Deep Learning in Computer Vision

Date:       January 16, 2020Time:       6-8 pm

Location:  AT&T Labs, 9505 Arboretum, Austin

 

Presenters: 

Semih Aslan, Ph.D.

Assoc. prof. & Lab Director, Ingram School of Engineering,

Texas State University, San Marcos

 

Vittal Siddaiah, Ph.D.

Ingram School of Engineering,

Texas State University, San Marcos

 

Abstract

Computer Vision has evolved into an interdisciplinary field of science that beams to perform computers process, analyze images and videos to extract information similar to that of human beings.

In recent years, there has been serious interest in the demand for deep learning techniques to the computer vision tasks, particularly in the context of search and social media businesses such as Facebook, Google, and others. These methodologies designed to automatically derive valuable information from images and videos that have been uploaded to the web by users.

Deep Neural Network (DNN) in computer vision has propelled this technology to a different level and made complicated tasks like self-driven cars to the analysis of three-dimensional images from CT scans a possibility. DNN outperforms traditional methods and delivers superior results both in inference accuracy and with reduced latency. Computer Vision, along with DNN, has now reached the stars.

 

Bios:

Dr. Semih Aslan is an Associate Professor of Electrical Engineering at Texas State University, where he joined in 2011. He previously worked as a Senior FPGA Design Engineer with Motorola and full-time instructor at ITT Technical Institute. Dr. Aslan is the founding director of the System Modeling and Green Technology (SMART) Lab in the Ingram School of Engineering at Texas State. He currently advises graduate and undergraduate senior students on green energy, multi-processor system design and data analysis projects and has numerous publications.

Dr. Semih Aslan received a B.Sc. degree in electrical engineering from Istanbul Technical University in 1994, M.Sc. degree in electrical engineering from Illinois Institute of Technology in 2003, and Ph.D. degree in computer engineering from Illinois Institute of Technology in 2010. He is a Senior IEEE member.

 

 

Vittal Siddaiah, Ingram School of Engineering, Texas State, San Marcos

Vittal is system engineer at Intel with 15 years of experience in silicon validation.

He led the design of machine learning strategies based Regression Tool Suite for emulation and post-silicon validation, enhancing performance, and reducing the time to triage. He is distinguished for his contributions to the high-performance design of tools in the field of data-analytics and measurements.  He has earned several recognitions and awards, including “One Generation Ahead Award” and  “Waste Elimination Award.”

Vittal is passionate about mentoring engineers and students.  He has won the “Best Trainer Award” at Intel.  Some of the domains include Hardware-software co-design, Operations Research, Image Processing, Operating System, Python, and C++.

Vittal has Bachelors in Electronics Engineering, and Masters in Management, M Phil in Management, Masters in Mathematics.