Ibrahim Mahmoud Ahmed, Ph.D., Developer in Perth, Western Australia, Australia
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Ibrahim Mahmoud Ahmed, Ph.D.

Verified Expert  in Engineering

Machine Learning Developer

Location
Perth, Western Australia, Australia
Toptal Member Since
April 22, 2019

Ibrahim is a veteran data scientist and software developer with a passion for deep learning and extensive experience in statistics and time series. Ibrahim is interested in helping businesses take full advantage of their data and take data-driven actions. His principle is to observe, model, and corroborate, disrupting the status quo with artificial intelligence.

Portfolio

AlgoTech, L.L.C-FZ
Machine Learning, Python, Docker, Scikit-learn
Johanson Dielectrics Incorporated
Python, PyTorch, Data Analytics, Data Visualization, Python 3...
NorthEast Monitoring Inc
Machine Learning, MATLAB, NLU, Deep Neural Networks...

Experience

Availability

Part-time

Preferred Environment

Jupyter Notebook, Google Cloud Platform (GCP), Azure, Amazon Web Services (AWS), ChatGPT, Bloomberg, OpenAI GPT-3 API, OpenAI GPT-4 API, Selenium, 3D Image Processing

The most amazing...

...berthing project I've developed for a mining company used SplatNet, an enabled hierarchical and spatially aware feature detection to be used by AR.

Work Experience

Python Machine Learning Developer

2024 - PRESENT
AlgoTech, L.L.C-FZ
  • Contributed to the required enhancements and updates to use the latest packages and tools—the code was old, and the client had old software to do regression for Bitcoin pricing.
  • Used parallel computing instead of threading as Python does not use threading well.
  • Optimized the trained models with metrics to enhance performance using the Bayesian optimization method.
  • Made the code faster and more robust as it used to crash each day.
Technologies: Machine Learning, Python, Docker, Scikit-learn

Senior Python Developer

2023 - PRESENT
Johanson Dielectrics Incorporated
  • Developed an application that optimizes the layout of capacitors in a Gerber file, meeting copper area and distance between polygons.
  • Wrote the code in Python, using Numba for speed. Used shape transformation to optimize location.
  • Generalized the code to work with any shape and given conditions.
  • Wrote DXF file for the layout that meets area and distance conditions.
Technologies: Python, PyTorch, Data Analytics, Data Visualization, Python 3, Natural Language Toolkit (NLTK), Optuna, Bayesian Inference & Modeling

Machine Learning Engineer

2024 - 2024
NorthEast Monitoring Inc
  • Developed MATLAB code to classify ECG signals using deep neural networks of mixed type that takes wave forms and other lab data.
  • Generated results and selected models based on the F1 score.
  • Built code that handles class imbalance for four classification types.
Technologies: Machine Learning, MATLAB, NLU, Deep Neural Networks, MATLAB Statistics & Machine Learning Toolbox

AI/ML Developer

2024 - 2024
Kevin O'Hagan
  • Built an application to load emails from Outlook and create a way to extract information from emails. The information includes an estimate of the effort, a summary of the case, and a title.
  • Extracted information from emails using large language models.
  • Estimated the effort done in the case to calculate the cost.
Technologies: Machine Learning, Python, Artificial Intelligence (AI), Natural Language Processing (NLP), Microsoft Graph API, Microsoft Exchange, Large Language Models (LLMs)

NLP Expert

2023 - 2024
MeetMonk Inc
  • Fine-tuned whisper voice to text to recognize the Indian language using medium and large models with Parameter Efficient Fine Tuning.
  • Utilized Bark, XTTS, METAVOICE, and text-to-speech for the voice cloning of an Indian speaker to speak the English translation.
  • Enhanced the MetaVoice-1B Facebook model with custom voice.
Technologies: Natural Language Processing (NLP), Python, Machine Learning, Artificial Intelligence (AI), TensorFlow, PyTorch, Deep Learning, Speech to Text, Speech Recognition, Large Language Models (LLMs), Text to Speech (TTS)

