I have been researching various AI, Machine Learning, and Deep Learning applications. My PhD was focused on Computational Biology, but since then, other interesting applications domains have been of Interest. I am working on Large Language Models and their applications in various domains. I am also working on Data Fusion for various streams from IoT devices and sensors for Robotic assistance in various applications. Generally, I am interested in these knowledge areas of computer science: AI, ML, Deep Learning different architectures and approaches, DL compression and large-scale deployment, Math for AI and ML, Robotics and Digital Twins and IoT, Bioinformatics and Computational Biology, generally scientific computing, Topology and knowledge graphs and various semantic networks.   

I have been teaching computer science to both undergraduate and postgraduates since 2011 in various institutions, and prior to PhD, I was assisting in teaching since undergraduate years. My experiences navigated various fundamental topics, such as programming fundamentals, parallel programming fundamentals, design patterns, networks, computer graphics, algorithms, operating systems, Pattern recognition, robot programming and operating systems, AI and machine learning among others. I received NSF/IEEE-TCPP Curriculum Initiative Early Adopter Program to adopt an undergraduate curriculum to include PDC (Parallel & Distributed Computing) for 2013. In 2022, I became an Nvidia Academic Ambassador for Deep Learning fundamentals. I align the theoretical contents with the practical content and assessments with the learning objectives to enable students at any level to benefit from the well-established academic content to benefit both the industry and research communities they can join later.