I build intelligent systems that solve real problems. With expertise in computer vision, NLP, and generative AI, I develop scalable solutions that deliver measurable impact.
My approach goes beyond just writing code—I design intelligent, intuitive experiences that push boundaries and explore new possibilities.
As a quick learner and creative problem-solver, I thrive when facing complex challenges and enjoy transforming ambitious ideas into reality. Ready to collaborate on something revolutionary?
Honored to receive best methodology report award for our work on Surgical Tool Detection at MICCAI 2024 (27th International Conference on Medical Image Computing and Computer Assisted Interventions), one of the premier conferences in medical imaging and computer-assisted interventions.
View CertificateHonored to receive 2nd place recognition for our work on Surgical Tool Detection at MICCAI 2024 (27th International Conference on Medical Image Computing and Computer Assisted Interventions), one of the premier conferences in medical imaging and computer-assisted interventions.
View CertificateHonored to receive 5th place recognition for our work on Surgical Task Recognition at MICCAI 2024 (27th International Conference on Medical Image Computing and Computer Assisted Interventions), one of the premier conferences in medical imaging and computer-assisted interventions.
Awarded by the International Society of Data Scientists (ISODS) for completing the Summer 2023 Practicum Program with High Distinction. Specialized in Computer Vision with a focus on creating functions for AI Proctoring Applications. Supervised by Drs. Christopher Do, Thanh Ha, Dinh-Lam Pham, and Tran-Minh-Khuong Vu at the George Washington Institute of Data Science & Artificial Intelligence.
View CertificateMAPR 2023
October 2023
This paper enhances the EvoGen framework by implementing an improved genetic algorithm with an elitism mechanism that preserves high-performing prompts during evolution. We introduce a cosine loss function as the fitness measure, resulting in faster convergence and better image guidance compared to previous approaches. Our modifications address the inconsistency issues in existing text-to-image prompt evolution methods, making the automatic generation of high-quality, preference-satisfying prompts more reliable.