Dr. Yunbin Deng
Massachusetts Institute of Technology, USA
Dr. Yunbin Deng is a technical staff member in the Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory. His research interests cover speech and language processing, machine learning, biometrics, and AI. Deng holds PhD and MS degrees from Johns Hopkins University. He has 50+ publications in books, book chapters, journals, and conference proceedings and holds three U.S. patents. He is a senior member of IEEE, and served as a guest editor for IEEE Transaction on Emerging Topics in Computing. He is an Associate Editor for IEEE Transaction for Audio, Speech, and Language Processing. He has received numerous awards and honors throughout his career, including the Highest Impact Award at the IEEE Computer Vision and Pattern Recognition (CVPR) Biometric Workshop in 2016.
Dr. Mario Flores
University of Texas at San Antonio, USA
Mario Flores is an Assistant Professor at the University of Texas at San Antonio whose research focuses on artificial intelligence, natural language processing (NLP), speech processing, and computational biology. He earned degrees in Applied Mathematics and Electrical Engineering with a specialization in Computational Biology in 2015 and completed postdoctoral training at the National Center for Biotechnology Information (NCBI) at the National Institutes of Health (NIH).
His research develops interpretable AI and deep learning models that predict disease phenotypes, identify biomarkers, and support clinical decision-making using multimodal biomedical data. While his laboratory has extensive experience developing AI methods for genomics, medical imaging, and electronic health records, his recent work has expanded to speech and language technologies for healthcare.
His current research focuses on multimodal AI frameworks that integrate acoustic speech features with natural language transcripts to enable the early detection and objective assessment of neurological and neuropsychiatric disorders, including aphasia and depression. By combining transformer-based language models, speech representation learning, and explainable AI, his goal is to develop scalable, objective, and clinically meaningful tools that assist clinicians in diagnosis, disease monitoring, and personalized patient care.