AI Expert

2023 - 2024
Getech Education Ltd
  • Architected a note-taking and summarization solution using OpenAI and LangChain.
  • Solved problems with summarizing large text from different documents.
  • Architected the AWS solution using Lambda/Fargate ECS.
Technologies: Amazon Web Services (AWS), Artificial Intelligence (AI), Generative Pre-trained Transformers (GPT), OpenAI GPT-3 API, Amazon S3 (AWS S3), Amazon DynamoDB, AWS Lambda, Large Language Models (LLMs)

Machine Learning Developer

2023 - 2023
Gutify Ltd
  • Used images with a multilabel associated with them, created a multilabel image classifier to classify images and obtain related tags.
  • Deployed the model in the Google Cloud platform, with a schedule to run daily.
  • Stored the images in Airtable so the solution obtains the new images from Airtable and labels them.
Technologies: Machine Learning, Artificial Intelligence (AI), TensorFlow, Python, Image Processing, Data Science, Google Cloud Platform (GCP), Kubernetes, Optuna

AI/Machine Learning Developer

2023 - 2023
Paragon Component Systems LLC
  • Optimized the truss webs using Bayesian optimization, having an unknown number of webs, and optimized the cost maintaining that the structure passed strength and structure requirements.
  • Worked with different truss structures and found an optimum number of webs to lower cost.
  • Designed a system to find a matching of previously designed truss structure with the structure that passed strength requirements.
Technologies: C#, Machine Learning, Artificial Intelligence (AI), Deep Learning, Vehicle Routing, Optuna, Bayesian Inference & Modeling

Senior Data Scientist

2018 - 2023
Alcoa
  • Designed and built Alcoa Azure data science pipelines using TSFRESH for feature engineering, Boruta for feature selection, TPOT/Optuna for model selection, and Shap for feature importance.
  • Attended stakeholder meetings, gathered requirements in A3 and DI, and created project objectives and deliverables.
  • Peer-reviewed the team code and checked if the code achieved the desired deliverable.
  • Built models and deployed them on-premises or Azure Cloud, sending output to PHD or OSI/PI (mining) to be consumed by dashboards for stakeholders.
  • Built deep learning models for image processing from drones to detect bauxite quality and estimate sand and reactive silica using images.
  • Preventively maintained, forecasted equipment failure, useful life, reliability, and optimization.
  • Optimized mining with mixed integer linear programming (MILP) using CPLEX, Optuna AnyLogic, and spatial data analysis using ArcGIS Server/Esri.
  • Worked on ETL/ELT using Azure Data Factory and FME for spatial data.
  • Worked in three business verticals, mining, refining, and smelting.
  • Used PyTorch, Pandas, NumPy, scikit-learn, TPOT, TSFRESH, Azure ML, and Azure Data Lake (Gen1 and Gen2).
Technologies: Agile Product Delivery, Algorithms, Machine Learning, Big Data, DataViz, Kalman Filtering, Signal Processing, Deep Learning, Azure ML Studio, CPLEX, Hugging Face, Optuna, Signal Protocols, FME, Financial Modeling, Design Language, Technical Leadership, Regression Modeling

Machine Learning Expert for Data Science POC

2021 - 2022
Breadcrumb Data Limited
  • Developed time series anomaly detection using autoencoders for bearing and mills.
  • Used a Controller Area Network (CAN bus) to build a vibration anomaly detection. The system uses the accelerometer acceleration data to estimate velocity and displacement.
  • Estimated power spectrum to estimate the harmonics of vibration and use that to estimate the health of the equipment.
  • Used TensorFlow to build this application and deployed it to Pi Zero and Pi 4.
Technologies: TensorFlow Deep Learning Library (TFLearn), Data Science, Machine Learning, Feature Engineering, Variational Autoencoders, TensorFlow, IOTA, Raspberry Pi, Autoencoders, CAN Bus, Data Analysis, Forecasting, Data Visualization, API Integration, Simulations, Data Inference, Fine-tuning, Recommendation Systems, Communication, Open Source, Pandas, Bash, Linux, NVIDIA CUDA, GPU Computing, NumPy, AI Design, Time Series, Neural Networks, Artificial Neural Networks (ANN), Hardware, Real-time Data, Scikit-optimize, Mathematics, Anomaly Detection, Internet of Things (IoT), Optuna, OpenAI

