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Assistant Professor, Biomedical Informatics & Intelligent Systems, University of Pittsburgh
Dr. Batmanghelich is at the intersection of medical vision (medical image analysis), machine learning, and bioinformatics. He develops algorithms to analyze and understand medical images along with genetic data and other electrical health records such as the clinical report. For example, he developed a probabilistic model to extract information from brain images (Magnetic Resonance Images) of patients with Alzheimer's disease and relates them the underlying genetic markers involved in the disease. He is interested in novel method development as well as real-world clinical applications.
Full Professor, Ophthalmology, University of Pittsburgh School of Medicine
Dr. Benosman's research is devoted to study and understand biological and computation machine learning operated by brains with the goal to build complete neuromorphic systems (sensors, brain-like computation devices, and algorithms) and solve open problems both in medecine, life long learning, robotics and general AI.
This stream of research is bidirectional in the sense that insights can come from both sides, whether from understanding neural recordings or by working in the same space-time mathematical spaces of the brain using silicon retinas and neural processors but also conventional hardware.
His research can then be summarized by the following three main tracks:
- Neuromorphic engineering development of bioinspired sensors, new brain inspired computation platforms and spike-based machine learning
- Computer Vision and Robotics for a new kind of biologically inspired computer vision and bio inspired robotics allowing dynamic data interpretation and decision-support
- Medicine: computational neurosciences, biomedical engineering, monitoring physiologic processes, neu-
ral stimulation devices for retina and cortex implants and optogenetics techniques
Dr. Benosman's lab develops a unique neuromorphic asynchronous silicon retina that provides both pathways of biological retinas while being 1000 times faster. This sensor is inspired by the biology of the eye and brain.
Professor and Chief Academic Officer, Toyota Technical Institute at Chicago
Dr. Blum's main research interests are in Machine Learning theory and on-line algorithms. His work involves designing algorithms with provable performance guarantees, as well as developing new models for analyzing emerging problems such as learning from labeled and unlabeled data.
Assistant Professor, Computer Science, Stanford University
Associate Professor, Computer Science, Federal University of Sao Carlos
Visiting Professor, Art and Machine Learning, Carnegie Mellon University
Eunsu Kang is an artist, a researcher, and an educator who explores the intersection of art and machine learning. She has been making interactive art installations and performances, teaching artmaking using machine learning methods, and now she is also looking into the possibility of creative AI. She was a tenured art professor and now is teaching at Carnegie Mellon University’s School of Computer Science. She started her artist career with video installations and single-channel videos. After more than 100 exhibitions, her works have transformed into interactive and interdisciplinary art projects, which currently focuses on the new area of AI art.
Her work has been invited to numerous places around the world including Korea, Japan, China, Switzerland, Sweden, France, Germany, and the US. All ten of her past solo shows, consisting of individual or collaborative projects, were invited or awarded. She has won the Korean National Grant for Arts three times. Her research has been presented at conferences such as ACM, ICMC, ISEA, and NeurIPS. Kang earned her Ph.D. in Digital Arts and Experimental Media from DXARTS at the University of Washington. She received an MA in Media Arts and Technology from UCSB and an MFA from Ewha Womans University."
Assistant Professor; Radiology, Biomedical Informatics & Bioengineering; University of Pittsburgh
Dr. Wu’s background is in computer vision with additional training in radiology and clinical imaging. His research interfaces a broad range of interdisciplinary in computational science and medicine for translational and clinical applications, with main areas in computational biomedical imaging analysis, big (health) data coupled with machine/deep learning, imaging-based clinical studies, radiomics/radiogenomics, and artificial intelligence in clinical informatics/workflows. Current research interests center on computational breast imaging and clinical studies for investigating quantitative imaging-derived biomarkers, models, and systems for breast cancer screening, risk assessment, diagnosis, prognosis, and treatment, towards improving individualized clinical decision-making and precision medicine.