Ali Torabi
PhD Student in Computer Science
Specializing in Computer Vision, Deep Learning, and Explainable AI
PhD Student in Computer Science
Specializing in Computer Vision, Deep Learning, and Explainable AI
I am a PhD student in Computer Science at the University of Wyoming, working under the supervision of Dr. Yaqoob Majeed. My research focuses on developing novel techniques in Explainable AI (XAI) for Computer Vision, particularly in Weakly Supervised Semantic Segmentation (WSSS).
I'm passionate about bridging the gap between theoretical research and practical applications in deep learning. My work involves developing influence-guided Class Activation Mapping (CAM) techniques with instance-level refinement and GPU-optimized scaling for large-scale datasets such as PASCAL VOC and COCO.
With a strong background in software engineering and industrial experience in iOS development with ML integration, I bring both academic rigor and practical expertise to my research. I have published in top venues including Springer Neural Processing Letters and have papers under review in leading journals.
PhD in Computer Science
University of Wyoming
Explainable AI
Computer Vision
NSF ART Grant
Multiple Publications
Developing novel CAM-based techniques with influence functions for improved segmentation with weak annotations.
Creating interpretable deep learning models with explanation methods to improve neural network predictions.
Exploring transformer architectures for computer vision tasks and multi-modal learning applications.
Optimizing model training with CUDA, mixed-precision training, and multi-GPU parallelism for large-scale datasets.
12 Production-Ready Computer Vision & Vision-Language Model Projects
Complete PyTorch implementation of ViT with attention visualization and transfer learning capabilities.
Semantic image search using OpenAI CLIP for natural language queries with FAISS integration.
Automatic caption generation using BLIP, BLIP-2, and GIT vision-language models.
Real-time object detection with YOLOv8 supporting 80+ classes with video processing.
Pixel-level classification using DeepLabV3 with 21-class PASCAL VOC segmentation.
Answer questions about images using BLIP-2 and multi-modal reasoning.
arXiv:2509.12496, 2025
Research Square (Preprint), July 2025
Springer, Neural Processing Letters, 2023
International Journal of Bio-Inspired Computation
Online Social Networks and Media
Conducting research on Explainable AI in Deep Learning and Image Recognition. Developing influence-guided CAM techniques for Weakly Supervised Semantic Segmentation with GPU-optimized scaling on PASCAL VOC and COCO datasets.
Designed and implemented a web-based 3D STL visualization platform with interactive cross-sectioning and advanced rendering using Three.js and WebGL for F3DT commercialization project.
Served as TA for five consecutive semesters, providing mentorship in algorithmic problem solving and complexity analysis.
Developed iOS applications with ML integration. Built Deep CNN models for product image recognition and implemented ML functionality across iOS devices including watchOS.
I'm always open to discussing research opportunities, collaborations, or potential positions in Computer Vision and Deep Learning.
+1 (949) 738-3001
University of Wyoming
Laramie, WY, USA