Data Scientist and AI Consultant

2021 - 2022
S Wave International Corp
  • Developed deep learning models to auto-tag music records using generated spectrogram images created from music records.
  • Tracked and fixed bugs using Jira as a reporting tool.
  • Deployed the production model in AWS Fargate with an elastic application load balancer to auto-scale the auto-tagging of music records depending on demand.
  • Estimated the beat per minute using signal processing and deep learning. The project used the ViT model to classify spectrograms.
  • Used PyTorch Lightning and DeepSpeed to train models using a large number of tracks.
Technologies: Data Science, Machine Learning, Data Engineering, AIOps, Amazon Web Services (AWS), Models, Data, Transformer Models, Fast.ai, Scikit-learn, Time Series Analysis, Docker, Audio, Signal Processing, Data Visualization, API Integration, Data Inference, Fine-tuning, DeepSpeed, PyTorch Lightning, Communication, Open Source, Pandas, Bash, Linux, NVIDIA CUDA, GPU Computing, Project Management, NumPy, AI Design, Time Series, Amazon DynamoDB, Neural Networks, Artificial Neural Networks (ANN), Text Analytics, Scikit-optimize, Mathematics, Agile Product Delivery, Redis, Kubernetes, Amazon Elastic Container Service (Amazon ECS), Hugging Face, Optuna, Signal Protocols, FME

GIS Platform Architect/Engineer

2021 - 2021
Birch Infrastructure, PBLLC
  • Replicated data from Velocity Suite to BigQuery, performed many ETL operations, and generated materialized views using DBT.
  • Converted geospatial data from shapefiles into BigQuery spatial.
  • Generated a materialized view of LMP prices with weather information, updated hourly.
  • Created Prefect flows to create jobs for data scrapping and downloading from various sources into BigQuery.
  • Created APIs to perform data downloads from various sources like FRED, LMP prices, and other spatial data sources.
Technologies: Python, Data Build Tool (dbt), Prefect, ArcGIS, GPS, Docker, Google Kubernetes Engine (GKE), Data Analysis, Statistics, Data Analytics, Data Visualization, API Integration, Analytics, Datasets, Data Inference, Fine-tuning, Google BigQuery, Communication, Open Source, Data Mining, ETL, Pandas, Bash, Data Reporting, Leadership, Architecture, NumPy, Time Series, Data Processing Automation, Text Analytics, Web Scraping, Mathematics, Mathematical Analysis, BigQuery, Geographic Information Systems, Agile Product Delivery, Neo4j, Arc, Selenium

Developer

2020 - 2021
Toptal Client
  • Created an API for a chatbot to train medical students with virtual patients for the Meksi project in Sydney.
  • Used pre-trained BERT models with examples sent from the client.
  • Created an API to infer the intent from user inquiries.
  • Built a chatbot using the intent classifier, Named Entity Recognition (NER).
  • Merged the Bert Response with an IBM Watson assistant response based on confidence to identify the intent. The results were 97% accurate across 512 intents.
Technologies: Language Models, BERT, Python 3, PyTorch, Fast.ai, Scikit-learn, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Generative Pre-trained Transformer 2 (GPT-2), Generative Pre-trained Transformer 3 (GPT-3), Docker, Data Analysis, Statistics, Data Visualization, Causal Inference, Text Generation, Data Inference, Fine-tuning, Communication, Open Source, ETL, Machine Learning Operations (MLOps), Pandas, Bash, Linux, NVIDIA CUDA, GPU Computing, NumPy, AI Design, Neural Networks, Artificial Neural Networks (ANN), Chatbots, Sentiment Analysis, Chatbot Conversation Design, Word Embedding, SpaCy, Amazon Elastic Container Service (Amazon ECS), Hugging Face

