Ahmad Beirami received the B.Sc. degree in electrical engineering from Sharif University of Technology, Tehran, Iran, in 2007 and the M.Sc. and Ph.D. degrees in electrical and computer engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 2011 and 2014, respectively. He is currently a postdoctoral fellow with the school of engineering and applied sciences at Harvard University, and also with electrical engineering and computer science department at MIT. Previously, he was a postdoctoral associate at Duke University. His research interests broadly include information theory, statistics, machine learning, and networks. He is the recipient of the 2013-2014 School of ECE Graduate Research Excellence Award and the 2015 Sigma Xi Best Ph.D. Thesis Award from the Georgia Institute of Technology.
Data-dependent randomized features for generalizability in large-scale supervised learning:
S. Shahrampour, A. Beirami, and V. Tarokh,
"On data-dependent random features for improved generalization in supervised learning,"
accepted in Proc. of The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018).
Computationally efficient approximate cross validation and parameter tuning in supervised/unsupervised learning:
A. Beirami, M. Razaviyayn, S. Shahrampour, and V. Tarokh,
"On optimal generalizability in parametric learning,"
accepted in Proc. of 2017 Advances in Neural Information Processing Systems (NIPS 2017).
Measuring data security against brute-force cyberattack:
A. Beirami, R. Calderbank, M. Christiansen, K. Duffy, and M. Médard,
"A characterization of guesswork on swiftly tilting curves,"
submitted to IEEE Transactions on Information Theory, 2017 (Short version appeared in Allerton 2015).
Information-theoretic techniques for converse sample complexity bounds in supervised learning:
M. Nokleby, A. Beirami, and R. Calderbank,
"Rate-distortion bounds on Bayes risk in supervised learning,"
submitted to IEEE Transactions on Information Theory, 2016 (Short version appeared in ISIT 2016).
Fast data compression in large-scale networks using unsupervised learning:
A. Beirami, M. Sardari, and F. Fekri,
"Packet-level network compression: realization and scaling of the network-wide benefits,"
IEEE/ACM Transactions on Networking, vol. 24, no. 3, pp. 1588-1604, June 2016 (Short version appeared in INFOCOM 2012).
See my Google Scholar
page for a complete list of publications.