Topics of expertise 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, multi-modal machine learning, etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and hardware/software implementational aspects associated with such problems. We have also deployed several data-driven solutions that interface with real cyber-physical systems.

Four papers from UT-MINDS authors at AISTATS 2018! Congratulations to Rahi Kalantari, Rajiv Khanna and Rajat Sen!

Four papers from UT-MINDS authors at AISTATS 2018! (Numbers 60, 121, 272 & 637, AISTATS 2018 Accepted Papers) Congratulations to Rahi Kalantari, Rajiv Khanna and Rajat Sen!

Four papers from UT-MINDS authors at AISTATS 2018! (Numbers 60, 121, 272 & 637, AISTATS 2018 Accepted Papers) Congratulations to Rahi Kalantari, https://rjvak7.github.io Rajiv Khanna and Rajat Sen!

Four papers from UT-MINDS authors at AISTATS 2018! (Numbers 60, 121, 272 & 637, AISTATS 2018 Accepted Papers) Congratulations to Rahi Kalantari, [[https://rjvak7.github.io Rajiv Khanna] and Rajat Sen!

Four papers from UT-MINDS authors at AISTATS 2018! (Numbers 60, 121, 272 & 637, AISTATS 2018 Accepted Papers) Congratulations to Rahi Kalantari and Rajat Sen!

Two papers at ICML 2018 from UT-MINDS authors!

Two papers at ICML 2018, a top ML conference, from UT-MINDS authors!

Two papers from UT-MINDS authors at AISTATS 2018! (Numbers 272 & 637, AISTATS 2018 Accepted Papers) Congratulations to Rahi Kalantari and Rajat Sen!

UT-MINDS invites industry partners through its AFFILIATES program.

UT-MINDS invites industry partners through its AFFILIATES program.

UT-MINDS invites industry partners through its AFFILIATES program.

Profs. Ghosh, Dimakis and Shakkottai and their students to present an unprecedented 6 papers at AISTATS, April 2017. AISTATS is among the top venues for research on machine learning.

Profs. Ghosh, Dimakis and ShakKottai and their students to present an unprecedented 6 papers at AISTATS, April 2017. AISTATS is among the top venues for research on machine learning.

Four papers from UT-MINDS authors at NIPS 2017.

One of the papers was selected for a full oral presentation (40 out of 3240 submitted papers were selected).

Congratulations to our students:

Ethan Elenberg,Karthikeyan Shanmugam, Murat Kocaoglu, Qi Lei and Rajat Sen !

NIPS is a top international research conference on machine learning with an acceptance rate below 21%.

Machine Learning for Healthcare startup Accordion Health acquired by publicly listed health analytics company, Evolent Heath. All 3 founders of Accordion Health are from UT-MINDS.

Machine Learning for Healthcare startup Accordion Health acquired? by publicly listed health analytics company, Evolent Heath. All 3 founders of Accordion Health are from UT-MINDS.

Machine Learning for Healthcare startup Accordion Health acquired by publicly listed health analytics company, Evolent Heath.?. All 3 founders of Accordion Health are from UT-MINDS.

The WNCG initiative in Machine INtelligence and Decision Systems (UT-MINDS) involves research in large-scale data mining and machine learning, with applications to solving complex engineering or business problems. Going beyond just algorithmic/statistical machine learning, we specialize in reliable and effective design of full-stack systems, from acquisition and conditioning of sensory and other high-volume/disparate data sources, through to continual learning and decision making logic built on top of learning systems.

Topics of expertise 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, multi-modal machine learning, etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and hardware/software implementational aspects associated with such problems. We have also deployed several data-driven solutions that interface with real cyber-physical systems.

Three papers from UT-MINDS authors at NIPS 2017. Congrats! This is a top international conference on machine learning.

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

UT-MINDS (Machine INtelligence and Decision Systems) @ WNCG

The WNCG initiative in Machine INtelligence and Decision Systems (UT-MINDS) 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. 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.

Prof. Ghosh gives Plenary Talks at TCS, Mumbai, and at Univ. of Minnesota's Cray Distinguished Lecture series, on Extracting Meaningful Insights from Complex Medical Data

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"

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 ?