Machine Learning Consultant

2019 - 2021
Oyu Tolgoi LLC
  • Converted seismic traces into the spectrogram to be used with image detection, a Resnet50 model.
  • Built a multi-input model with the height of the event as an embedding input and spectrogram as the second input. The model and approach will be published in the scientific journal Bulletin of the Seismological Society of America.
  • Deployed the model successfully. It is planned to go into production in 2020.
Technologies: Computer Vision, Deep Learning, Dlib, OpenCV, Qt, C++, PyTorch, TensorFlow, Keras, Python, Time Series Analysis, Google Kubernetes Engine (GKE), Audio, Data Visualization, Data Inference, Fine-tuning, Communication, Open Source, Azure Databricks, Pandas, Bash, Linux, NVIDIA CUDA, GPU Computing, NumPy, AI Design, Neural Networks, Artificial Neural Networks (ANN), Mathematics, Mathematical Analysis, Agile Product Delivery, Kubernetes

Machine Learning Consultant

2015 - 2021
Global Unmanned System
  • Developed algorithms to estimate above-ground biomass using point cloud data from drone images.
  • Created object detection software for sealion detection in drone images.
  • Classified images for sandalwood detection in drone images.
  • Developed various image analysis software for drone images using OpenCV.
  • Created various GIS applications for satellite images and point cloud data.
  • Developed a program to help ship berthing at Fremantle Port using a SLAM algorithm and graph network.
  • Gathered requirements and met with mine managers and refineries to learn about their problems and find possible projects.
  • Converted raster layers into vector layers using kernel methods/OpenCV.
Technologies: Amazon Web Services (AWS), ArcPy, Point Clouds, Amazon SageMaker, 3D Image Processing, Image Processing, Predictive Analytics, Computer Vision, ArcGIS Runtime SDK for .NET, PyTorch, Data Science, GIS, MySQL, Agile Software Development, Deep Learning, Git, XGBoost, Keras, OpenCV, R, Python, Data Analysis, Object Detection, Video Processing, Statistics, Data Visualization, Data Inference, Fine-tuning, Algorithms, Open Source, Pandas, Bash, Data Reporting, Leadership, Real-time Vision Systems, Architecture, NumPy, AI Design, Neural Networks, Artificial Neural Networks (ANN), Real-time Data, Data Processing Automation, Mathematical Analysis, Geographic Information Systems, Amazon Elastic Container Service (Amazon ECS), Regression Modeling

Senior Data Scientist

2018 - 2020
Freelance Work
  • Developed refineries predictive maintenance using machine learning in Databricks, Azure Machine Learning service, and Azure Machine Learning Studio.
  • Built time series prediction using Keras and PyTorch for anomaly detection.
  • Built time series prediction with LSTM/CNN using multivariate one-minute sensors data.
  • Developed a PowerBI dashboard for mining with Fleet Management System.
  • Built a sound and vibration equipment health using a convolution neural network.
  • Managed external contractors to evaluate cloud technology and perform a proof-of-concept solution to common anomaly detection in time series data and apply it to all pumps in the refinery.
Technologies: Transformer Models, BERT, Language Models, Reinforcement Learning, Uniformance Process History Database (PHD), OSI Model, Data Engineering, ArcPy, Point Clouds, RStan, Image Processing, Predictive Analytics, Kubernetes, Microsoft Power BI, ArcGIS Runtime SDK for .NET, PyTorch, Data Science, GIS, SQL, Agile Software Development, Deep Learning, Artificial Intelligence (AI), Statistical Learning, Databricks, Azure ML Studio, Azure Machine Learning, C++, R, Python, Scikit-learn, Time Series Analysis, Signal Processing, Forecasting, Statistics, Data Analytics, Data Visualization, Physics Simulations, Data Inference, Fine-tuning, Open Source, ETL, Azure Databricks, Machine Learning Operations (MLOps), XGBoost, Pandas, Bash, Data Reporting, NVIDIA CUDA, NumPy, Time Series, Neural Networks, Artificial Neural Networks (ANN), Data Processing Automation, Mathematics, Mathematical Analysis, Anomaly Detection, Amazon Elastic Container Service (Amazon ECS), Regression Modeling

