Rohit Pal Singh

Rohit Pal Singh

Assistant Professor

Profile Summary

Rohit Pal Singh is an Assistant Professor and researcher specializing in Topological Data Analysis (TDA). He earned his Ph.D. from the University of Cincinnati, where he worked at the intersection of mathematics and computer science.

His research has been published in leading international conferences, including IEEE ICDM, IEEE Big Data, and IEEE Cluster. His work focuses on integrating mathematical rigor, computational methods, and data science applications to solve complex analytical problems.

Work Experience

Before joining UPES, he served as a teaching faculty member at the University of Wisconsin–Milwaukee and also taught at Jaypee Institute of Information Technology. His experience spans undergraduate and postgraduate teaching, curriculum delivery, student mentoring, and interdisciplinary research-oriented education.

Research Interests

Topological Data Analysis | Computational Geometry and Topology | Distributed and Parallel Processing | Statistical Modeling | Data Science | Applied Mathematics

Teaching Philosophy

Rohit Pal Singh follows a student-centric teaching philosophy that emphasizes conceptual clarity, critical thinking, and applied learning. He encourages students to connect theoretical foundations with real-world applications and fosters an interactive and inquiry-driven learning environment.

Courses Taught

Data Structures and Algorithms | Algorithm Design and Analysis | Scientific Data Visualization | Computer Graphics | Object-Oriented Programming | Core Computer Science Courses

Awards and Achievements

Recipient of the Best Indian Research Award by Apeejay School of Management.

Scholarly Activities

His scholarly work focuses on Topological Data Analysis and computational mathematics, with contributions presented at reputed international platforms such as IEEE ICDM, IEEE Big Data, and IEEE Cluster. His research explores the use of topology, geometry, and scalable computational techniques for analyzing complex datasets.

He continues to actively engage in interdisciplinary research connecting mathematics, computer science, and data science to address modern analytical challenges.