Rimsha Tariq

Artificial Intelligence and Computer Vision researcher with a Master's in Computer Engineering and a strong foundation in mathematics and research. Proficient in image processing, 3D computer vision, and deep learning, with published work in scientific journals. Skilled at collaborating with multidisciplinary teams, employing analytical thinking, and solving complex problems. Explored cutting-edge research in computer vision, deep learning, and generative models. Passionate about utilizing AI to optimize healthcare delivery by addressing unscheduled care pathways, enhancing post-operative monitoring, and personalizing treatment strategies. Leveraging scalable, AI-powered solutions to improve patient outcomes, streamline clinical decision-making, and optimize resource allocation in resource-constrained environments. Motivated to explore the intersection of AI, science, and human-centered design through high-impact, collaborative projects.

• CloudUP - Upsampling Vibrant Color Point Clouds using Multi-Scale Spatial Attention, Yongju Cho, Rimsha Tariq, Usama Hassan, Javed Iqbal, Abdul Basit, Hyon-Gon Choo, Rehan Hafiz, Mohsen Ali, IEEE ACCESS, 11, 128569-128579, 2023, 10.1109/ACCESS.2023.3332141, 3.9
• Resource-Restricted Environments Based Memory-Efficient Compressed Convolutional Neural Network Model for Image-Level Object Classification, Zahra Waheed, Shehzad Khalid, Syed Mursleen Shehzad, Sajid Gul Khawaja, Rimsha Tariq, IEEE Access, 11, 1386-1406, 2023, 10.1109/ACCESS.2022.3230008, 3.4

• Reconfigurable Architecture for Real-time Decoding of Canonical Huffman Codes, Rimsha Tariq, Sajid Gul Khawaja, Muhammad Usman Akram, Farhan Hussain, 2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2)
• Masked Face Detection and Recognition Using a Unified Feature Extractor, Aroobah Iftikhar, Arslan Shaukat, Rimsha Tariq, 2024 5th International Conference on Advancements in Computational Sciences (ICACS)
• Automatic Classification of White Blood Cell Images using Convolutional Neural Network, Rabia Asghar, Arslan Shaukat, Usman Akram, Rimsha Tariq, Accepted in The 2022 World Congress In Computer Science Computer Engineering Applied Computing, 07/25/22 - 07/28/22, Las Vegas, Nevada, USA, https://american-cse.org/static/CSCE22-book-abstracts-printing.pdf

• Machine Learning & Deep Learning: Model development, hyperparameter tuning, and evaluation (DNNs, CNNs, RNNs, Transformers).
• Transfer Learning: Fine-tuning pre-trained models (e.g., ResNet, VGG) for domain-specific tasks.
• Model Optimization: Quantization, pruning, hyperparameter optimization (Grid Search, Random Search).
• Pattern Recognition: Feature engineering, dimensionality reduction (PCA), clustering (K-Means), object detection (Yolo, FasterRCNs).
• Image Processing: Image enhancement, segmentation (thresholding, edge detection, SAM).