- Director, Center for Biomedical Image Computing and Analytics (CBICA)
- Director, Artificial Intelligence in Biomedical Imaging Lab (AIBIL)
- Joint Affiliations: Bioengineering and Applied Math graduate groups
- University of Pennsylvania, Philadelphia, USA
- Tebessa, ALGERIA
- Lamis Laboratory
- University of Larbi Tebessi,Tebessa,Algeria
Pr. Christos Davatzikos is the Wallace T. Miller Sr. Professor of Radiology at the University of Pennsylvania, and Director of the Center for Biomedical Image Computing and Analytics. He holds a secondary appointment in Electrical and Systems Engineering at Penn as well as at the Bioengineering an Applied Mathematics graduate groups. Dr. Davatzikos’s interests are in medical image analysis. He oversees a diverse research program ranging from basic problems of imaging pattern analysis and machine learning, to a variety of clinical studies of aging and Alzheimer’s Disease, schizophrenia, brain cancer, and brain development. Dr. Davatzikos has served on a variety of scientific journal editorial boards and grant review committees. He is an IEEE fellow, and a fellow of the American Institute for Medical and Biological Engineering.
Title : Machine learning methods for understanding heterogeneity of neurologic and neuropsychiatric diseases
Dr. Aouiche Chaima, Ph.D. received her bachelor and Master degrees in computer science from Larbi Tebessi University, Tebessa, Algeria, in 2011, 2013, respectively and Ph.D. degree in computer science from Northwestern Polytechnical University, Xi’an, China, in 2021. She joined key laboratory of big data storage and management as a researcher in 2014. Since September 2022, she has been with the Faculty of science and technology, Larbi Tebessi University, Algeria, where she is currently an assistant Professor. Her research interests include data mining, bioinformatics, big data and machine learning. Dr. Chaima has authored/co‐authored many publications in various high impact factor, peer‐reviewed, journals and international conferences.
Title : Predicting Cancer-related Genes from Integrated Data Sets