Salman Ahmed, Artificial Intelligence Engineer and Developer in Houston, United States
Salman Ahmed

Artificial Intelligence Engineer and Developer in Houston, United States

Member since August 11, 2022
Salman is a data scientist with more than four years of experience designing and implementing data and machine learning pipelines. He has won three International grand challenges sponsored by Amazon Web Services (AWS) and published multiple research papers in top journal and conferences.
Salman is now available for hire

Portfolio

  • RunKicker Pte Ltd
    Artificial Intelligence (AI), Image Processing, Python, Signal Processing...
  • Psi.Wave LLC
    Python, Machine Learning, Deep Learning, Artificial Intelligence (AI)...
  • HamzaAi
    Computer Vision, Natural Language Processing (NLP), PyTorch, TensorFlow...

Experience

Location

Houston, United States

Availability

Part-time

Preferred Environment

PyCharm, PyTorch, TensorFlow, Jupyter Notebook, OpenCV, Computer Vision Algorithms, Pandas, AI Programming, Large Language Models (LLM)

The most amazing...

...project I've led won 1st place in an Amazon contest and was #1 at the International Conference on Medical Image Computing and Computer Assisted Intervention.

Employment

  • AI Expert for Healthcare Personal Assistant

    2022 - 2023
    RunKicker Pte Ltd
    • Developed deep learning pipelines for BMI detection on complex data.
    • Designed deep learning algorithms to handle small data and make it robust, giving a distribution gathered from a small amount of data.
    • Optimized existing models and reduced sizes of the models from 250 MB to just 50 MB.
    Technologies: Artificial Intelligence (AI), Image Processing, Python, Signal Processing, Health, Computer Vision, C++, Models, PyTorch, TensorFlow, Mobile, AI Programming, Image Generation, APIs, AI Design, Data Pipelines, Data Visualization, Large Language Models (LLM)
  • Machine Learning Developer | Models Build and Models Fine Tune

    2022 - 2022
    Psi.Wave LLC
    • Designed and Implemented deep learning LLM pipelines on huge data sets.
    • Optimized the existing training pipeline from both time and computation perspectives.
    • Implemented custom attention heads for multiple LLMs.
    Technologies: Python, Machine Learning, Deep Learning, Artificial Intelligence (AI), AI Programming, APIs, AI Design, Data Pipelines, Data Visualization, Large Language Models (LLM)
  • Senior Data Scientist

    2021 - 2022
    HamzaAi
    • Implemented a machine learning pipeline for vessel delay prediction at Khalifa Port in the UAE. Reduction in error rate from more than 24 hours to two hours. This resulted in better use of resources, including data mining and ML at Khalifa Port.
    • Executed the machine learning pipeline for job category detection through text mining.
    • Implemented the pipeline to detect Arabic content originality through text mining.
    Technologies: Computer Vision, Natural Language Processing (NLP), PyTorch, TensorFlow, Deep Learning, Image Processing, Machine Learning, Python, Custom Models, Artificial Intelligence (AI), Neural Networks, Artificial Neural Networks (ANN), Generative Adversarial Networks (GANs), Facial Recognition, OpenCV, Computer Vision Algorithms, Azure Machine Learning, Pandas, Azure, Spark ML, Best Practices, Performance Optimization, Language Models, Text Generation, Fine-tuning, Inference, Stable Diffusion, Diffusion, AI Programming, Image Generation, APIs, Chatbots, AI Design, PostgreSQL, Data Pipelines, Data Visualization, Financial Forecasting, Large Language Models (LLM)
  • Graduate Research Assistant

    2021 - 2021
    Texas A&M University
    • Researched T-cell and Receptor sequence contact prediction on human protein sequences using deep learning. (NLP).
    • Investigated cancer region detection in whole slide images (WSI) in collaboration with the University of Chicago.
    • Achieved the challenge of each WSI taking GBs to be stored, so it's impossible to use direct deep learning methods like image classification and segmentation.
    Technologies: Computer Vision, Natural Language Processing (NLP), PyTorch, Deep Learning, Image Processing, Machine Learning, Python, Custom Models, Artificial Intelligence (AI), Neural Networks, Artificial Neural Networks (ANN), Generative Adversarial Networks (GANs), Facial Recognition, OpenCV, Computer Vision Algorithms, Pandas, Best Practices, Language Models, Text Generation, Inference, AI Programming, Data Visualization, Large Language Models (LLM)
  • Data Scientist

