Salman is available for hire
Hire SalmanSalman Ahmed
Verified Expert in Engineering
Artificial Intelligence Engineer and Developer
Location
Houston, TX, United States
Toptal Member Since
August 11, 2022
Salman is a data scientist with half a decade 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 journals and conferences.
Portfolio
Designstripe
Machine Learning, Artificial Intelligence (AI)...
NIC MAP Vision LLC
Artificial Intelligence (AI), Natural Language Processing (NLP)...
Atmospheric Data Solutions
Machine Learning, Python, Pandas, NumPy, R, JupyterLab, Weather, MySQL...
Experience
Availability
Part-time
Preferred Environment
PyCharm, PyTorch, TensorFlow, Jupyter Notebook, OpenCV, Computer Vision Algorithms, Pandas, AI Programming, Large Language Models (LLMs), Cloud
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.
Work Experience
LLM Fine-tuning Expert
2023 - 2023
Designstripe
- Developed pipelines to fine-tune open-source LLMs on custom data.
- Built Stable Diffusion pipelines to fine-tune custom data.
- Developed a LangChain-based agent to optimize the workflow.
Technologies: Machine Learning, Artificial Intelligence (AI), Natural Language Processing (NLP), Neural Networks, Web Design, OpenAI GPT-3 API, OpenAI GPT-4 API, Integration, Time Series, ChatGPT, OpenAI
LLM Prompt Engineer
2023 - 2023
NIC MAP Vision LLC
- Engineered LLM prompts to design the legal document Q/A in the chatbot.
- Engineered LLM prompts to design the Q/A on document queries for the platform.
- Engineered LLM prompts to design the summarization of legal documents.
Technologies: Artificial Intelligence (AI), Natural Language Processing (NLP), OpenAI GPT-3 API, OpenAI GPT-4 API, Generative Pre-trained Transformers (GPT), Generative Pre-trained Transformer 3 (GPT-3), Language Models, Research, API Integration, Integration, Time Series, ChatGPT, OpenAI
Machine Learning Developer
2023 - 2023
Atmospheric Data Solutions
- Designed data pipelines to manage vast amounts of data for an atmospheric-related project.
- Designed machine learning algorithms to improve wind speed predictions.
- Converted existing codebase from R to Python and optimized machine learning pipelines.
Technologies: Machine Learning, Python, Pandas, NumPy, R, JupyterLab, Weather, MySQL, Random Forests, XGBoost, Data Scientist, Algorithms, Keras, Data Analysis, Benchmarking, Research, API Integration, Integration, Time Series, ChatGPT, OpenAI
AI Expert
2022 - 2023
RunKicker
- Developed deep learning pipelines for BMI detection on complex data for a personal healthcare assistant.
- Developed deep learning algorithms that efficiently handle small datasets, enhancing their robustness by leveraging the distribution derived from limited data.
- Optimized existing models and reduced sizes of the models from 250MB to just 50MB.
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 (LLMs), Data Scientist, Algorithms, Keras, Data Analysis, Open Neural Network Exchange (ONNX), Research, API Integration, Integration, Time Series, Statistical Analysis, Data Analytics, OpenAI
Machine Learning Developer | Models Build and Models Fine Tune
2022 - 2022
Psi.Wave LLC
- Designed and implemented deep learning large language model (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 (LLMs), Data Scientist, Algorithms, Keras, Research, API Integration, Integration, Statistical Analysis, OpenAI
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.
- Implemented auto fault prediction in chips during manufacturing.
Technologies: Computer Vision, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), GPT, 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, Data Inference, Stable Diffusion, AI Programming, Image Generation, APIs, Chatbots, AI Design, PostgreSQL, Data Pipelines, Data Visualization, Financial Forecasting, Large Language Models (LLMs), OpenAI GPT-4 API, OpenAI GPT-3 API, Leadership, Data Scientist, Algorithms, Reinforcement Learning, Keras, Data Analysis, Dashboards, Business Intelligence (BI), Reports, Google Data Studio, BigQuery, Legal Documentation, Research, API Integration, Wearables, Biometrics, Time Series, Statistical Analysis, Data Analytics, Data Reporting, Amazon SageMaker, JSTransformers
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), Generative Pre-trained Transformers (GPT), GPT, 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, Data Inference, AI Programming, Data Visualization, Large Language Models (LLMs), Algorithms, Research, Biometrics, Statistical Analysis
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), GPT, Generative Pre-trained Transformers (GPT), 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, Data Inference, AI Programming, AI Design, Data Pipelines, Data Visualization, Large Language Models (LLMs), Algorithms, Data Analysis, Dashboards, Business Intelligence (BI), Reports, BigQuery, Research, Biometrics, Web Development, Statistical Analysis, Data Analytics, Data Reporting
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, Data Inference, AI Programming, AI Design, Data Visualization, Algorithms, Data Analysis, Dashboards, Business Intelligence (BI), Reports, BigQuery, Research, Web Development, Data Analytics
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, Data Inference, AI Programming, AI Design, Data Visualization, Algorithms
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.
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.
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/9708553COVID-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.
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/2105.00619Deep 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.
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++, R
Libraries/APIs
PyTorch, TensorFlow, OpenCV, Pandas, Keras, Spark ML, NumPy, XGBoost
Tools
Amazon SageMaker, PyCharm, Azure Machine Learning, BigQuery
Paradigms
Data Science, Best Practices, Business Intelligence (BI)
Platforms
Jupyter Notebook, Azure, Mobile
Storage
Data Pipelines, PostgreSQL, MySQL
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, Data Inference, Classification Algorithms, Classification, Text Classification, AI Programming, AI Design, Large Language Models (LLMs), GPT, Generative Pre-trained Transformers (GPT), OpenAI GPT-4 API, Generative Pre-trained Transformer 3 (GPT-3), OpenAI GPT-3 API, Data Scientist, Algorithms, Data Analysis, Dashboards, Reports, Research, Time Series, Statistical Analysis, Data Analytics, ChatGPT, OpenAI, Object Detection, Performance Optimization, Stable Diffusion, APIs, Chatbots, Data Visualization, Financial Forecasting, Image Generation, Leadership, Reinforcement Learning, Legal Documentation, Benchmarking, Open Neural Network Exchange (ONNX), API Integration, Integration, Wearables, Biometrics, Data Reporting, Point Clouds, Signal Processing, Health, Models, JupyterLab, Weather, Random Forests, Google Data Studio, Web Development
Industry Expertise
Web Design
Education
2021 - 2023
PhD in Computer Science
Texas A&M University - College Station, TX, USA
2015 - 2019
Bachelor's Degree in Computer Science
National University of Computer and Emerging Sciences - Islamabad, Pakistan
Certifications
JULY 2022 - PRESENT
Winner of Object Detection for Dash CAM Images AI-challenege
Motive (Former KeepTruckin)
SEPTEMBER 2021 - PRESENT
Winner of Chest-XRAY COVID-19 Grand Challenge
Amazon Web Services
AUGUST 2021 - PRESENT
Winner of Diabetic Foot Ulcer Detection Grand Challenge
MICCAI
AUGUST 2021 - PRESENT
Certificate of Achievement
The Manchester Metropolitan University