Research Focus Pawar Shrikant
Education and Workforce Development through AI-Driven Healthcare Research
Dr. Pawar Shrikant is an Assistant Professor in the Department of Computer Science and Biology at Claflin University. He’s also a co-founder of a Connecticut-based AI company, ChestAi. The research in his group focuses on big data (next-generation sequencing, microarrays, X-ray crystallography, etc.) analysis using machine learning techniques (Neural networks, SVMs, Restricted Boltzmann machines, Clustering algorithms, etc.). Dr. Shrikant is the Co-Principal Investigator from Claflin University of the ADAPT in SC Project. He is a contributor to Thrust II of the ADAPT in SC project, on computer vision techniques for image analysis. He is also involved with education & workforce development and is a member of the project implementation team from Claflin University. Identifying cancer drug targets, developing regression and tree-based classification prognostic models, and deep learning & computer vision are his other research interests. He’s also a recipient of awards like the Google HBCU Career Readiness Capacity Grant.
As part of the ADAPT project, Dr. Shrikant integrated artificial intelligence (AI) research with student education and workforce development at Claflin University. The project provides undergraduate and graduate students with hands-on training in machine learning, biomedical image analysis, high-performance computing, and interdisciplinary healthcare research while addressing clinically important challenges in the diagnosis of Peripheral Artery Disease (PAD).
Through this research initiative, students gained practical experience in developing machine learning models for the analysis of Computed Tomography Angiography (CTA) images to improve the early detection and diagnosis of PAD. Students participated in the complete research workflow, including data preprocessing, AI model development, performance evaluation, scientific writing, poster preparation, and presentation of research findings. This experiential learning approach prepares students with technical and professional skills that are highly sought after in the rapidly growing fields of artificial intelligence, biomedical informatics, and healthcare analytics.
The project has resulted in peer-reviewed scholarly abstracts, including Machine Learning Applications on Computed Tomography Angiography (CTA) Data for Diagnosis of Peripheral Artery Diseases (PAD) published in the Proceedings of the 6th National Big Data Health Science Conference (BMC Proceedings, 2025). Student researchers served as lead authors, demonstrating the project’s commitment to developing future scientists through meaningful research experiences.
Beyond publication, students have disseminated their research at numerous regional and national conferences, including the National Big Data Health Science Conference, the Association of Computer Science Departments at Minority Institutions (ADMI) Symposium, Claflin University’s Research, Scholarship & Creative Expression Day, and the South Carolina EPSCoR State Conference. These opportunities have strengthened students’ scientific communication, networking, and professional development while exposing them to collaborations with researchers from multiple disciplines.
Briefly, the PAD validation metrics remained stable through 440 epochs, indicating effective generalization and avoiding overfitting, with an overall F1- score of approximately 0.93. Strong performance on held-out data suggests that the strategy of the LoRA-driven diffusion synthetic data captured generalized PAD imaging characteristics rather than memorizing the training set (Figure 1). The ADAPT project has also fostered interdisciplinary collaboration by bringing together students and faculty from computer science, biology, engineering, and biomedical research. Students have worked alongside faculty mentors and research collaborators on projects involving AI-enabled medical imaging, machine learning, and advanced biomedical technologies, creating an educational environment that mirrors modern interdisciplinary research laboratories. Students are introduced to real-world biomedical datasets and computational tools, allowing classroom learning to directly support ongoing research activities. This integration of research and education strengthens students’ computational thinking, data analysis, and problem-solving abilities while preparing them for graduate education and careers in artificial intelligence, biomedical engineering, healthcare informatics, and data science.
Overall, the ADAPT project has significantly expanded research opportunities for students at Claflin University by combining innovative AI research with experiential learning, mentorship, scholarly dissemination, and interdisciplinary collaboration. Through these efforts, the project is helping develop South Carolina’s next generation of a diverse, highly skilled STEM workforce equipped to address emerging challenges in precision healthcare and artificial intelligence.