Data Science & Engineering @ WNCG

The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, with applications to solving complex engineering problems. Topics include modeling and analysis of complex networks, both social and physical, design of large-scale recommender systems, analysis of heterogeneous and complex EHR and other health data, learning from distributed sensor data acquired from intelligent transportation systems, etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational aspects associated with such problems. We have also deployed several data-driven solutions that interface with real cyber-physical systems.


Prof. Alex Dimakis has 6 papers accepted at ICML, 2017. Congrats! This is a top international conference on machine learning.

Prof. Ghosh appointed as Chief Scientific Officer of CognitiveScale. CognitiveScale incorporates several AI technologies, including advanced machine learning, for scalable, enterprise-level solutions, and counts several Fortune 500 Companies among its clients. Prof. Ghosh will continue to be a faculty at UT; however this engagement showcases the relevance of our group in enabling innovative and industry-strength products and solutions.

Team headed by Prof. Ghosh receives 2017 Distinguished Clinical Informatics Research Paper Award at AMIA for "PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature"

Alumna Suju Rajan receives the 'Best Paper Award at RecSys14 for "Beyond Clicks: Dwell Time for Personalization"

Joyce Ho and Yubin Park are 2014 Code-a-Palooza Winners at Datapalooza 2014