About Me

I am a recent Ph.D. graduate in Computer Science from the University of Southern California (USC), specializing in Artificial Intelligence, Computer Vision, and Deep Learning. I was advised by Dr. Prem Natarajan at the USC Information Sciences Institute , where my doctoral research focused on developing state-space and information-theoretic deep learning models for AI-generated image forgery detection, generative model fingerprinting, and vision-language understanding. My Ph.D. thesis, titled “Context-Aware Semantic Forgery Detection in Biomedical and Natural Images” , explored the integration of semantic and non-semantic cues for robust manipulation detection.

I bring hands-on experience across the full AI stack — from model design and optimization (SSMs, VLMs, Diffusion Models, GANs, CNNs, Transformers) to data curation, multimodal learning, and large-scale deployment. My projects bridge vision, language, and generative modeling, with applications in biomedical imaging, content integrity, and trustworthy AI systems.

Over the years, I have gained extensive experience in AI-driven image analysis, generative model forensics, and visual-language alignment, spanning forgery detection, diffusion modeling, self-supervised learning, and anomaly detection. My research further encompasses image manipulation detection and localization — including splicing, copy-move, inpainting, and enhancement forgeries — as well as deepfake detection and fingerprinting of generative synthetic images from GANs, VAEs, flows, and diffusion models. I am also interested in the scientific integrity of research documents, document visual question answering, and zero-shot semantic segmentation, along with image description, captioning, scene-graph generation, image repurpose detection, and visual question answering. These directions collectively aim to advance explainable, robust, and generalizable visual intelligence systems that reason jointly over semantic and non-semantic cues.

Beyond academia, I have industry experience at ABB Robotics and ON Semiconductor, applying computer vision, 3D mapping, and reinforcement learning to real-world automation systems. I also worked at the Kansas Geological Survey and the Center for Remote Sensing of Ice Sheets, developing signal and image processing pipelines for radar and seismic data. During my Master’s in Electrical Engineering at the University of Kansas, I completed my thesis under Dr. Richard Wang titled “Robust Object Tracking and Adaptive Detection for Auto Navigation of Unmanned Aerial Vehicles” , which explored object tracking, motion estimation, and adaptive detection using classical computer vision and signal processing techniques — forming the foundation for my later research in deep learning and generative AI.

With a strong foundation in AI model development, generative systems, and applied computer vision, I am passionate about building scalable, and interpretable AI solutions that drive innovation in visual intelligence and generative trustworthiness.

Highlights