Research Scientist | Neuroscience | Data Science | Machine Learning | Imaging Techniques
Designed & developed an image processing algorithm to enable segmentation & detection of small (<5%) dilation from noisy imaging time series of brain arterioles.
Created first time-series model to predict astrocyte activity using regression & GLM
Applied multiple deep learning and computer vision algorithms (CNNs, ViTs) for image classification (Python, Tensorflow, Keras, OpenCV, sklearn)
Awarded competitive NIH R01 funding ($1M, 5-year grant) for image processing neuroscience research project, demonstrating excellence in scientific communication, project management.
Developed & taught highly rated "Data Evaluation Principles" course, focusing on data analysis, probability & statistical models for advanced engineering students.
Mentored 10 Senior Design teams, providing guidance to engineering students, acting as bridge to the industry sponsor | Supervised 3 PhD students | Served on 5 neuroscience PhD theses | Chair of PhD Qualifying Exam Committee.
Secured $400,000/year in funding, overseeing all aspects of the lab's operations, including experimental design, data collection, data analysis, and publication of results
Multiple leadership roles: Elected Research Group Leader representative to the Director | Chair of IACUC | Institutional Official to NIH (representing the Institute on key regulatory issues)
Designed, planned, managed & executed multiple research projects incorporating first ever use of in vivo two-photon (2p) laser-scanning microscopy to measure functional calcium signals in non-murine brain. Responsible for all aspects of data analysis, graphical data representation, scientific and technical writing.
Assembled, tested and employed a custom two-photon laser-scanning system, including all aspects of design, layout, procurement, etc.
Made highly-cited discovery of interactions between neurons, astrocytes & neurovascular coupling, published in world-leading academic journal, Science.