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Jay Urbain, Ph.D. - Milwaukee School of Engineering. Milwaukee, WI, US

Jay Urbain, Ph.D.

Adjunct Professor | Milwaukee School of Engineering


Dr. Jay Urbain is an expert in machine learning, data science, artificial intelligence, database systems, and software architecture.

Education, Licensure and Certification (5)

Ph.D.: Computer Science, Illinois Institute of Technology 2008

MBA: Business, University of Wisconsin-Madison 1997

MSEE: Computer Engineering, Illinois Institute of Technology 1989

B.S.: Engineering, Illinois Institute of Technology 1981

B.S.: Biology, Northeastern Illinois University 1979

Areas of Expertise (7)

Artificial Intelligence

Computer Science

Data Science

Database Systems

Mobile Computing

Software Architecture

Web Development

Accomplishments (1)

Karl O. Werwath Engineering Research Award, MSOE (professional)



Research Grants (5)

Identifying Opioid Response Phenotypes in Low Back Pain Electronic Health Data

NIH Career Grant Mentor for Dr. Meredith Adams 


Patient Centric Medical Records Translation

NIH CTSI Pilot Award Grant 

April 2018-2019, joint-PI

Honeywell, Big Data: Machine Learning of Structured and Unstructured Data

National Nuclear Security Administration 

2017, Principal investigator

Honeywell, Data Modeling and Predictive Analytics of Sensor Data

National Nuclear Security Administration 

2016, Principal investigator

Participated in the Biomedical Informatics Working Group, and Compiled the Biomedical Informatics Section of the Grant

NIH CTSA Grant, CTSI of SE Wisconsin 

2015-2018, Co-lead, Biomedical Informatics

Selected Publications (5)

Natural interfaces for evaluation and management of shoulder dysfunction

Innovation in Aging

Korducki, J., Tidemann, A., Tarima, S., Grindel, S., Urbain, J., Mickschl, D., Rosenthal, A., Burns, E.


Shoulder dysfunction affects >50% of individuals >/=60 years. Physical therapy (PT) is an effective and standard treatment component, yet adherence is low and knowledge on home exercise adherence is sparse. We developed an exploratory study to determine whether supplementing PT with objective visual feedback increases adherence to PT and home exercise (HE). Novel software was developed for a motion-sensing Kinect camera, translating video images of patients performing standard range of motion (ROM) maneuvers into skeletal avatars. Images were recorded, stored, and played back, providing objective visual documentation of progress. Participants were randomized to the intervention group (IG, viewed current and previous images a teach of 4 study visits) or control group (CG, imaged but did not view clips). All participants completed questionnaires regarding daily function, perceived improvement, PT effectiveness, and time spent on HE. 21 patients mean age 66.8 + 6.5 years attending physical therapy for first-time management of shoulder impairment were consented and enrolled. IG and CG were similar in age, demographics, and dysfunction due to shoulder impairment. IG spent more time performing HE, 42.1+/-29.8 min/day vs. 19.1+/-8.2min/day (p = 0.04, 2-sample t test). IG experienced 0% study dropout rate vs. 40% for CG (Fisher exact test; p=0.04). Adjusting for baseline ROM, IG demonstrated a trend towards greater gain in forward elevation ROM from visit to visit, compared to CG (9.25o vs.0.49o, p=0.17). Providing objective visual feedback on progress was associated with greater adherence to HE, lower attrition rates from PT, and possibly greater increases in ROM recovery.

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Distributional semantic concept models for entity relation discovery

Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

Urbain, J., Bushee, G., Kowalski, G.


We present an ad hoc concept modeling approach using distributional semantic models to identify fine-grained entities and their relations in an online search setting. Concepts are generated from user-defined seed terms, distributional evidence, and a relational model over concept distributions. A dimensional indexing model is used for efficient aggregation of distributional, syntactic, and relational evidence. The proposed semi-supervised model allows concepts to be defined and related at varying levels of granularity and scope. Qualitative evaluations on medical records, intelligence documents, and open domain web data demonstrate the efficacy of our approach.

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User-driven relational models for entity-relation search and extraction

JIWES '12 Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search

Urbain, J.


The ability to extract new knowledge from large datasets is one of the most significant challenges facing society. The problem spans across domains from intelligence analysis and scientific research to basic web search. Current information extraction and retrieval tools either lack the flexibility to adapt to evolving information needs or require users to sift through search results and piece together relevant information. With so much data compounded by the criticality of finding relevant information, new tools and methods are needed to discover and relate relevant pieces of information in ever expanding repositories of data.

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Exploring contextual models in chemical patent search

Information Retrieval Facility Conference

Urbain, J., Frieder, O.


We explore the development of probabilistic retrieval models for integrating term statistics with entity search using multiple levels of document context to improve the performance of chemical patent search. A distributed indexing model was developed to enable efficient named entity search and aggregation of term statistics at multiple levels of patent structure including individual words, sentences, claims, descriptions, abstracts, and titles. The system can be scaled to an arbitrary number of compute instances in a cloud computing environment to support concurrent indexing and query processing operations on large patent collections.

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A dimensional retrieval model for integrating semantics and statistical evidence in context for genomics literature search

Computers in Biology and Medicine

Urbain, J., Goharian, N., Frieder, O.


We present a dimensional information retrieval model for combining concept-based semantics and term statistics within multiple levels of document context to identify concise, variable length passages of text that answer a user query. Our results demonstrate improved search results in the presence of varying levels of semantic evidence, and higher performance using retrieval functions that combine document, as well as sentence and passage level information. Experimental results are promising. When ranking documents based on the most relevant extracted passages, the results exceed the state-of-the-art by 15.28% as assessed by the TREC 2005 Genomics track collection of 4.5 million MEDLINE citations.

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