Welcome to the GitHub repository of the Applied AI
research lab. This page is dedicated to disseminating research
products/publications on artificial intelligence, machine learning,
pattern recognition, computer vision, image processing, data mining and
big data with various application domains such as healthcare informatics
and medical imaging, document imaging, biometrics, forensics, speech
analysis and Internet of Things.
We actively participate and/or sponsor the International Conf on Recent Trends in Image Processing & Pattern Recognition (RTIP2R).
The growing list of projects can be found here.
With over 25 years of industry experience, Athavale is currently a senior technical leader in automotive functional safety at NVIDIA, and is driving capability development, safety architectures and methodologies, system safety engineering activities and pathfinding for safety critical markets such as autonomous driving and avionics. Prior to NVIDIA, she was principal engineer (director) at Intel Corporation where she led functional safety platform architectures for automotive and avionics use cases and drove methodologies for radiation effects modeling and product qualification activities.
Gundeti is an acclaimed innovator and pioneer in the field of pediatric robotic surgery. He is credited with some of the first procedures performed in the world. In addition to being a compassionate surgeon, he frequently shares his knowledge through worldwide surgical workshops and lectures, which have benefited thousands of patients and surgeons across the world. He is a dedicated educator and continues to teach at the University of Chicago and worldwide. He has authored and edited several manuscripts and textbooks of pediatric and robotic urological surgery to reach a global audience.
At the University of Colorado Denver, Roberts listens for “dark matter” with ultra-cold detectors. Although gravitational measurements suggest this “dark matter” makes up fully 80% of the universe’s mass, it has never been directly detected. Roberts is a member of the Cryogenic Dark Matter Search collaboration (SuperCDMS), which specializes in sensing the faintest possible signals from highly sensitive phonon detectors. Her work focuses on understanding detector response at low energies, novel signal analysis, and building software tools that make data and analysis accessible to all scientists.