Recommended expert
Musaib Parray
Research Intern – Exploring Reasoning with Diffusion Models
Experience
Oct 2025 - Present
4 monthsGermany
Research Intern – Exploring Reasoning with Diffusion Models
Machine Learning and Perception group, FAU Erlangen-Nürnberg
- Investigating the equivalence between the Tiny Reasoning Model (TRM) and diffusion models for structured reasoning tasks such as Sudoku and maze solving.
- Exploring the reasoning and generative capabilities of diffusion models in symbolic problem-solving environments.
Apr 2025 - Aug 2025
5 monthsGermany
Machine Learning Lab – Autonomous Driving & Generative Models
LMS, FAU Erlangen-Nürnberg
- Built an ML pipeline for autonomous driving in OpenAI Gymnasium, generating labeled datasets from simulation frames and control actions.
- Designed and trained lightweight CNN and RNN models for end-to-end control and implemented model compression for real-time inference.
- Designed and trained a diffusion model from scratch on MNIST, implementing denoising, sampling, and image generation during inference.
Jan 2025 - Mar 2025
3 monthsGermany
Canny Edge Detection Optimization (DSP Lab Project)
LMS, FAU Erlangen-Nürnberg
- Implemented the full Canny Edge Detection pipeline in Python using OpenCV, modularizing key stages such as gradient computation and hysteresis thresholding.
- Enhanced performance by integrating CLAHE preprocessing and multi-scale Gaussian filtering, improving edge contrast and localization.
- Conducted experiments on the BSDS500 dataset to analyze the impact of parameter tuning and multi-scale edge combination.
Sep 2022 - May 2023
9 monthsIndia
Bachelor Thesis – EEG Seizure Detection Using CNN
Aligarh Muslim University
- Developed a CNN-based model for epileptic seizure detection using CHB-MIT EEG data.
- Compared time-domain and FFT-based features, achieving superior performance with frequency-domain inputs.
- Designed a lightweight CNN for real-time classification of seizure states (Interictal, Preictal, Ictal).
Summary
M.Sc. student majoring in Machine learning in Signal Processing at FAU Erlangen with a strong foundation in machine learning, signal processing, and deep generative models. Experienced in implementing neural architectures for perception and generation tasks.
Skills
- Programming: Python, Matlab, C++
- Libraries & Frameworks: Pytorch, Numpy, Scipy, Gymnasium, Librosa
- Tools: Jupyter, Matlab, Git
- Domains: Deep Learning, Generative Models (Gans, Diffusion Models), Reinforcement Learning, Audio And Image Processing, Adaptive Filters
- Other Skills: Data Visualization, Version Control, Scientific Writing
Languages
English
AdvancedGerman
ElementaryEducation
Oct 2023 - Present
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Master of Science, Communications and Multimedia Engineering · Communications and Multimedia Engineering · Erlangen, Germany
Oct 2019 - Jun 2023
Aligarh Muslim University
Bachelor of Technology, Electrical Engineering · Electrical Engineering · Aligarh, India · First Division Honours, 8.95/10.0
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