Dynamic and proactive professional with expertise in conceptualizing and developing advanced industrial computer vision software solutions, collaborating and building ideas with other team members, and delivering solutions to wide ranging projects. Enthusiastic, creative, technical highflyer with a Ph.D. in Electrical and Computer Engineering and hands-on algorithm and software development. Proven ability in inventing, implementing, and testing software algorithms and improving algorithms of others to offer customers optimal solutions. Demonstrated capabilities in leading computer vision research projects, investigating machine learning approaches to MRI reconstruction, and maintaining custom-built system software. Excellent communication and leadership acumen, with the capability to train, coach, and mentor project teams and engage effectively with relevant stakeholders.
I earned my Ph.D. in Electrical and Computer Engineering with a minor in Mathematics from the University of Arizona in Spring 2020 under the guidance of Dr. Jeffrey Rodriguez in the Signal and Image Laboratory. The focus of my Ph.D. dissertation was on developing novel generalized precision and recall metrics for object detection under hybrid/partial detection scenarios. I delivered a computer vision solution to analyze zebrafish swimming behavior by acquiring video data, detecting zebrafish, determining their orientation, computing rheotaxis index, and tracking zebrafish across multiple frames to our collaborators at the Dept. of Otolaryngology. In addition, I was also responsible for delivering an automated machine learning solution to classify damages sustained by zebrafish neuromasts into one of three damage levels using supervised classifiers and deep learning.
I earned my M.S. in Electrical and Computer Engineering from the University of Arizona in Fall 2008 under the guidance of Dr. Jeffrey Rodriguez in the Signal and Image Laboratory. The focus of my Masters thesis was on segmenting breast cancer tissue from high resolution immunohistochemical images. I earned my B.E. in Electronics and Communication Engineering from Anna University, India, in 2005 where I was fascinated by digital signal processing and communication theory.
During the summer of 2014, I interned with Takeda Pharmaceuticals where I built a machine learning classifier to identify developmental pharmaceutical compounds that reduced tumor size, by extracting features of interest including size, shape, intensity, and texture features from PET scans of tumors implanted in mice. In a separate project, these features were also used to build a machine learning classifier that identified the four chambers of the heart from MRI images. In addition, I have also worked as a data scientist on electrophysiology data collected from non-human primates to study connections in the amygdala, a brain region responsible for emotion and the fear response. I have also worked on studying and simulating pharmacological and pharmacokinetic drug profiles for several pharmaceutical compounds.
Currently, I work as a postdoctoral research associate in medical imaging, where I learn about reconstructing MRI images, plaque in the carotid artery, and quantifying the risk of stroke to patients with extracranial carotid atherosclerotic disease (ECAD). I also use a bunch of neuroimaging tools such as Freesurfer, FSL, and MRTrix3 to study white matter pathways from diffusion weighted MRI images of the brain.
Lastly, here is my Google Scholar profile and a picture of my dog, Ella, a 110 lb Great Dane whom I adore!