BrainHub Hires Four Faculty Members
Carnegie Mellon University’s BrainHub initiative is pleased to announce that four new faculty members have accepted offers to join the university.
The new hires, which will add significant strengths and capabilities to CMU’s approach to brain science, have appointments in the Departments of Computational Biology, Electrical and Computer Engineering, Biomedical Engineering and Machine Learning.
The BrainHub steering committee expects the hires to expand CMU’s interdisciplinary community of scholars focused on the intersection of engineering and computational, cognitive and biological neuroscience.
The new faculty members are:
Chamanzar is working on developing next generation opto-acousto-electrical neural interfaces and will integrate in the campus-wide BrainHub research efforts to apply these interfaces. Chamanzar received his Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in 2012. His Ph.D. thesis on developing novel hybrid plasmonic-photonic on-chip biochemical sensors was recognized as the best Ph.D. thesis in 2013, and he received the Sigma Xi best Ph.D. thesis award. Chamanzar received his M.Sc. in Electrical Engineering majoring in Microsystems from the Georgia Institute of Technology in 2008. Chamanzar has published more than 25 Journal and conference papers. He has received a number of awards such as the SPIE research excellence award, GTRIC innovation award, OSA Emil Wolf best paper award, and Edison innovation award.
Kainerstorfer’s research in brain hemodynamics and physiological modeling exemplifies how non-invasive measurements of hemodynamic changes can be used to study the effect of dialysis on brain perfusion, as well as can be used for measuring auto-regulation in tissue, which is important in various diseases. Kainerstorfer’s research is focused on clinical translation of non-invasive optical methods in humans for disease monitoring and disease detection. Kainerstorfer has developed numerous imaging modalities, have focused on numerical methods and physiological modeling of tissue, and has demonstrated clinical translation. Kainerstorfer received her Ph.D. through a joint program between the University of Vienna and the National Institutes of Health. Her degrees are in physics, with a concentration in biomedical optical imaging and clinical translation. She had published 20 peer-reviewed journal articles.
Pfenning is working to better understand the principles that govern neurological disorders and complex vertebrate behaviors from a genetic and evolutionary perspective. He has conducted research on the genetic mechanisms of Alzheimer’s disease progression and evolution of vocal learning behavior. Andreas has a broad base of knowledge in computational biology, neurobiology, genetics, and epigenetics. He is currently working as a postdoctoral associate in a joint position between the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology and the Genetics Department of Harvard Medical School. He has a Ph.D. in computational biology and bioinformatics from Duke University and a B.S. in Computer Science, from Carnegie Mellon (2006). He has published a number of high-impact papers, including three in Nature and three in Science.
Salakhutdinov’s work is in the field of statistical machine learning. His research interests include deep learning, probabilistic graphical models and large-scale optimization. Salakhutdinov has a Ph.D. in computer science from the University of Toronto. His most recent papers include: “Deep Learning for Neuroimaging: a Validation Study,” “Multimodal Neural Language Models” and “Restricted Boltzmann Machines for Neuroimaging: An Application in Identifying Intrinsic Networks.” After spending two post-doctoral years at MIT, he joined the University of Toronto in 2011 where he was an assistant professor of computer science and statistics.