Javad Manashti

SKILLS & SPECIALTIES

• Artificial Intelligence (AI)
• Deep learning: Proficiency in developing, validating, and optimizing deep learning models, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and natural language processing (NLP) models using Python, MATLAB, and CNTK Experience training models using GPUs and NVIDIA CUDA, as well as in cloud Proficiency in using popular deep learning frameworks such as TensorFlow and PyTorch
• Machine learning: Strong experience in developing supervised machine learning algorithms, including neural networks, linear regression, and support vector machines (SVMs) Experience in fine-tuning parameters, training and validating machine learning systems, and improving models through statistical analysis and testing
• Computer vision: Skill in exploring, processing, and analyzing images, as well as developing, evaluating, and optimizing computer vision models Expertise in detecting statistical, frequency-based, and local patterns in image feature extraction Experience using convolutional neural networks and pretrained convolutional models for feature extraction and transfer learning Skills in object detection and segmentation

• Programming: Strong scripting and programming skills Expertise in Python, including popular ML frameworks such as TensorFlow, PyTorch, Keras, and Scikit-Learn, as well as data analysis libraries (such as NumPy, SciPy, and Pandas) and data visualization libraries Expertise in MATLAB for deep learning, machine learning, computer vision, and data analysis Ability to write queries in SQL and familiarity with NoSQL (Cloud Firestore) Expertise in using Visual Basic for Applications (VBA) to automate tasks in Excel, Word, Access, and AutoCAD
• Data analysis: Expertise in data analysis, including building models to solve real-world problems and collecting, integrating, validating, analyzing, and reporting data Experience with data accuracy analysis, troubleshooting, and problem resolution Skills in data cleaning and missing data identification and research Direct experience in supervised learning, including regression, classification, and time series analysis Ability to utilize statistical and machine learning methods to solve problems Experience in data processing and visualization development
• Research: Understanding of academic papers and state-of-the-art algorithms for specific problems, especially in deep/machine learning, computer vision, and convolutional neural networks
• Big data: Experience in analyzing and processing large datasets (>12TB) using R and Python, including NASA NEX datasets
• Cloud computing: Experience in building and deploying deep learning models on Amazon Web Services (AWS) and Microsoft Azure, and familiarity with Google Cloud Working knowledge of related tools, databases, and virtual machines