Ms. Ambika Sewaniya

Designation: Teaching Assistant

Academic Area: B.E in Computer Science, M.Tech in Computer Science (Image Processing), Pursing Ph.D in Deep Learning Healthcare Informatics & Medical Data Analytics, AI in Healthcare (Medical Imaging, Predictive Models, Decision Support)

Brief Profile:

I am working as an Teaching Assistant at Adani University in Computer Science Department. With a robust academic foundation in Computer Science (B.Tech, M.Tech), I am currently pursuing a Ph.D. in applying Deep Learning techniques to Healthcare domains, particularly in medical image analysis, predictive healthcare analytics, and AI-driven clinical decision support systems.

Before joining Adani University, I held an esteemed position as an Assistant Professor at LJ University, where I contributed significantly to both technical education and the development of academic curricula in computer science and engineering. In addition to teaching, I have been deeply involved in multiple National Board of Accreditation (NBA) and National Assessment and Accreditation Council (NAAC) committees, contributing expertise to improve institutional quality standards and accreditation processes.

I also led and coordinated interdisciplinary technical fests and cultural events at the university level, fostering a culture of innovation and collaboration among students. This experience further honed leadership skills while promoting an active learning environment that merged technical excellence with creativity.


  • B.E in Computer Science
  • M.Tech in Computer Science (Image Processing)
  • and Pursing Phd in Deep Learning techniques to Healthcare domains, particularly in medical image analysis, predictive healthcare analytics.

  • 4.5 Years as Assistant Professor

  • Having NCC Certificate and awarded as NBA coordinator

  • Prediction of Heart Stroke Using Deep Learning Techniques: My current research focuses on the application of deep learning techniques to healthcare challenges, including early disease detection, personalized treatment plans, and improving healthcare outcomes through AI-driven systems. With an emphasis on analyzing large-scale medical data, the research also explores the integration of AI technologies into clinical practice to improve decision-making processes and patient care.

  • AI & Machine Learning for Healthcare (Predictive models, diagnostics, decision support)
  • Medical Imaging & Deep Learning Solutions (Image classification, anomaly detection)
  • Healthcare Data Analytics & Informatics (Patient data analysis, personalized medicine)

  • Attended various FDPS ON MOOCS, NPTEL, Flutter and Figma at University Level.