Talha Nadeem

• Conducted extensive research in signal processing and machine learning, focusing on algorithm development at Lahore University of Management Sciences (LUMS).
• Designed and implemented model-based algorithms, including Kalman Filtering and outlier mitigation strategies, improving system accuracy by 20%.
• Developed hybrid approaches combining data-driven methods with model-based techniques, such as deep unfolding networks, reducing computational complexity by 15%.
• Applied advanced methods for image denoising, resulting in a 25% enhancement in image quality.
• Contributed to the development of cutting-edge solutions, advancing research in signal processing and machine learning domains.
• Kept abreast of current industry, scientific and regulatory developments and issues

• Developed efficient algorithm for real-time 3D brain imaging, focusing on source localization and related brain activities.
• Utilized EEG cap to capture brain activity, enabling depiction of source-localized activity within brain, visible on a 3D brain model.
• Employed Low Resolution Electromagnetic Tomography (LORETA) and Dipole Fitting methods for observing brain activity and source localization.
• Designed algorithm to acquire EEG signals online through lab streaming layer in Open Vibe, ensuring real-time source localization.
• Effectively removed artifacts from EEG signals, including those caused by eye blinks, movements, or other external disturbances.
• Contributed to advancements in understanding brain activity in response to specific events.

Research intern at Power &Control Systems Lab