    2020 - 2021
    HamzaAi
    • Implemented a deep learning pipeline for event and accident detection on self-driving car synthetic data.
    • Executed an Arabic OCR detection pipeline based on EasyOCR adjustments.
    • Worked on a handwriting recognition tool for Arabic schools.
    Technologies: Computer Vision, Natural Language Processing (NLP), PyTorch, Deep Learning, Image Processing, Machine Learning, Python, Custom Models, Artificial Intelligence (AI), Neural Networks, Point Clouds, Artificial Neural Networks (ANN), Generative Adversarial Networks (GANs), OpenCV, Computer Vision Algorithms, Azure Machine Learning, Pandas, Azure, Spark ML, Best Practices, Performance Optimization, Language Models, Text Generation, Fine-tuning, Inference, AI Programming, AI Design, Data Pipelines, Data Visualization, Large Language Models (LLM)
  • Data Scientist

    2020 - 2021
    National University of Computer and Emerging Sciences
    • Researched breast cancer detection using whole slide images, computerized medical imaging, and graphics.
    • Worked on a low-cost pathology project that received a $13.68 million grant for breast cancer detection.
    • Worked on Amal. It wasn't just a project but served as an awareness campaign too. I was the lead to start a movement about low-cost pathology—breast cancer detection—in Pakistan using artificial intelligence.
    Technologies: Computer Vision, Machine Learning, Deep Learning, PyTorch, Image Processing, Python, Custom Models, Artificial Intelligence (AI), Neural Networks, Point Clouds, Artificial Neural Networks (ANN), Generative Adversarial Networks (GANs), OpenCV, Computer Vision Algorithms, Pandas, Best Practices, Language Models, Inference, AI Programming, AI Design, Data Visualization
  • Software Engineer

    2019 - 2020
    National University of Computer and Emerging Sciences
    • Developed a deep learning pipeline to detect breast cancer based on low-cost pathology by extracting whole slide images from a scanned microscopic mobile video.
    • Designed a Python library and package to optimize training for whole slide images called OpTorch. Optimized the PyTorch training pipeline library for WSI. Published OpTorch research paper in a well-reputed conference.
    • Built a deep learning pipeline to detect brain tumors based on CAT scan Images.
    Technologies: Machine Learning, Deep Learning, PyTorch, TensorFlow, Computer Vision, Generative Adversarial Networks (GANs), OpenCV, Computer Vision Algorithms, Pandas, Language Models, Inference, AI Programming, AI Design, Data Visualization

Experience

  • PMNet | A Probability Map-based Scaled Network for Breast Cancer Diagnosis
    https://pubmed.ncbi.nlm.nih.gov/33578222/

    Our method employs scaled networks for detecting breast cancer in whole slide images. It classifies entire slide images on a patch level into normal, benign, in situ, and invasive tumors.

    Our approach yielded an f1-score of 88.9 (±1.7)%, which outperformed the benchmark f1-score of 81.2 (±1.3)% on patch level and achieved an average dice coefficient of 69.8% on 10 whole slide images compared to the benchmark average dice coefficient of 61.5% on BACH dataset.

    Similarly, on the Dryad test dataset comprising 173 whole slide images, we achieved an average dice coefficient of 82.7% compared to the previous state-of-art of 76% without fine-tuning on this dataset. We further proposed a method to generate patch-level annotations for the image-level TCGA breast cancer database that will be useful for future deep learning methods.

  • Bias Adjustable Activation Network for Imbalanced Data | Diabetic Foot Ulcer Challenge 2021

    Despite great success, deep learning models still face a critical obstacle in classifying highly imbalanced real-life data.