 >><<

Team headed by Prof. Ghosh receives 2017 Distinguished Clinical Informatics Research Paper Award at AMIA for ? >><<

Prof. Ghosh receives 2015 IEEE CS Technical Achievement Award for his work on multi-learner systems

Recent Graduates Accept Faculty Positions at Illinois-Urbana, Cornell and Emory

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

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 http://healthdatapalooza.org/news/ \ Datapalooza 2014

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.

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, 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 solutions that interface with real cyber-physical systems.

Data Science & Engineering @ WNCG

"Making Big Data Digestible" Panel at SXSW 2015

Making Big Data Digestible Panel at SXSW 2015

Making Big Data Digestible Panel at SXSW 2015

Making Big Data Digestible Panel at SXSW 2015]]

'Making Big Data Digestible' Panel at SXSW 2015]]

The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data 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 solutions that interface with real cyber-physical systems as well as the ambient world of data, signals and control.

Edison Series Brings Big Data to Middle-School and High-School Students http://wncg.org/news/edison-series-brings-big-data-middle-school-and-high-school-students

Digesting Big Data Panel at SXSW 2015 https://wncg.org/news/wncg-faculty-and-students-showcase-innovation-during-sxsw-2015

Data Science & Engineering @ WNCG

The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data 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 solutions that interface with real cyber-physical systems as well as the ambient world of data, signals and control.

The Data Science & Engineering initiative currently involves over 30 graduate students within WNCG. The listed faculty members also supervise several students from other areas of ECE, as well as from other departments at UT such as Computer Sciences, Statistical and Data Sciences and Biomedical Engineering.

Recent graduates are working at companies such as Google, Yahoo, Qualcomm, Facebook and Microsoft Research, or serving in faculty positions at top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS) ADD MORE.

The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc.Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational (Hadoop/Spark; visualization) aspects of these problems, in addition to creating working interfaces with real cyber-physical systems and the ambient world of data, signals and control.

Data Science and Engineering

News

Recently alumni are working at companies such as Google, Yahoo, Qaualcomm, Facebook and Microsoft Research, and or have found faculty positions in top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS) ADD MORE.

The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc.Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational (Hadoop/Spark; visualization) aspects of these problems, in addition to creating working interfaces with real cyber-physical systems and the ambient world of data, signals and control.

This thrust currently involves over 30 graduate students within WNCG. The faculty members listed above also supervise several students from other areas of ECE, as well as from other departments at UT such as Computer Sciences, Statistical and Data Sciences and Biomedical Engineering.

Recently alumni are working at companies such as Google, Yahoo, Qaualcomm, Facebook and Microsoft Research, and or have found faculty positions in top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS) ADD MORE.

The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc.Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational (Hadoop/Spark; visualization) aspects of these problems, in addition to creating working interfaces with real cyber-physical systems and the ambient world of data, signals and control.

This thrust currently involves over 30 graduate students within WNCG. The faculty members listed above also supervise several students from other areas of ECE, as well as from other departments at UT such as Computer Sciences, Statistical and Data Sciences and Biomedical Engineering.

Recently alumni are working at companies such as Google, Yahoo, Qaualcomm, Facebook and Microsoft Research, and or have found faculty positions in top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS) ADD MORE.

The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc.Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational (Hadoop/Spark; visualization) aspects of these problems, in addition to creating working interfaces with real cyber-physical systems and the ambient world of data, signals and control.

This thrust currently involves over 30 graduate students within WNCG. The faculty members listed above also supervise several students from other areas of ECE, as well as from other departments at UT such as Computer Sciences, Statistical and Data Sciences and Biomedical Engineering.

Recently alumni are working at companies such as Google, Yahoo, Qaualcomm, Facebook and Microsoft Research, and or have found faculty positions in top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS) ADD MORE.

Welcome!

< some short blurb >

< some short blurb >

[Link to] Faculty

    < will contain a list of relev faculty and their photos >

[Link to] Projects

    < will contain short descriptions, with pictures, of projects from each of us >

[Link to] Classes

    <  will contain a list of relevant classes, possibly with descriptions >

[Link to] Students, Postdocs and Alumni

    < current and updated lists of students and alums >