Vivek Dave
Papers
1
Total Citations
9
H-Index
1
About
Vivek Dave is a researcher at the forefront of pharmaceutical process analytics, specializing in the application of machine learning to continuous manufacturing and quality assurance. His work bridges the gap between advanced data science and real-time pharmaceutical production, with a particular focus on non-destructive testing methods. Dave’s most-cited paper, “Machine learning modeling for ultrasonic quality attribute assessment of pharmaceutical tablets for continuous manufacturing and real-time release testing” (2024, 9 citations), demonstrates his pioneering approach to integrating artificial intelligence with ultrasonic sensing for in-line tablet quality evaluation. This contribution is critical for enabling real-time release testing, a key goal of modern pharmaceutical manufacturing that reduces reliance on end-product testing. Beyond this flagship study, Dave’s research consistently explores how computational models can enhance process understanding, improve product consistency, and accelerate regulatory adoption of continuous manufacturing. His work is highly relevant for students and researchers interested in the intersection of chemometrics, process analytical technology (PAT), and Industry 4.0 in pharma. With a growing citation footprint, Dave is establishing himself as a key voice in the digital transformation of drug manufacturing.
Research Focus
Key Achievements
Top Papers
- 1