Dr. El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master’s degrees in Electrical Engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contributions to the field of biomedical translational research. Dr. El-Baz has 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 450 technical articles (105 journals, 15 books, 50 book chapters, 175 refereed-conference papers, 100 abstracts, and 15 US patents). Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General’s Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.

• 1. Prediction of outcome in children with autism spectrum disorders2. Autism spectrum disorder and sleep: pharmacology management3. Diagnosis of autism spectrum disorder with convolutional autoencoder and structural MRI images4. Explainable and scalable machine learning algorithms for detection of autism spectrum disorder using fMRI data5. Smart architectures for evaluating the autonomy and behaviors of people with autism spectrum disorder in smart homes6. Data mining and machine learning techniques for early detection in autism spectrum disorder7. Altered gut–brain signaling in autism spectrum disorders—from biomarkers to possible intervention strategies8. Machine learning methods for autism spectrum disorder classification9.Exploring tree-based machine learning methods to predict autism spectrum disorder10. Blood serum–infrared spectra-based chemometric models for auxiliary diagnosis of autism spectrum disorder11. A deep learning predictive classifier for autism screening and diagnosis12. Diagnosis of autism spectrum disorder by causal influence strength learned from resting-state fMRI data13. Adapting multisystemic therapy to the treatment of disruptive behavior problems in youths with autism spectrum disorder: toward improving the practice of health care14. Machine learning–based patient-specific processor for the early intervention in autistic children through emotion detection15. Autism spectrum disorders and anxiety: measurement and treatment16. Extract image markers of autism using hierarchical feature selection technique17. Early autism analysis and diagnosis system using task-based fMRI in a response to speech task18. Identifying brain pathological abnormalities of autism for classification using diffusion tensor imaging