Projects, Members, and Resources


Recent advances in artificial intelligence have enabled unprecedented success in face and speech recognition, language translation, self-driving vehicles, and game playing. Some examples are FaceID in iPhone X, automatic face tagging in Facebook, speech recognition in Siri, HeyGoogle, and Cortana, language translation, and skin cancer detection using the SkinVision mobile application in GooglePlay. For many of these problems where human-level performance is the benchmark, a wealth of deep learning methods have been developed and tested. For instance, deep learning methods can detect skin cancer as good as dermatologists. For many other important scientific problems, however, the full potential of deep learning has not been fully explored yet.

Current Research

Our overall interest is on investigating how deep neural network methods may be developed to improve human health. Currently, we are focused on solving a bioinformatics problem known as 'protein distance prediction'. This problem lies at the heart of a notoriously challenging problem known as protein folding—a problem that is one of the top 100 unsolved questions. The problem remains largely unsolved. However, deep learning now shines another hope to solve it. Many groups all around the world, including and DeepMind and Microsoft are now attacking the problem with new success stories, and the field has now become highly competitive. My ever growing interest in deep learning uniquely positions me to continue my research in this area. We are currently investigating how deep learning algorithms can be best designed, engineered, and understood for predicting more accurate protein distances.


I am thankful to all the organizations who have made our research prossible by providing us financial support in many ways. NVIDIA awarded me with an state-of-the art GPU (physical device) and Google awarded me thousands of dollars worth of Google Cloud Credits to use their GPUs. The U.S. National Science Foundation provided me the funds to continue my 'deep learning for protein distance prediction' research. In addition to the UM-System's high performance computing cluster with thousands of computing nodes, we are now fortunate to own a high-end deep learning server with multiple GPUs that we can use for our research. I am also thankful to the National Aeronautics and Space Administration (NASA) for providing me with funds to investigate the application of reinforcement learning for detecting gas leakages.


Badri Adhikari, PhD
Assistant Professor of Computer Science
Department of Mathematics and Computer Science
University of Missouri-St. Louis

312 Express Scripts Hall
St. Louis, MO 63121

+1 314-516-7393
Email: adhikarib@edu.umsl

Google scholar: publications
UMSL page: profile page
ORCID: 0000-0002-1547-0238
CV and Short Bio.: CV Bio

© 2017 Adhikari Lab at University of Missouri-St. Louis, Last Updated: Nov 5, 2020