top of page

PHD EECS, Electrical Engineering, Computer Science, Vision, Machine Learning, Medical Applications

Updated: Jan 24

I was raised in a small village in India by parents who had not benefited from much education. Educating girls was not a priority in my culture, and it took great determination and hard work to achieve what I had. Early on, it was clear that I had a natural facility for dealing with numbers, which quickly developed into a passion for mathematics and, subsequently, computer science. I have never regretted choosing such a fascinating field where many rapid and exciting advances are made.

I was awarded my Bachelor's degree in I.T. at the prestigious Jaypee Institute of Information Technology. I was awarded an Indian Army ‘Bright Student Scholarship’, my father being an Indian Army veteran. I earned my Master’s in Computer Science at the University of Massachusetts, graduating with a creditable GPA of 3.77.

My undergraduate studies centered on mathematics: Calculus, Applied Linear Algebra, Numerical Optimization, and Probability. In my senior year, I worked on developing a ‘Military Simulator’ as my ‘capstone project.’ This was prompted by an interest in military matters arising from my father’s career as a soldier. This project was designed in Unity 3D and involved data extraction from designed simulation systems. I also applied Data Mining techniques to predict the probable outcome of certain events and published two Journal Research papers at IJCA and IJCSIT. These experiences fired my interest in research, which I now passionately hope to pursue within the program.

My internship was spent working under the supervision of Professor XXXX of the Indian Institute of Science Education and Research. During this time, I explored the applications of Tropical Geometry and completed a project on the formation and simulation of phylogenetic trees using hamming distance.

To gain practical experience, after my graduation in June 2014, I joined SAP Labs as a Software Engineer in the S/4HANA suite. I handled the development and deployment of a Product Lifestyle Management application. I then decided to enhance my skills and knowledge in Machine Learning by pursuing a relevant Master’s program, which led to my joining the Computer Science program at the University of Massachusetts.

During my time at UMass, I focused on Data Science and enrolled in courses: Machine Learning, Algorithms for Data Science, Computer Vision, Neural Networks, etc. To explore my domain interest in Machine Learning, I initially decided to work on an NLP-based research project, ‘Concept/Theme Generation,’ under Professor Andrew McCallum’s supervision. After exploring NLP, I focused on computer vision research topics such as Capsule Networks, One-Shot Learning, and Video Summarization. My work on One-Shot Learning involved improving Siamese Networks Architecture embedding learning using Kernel Functions. It was accepted at NeurIPS’18 Workshop as Poster Presentation and will be published in Springer AISC’20 proceedings. At the same time, my work on Video Summarization using keyframe extraction methods has been widely appreciated on Github and was a trending paper on arxiv.

I have also worked on the Multi-Arm Bandit problem besides computer vision under Professor XXXX's guidance. This involved designing an algorithm to find a Supremum (Lower upper bound) on an unknown data sample compared to the Central-Limit theorem. For my internship, I worked at Autodesk as Machine Learning Intern and explored practical aspects of AI. I worked on Revit, modeling software for architects, and improved the recommendation algorithm by modeling a probabilistic graph that traverses using the Bayesian approach. This internship helped me understand one of the practical aspects of AI and the potentially significant effects and potential of machine learning on business success.

Since being awarded my Master’s degree, I have acquired valuable and relevant experience. I worked for eight months as a Machine Learning Engineer on various vision-related projects. I have also worked under the guidance of Dr. XXXX at the Brain Injury Outcomes Lab at John Hopkins on a Brain Injury Segmentation project, during which I was exposed to applications relating to Computer Vision and Machine Learning in the healthcare domain. My interest in this field led me to work with Bayer on CTEPH classification cases using lung scans. I became aware of the limitations of AI in healthcare due to data constraints but decided to explore fields other than Computer Vision and initiated a project with UMass IESL on ‘Disease Progression Using Clinical Observations’ in which we experimented time-series models to predict Sepsis progression using ECG data.

I am the author of a book, ' Hands-on One-Shot Learning’, which is due for publication in February. The book summarizes the latest ‘one-shot’ learning methods in Deep Learning and Probability, and I am confident that it will be helpful to many in the field.