. Naderializadeh, M. Maddah-Ali, and A. S. Avestimehr, “Fundamental Limits of Cache-Aided Interference Management,” in IEEE Transactions on Information Theory, vol. 63, no. 5, pp. 3092–3107, May 2017.[30]
  • S. Li, M. A. Maddah-Ali and A. S. Avestimehr, “Coding for Distributed Fog Computing,” IEEE Communications Magazine, vol. 55, no. 4, pp. 34–40, April 2017.[31]
  • Q. Yu, M. A. Maddah-Ali and A. S. Avestimehr, “Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication,” in NIPS, 2017.
  • C. Geng, N. Naderializadeh, A. S. Avestimehr, and S. Jafar, “On the Optimality of Treating Interference as Noise,” IEEE Transactions on Information Theory, Vol 61, No 7, 2015.
  • N. Naderializadeh and A.S. Avestimehr, “ITLinQ: A New Approach for Spectrum Sharing in Device-to-Device Communication Systems,” IEEE Journal on Selected Areas in Communications Special Issue on 5G Wireless Communication Systems, Vol 32, No 6, 2014.
  • I. Shomorony and A. S. Avestimehr, “Worst-Case Additive Noise in Wireless Networks,” IEEE Transactions on Information Theory, Vol 59, No 6, June 2013
  • I. Shomorony and A. S. Avestimehr, “Two-Unicast Wireless Networks: Characterizing the Degrees-of-Freedom,” IEEE Transactions on Information Theory, Vol 59, No 1, January, 2013.
  • V. Aggarwal, A. S. Avestimehr, and A. Sabharwal, “On Achieving Local View Capacity Via Maximal Independent Graph Scheduling,” IEEE Transactions on Information Theory, Special Issue on Interference Networks, Vol 57, No 5, May 2011.
  • A. S. Avestimehr, S. N. Diggavi, and D. N. C. Tse, “Wireless Network Information Flow: A Deterministic Approach,” IEEE Transactions on Information Theory, Vol. 57, No. 4, pp. 1872–1905, April 2011.

References

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  1. ^ a b “President Obama Honors Outstanding Early-Career Scientists”
  2. ^ a b “James L. Massey Research & Teaching Award for Young Scholars”
  3. ^ a b “Cornell Chronicle"Salman Avestimehr receives NSF early career award”
  4. ^ a b “NSF:"Award Abstract #0953117”
  5. ^ a b A. Avestimehr Salman; Suhas N. Diggavi; Chao Tian; David N. C. Tse (2015). An Approximation Approach to Network Information Theory. Now Publishers. p. 198. ISBN 978-1-68083-026-2.
  6. ^ “USC-Amazon Center for Secure and Trusted Machine Learning”
  7. ^ “USC Viterbi Faculty Directory”
  8. ^ “USC-Amazon Center on Secure and Trusted Machine Learning”
  9. ^ “FedML, Inc.”
  10. ^ “Professor Avestimehr publications and research”
  11. ^ a b Laboratory for Information and Decision Systems: Coded Computing: A Transformative Framework for Resilient, Secure, and Private Distributed Learning
  12. ^ “Institute of Electrical and Electronics Engineers ”
  13. ^ a b 2020 Newly Elevated Fellows
  14. ^ “Berkeley EECS:"Wireless network information flow: a deterministic approach”
  15. ^ “MIT LABORATORY FOR INFORMATION & DECISION SYSTEMS”
  16. ^ “Avestimehr Linkedin Profile”
  17. ^ “University of Minnesota: "Department of Electrical and Computer Engineering”
  18. ^ “The Okawa Foundation for Information and Telecommunications”
  19. ^ “Communications Society & Information Theory Society Joint Paper Award”
  20. ^ “Iranian Who's who”
  21. ^ I. Shomorony; A. Avestimehr Salman (2014). Multihop Wireless Networks: A Unified Approach to Relaying and Interference Management. Now Publishers. p. 131. ISBN 978-1-60198-904-8.
  22. ^ Songze Li; A. Avestimehr Salman (2020). Coded Computing. Now Publishers. p. 148. ISBN 978-1-68083-704-9.
  23. ^ Kiana Avestimehr & A. Avestimehr Salman (2020). Problem Solving Strategies for Elementary-School Math. Now Publishers. p. 124. ISBN 978-1-68083-984-5.
  24. ^ “Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge”
  25. ^ “FedML: A research library and benchmark for federated machine learning”
  26. ^ “Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy”
  27. ^ “Illinois experts:" Vector quantization for bandwidth-efficient gradient aggregation in distributed CNN training”
  28. ^ “ResearchGate:"A Fundamental Tradeoff between Computation and Communication in Distributed Computing”
  29. ^ “IEEE/ACM Transactions on Networking Journal ”
  30. ^ “IEEE Digital Library”
  31. ^ “Semantic Scholar:"Coding for Distributed Fog Computing”
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