Sai Karthik Nallamothu

సాయి కార్తీక్ నల్లమోతు

I am currently pursuing an M.S. in Computer and Information Sciences at the University of Central Florida, working under the guidance of Dr. Pegah Khosravi and Dr. Amrit Singh Bedi. Previously, I graduated with Computer Science and Engineering from SRM University.

I am particularly interested in the intersection of Human Interaction and Intelligent Systems in healthcare and medical applications.

skarthik.nallamothu@gmail.com

Resume / LinkedIn / Github / Scholar

Sai Karthik Nallamothu - Professional Photo. Copyright owned exclusively by Sai Karthik Nallamothu. Not AI-generated.

Updates

Aug 2025: Joined University of Central Florida to pursue M.S. in Computer Science. Looking forward to this new chapter in life!!

Selected Publications (Full list here)

[1] Application of Machine Learning Algorithms and Feature Selection using Genetic Algorithm: A Case Study on Cyber Attack Detection.
YVK Tapas Kumar Mishra, Sai Karthik, Pradeep Sai Teja, Raja Pavan Vignesh.
In Proceedings of the 9th International Conference on Innovations in Information and Communication Technology (I2CT), 2024.

[2] Comparative Analysis of Feature Representations for Topic Modeling with Latent Dirichlet Allocation.
SK Nallamothu, RKK Yenduri, SS Pippalla, K Karthik, BS Alapati.
In Proceedings of the 15th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IIT Mandi, 2024.

[3] Early Childhood Autism Screening Through Facial Feature Extraction.
SK Nallamothu, N Dutta, G Ghinea, SS Beri, SK Mekala, A Abdul.
In Proceedings of the 8th International Conference on Parallel, Distributed and Grid Computing (PDGC), 2024.

[4] Assessment of Data Augmentation Paradigms in Pathology Identification.
SK Nallamothu, PST Sanka, S Vaka, RPV Kajjayam, Y Vidavaluru.
In Proceedings of the 22nd International Conference on Information Technology (OCIT), 2024.

Projects

Adversarial-Resilient ANN for Medical Diagnostics
Code

Developed a deep neural network optimized via Genetic Algorithms for hyperparameter tuning on clinical MRI datasets, achieving 94% classification accuracy under noisy and adversarial conditions. Integrated fuzzy logic to enhance model interpretability and robustness.