I am a dedicated Ph.D. student in Computer Science at The Ohio State University, focusing on Computer Vision, Vision-Language Models, and Self-Supervised Learning under the supervision of Professor Rajiv Ramnath. My research encompasses image and video generation, as well as self-supervised learning techniques to advance the field of computer vision.

I am actively looking for a research internship for summer 2025!

Research Interests:

Image and Video Generation:

• Developing innovative methods for generating high-quality images and videos.
• Projects include Multi-Guided Image Inpainting and Multi-Modal Conditional Video Generation.
• Exploring how the creativity of Large Language Models (LLMs) can be utilized in video generation with diffusion models.

Self-Supervised Learning for Vision:

• Designing self-supervised approaches to learn meaningful representations from unlabeled data.
• Projects include Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning and a self-supervised approach for general images using multimodal architectures like CLIP.
• Applying self-supervised learning to medical image analysis to overcome the challenge of limited labeled data.

Medical Image Analysis:

• Utilizing self-supervised learning to train models on unlabeled medical images.
• It aims to extract valuable features for better analysis and interpretation in the medical domain.
• Developed Masked LoGoNet, a neural network architecture with tailored self-supervised learning for efficient medical image segmentation.

Recent News and Updates:

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Bachelor and Master

My Bachelor’s thesis focused on applying reinforcement learning in a multi-object environment. In this unique setting, each object had the ability to train individually. Additionally, I incorporated federated learning techniques to enable the objects to generalize their models to each other. This research explored the potential of combining these approaches to enhance learning and decision-making in complex environments.

For my Master’s thesis, I delved into the realm of software testing. Specifically, I proposed an innovative approach to generating datasets using machine learning techniques. This approach aimed to cover the main paths within the software, enabling effective fault detection. By leveraging machine learning, I sought to enhance the efficiency and accuracy of software testing processes, ultimately improving software systems’ overall quality and reliability.