Professional Experience
Kitware Inc.
Summer Internship
Minneapolis, MN
May 2024 - August 2024
- • Apply deep learning and other computer vision methods for object detection, event/activity recognition, video/image search or understanding in calculating the uncertainty in object recognition and detection in long-range video footage
- • Utilized multi-source imagery and video data (ground, handheld, aerial, and satellite cameras) to advance real-time segmentation and enhance degraded video quality. Optimized performance with emphasis on object feature preservation and integrated the pipeline into GitLab.
- • Work on deep learning libraries like PyTorch or TensorFlow; large-scale computer vision and framework on the cloud
- • Developed an automated pipeline for DeepFake detection, evaluating hundreds of models to distinguish AI-generated from real images and identify the optimal encoder for the project.
Lightsense Technology Inc.
Summer Intern
Tucson, AZ
June 2022 - August 2022
- • Developed AI model for Covid-19 classification using spectral data
- • Pioneered spectral unmixing solutions for bacteria samples
- • Enhanced component identification for drug detection and pathogen identification
- • Led transitioning core functionalities into Python
Alphacore Inc.
Doctorate Student Collaborator
Tempe, AZ
March 2021 - August 2023
- • Managed onsite field experiments setup with several telescopes, drones, cameras, weather stations and scintillometers
- • Built a deep learning model for Atmospheric Turbulence estimation
- • Analyzed and processed extensive multidimensional data from various sensors
- • Participated in producing and disseminating original research contributions
Imaging Lyceum Lab, Arizona State University
Research Assistant
Tempe, AZ
January 2021 - Present
- • Design and develop a physics-based deep learning model for dynamic scene restoration affected by the atmospheric turbulence taken with Ultra-Zoom or astrophotography camera
- • Gather, analyze and validate data while performing research studies related to computer vision and machine learning
- • Contribute to research on computational imaging and photography, computer vision and visual or perceptual experience
- • Write research papers, reports and proposals while conducting literature review on appropriate topics
NeuroPhotonics Lab, GIST
Research Assistant
Gwangju, South Korea
August 2018 - December 2020
- • Designed multimodal deep learning architecture for Meibomian Gland analysis
- • Enabled automated assessment of infrared images of tear film
- • Achieved ophthalmologist-level quality assessment with Meiboscore
- • Participated in research on various microscopy techniques
Teaching Experience
EEE 598: Deep Learning - Lab (ASU)
Fall 2024
- • Led hands-on coding and lab sessions focusing on:
- • Model development lifecycle: from architecture design to training and efficient inference on GPU clusters
- • PyTorch fundamentals and advanced implementation strategies
- • Custom CNN architecture development for regression and classification tasks on custom datasets
- • Implementation of state-of-the-art computer vision models for classification (ViT, ResNet), detection (YOLO), and image/video segmentation (Mask R-CNN)
- • Transformer architecture implementation from scratch, including self-attention and multi-head attention mechanisms
- • End-to-end LLM development: architecture design, training on curated textbook datasets, and optimization for text generation
- • Complete implementation of Denoising Diffusion Models from scratch, incorporating advanced sampling strategies for high-quality image generation
- • Graph Neural Network development: graph convolutions, node classification, and embedding techniques
- • Integration of modern AI frameworks (DINO, SAM, LLAMA, Phi) using Hugging Face
- • Full-stack AI application development: from prototyping to web deployment
AME 494: Minds and Machines
Spring 2023
- • Conducted regular office hours for student consultations
- • Managed grading responsibilities for course assignments and exams
- • Provided additional support for students struggling with course material