Senior Data Scientist

2019 - 2019
Western Power
  • Built energy demand time series prediction with multivariate half-hourly input data using LSTM/CNN neural networks.
  • Created a production solution to use forecasted weather data to forecast demand deployed to AWS Fargate.
  • Project-managed through Jira tickets and code stored in Bitbucket.
Technologies: ArcGIS Runtime SDK for .NET, Data Science, GIS, Agile Software Development, Artificial Intelligence (AI), TensorFlow, Keras, Python, ArcGIS, Time Series Analysis, Data Analysis, Sensor Fusion, Forecasting, Statistics, Data Visualization, Analytics, Physics Simulations, Data Inference, Fine-tuning, JavaScript, Open Source, Plotly, ETL, Pandas, Bash, Data Reporting, Linux, Leadership, NVIDIA CUDA, Time Series, Neural Networks, Artificial Neural Networks (ANN), Text Analytics, Mathematics, Geographic Information Systems, Agile Product Delivery, Internet of Things (IoT), Regression Modeling

Senior Spatial Engineer

2016 - 2018
BHP
  • Helped a big mining company to take advantage of its spatial data.
  • Created driver behavior analysis software for a mining operation.
  • Worked with natural language processing with Keras.
  • Developed various GIS projects using ArcPy and C# ArcObjects.
  • Created predictive models using machine learning and Apache Spark/Databricks.
  • Completed a time series data analysis using Kalman filters for vehicle tracking.
  • Mounted edge devices on diggers in an underground mine (Olympic Dam Mine) to classify the underground signs and determine if the bucket was full or empty.
  • Converted raster layers of roads and vegetation into vector layers for processing in ArcGIS.
  • Gathered requirements and met with mine managers and refineries to learn about their problems and find possible projects.
  • Developed a Shiny (in R) dashboard for mining, a hotspot of high-rack events.
Technologies: Amazon Web Services (AWS), Cloudera, Hortonworks Data Platform (HDP), OSI Model, Data Engineering, Point Clouds, RStan, ArcGIS GeoEvent Server, 3D Image Processing, Redshift, Spotfire, Microsoft Power BI, Hadoop, ArcGIS Runtime SDK for .NET, GIS, SQL, Agile Software Development, Statistical Learning, RStudio Shiny, Kibana, Elasticsearch, Oracle, Microsoft SQL Server, Git, Apache Kafka, C#, ArcPy, Keras, Python, Esri, Kalman Filtering, ArcGIS Server, ArcGIS, GPS, Docker, Data Analysis, Sensor Fusion, Video Processing, Data Analytics, Data Visualization, Analytics, JavaScript, Plotly, ETL, Spark, Big Data, XGBoost, Pandas, Bash, Data Reporting, Linux, Architecture, Neural Networks, Hardware, Data Processing Automation, Text Analytics, Mathematics, Data Modeling, Geographic Information Systems, Vehicle Routing, Agile Product Delivery, SpaCy, Natural Language Toolkit (NLTK), Internet of Things (IoT), FME, Neo4j, Technical Leadership

Senior Algorithm Engineer

2014 - 2016
Fugro
  • Developed image analysis software for underwater object detection.
  • Processed point cloud data using C++/Python for both underwater objects and above ground.
  • Created image classification for a remote sensing Lidar point cloud using Python running in AWS and Fugro Roames for Ergon.
  • Created C++ numerical algorithms for echo sounder calculation using Armadillo C++, OpenBLAS, and algorithms including Kalman filter, LAZ smoothing, and classification.
  • Created various Julia and R regression machine learning applications.
  • Converted raster layers underwater to detect cables.
Technologies: Amazon Web Services (AWS), Data Engineering, Point Clouds, ArcGIS Runtime SDK for .NET, GIS, SQL, Artificial Intelligence (AI), Statistical Learning, MATLAB, Esri, Oracle, Microsoft SQL Server, Git, Python, C++, Kalman Filtering, ArcGIS, GPS, Sensor Fusion, Object Detection, Audio, Data Analytics, Data Visualization, Algorithms, JavaScript, XGBoost, Pandas, Bash, Architecture, Mathematics, Data Modeling, Geographic Information Systems, Agile Product Delivery, Armadillo

