P.h.D Engineering - University of Georgia, USA
I am an inquisitive researcher interested in finding creative and meaningful solutions using A.I and robotics to address challenges in precision ag.
Did my Bachelor's in Electrical Engineering from the School of Electrical Engineering and Computer Science (SEECS) at the National University of Sciences and Technology, (NUST) H-12 ISB. I was honored to be appointed as a Cultural Ambassador of Pakistan by the U.S Dept of State at South Dakota State University in Spring 2022.
I've engaged in multidisciplinary research, including Deep Learning for Healthcare and Remote Sensing; Robotic Development and trustworthy LLMs in NLP
Beyond engineering, I have a keen interest in entrepreneurship. My latest venture, Dawg Housing is currently under development in collaboration with the UGA Entrepreneurship Program, aiming to bring innovative solutions to housing challenges.Other than that I have been involved with Millennium Fellowship 2021 and General Secretary NSF. Beyond tech, I enjoy hiking, tennis and capturing moments through photography. 📸🌲.
Designed a novel approach for forest damage assessment in data-scarce regions like Pakistan leveraging clustering techniques on multi-spectral satellite imagery. Developed a Web Portal for Pakistan's Forestry Department to accurately monitor wildfires, enabling better conservation and disaster management.
Developed a safety-enhancing system that integrates embedded systems, audio processing, machine learning, and Wi-Fi communications.
The system utilizes ESP-32 and FreeRTOS to enable real-time audio analysis and transmission of classified audio signals as alerts or notifications over a Wi-Fi network.
Tech Stack & Hardware: FREE RTOS, ESP-32, INMP441 I2S Mic, Arduino IDE
GitHub Link
The Car Drowsiness Detector is an innovative solution that helps prevent accidents caused by drowsy driving.
By using computer vision and deep learning techniques, the system monitors the driver's eyes in real-time and alerts
them if they start to close or show signs of drowsiness.
Tech Stack & Libraries : OpenCV, Deep Learning, Pygame
GitHub Link
Developed HemoDetect, an innovative deep-learning solution designed for accurate disease diagnosis and classification. By leveraging Faster R-CNN,
the model effectively performs multi-class disease classification on peripheral blood cell images. This enables rapid and precise diagnosis of life-threatening
conditions such as Malaria, Leukemia, and Sickle Cell Anemia
Tech Stack & Libraries : Python, Pytorch, ResNET-50, TensorFlow
Github Link
Designed an instance segmentation model by augmenting multi-head attention in Mask RCNN structure. Deployed the model for apple disease detection in complex environments.
Tech Stack & Libraries : Python, Pytorch, ResNET-50, TensorFlow
Github Link:
Implemented Soft Watermarking approach on Bart pre-trained on CNN Daily Mail dataset. Designed watermark detector, analyzed performance in low-entropy text and strong watermarking etc.
Github Link:
Designed a novel approach using Cascaded K-Means clustering to identify anomalies in Sentinel-2 multispectral data,
uncovering forest fire damage patterns. Leveraging a supervised classifier trained on spectral indices, the clusters were
classified into burn severity classes. To enhance monitoring capabilities, a user-friendly Web App was developed, empowering
the Forest Department to track and analyze Northern Punjab Forest Fires over the past 5 years.
Tech Stack & Libraries : Javascript, Google Earth Engine, Sentinel-2, Weka
GitHub Link |
Paper (Coming Soon)
Implemented Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward of the Transformer model.
Tech Stack & Libraries : Verilog, , OpenAI
GitHub Link | Paper (Coming Soon)
Contact Me
muneebellahi2001@gmail.com
+92 313 762 6668