About
I am a third-year PhD research scholar at the IRIMAS Lab, Université de Haute-Alsace (UHA), Mulhouse, France. Previously, I worked at Gomal University and Virtual University of Pakistan as a Tutor/Instructor teaching multiple computer science courses.
Before that, I was a research assistant at the Medical Imaging and Diagnostics (MID) Lab, where I proposed approaches for brain tumor segmentation, survival prediction, and breast pectoral muscle segmentation.
- AutoML & Neural Architecture Search (2D/3D)
- Medical image classification & segmentation
- Robust NAS and multi-objective optimization
Work Experience
Ph.D. Research Scholar — UHA | Dec 2021 – Dec 2024
- Designed AutoML approaches for medical image analysis.
- Proposed NAS methods for 2D/3D segmentation and classification.
- Developed robust NAS against adversarial attacks.
- Formulated multi-objective NAS for lightweight architectures.
Technical Skills
A/B testing, optimization, big data pipeline (cleansing, wrangling, visualization, modeling, interpretation), AutoML, Image Processing
MONAI, nnUNet, nibabel, pydicom
Flask
Python (Pandas, scikit-learn, pytest, TensorFlow, PyTorch, SciPy, NLTK, Gensim), SQL, R, C++, Java
AWS (SageMaker, ECR, EMR, S3, Redshift), Spark, Databricks, Airflow
Projects
Evolutionary NAS for 2D/3D Medical Image Classification
Proposed an evolutionary NAS approach for medical image classification and a comparative study of multiple metaheuristics.
Robust NAS Using Differential Evolution for Medical Images
DE-based NAS with attention-rich search space across convolution and pooling operations.
Tashkhees (Breast Cancer AI Diagnostic App)
Integrated segmentation AI modules and built a Flask frontend for clinical workflow.
CNNs via Surrogate-Assisted GA
Search of CNN architectures for medical image classification using surrogate models.
Brain Tumor Segmentation & Survival Prediction
DL pipeline for segmentation and survival prediction; ranked in BraTS 2020.
Hardware-Aware NAS (NSGA-II)
Multi-objective NAS for lightweight medical imaging architectures. Under review.
Training-Free U-Net for Retinal Vessel Segmentation
NAS for U-Net shaped architectures. Under review.
Selected Publications
Review of AutoML Optimization Techniques for Medical Image Applications
Computerized Medical Imaging and Graphics
Multi-task learning architecture for brain tumor detection and segmentation in MRI images
Journal of Electronic Imaging 31(5), 2022
Multi-level Kronecker CNN (ML-KCNN) for Gliomas Segmentation from Multi-Modal MRI
Journal of Digital Imaging
Enhancing breast pectoral muscle segmentation using skip connections in FCN
Int J Imaging Syst Technol, 2020
U-Net Based Glioblastoma Segmentation with OS Prediction
Intl. Symposium on Intelligent Computing Systems, 2020
Designing CNNs with Surrogate-Assisted GA for Medical Image Classification
GECCO 2023
Evolutionary NAS for 2D/3D Medical Image Classification
ICCS 2024
Robust NAS using Differential Evolution for Medical Images
EVOAPPS 2024
Full list available on Google Scholar.
Education
Université de Haute Alsace — 2021–Present
COMSATS University Islamabad — 2018–2020
National University of Computer and Emerging Sciences — 2013–2017
Awards
MICCAI Society — Intel monetary award (2020)
BRATS 2020 Challenge
University of Upper Alsace (2021–2024)
National University of Computer and Emerging Sciences (2013–2017)
Languages
Fluent (PhD studies in English)
A1
National Language
Native
Contact
The best way to reach me is email. I'm open to collaborations and applied AI projects in medical imaging.
- Email: junaid199f@gmail.com
- GitHub: github.com/junaid199f
- Scholar: Google Scholar
- LinkedIn: LinkedIn