Senior GIS Developer

2010 - 2014
Department of Mines and Petroleum
  • Developed various GIS software to help surveyors in their work.
  • Wrote classification and regression software for a GIS application.
  • Developed GeoMap.WA, which is used to display the department's GIS products.
  • Developed projects to do point cloud data analysis using ArcGIS software.
  • Wrote various SQL server scripts to optimize retrieval of data.
Technologies: ArcPy, .NET, GIS, SQL, Microsoft SQL Server, Git, R, Python, GPS, Esri, C#, 3D Image Processing, ArcGIS, ArcSDE, ArcGIS Server, Data Visualization, JavaScript, Sentiment Analysis, Data Processing Automation, Mathematical Analysis, Data Modeling, Geographic Information Systems, Agile Product Delivery, FME

Software Engineer

2008 - 2010
Western Power
  • Supported GIS software to show Western Power Assets in Western Australia.
  • Created predictive models for wooden pole maintenance and inspection using R.
  • Developed and helped in the establishment of the wooden pole serviceability index.
  • Wrote classification software for pole top fire prediction and pole serviceability index.
  • Wrote Oracle scripts to download data for Oracle reports and optimize database queries.
Technologies: SQL, Java, Oracle, C++, C#, R, Time Series Analysis, Kalman Filtering, Signal Processing, Forecasting, Data Visualization, JavaScript, Agile Product Delivery

Senior Software Engineer

2006 - 2008
Comsec
  • Served as a senior software engineer and worked with various kinds of share trading software.
  • Developed and designed NAB Equity Lending's online margin lending software.
  • Supported various kinds of online trading software and managed funds.
  • Created a SQL Server and Oracle database that shared procedures and database optimization.
  • Participated in the design of NAB Equity Lending's migration to the Commsec Apollo project.
  • Provided bug fixes and problem-solving for issues with trading software.
Technologies: T-SQL (Transact-SQL), SQL, Java, .NET, C++, Quantitative Finance, Financial Data, Computational Finance

Senior Software Engineer

2000 - 2006
ERG
  • Wrote Various C++ and Java applications for smart rider ticketing.
  • Wrote transaction processing software in C++ to run under Solaris Unix and Windows operating systems.
  • Supported existing software and bug fixes, code debugging, code executing speed (profiling), and peer review.
  • Generated Oracle reports for Ventura bus travel times, loading, and peak time analysis.
  • Optimized various PL and SQL queries for reports using Toad and Oracle query analysis.
Technologies: T-SQL (Transact-SQL), SQL, PostgreSQL, C#, Oracle, C++, Bash

Postdoctoral Research Fellow

1999 - 2000
Columbia University, New York
  • Conducted research on the Columbia Linear Machine (CLM) as a postdoctoral research fellow at Columbia University.
  • Supervised PhD and honor students and assisted with lectures and labs.
  • Worked with LabVIEW on their National Instrument (NI) products control experiment.
  • Looked after the lab supplies and placed orders to maintain operations.
  • Wrote signal processing software (filtering) for probe measurements to remove noise.
  • Wrote scientific papers and published the results of experiments.
Technologies: NAG Numerical Library, MATLAB, Fortran, C++, Time Series Analysis, Signal Processing, Physics Simulations, Simulations, Research, Regression Modeling

Postdoctoral Research Fellow

1996 - 1999
Flinders University of South Australia
  • Supervised PhD students and honour students during their studies.
  • Wrote numerical analysis software for signal processing.
  • Helped with lectures and lab tutorials and demonstrations.
  • Maintained the lab by ordering supplies and repairs.
  • Wrote scientific papers and published results of experiments.
Technologies: NAG Numerical Library, MATLAB, Fortran, LabWindows/CVI, Windows, C++, Signal Processing, Simulations, Research, Regression Modeling

Automatic Processing of Mine Micro-seismic: Application to the Oyu Tolgoi Mine

Developed an automatic processing system that outperforms the analysts for routine processing in terms of processing quality and speed.

