About Us QPSi trainees

Varun Bohara is pursuing a Master of Science in Computer Science at the University of Florida, following a Bachelor of Engineering in Computer Science from KLE Technological University, and is particularly interested in AI in healthcare and patient safety. 
Prior to joining QPSi, Varun built expertise in applied AI and data engineering at Deloitte, where he developed RAG pipelines, semantic search systems, and event-driven microservices for enterprise clients. 
At QPSi, Varun supports projects related to agentic AI automation, data collection and analysis, contributing to efforts that strengthen care delivery and patient outcomes. 
Outside of QPSi, Varun is passionate about the intersection of AI and health systems, and enjoys playing guitar solos, exploring blues and rock and roll music. 

During his time with QPSi, Ganesh worked on machine learning infrastructure projects focused on inference pipeline development, ML platform reliability, and MLOps scaling. Currently, he is focused on building and maintaining scalable ML systems, where he works on deployment workflows, production infrastructure, and efficient model serving. He is applying his skills in Python, infrastructure automation, backend systems, and DevOps practices, and working with Docker, HPC environments, CI/CD pipelines, and Ansible to support reliable and scalable ML operations. Ganesh aims to advance robust ML infrastructure for real-world AI applications, while continuing to deepen his expertise in MLOps, model deployment, and large-scale production systems through his work at QPSi. 

During his Fall ’25 session with QPSI, Prajay worked on the patient experience feedback pipeline and building the standardized LLM analyzer, with a particular focus on improving offline LLM inference latency. Currently, he is focused on the RN Health Coach and LLM Evaluator project, while also integrating the standardized LLM analyzer into the existing pipelines to improve daily run efficiency. He is applying his skills in Natural Language Processing (NLP), large language models, Python infrastructure, and working with vLLM for inference pipelines to optimize throughput. Prajay aims to enhance healthcare outcomes at QPSI while continuing to learn and apply NLP and LLMs to large-scale medical data through his contributions at QPSI. 

Himanshu PS is pursuing a Master’s in Computer and Information Sciences at the University of Florida, following a Bachelor of Technology in Computer Science and Engineering from the Indian Institute of Technology Kanpur, and is particularly interested in AI automation. 
Prior to joining QPSi, Himanshu worked as a Software Developer at SAP Labs India, where he architected optimized databases and containerized applications to enable high scalability and availability. 
At QPSi, Himanshu supports projects related to deploying Large Language Model (LLM) endpoints, engineering medical knowledge graphs, and developing an Agentic AI automated prompt engineering framework, contributing to efforts that strengthen care delivery and patient outcomes. 
Outside of QPSi, Himanshu is passionate about applying deep learning to solve complex challenges, and enjoys reading books, cooking Indian cuisine, and exploring new video games.

Abhinava Sai Tirunagari is pursuing a Master of Science in Computer Science at the University of Florida, following a Bachelor of Technology in Computer Science Engineering from Mahindra Ecole Centrale, and is particularly interested in the application of AI and intelligent retrieval systems to improve healthcare quality and patient safety. 
Prior to joining QPSi, Abhinava built hands-on experience in AI and software engineering through roles at Cognera Health, where he developed LLM-powered analytics tools, and at ADP, where he contributed to large-scale enterprise backend systems. He also completed a research internship at ADRIN-ISRO, where he trained and evaluated deep learning models for speech recognition applications. 
At QPSi, Abhinava supports projects related to Retrieval-Augmented Generation (RAG), specifically GraphRAG models — benchmarking their performance and fine-tuning them for healthcare-specific use cases — contributing to efforts that strengthen care delivery and patient outcomes through smarter, AI-assisted information systems. 
Outside of QPSi, Abhinava is passionate about applying AI to real-world healthcare systems and building full-stack interactive applications that make intelligent tools accessible and intuitive for end users. He is an avid cricket player and proudly represents the Gators cricket team and enjoys unwinding over a game of pool or snooker. He is also a dedicated pet lover. 

Helen is pursuing a Master of Health Administration at UF, after earning a B.S. in Biology from the University of Florida. Before joining QPSi, Helen worked in the pathology department of a cancer center, participated in health administration and public health case competitions, and volunteered in setting up a free clinic and running educational programs for children.
At QPSi, Helen supports process improvement and patient safety education projects to enhance care delivery. Outside of QPSi, Helen enjoys cooking and exploring local coffee shops.

