Team Members

Each of our researchers are experts in emerging technologies such as machine learning algorithms, forecasting models, intelligent image and video search, automated scene analysis, voice biometrics, recommender systems, personalization, and deep metadata. Combined they hold over 100 US patents across their areas of specialty, and have contributed to and presented more than 50 research projects.

We are a distributed team, with locations across the United States such as Washington, DC,  Philadelphia, Chicago, Denver and Silicon Valley,

Group Leads

Amit Bagga, Ph.D., VP Research

Jan Neumann, Ph.D., Director, Technical R&D

Applied Machine Learning 

Bernard Burg, Ph.D., Senior Manager, Data Science

Patrick Dwyer, CORE Tech Intern

Oliver Jojic, Distinguished Engineer

Ryan March, Software Engineer

Abhijit Mulye,Lead Engineer, Machine Learning

Nicholas Pinckernell, Senior Principal Engineer

Vamsi Potluru, Ph.D., Senior Researcher

Abel Villca Rocque, Principal Engineer, Machine Learning

Brian Xu, Ph.D., Senior Data Scientist

Natural Language Processing & Content Discovery

Hassan Sayyadi, Ph.D., Senior Manager

Nima Bina, Senior Engineer

James Cahill, Principal Engineer

Anupam Guha, Senior Researcher

Craig Murray, Lead Engineer

Raul Guerra Paredes, Engineer, NLP

Robert Rubinoff, Ph.D., Principal Researcher II

Vikrant Sagar, Lead Engineer NLP

Shahin Sefati, Ph.D., Senior Researcher

Ferhan Ture, Ph.D., Lead Researcher

Media Analytics

Faisal Ishtiaq, Ph.D., Director, Technical R&D

Tony Braskich, Senior Research Engineer

Richard Li, Ph.D., Senior Researcher

Isselmou Maloum, Ph.D., Senior Researcher

Ehsan Younessian, Ph.D., Lead Researcher

Video Analytics

Hongcheng Wang, Ph.D., Manager Research

Mahmudul Hasan, Ph.D., Senior Researcher

Cuong Vu, Ph.D., Computer Vision Engineer

PhD Interns 2017

Sardar Hamidian, George Washington, University

Mohamed Ibrahim, Rutgers University

Upal Mahbub, University of Maryland, College Park

Md Iftekhar Tanveer, University of Rochester

Jinfeng Rao, University of Maryland, College Park

Weiwei Yang, University of Maryland, College Park

Ruichi Yu, University of Maryland, College Par

Comcast Applied Artificial Intelligence Research

The Comcast Applied Artificial Intelligence Research Team invents the technological foundations for the Xfinity experiences of the future. Besides long-term research efforts in NLP, computer vision, and machine learning (with a focus on deep learning), we support the Comcast Technology & Products organization through innovations and technical expertise in these product domains:

Voice: Communicate with your TV, mobile device or home using natural language.

Scene-Level Metadata: Understand content at a deeper level to label and categorize the moments and segments of interest with computer vision and machine learning.

Personalization for Search & Recommendations: Improve the quality of search, recommendations, predictive analytics, and more using advanced machine learning techniques.

Smart Home Video Analytics & IoT:  Build intelligent product thats secure your home and anticipate your needs using machine learning.

Knowledge Representations:  Organize and structure information using novel representations that allow for more human-like queries and insights.

Customer Care:  Improve customer service experiences or automatically detect and repair outages before they happen with advanced machine learning models.

Selected Publications & Talks

The Comcast Labs DC Researchers are experts in their fields of study and are sought after to present at academic and industry conferences:

Talking to Your TV: Context-Aware Voice Search with Hierarchical Recurrent Neural Networks. Jinfeng Rao, Ferhan Ture, Hua He, Oliver Jojic, and Jimmy Lin, In Proc. of International Conference on Information and Knowledge Management (CIKM 2017) 

No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering “Simple” Questions), Ferhan Ture and Oliver Jojic, In Proc. of Empirical Methods in NLP (EMNLP 2017)

How AI Powers the Comcast X1 Voice Interface, Jan Neumann, Ferhan Ture & Oliver Jojic, O’Reilly AI Conference, June 2017, New York NY [Conference link]

Data Science & Machine Learning to Improve the Customer Experience, Jan Neumann, Business Analytics Innovation Summit,May 2017, Chicago,IL [Conference Schedule]

How GPUs Power Comcast’s X1 Voice Remote and Smart Video Analytics, Jan Neumann, GPU Technology Conference, May 2017, San Jose, CA [Conference Link]

How AI Powers the X1 Entertainment System. Jan Neumann, AI Summit NYC, Dec 2016, New York, NY [Conference web site]

Learning to Translate for Multilingual Question Answering. Ferhan Ture and Elizabeth Boshee, 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX [PDF]

3rd Workshop on Recommender Systems for TV and Online Video (RecSysTV 2016) organized by Jan Neumann, John Hannon, and Hassan Sayyadi  in conjunction with ACM RecSys 2016, Boston, MA

Ask Your TV: Real-Time Question Answering with Recurrent Neural Networks. Ferhan Ture and Oliver Jojic, SIGIR 2016, Pisa, Italy [ACM DL] [Arxiv pdf]

Learning Temporal Regularity in Video Sequences. Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K. Roy-Chowdhury, Larry S. Davis, CVPR 2016. [PDF] [Supplementary] [Project Page] [Code] [Video1] [Video2] [Poster]
arXiv:1604.04574 [PDF-Preprint]

How to Fake Multiply with a Gaussian Matrix. Michael Kapralov, Vamsi K. Potluru and David P. Woodruff, ICML 2016. (paper, code, arxiv)

Structured TV Shows — “You have been Chopped”. Ferhan Ture, Jonghyun Choi, Hongcheng Wang and Vamsi K. Potluru, ICML workshop 2016. (pdf)

Data Science and Machine Learning to Improve the Customer Experience. Jan Neumann, Business Analytics Summit – Innovation Enterprise, Chicago, IL, May 2016

How Automatic Content Analytics enables the TV Experiences of the Future. Jan Neumann, INTX 2016, Boston, MA

How Comcast Uses Data Science and ML to Improve the Customer Experience. Jan Neumann, Global Big Data Conference March 2016, Santa Clara, US

Knowledge Transfer with Interactive Learning of Semantic Relationships. Jonghyun Choi, Sung Ju Hwang, Leonid Sigal and Larry S. Davis,
AAAI Conference on Artificial Intelligence (AAAI) 2016 (PDF)

How Spark is working out at Comcast Scale. Sridhar Alla and Jan Neumann, Strata Hadop NYC 2015

Recommendations for Live TV. Jan Neumann and Hassan Sayyadi, RecSys 2015, Vienna Austria

2nd Workshop on Recommender Systems for TV and Online Video (RecSysTV 2015) organized by Jan Neumann, Hassan Sayyadi, John Hannon, Roberto Turrin, and Danny Bickson in conjunction with ACM RecSys 2015, Vienna, Austria

Data Science for Customer Service at Comcast. Jan Neumann, Dato Data Science Summit, San Francisco 2015 (Video)

Comcast – Real-time Recommendations with Spark. Jan Neumann, DC Spark Interactive Meetup, Washington, DC, May 2015 (Video)

Real-time Recommendations using Apache Spark. Jan Neumann &  Sirdhar Alla, Spark Summit East 2015 (Video)

1st Workshop on Recommender Systems for TV and Online Video (RecSysTV 2014) organized by Jan Neumann, Hassan Sayyadi, John Hannon, Roberto Turrin, and Danny Bickson in conjunction with ACM RecSys 2014, Foster City, USA