Ahmed Hefny Photo

Ahmed Hefny

Machine Learning Department
Carnegie Mellon University

Office: GHC 8223
E-mail: ahefny AT CSdotCMU DOT edu
Home | Publications | Resume | Misc

Welcome to my web-page. I am a PhD student in the Machine Learning Department. I am currently working with Prof. Geoffrey Gordon. My current research focuses on building flexible models for dynamical systems. Previously, I worked on building probabilistic models for textual and social data.

Prior to joining CMU, I got my BSc and MSc in computer engineering from Cairo University, Egypt. My MSc thesis was supervised by Prof. Amir Atiya. While working as a teaching assistant at Cairo University, I was also an R&D engineer at IBM Cairo Technology Development Center and then a research assistant at Cairo Microsoft Innovation Center (currently known as Advanced Technology Labs Cairo) with Dr Kareem Darwish.


  • Our team won the first place in the first neurohackathon hosted by CMU BrainHub.
  • Our paper on predictive belief methods for learning dynamical systems has been accepted in NIPS 2015. (preprint)
  • Our paper on asynchronous variants of variance-reduced SGD has been accepted in NIPS 2015.
  • I spent summer 2015 in Google[x], working on behavior prediction for the self-driving car .
  • Our paper on large scale coordinate-descent with linear coupling constrains has been accepted in UAI 2015.
  • I am TAing spring 2015 offering of 10601 (Machine Learning) with Tom Mitchell and Nina Balcan.
  • I spent summer 2014 in Google Mountain View as a software engineering intern, building models for user prediction.
  • I am TAing fall 2013 offering of 10701 (Machine Learning) with Geoff Gordon and Alex Smola.
  • I spent summer 2013 in a Bing/MSR joint internship in Redmond.
  • My paper with Avinava Dubey, Sinead Williamson and Eric Xing on non-parametric topic modeling over time has been accepted in SIAM Datamining Conference 2013.
  • I spent summer 2012 in Bellevue as a Microsoft Research intern, working with Bing Document Understanding team.


  • Machine Learning (Eric Xing)
  • Intermediate Statistics (Larry Wasserman)
  • Statistical Machine Learning (Larry Wasserman)
  • Graduate Algorithms (Manuel Blum)
  • Optimization (Geoffrey Gordon and Ryan Tibshirani)
  • Multimedia Databases and Data Mining (Christos Faloutsos)
  • Advanced Probability Overview (Jing Lei)
  • Probabilistic Graphical Models (Eric Xing)
  • Spectral Graph Theory (Gary Miller)
  • Deep Learning (Bhishka Raj)
  • Randomized Algorithms and Advanced Optimization (Alexander Smola and Suvrit Sra)
  • Topics in Deep Learning (Ruslan Salakhutdinov)

Last update: Oct 28, 2016