Welcome to my
web-page. I am a PhD student in the
Machine Learning Department. I am currently working with
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 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)