ASME

Journal of Computing and Information Science in Engineering

Tonya  
Custis Ph.D.
CustisPS

Areas of Interest

Generative AI
Geometric Deep Learning
Geometry Understanding
Information Retrieval
MACHINE LEARNING
Natural Language Processing & Understanding
Autodesk
Dr. Tonya Custis has over 15 years of experience performing Artificial Intelligence research and leading AI research teams & projects at Autodesk, Thomson Reuters, eBay, and Honeywell. Tonya earned a Ph.D. in Linguistics, an M.S. in Computer Science, and an M.A. in Linguistics, all from the University of Minnesota. She also has a B.A. in Music from the University of Connecticut. Her research interests include Natural Language Processing & Understanding, Information Retrieval, Machine Learning, Geometric Deep Learning, Geometry Understanding, and Generative AI. In her current position as the Director of AI Research at Autodesk, she leads a team of research scientists carrying out foundational and applied algorithmic research in Artificial Intelligence in the context of the Manufacturing, AEC, and Media & Entertainment industries.

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Announcements

July 17 Spotlight: “Information Embedding in Additively Manufactured Parts Through Printing Speed Control” 

A recording is now available for the July 17, 2024 JCISE Spotlight talk by Professor Jitesh Panchal on paper co-authored with Karim A. ElSayed entitled “Information Embedding in Additively Manufactured Parts Through Printing Speed Control” J. Comput. Inf. Sci. Eng. J. Comput. Inf. Sci. Eng. Jul 2024, 24(7): 071005 (10 pages) Paper No: JCISE-23-1496 https://doi.org/10.1115/1.4065089.

Announcements

June 18, 2024 Spotlight: “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset”

A recording is now available on Youtube for the June 18, 2024 Spotlight talk by Professor Christian Soize (Université Gustave Eiffel) on his paper “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset,”

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