My research is of interdisciplinary nature and includes:

- Graph- and network-based learning and data science (pattern recognition, classification and clustering);
- Random geometric graphs (Proximity Catch Digraphs, their construction
and characterization,

domination, edge and arc density); - Network optimization (in relation to Stochastic Obstacle Scene Problem and Canadian Traveler's Problem)
- Spatial point pattern and data analysis (by nearest neighbor and graph theoretic methods) and their applications;
- Statistical Methods for Medical Data and Image Analysis.

Software: I have considerable experience in the below statistical and mathematical software.

Statistical Software: R and SAS

R is a free and open-source (i.e., users
can contribute add-on packages) software environment for statistical computing
and graphics. You can download it thru CRAN.

SAS is a commercial software environment with many branches, and mostly known for statistical analysis.

R Programming in SPARK environment

Symbolic Math Software: Maple and Mathematica

Maple
is a symbolic math software that is also powerful in numerical methods, and user-friendly for programmers.

Mathematica is also a symbolic math software that is also powerful in numerical methods.