    Detecting diabetic foot ulcers is fundamental for healthcare specialists to prevent amputations. In this work, we performed multiple experiments to benchmark results on the grand. To adjust the bias of the convolutional neural networks, we also proposed a custom-designed activation layer based on softmax to handle the probability skew of the classes.

    We achieved the second position in the validation set with a macro F1 score of 0.593 and the third position in the test set with a macro F1 score of 0.596 for the Diabetic Foot Ulcer Detection 2021 Grand Challenge.

  • PRNet | A Progressive Resolution-based Network for Radiograph-based Disease Classification
    https://ieeexplore.ieee.org/document/9708553

    COVID-19 and pneumonia have impacted human life significantly. The number of infected people and deaths is increasing daily due to COVID-19. Rapid COVID-19 detection is vital to control and stop the spread of the disease.

    Considering AI can play a significant role in accurately detecting such diseases, EE-RDS conducted a multi-class classification challenge by providing chest X-rays of pneumonia, COVID-19, and regular patients. We proposed PRNet, a novel deep learning pipeline, and achieved 96.3% accuracy, winning the second position on the test set leader board.

  • OpTorch | Optimized Deep Learning Architectures for Resource Limited Environments
    https://arxiv.org/abs/2105.00619

    Deep learning algorithms have made many breakthroughs and various real-life applications. Computational resources become a bottleneck as the data and complexity of the deep learning pipeline increase.

    In this paper, we proposed optimized deep learning pipelines in multiple aspects of training, including time and memory. OpTorch is a machine learning library designed to overcome weaknesses in existing implementations of neural network training. It provides features to train complex neural networks with limited computational resources.

    OpTorch achieved the same accuracy as existing libraries on CIFAR-10 and CIFAR-100 datasets while reducing memory usage to approximately 50%. We also explored the effect of weights on total memory usage in deep learning pipelines.

    In our experiments, parallel encoding-decoding along with sequential checkpoints result in a much-improved memory and time usage while keeping the accuracy similar to existing pipelines.

Skills

  • Languages

    Python, C++
  • Libraries/APIs

    PyTorch, TensorFlow, OpenCV, Pandas, Spark ML
  • Tools

    Amazon SageMaker, PyCharm, Azure Machine Learning
  • Paradigms

    Data Science, Best Practices
  • Platforms

    Jupyter Notebook, Azure, Mobile
  • Storage

    Data Pipelines, PostgreSQL
  • Other

    Machine Learning, Computer Vision, Natural Language Processing (NLP), Deep Learning, Image Processing, JSTransformers, Custom Models, Artificial Intelligence (AI), Cloud, Neural Networks, Artificial Neural Networks (ANN), Generative Adversarial Networks (GANs), Code Review, Source Code Review, Task Analysis, Technical Hiring, Interviewing, Facial Recognition, Computer Vision Algorithms, Language Models, Text Generation, Fine-tuning, Inference, Classification Algorithms, Classification, Text Classification, AI Programming, AI Design, Large Language Models (LLM), Object Detection, Performance Optimization, Stable Diffusion, Diffusion, APIs, Chatbots, Data Visualization, Financial Forecasting, Image Generation, Point Clouds, Signal Processing, Health, Models

Education

  • Master's Degree in Computer Science
    2020 - 2022
    University of Michigan - Ann Arbour
  • Bachelor's Degree in Computer Science
    2015 - 2019
    National University of Computer and Emerging Sciences - Islamabad, Pakistan

Certifications

  • Winner of Object Detection for Dash CAM Images AI-challenege
    JULY 2022 - PRESENT
    Motive (Former KeepTruckin)
  • Winner of Chest-XRAY COVID-19 Grand Challenge
    SEPTEMBER 2021 - PRESENT
    Amazon Web Services
  • Winner of Diabetic Foot Ulcer Detection Grand Challenge
    AUGUST 2021 - PRESENT
    MICCAI
  • Certificate of Achievement
    AUGUST 2021 - PRESENT
    The Manchester Metropolitan University

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