Our processing scheme mixes novel approaches and a more traditional method relying on energy variation-based event detection and on the picking of arrival times. Our platform directly employs the raw events detected by the seismic system. The event detection is set to be highly sensitive. When an event is detected, a block of continuous data is extracted and the event is located using a grid-based event location method. It classifies the event into ten categories, picks the arrival times, locates the events, and calculates the source parameters. The Oyu Tolgoi mine is currently using this system to process data in a production setting.

The system is deployed on Kubernetes clusters. Automatically processing seismic data requires the adoption of a holistic approach that extends to the design of the seismic array, the choice of sensors and the type and shape of the input data.

Using convolution neural networks and mixed networks with other features available, an automatic seismic identifier was created that outperforms other models in the market.

Auto-tagging Music Records

The project is to auto-tag production-level music records to run under AWS to enable auto-tagging of music as they are used. The auto-tagging included mood/fee, genre, Instruments, vocals, and Type. The project converted the audio track into a mel spectrogram and did image classification. For the beats or beats per minute (BPM), I used wave analysis and some property software written to estimate BPM. The project achieved over 80% macro precision and 60% macro recall.

Electricity Demand Prediction

https://github.com/cobleg/Hack-A-Gig
Electricity demand prediction using current population categories and weather conditions helps companies plan their usage of electricity to reduce costs.

Western Power had a hack-a-gig competition to help them use their smart meter collected data, weather conditions, and population categories to determine their electricity demand. This helps clients to achieve minimum costs and helps Western Power plan its electricity network.

Mine was the winning solution and was written in Python using Keras for neural network and a mixed input model neural network, which consisted of the following inputs:

• Embedding of categorical fields like household categories, days to public holidays, and days after a public holiday (up to 5 days).
• Tabular numerical data for weather conditions.
• LSTM of the previous usage pattern of electricity.

These inputs were then concatenated into a fully connected deep neural network with one output variable to estimate the current usage of electricity per household. The data is then aggregated to reduce error by localities. My solution gave the best result in the competition, and the best reward was getting contracted by Western Power to do the project.

Driver Behavior Analysis

BHP mines have a large number of haul trucks, light vehicles, dozers, excavators, and others. There is a need to detect when drivers exceed the speed limit, hit the brakes harshly, and take turns at high speed. These events lead to fast wear of tires and required coaching drivers to stop doing that. Light vehicles should not approach haul trucks less than 50m unless they have positive communication since the haul truck driver may not be able to see the light vehicle. Detecting these events helps increase safety in mines.

I wrote a C# application to collect GPS location data from various sources streamed through Kafka and then calculate the speed and orientation using the Kalman filter to reduce errors in the GPS measurements, and I stored the data in an Elasticsearch spatiotemporal database. I then used this data to determine events using the Elasticsearch database. I was the lead developer in the project.
2010 - 2016

Master's Degree in Actuarial Science

Curtin University - Perth, Western Australia

2009 - 2010

Graduate Diploma in GIS

Curtin University - Perth, Western Australia

1992 - 1996

PhD in Science and Engineering

Flinders University - Adelaide, South Australia

1990 - 1992

Master's Degree in Communications Engineering

Cairo University - Cairo, Egypt

1981 - 1986

Bachelor's Degree in Communications and Electronics

Cairo University - Cairo, Egypt

MARCH 2020 - MARCH 2022

GCP Associate Cloud Engineer

GCP

JULY 2017 - PRESENT

Artificial Intelligence Nanodegree

Udacity

Libraries/APIs

Scikit-learn, PyTorch, Keras, TensorFlow, ArcGIS, XGBoost, Pandas, NumPy, Scikit-optimize, SpaCy, OpenCV, DeepSpeed, PyTorch Lightning, Natural Language Toolkit (NLTK), Dlib, Fast.ai, TensorFlow Deep Learning Library (TFLearn), Armadillo

