PROFESSIONAL SUMMARY
9+ years of experience in research, AI, deep learning, machine learning, image processing, NLP, LLM, and data analysis.
SKILLS
- Programming Languages: Python, MATLAB, C.
- Data Science Tools: Python packages, including TensorFlow, Keras, PyTorch, Scikit-Learn, Pandas, SciPy.
- Deep Learning: Deep Convolutional Networks, Generative AI (ResNet, GANs, VAE, Transformers, VIT, Diffusion, SETR, PVT), Segmentation Networks (SegNet, UNET),
Detection Networks (RCNN), NLP models (BERT, GPT series), LLMs, and MLLMs.
- Machine Learning: KNN, SVM, Naive Bayes, Decision Tree, GLM, Random Forest.
- Computer Vision: Python packages including Scikit-image, OpenCV, Pillow, ITK, Imageio, and MATLAB image toolboxes.
- Natural Language Processing (NLP): Experience in text analysis, sentiment analysis, language modeling, and information extraction using advanced NLP techniques and LLMs.
SKILLS PROFICIENCY
- Python
- PyTorch
- Deep Learning
- Machine Learning
- Computer Vision
- Natural Language Processing (NLP)
PROFESSIONAL EXPERIENCE
Imaging Scientist II, Philips Pharma Solutions | Rochester, NY, USA. | 05/2021 – Now
- Developer: Design computer vision and deep learning methods to develop algorithms for 3D stitching, regression, classification, out-painting, registration, segmentation, super-resolution, parameter estimation, etc.
- Developing an Image generation algorithm to reconstruct the full leg and calculate knee angle with robust results for the Philips for the first time.
- Developing 3D image alignment to align 3D MRI images and reduce the cost of redoing MRI images.
- Developing a pipeline for loading datasets, categorizing them, and stitching the related ones. Applicable to reduce data loading time for the company from 2 hours to 10 minutes.
- Designed advanced NLP models for text classification, sentiment analysis, and language generation tasks.
- Implemented and fine-tuned Large Language Models (LLMs) for complex language understanding and generation problems.
- Researcher: Research and develop new algorithms for image processing, computer vision, and deep learning.
- Tech Lead: MRI and CT Imaging evaluation.
Medical imaging analytical intern at Siemens Healthineers | Malvern, PA, USA. | 06/2019 – 08/2019
- Development and assessment of a new deep learning algorithm to classify different views of echocardiogram imaging with 95% classification accuracy.
- Presenting image processing tools to prepare the dataset for the purpose of classification.
- Analyzing and proposing the best deep learning algorithm effective for this task.
Research assistant and software developer, University of Oklahoma | Norman, OK, USA. | 01/2017 – 05/2021
- Published 13+ papers in top-tier journals in machine learning, deep learning, artificial intelligence, medical imaging, and data science.
- Developed deep learning model to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. The system
provided 94.5% accuracy for three classes of classification.
- Developed a hybrid technique to optimize CNN (Xception, InceptionV3) features with classical data optimization techniques for lesion
classification. 91% of accuracy is achieved for lesion classification for this system.
- Feature extraction, optimization, and data evaluation:
- Applied a random projection algorithm to optimize the machine learning model for breast lesion classification. An overall AUC value of 0.84 showed the effectiveness of my method.
- Proposing data regeneration as an optimal way to reduce redundancy of features for breast mass classification and predicting the likelihood of malignant cases.
- Developed and assessed a new global mammographic image feature analysis scheme to predict the likelihood of malignant cases. The algorithm provided 92% of accuracy for malignant analysis.
- Designed a bilateral scheme to mimic radiologists' vision system to predict risk score of the likelihood of cancer in breast images. The algorithm showed a 9.7% improvement in risk prediction accuracy in comparison with the baseline.
- Designed a machine learning-based algorithm for Speech segmentation and enhancement to detach vowels and consonants and prepare them for steganography systems.
- Developed an algorithm for image steganalysis based on frequency domain features and machine learning algorithms.
SELECTED PUBLICATIONS
- Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. International journal of medical informatics, 2021
- Applying a random projection algorithm to optimize machine learning model for breast lesion classification. IEEE Transactions on Biomedical Engineering. 2021
- Development and assessment of a new global mammographic image feature analysis scheme to predict likelihood of malignant cases. IEEE transactions on medical imaging. 2019
- DevelPrediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm. Physics in Medicine & Biology 2018
EDUCATION
Ph.D. | University of Oklahoma, Electrical and Computer Engineering, 2021.
M.Sc. | Sharif University of Technology, Electrical and Computer Engineering, 2013.
B.Sc. | Isfahan University of Technology, Electrical and Computer Engineering, 2011.
ACCOMPLISHMENTS
- Student member in ECE faculty search committee at OU, 2021
- Gallogly College of Engineering Dissertation Excellence Award (DEA), 2020
- 40+ published papers and 1100+ citations for the papers on the google scholar page.
- Robert F. Wagner All SPIE Medical Imaging Conference Best Student Paper Award, 2018.