Mahdi Namazifar

Mahdi Namazifar, PhD

         Email: < my first name > AT < my last name > DOT com


About Me




Work Experience


Platform Data Science Tech Lead (April 2019 - Present:) Led variaous critical cross-organizational and high impact projects at Uber using advanced scientific techniques in collaboration with several engineering and data science teams, as well as executive leadership members.
NLP and Conversational AI Tech Lead: Helped with building out Uber's NLP and conversational AI platform. We brought the state of the art in NLP and conversational AI to Uber. Organized and co-chaired the first and second Uber Science Symposium series. Co-authored Uber's open source research dialogue system, Plato.

I worked on applications of deep learning in natural language processing (NLP) and other areas. Some of my works for Twitter include: a deep neural architecture model for classification of abusive tweets; a deep learning based Named Entity Recgonition (NER) systems for tweets; vectorizing users based on the Twitter's follow graph using skipgram; a language model for sequence of actions in user sessions based on Recurrent Neural Networks (RNNs) for sessions clustering and action prediction; a deep encoder-decoder based model for vectorizing tweets; and a video recommendation engine for promoted videos.

I was a member of the Talos team of the Security Business Group. Using the Big Data technology and machine learning techniques I contributed to designing, building, and improving Cisco’s security appliances and technologies.

I performed applied research on a variety of predictive analytics problems coming from different industries such as healthcare and finance. I developed predictive signals and machine learning models for problems such as hospital readmission predictions for a major U.S. hospital chain and predicting the probability of credit default for a major U.S. bank.

My research was on parallel branch and bound algorithms for mixed integer linear programming problems and also primal heuristics for these problems. I developed two novel primal heuristics for integer programming, namely Randomized Rounding and Pivot-and-Fix. These primal heuristcs have been in a part of COIN-Cbc since 2009.

Under the NSF grant for Cyberinfrastructure Experience for Graduate Students (CIEG) I did research on high performace computing for mixed integer programming. I developed PMaP (Parallel Macro Partitioning) which is a parallel solver for mixed integer programs on shared-memory parallel computing frameworks.

As a PhD student, I performed theoretical and computational research on different aspects of mathematical optimization including mixed integer linear and nonlinear programming, parallel computing in mathematical programming, and global optimization. I also worked on the application of parallel computing in mixed integer linear programming. My PhD thesis was on theoretical and computational aspects of linear convexifications for multilinear function in optimization problems. I studied the strength of convex relaxations for nonconvex functions in general and multilinear functions in specific.


Consulting Experience


I built deep learning models for early detection of cancer based on genome data.





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Conference Presentations