Enhancements    

Applied Artificial Intelligence (AAI)
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Description

The AAI Community of Practice is comprised of faculty, staff, graduate students, government partners, and private industry sponsors. Members come from diverse academic fields but are all interested in the research and application of Artificial Intelligence to multiple domains which includes, but is not limited to healthcare, medicine, systems engineering, education, as well as testing and evaluation of autonomous systems.

Aspirations

Members of the AAI Community of Practice aspire to afford students, partners, and faculty exemplary opportunities to enhance understanding, identify funding sources, and network with experts in the field of applied artificial intelligence and machine learning. We plan to do this through discourse, seminars, conferences, class projects, and guest speakers interested in identifying and expanding the domains which may benefit from applying AI and Machine Learning to society’s grand engineering challenges. Join Zoom Meeting https://utep-edu.zoom.us/j/82560832530?pwd=d0pGdS9neElvdVVWY3Zub3l0MHI1Zz09 Meeting ID: 825 6083 2530 Passcode: ZT5NPy6B

KEYWORDS

  1. Artificial Intelligence
  2. Artificial Neural Networks
  3. Cluster Analysis
  4. Convolutional Neural Networks
  5. Data science
  6. Deep Learning
  7. Machine Intelligence
  8. Machine Learning
  9. Supervised and unsupervised learning

COMMUNITY MEMBERS

Sang, Yuanrui
Member
Assistant Professor
Engineering - Electrical and Computer Engineering
flexible power transmission systems, power system economics, power system reliability and resilience, power system optimization, renewable energy for protecting the healthy environment, artificial intelligence, data analytics, high-performance computing
Featured: Awards

Shuvo, Md Maruf Hossain
Member
Assistant Professor
Engineering - Electrical and Computer Engineering
Applied machine learning, Deep learning, Edge AI, Biomedical signal and image analysis, Circuits and devices for AI, Application-specific integrated circuits

Smith, Eric
Member
Associate Professor
Engineering - Industrial, Manufacturing, and Systems Engineering (IMSE)
Systems Engineering, Industrial Engineering, Tradeoff Studies, Cognitive Biases

Velez-Reyes, Miguel
Member
Professor
Engineering - Electrical and Computer Engineering
Signal Processing and Machine learning; Remote Sensing; Hyperspectral Imaging; Cyber-physical systems; Space Domain Awareness; Data Science
Featured: Awards

MEMBERS FROM PARTNER INSTITUTIONS
Jesus, Jimenez  (View)
Texas State University San Marcos
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Lucero, Sergio  (View)
Barrio Technologies
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Montoya, Yazmin  (View)
Intel Corporation
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Quiros, Ondrea  (View)
El Paso Community College
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Salcedo, Andrea E.  (View)
City of El Paso Public Health
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Servin, Christian  (View)
El Paso Community College
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Villegas, Oscar  (View)
University of Texas at El Paso
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National Artificial Intelligence Research Institutes
External Resource
Other
Resource Link

The President’s Council of Advisors for Science and Technology has published Recommendations for Strengthening American Leadership in Industries of the Future, including AI, and calls for new and sustained research in AI to drive science and technology progress. The National AI Research Institutes program enables longer-term research and U.S. leadership in AI through the creation of AI Research Institutes. New to the program this year are contributions from partners in U.S. industry who share in the government’s goal to advance national competitiveness through National AI Research Institutes. This year’s industry partners are Accenture, Amazon, Google, and Intel Corporation.

artificial intelligence for all (AI4all)
UTEP Resource
Education
Resource Link

AI4ALL Summer Program is a UTEP resource for increasing El Paso High School students' awareness of Artificial Intelligence and how it can help the Borderland and the world.

The National Science Foundation for Engaged Learning
External Resource
Education
Resource Link

The National Science Foundation AI Institute for Engaged Learning is guided by a vision that supports & extends the capabilities of teachers and students using AI. The Institute conducts research on AI-driven narrative-centered learning environments with embodied conversational agents and powerful multimodal sensing capabilities to create deeply engaging collaborative story-based learning experiences. The Institute’s AI-driven learning environments build on its advances in core AI on natural language processing, computer vision, and machine learning.

Udemy course: AI A to Z: learn how to build an Artificial Intelligence
External Resource
Education
Resource Link

Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! (Course cost is $23.99)

DIALOG SOURCES

I3 Move Communities provide a way to share expertise, resources, and community information to the general public. There are numerous tools available, both open source and proprietary, for engaging in dialog and sharing information and knowledge within a closed group.

This community engages in dialog as follows:

None specified to date.