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ISR congratulates Behtash Babadi (ECE/ISR) on his promotion to the rank of associate professor with tenure by University of Maryland President Wallace Loh. The promotion is effective July 1, 2020.
Babadi has broad research interests in statistical and adaptive signal processing, neural signal processing, systems neuroscience. He works with a broad range of ISR faculty members in projects across a variety of domains.
This year, Babadi contributed to a new online version of the Springer reference work, Computer Vision: A Reference Guide. He wrote the entry, "Learning from a Neuroscience Perspective." Babadi’s entry gives an overview of learning from a neuroscience perspective by highlighting some key chronological findings in neuroscience that have given rise to various theories of learning and have particularly inspired major developments in artificial intelligence.
Also in 2020, along with Reza Ghodssi and Pamela Abshire, Babadi is involved in the UMD/UMB AIM-HI (AI + Medicine for High Impact) program to target major health care challenges. “Tackling Chronic Pain: Machine Learning-Enabled Biomarker Discovery and Sensing” will search for novel, localized biomarkers associated with gastrointestinal pain through mass spectrometry imaging as well as proteomic, lipidomic and RNA sequence analysis; miniaturized, multiplexed biochemical sensors to measure localized biomarkers; and machine learning.
In 2019, Babadi, Patrick Kanold and Wolfgang Losert became part of a BRAIN Initiative U19 center grant awarded by the National Institutes of Health. Their project, “Readout and control of spatiotemporal neuronal codes for behavior,” was funded for a total of $20M for five-years. Babadi is in charge of neural modeling, statistical data analysis, and model-based experimental design for the three science projects. He is also contributing data science tools to the Data Science Core.
Babadi also won the 2019 A. James Clark School of Engineering’s E. Robert Kent Teaching Award for Junior Faculty "in recognition of outstanding teaching evaluations, coursework development in ENEE 101 and senior and graduate level coursework supporting new specialization and graduate programs in machine learning."
In 2018, Babadi was part of a team of four ISR researchers awarded a $1M, 18-month cooperative agreement by the Defense Advanced Research Projects Agency (DARPA) for “An Optimization-Based Approach to Breaking the Neural Code.” The project was part of DARPA’s Lagrange program. The team was led by Steve Marcus and also included Michael Fu and Jonathan Simon. The team developed an innovative optimization framework tailored to noninvasive neuroimaging data from the human brain that is scalable, risk-sensitive and real time, and is capable of capturing the stochastic and geometric features unique to cortical function.
Babadi won a National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award in 2016 for “Deciphering Brain Function Through Dynamic Sparse Signal Processing.” Through this project he is developing a mathematically principled methodology that captures the dynamicity and sparsity of neural data in a scalable fashion with high accuracy and to employ it in studying the brain function with a focus on the auditory system.
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