Varun is pursuing an M.S. in Computer and Information Science at UF, after earning a B. Tech in Computer Science and Engineering from Amrita Vishwa Vidyapeetham, India. He previously developed deep learning models to detect anomalies in NASA aviation data as a Research Assistant at UF and built a full-stack digital election platform.
At QPSi, Varun works on probabilistic graph modeling using Gaussian Processes and Markov Chains to analyze patient care pathways and identify patterns associated with improved outcomes. Outside of QPSi, he enjoys playing tennis, coding side projects, and contributing to open source.

Ganesh is pursuing an M.S. in Computer Science at UF, after earning a B. Tech in Information Technology from Jawaharlal Nehru Technological University. He previously led DevOps automation initiatives at Amadeus, enhancing CI/CD pipelines and system security. He has also developed a sentiment analysis tool for patient feedback and a deep learning model to detect chest X-ray abnormalities.
At QPSi, Ganesh works on machine learning projects aimed at improving healthcare outcomes through data analysis, predictive modeling, and machine learning operations. Outside of QPSi, Ganesh is passionate about AI-driven innovation and enjoys photography, basketball, freediving and travelling.

During his spring term with QPSi, Prajay worked on graph machine learning techniques for Heart Failure care pathways, with a particular focus on graph anomaly detection and graph community analysis and comparison. This time, he is focused on a patient experience feedback pipeline, tasked with analyzing and classifying patient feedback using BERT. He is applying his skills in Natural Language Processing (NLP) and working with transformer-based models. Prajay aims to enhance healthcare outcomes at QPSi while learning to apply NLP and LLMs to large-scale medical data through these new contributions at QPSi.

During his spring term with QPSi, Udayakumar worked on evaluating the performance of large language models for clinical applications using the CLUE framework. This semester, he is focused on fine-tuning UF’s GatorTron-BERT model to predict readmission and respiratory failure based on summarized clinical notes. He is applying skills in prompt engineering and model fine-tuning developed since his last term in the training program. Through this work at QPSi, Udayakumar aims to leverage unstructured clinical data to improve the predictive performance of machine learning models and support clinical decision-making.

Kathryn Zhou is pursuing a bachelor’s in management information systems at the University of Florida and is particularly interested in AI and the ways it will be able to contribute to the healthcare quality.
At QPSi, Kathryn supports projects related to agentic AI to help automate processes within project management, contributing to efforts that strengthen care delivery and patient outcomes.
Outside of QPSi, Kathryn is passionate about learning more about AI and enjoys pickleball and exploring local coffee shops.
 

Karley earned a BS in Biomedical Engineering (UF, 2024) and is pursuing an MS in Industrial & Systems Engineering with a focus on human-systems engineering. She has research experience in biomechanics at UF and deep brain stimulation at Duke. As a quality engineering intern at Exactech, she developed a passion for healthcare quality. She aims to become a healthcare systems or medical device quality engineer, using this opportunity to enhance her skills in healthcare quality and AI-driven processes. Her interests include knitting, cooking, podcasts, and hiking.

Rupakali is a Master’s student in Business Analytics at UF with a background in Computer Science Engineering from Jawaharlal Nehru Technological University, India. She has experience as a Business Analyst at Bonnie Plants and OpenText, along with AI research at UF’s Fischer School of Accounting. Passionate about data-driven decision-making and workflow optimization, she aims to apply AI to real-world problem-solving. An avid traveler, she sees this program as an opportunity to enhance her skills, collaborate, and make a meaningful impact.

Prajay is a Computer Science graduate student at the University of Florida with two years of experience at Dell Technologies developing full-stack applications using Angular and Python/FastAPI. Passionate about healthcare AI, machine learning, and NLP, he aims to apply AI to patient care. He enjoys badminton, squash, golf, gaming, and Florida road trips. Through the QPSi training program, he looks forward to working with real healthcare datasets and learning from industry experts.

Utkarsh is a Computer Science graduate student at the University of Florida with a bachelor’s in Computer and Communication Engineering from Manipal University Jaipur. He worked at Accenture in Bangalore, supporting clients through Workday, and interned at IIT Kanpur, focusing on AI. Passionate about AI-driven innovation, he aims to apply AI in medical science. He enjoys story-driven video games, dancing, singing, and learning the synthesizer. This training program will enhance his AI expertise and its applications in healthcare.

Udayakumar holds a B.Tech in Information and Communication Technology (2021) and is pursuing an MS in Information Systems and Operations Management (2025). He worked as a Software Engineer at Optum Global Solutions (2021–2023) and is passionate about big data and data-driven decision-making. Aspiring to be a Data Engineer, he aims to leverage AI for optimizing healthcare data pipelines. This training program will provide experience in applying LLMs to clinical data analysis and patient safety insights. In his free time, he enjoys baking.