I have a Masters in Computer Science from the University of Minnesota,
specializing in Deep Learning architectures.
All of my work is motivated by the passion of real-world problem-solving. I tackle challenges head-on,
identify areas of improvement, and deliver data-driven solutions.
I previously worked as a Lead Software Engineer at a Japanese manufacturing company.
I had strategically driven the firm’s digital transformation. I built their in-house software
infrastructure from the ground up, which allowed them to migrate from old-style Excel sheets to
a new web-based reporting system. I also took the initiative to set up an online training system
that changed their old in-person training to virtual training, which proved essential during the pandemic.
I also wanted to set up a chatbot in our reporting system that would help the employees with any
quality-related or SOP-related questions. So, I started learning about chatbot systems. This sparked
my interest in NLP, but I quickly realized that NLP is still a growing technology, and there are very
few materials available online for self-learning. I knews that I had to attend school in order to really
understand deep learning and different NLP architectures.
So, I joined the graduate program at the University of Minnesota. This allowed me to perform several
academic research projects. For example, improving Large Language Models for Question Answering systems
in the medical field and privatizing Large Language Models to protect sensitive data. This experience
really helped me understand the complex architecture of these deep learning models.
This helped me get a Data Scientist internship at Boston Scientific, where I leveraged AI to create a
search engine app. The app accessed thousands of internal files like pdf, word, and outlook messages to
read text data, and used a large language model to create a version of this data such that when a user
has a query, relative documents can be shortlisted based on the semantic understanding of this query.
This was aimed at reducing the time-to-market for medical devices. This hands-on experience allowed me
to shape the AI strategy at the firm and deepened my understanding of the AI/ML lifecycle.
I also worked as a Data Scientist at the university’s medical school, where I sharpened my abilities
in big data analytics. I planned the methods and analyzed data for several research projects. For example,
I led a project to devise a tool that will measure resident-doctor interaction using electronic health record
(EHR). I captured important details like doctor and resident interactions with health records which demonstrated
diverse supervision styles. I also co-authored two published research papers, one of them in JAMA.
I also like to stay active in the community. I am part of the Minneapolis AI community where I learn about
different advancements in the AI.
I am excited about the prospect of using these rich experiences in ML, data analytics, and software engineering
to drive projects in your team.
