For more recent updates and information, please check my Curriculum Vitae.
I'm currently a Research Scientist at Intel Labs. I received my Ph.D. from the Computer Science Department at Purdue University, and my M.S. in statistics and computer science from Purdue University. Before joining Intel Labs, I had the opportunity to work at a number of industrial research labs including Facebook data science, Adobe research Labs, Technicolor research and innovation Labs, Intel data analytics, and the data mining and computer modeling center of excellence in Egypt.
My research lies in machine learning and data mining and spans the theory and algorithms of large-scale graph mining, statistical machine learning, and their applications in social and information networks. I have authored numerous papers and tutorials in top-tier conferences and journals. My research was selected among the best papers of ICDM in 2015, BigMine in 2012, and covered by popular press such as the MIT Technology Review. I'm honored to be selected by UC Berkeley among the rising stars in computer science and engineering in 2014. In addition, I co-founded the open source network repository project, the first data repository with interactive visual graph analytics to help researchers find, explore, and understand graph data in real-time over the web.
New paper on "Exact and Estimation of Local Edge-centric Graphlet Counts" accepted at the SIGKDD workshop on big data Bigmine
New paper on "Relational Similarity Machines" accepted at the SIGKDD workshop on mining and learning with graphs MLG
Invited panelist at Stanford AI Lab Summer Outreach SAILORS
Invited Panelist/Mentor at AAAI 2016 women's event
New paper on "An Interactive Data Repository with Visual Analytics" accepted at ACM SIGKDD Explorations Newsletter
Parallel Parameterized Graphlet Decomposition (PGD) Library - link
Tutorial - with Mohamed Hasan and Jennifer Neville at SDM 2015 Methods and Applications of Network Sampling
Rising Stars Award in EECS, UC Berkeley
New paper Role Discovery in Networks accepted at IEEE Transactions on Knowledge and Data Engineering (TKDE)