About
I am a researcher and educator specializing in Applied Computer Science and Data Science. With a Ph.D. in Applied Computer Science and an anticipated Ph.D. in Data Science, I bridge computational science, AI, and bioinformatics. Currently, I teach at Rutgers University, integrating cutting-edge research into courses on machine learning, cloud computing, and big data analytics.
My research focuses on AI-driven solutions, particularly deep learning in quantum and classical computing for glioblastoma gene analysis. I also explore secure computational systems and blockchain applications. Passionate about mentorship, I guide students in independent research on big data's role in elections, blockchain, and cybersecurity.
By combining advanced AI methodologies with quantum computing, I aim to revolutionize bioinformatics, precision medicine, and computational oncology. My work fosters interdisciplinary collaboration, advancing both research and education in next-generation computing solutions.
Current Work
I am currently developing a hybrid quantum-classical deep learning framework to analyze glioblastoma multiforme (GBM) mutation data. This research enhances genetic analysis using quantum computing to uncover key genetic hierarchies for cancer treatment.
At Rutgers University, I teach data science, cloud computing, and machine learning, mentoring students in big data analytics, blockchain, and AI applications. My research also explores secure data transfer and encryption models for privacy-preserving AI solutions.
By integrating deep learning and quantum computing, my work advances computational oncology, bioinformatics, and personalized medicine. Through interdisciplinary collaboration, I aim to drive innovation in precision healthcare and AI-driven research.