Tools

GIS, Esri, Azure Machine Learning, MATLAB, Amazon SageMaker, Google Kubernetes Engine (GKE), Plotly, Google AI Platform, Amazon Elastic Container Service (Amazon ECS), FME, ArcGIS Runtime SDK for .NET, Microsoft Power BI, Spotfire, Kibana, Azure ML Studio, Cloudera, ChatGPT, BigQuery, Google Analytics, Bloomberg, Git, LabWindows/CVI, Protégé, DataViz, CPLEX, Microsoft Exchange, MATLAB Statistics & Machine Learning Toolbox

Frameworks

.NET, Optuna, Selenium, Hadoop, RStudio Shiny, Spark, Qt

Languages

C++, C#, R, Python, SQL, Python 3, JavaScript, Bash, T-SQL (Transact-SQL), Java, Fortran, SPARQL, Arc

Paradigms

Agile Software Development, Data Science, Anomaly Detection, ETL, Functional Programming, Radio Frequency (RF) Protocol

Platforms

Ubuntu, Azure, Jupyter Notebook, Oracle, Windows, Amazon Web Services (AWS), Google Cloud Platform (GCP), Linux, ArcGIS Server, Apache Kafka, Docker, NVIDIA CUDA, Kubernetes, Databricks, Hortonworks Data Platform (HDP), Raspberry Pi, AWS Lambda

Storage

Microsoft SQL Server, Elasticsearch, MySQL, PostgreSQL, Amazon S3 (AWS S3), Neo4j, PL/SQL, Redshift, Amazon DynamoDB, Redis

Industry Expertise

Project Management, Insurance

Other

BERT, Natural Language Processing (NLP), Artificial Intelligence (AI), Deep Learning, Statistical Learning, ArcGIS GeoEvent Server, ArcPy, Predictive Analytics, Data Engineering, RStan, Image Processing, 3D Image Processing, RSelenium, Computer Vision, Machine Learning, Models, Generative Pre-trained Transformer 2 (GPT-2), Time Series Analysis, Data Analysis, Kalman Filtering, Sensor Fusion, Object Detection, Audio, Signal Processing, Video Processing, Forecasting, Statistics, Quantitative Finance, Finance, Data Analytics, Data Visualization, API Integration, Analytics, Physics Simulations, Simulations, Datasets, Language Models, Text Generation, Data Inference, Fine-tuning, Google BigQuery, Algorithms, Recommendation Systems, Communication, Open Source, Data Mining, Azure Databricks, Machine Learning Operations (MLOps), Data Reporting, Software Development, Leadership, GPU Computing, Architecture, AI Design, Time Series, Neural Networks, Artificial Neural Networks (ANN), Chatbots, Sentiment Analysis, Chatbot Conversation Design, Real-time Data, Data Processing Automation, Text Analytics, Web Scraping, Mathematics, Mathematical Analysis, Data Modeling, Financial Data, Word Embedding, Generative Pre-trained Transformers (GPT), Research, Geographic Information Systems, OpenAI GPT-3 API, Agile Product Delivery, Internet of Things (IoT), Hugging Face, Bayesian Inference & Modeling, Design Language, OpenAI, Technical Leadership, Regression Modeling, Large Language Models (LLMs), Text to Speech (TTS), Uniformance Process History Database (PHD), OSI Model, Point Clouds, Reinforcement Learning, Generative Adversarial Networks (GANs), NAG Numerical Library, Graph Neural Networks, Generative Pre-trained Transformer 3 (GPT-3), Stable Diffusion, Causal Inference, Big Data, Real-time Vision Systems, Financial Modeling, Hardware, Computational Finance, Vehicle Routing, OpenAI GPT-4 API, Frameworks, Raster to Vector, Windows 10, GPS, Transformer Models, TAPAS, OWL2, Data Build Tool (dbt), Prefect, Variational Autoencoders, ArcSDE, AIOps, Data, Feature Engineering, IOTA, Autoencoders, CAN Bus, Text to Image, Signal Protocols, Electronics, Digital Communication, Vector Databases, Pinecone, Speech to Text, Speech Recognition, Microsoft Graph API, NLU, Deep Neural Networks

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