Emad Elsebakhi

As a head of data science, my focus is to develop innovative R&D technology through hybrid data science talent team in Petronas PRSB-PD&T-GR&T: Harnessing big data analytics with the state of the art of AI and novel machine/deep learning algorithms, robotics through IOT-HPC & cloud-based platform to drive action insights in oil and gas: E&P efficiency: Maximize ROI, optimize operations, while reducing cost of maintenance & identify sever risk. Lead a data science team to build big data hub with deep insights platform of seismic/well logs, core, integrating real-time drilling with daily drilling reports data and historian data; then develop novel AI-Deep Learning Predictive Models to improve performance across Petronas oil and gas operations (optimize operations, digital oilfield, drilling optimization and reduce NPT, EOR and reservoir management to increase hydrocarbon production, automated subsea factory, monitoring and diagnoses, identify stuck pipes, minimize drilling cost, optimize drilling operation; while minimize NPT, rotating equipment predictive maintenance models (PdMs) & APM, and supply chain...etc. In the era of digital transformation of upstream and downstream oil and gas: E&P efficiency; I am focusing on: - Bringing together data scientists, geophysicist(s), petroleum and software engineers to provide innovative analytics solutions for the common challenges in Petronas oil and gas operations, services; and productions. - Drawing vision and roadmap for the team to carry cutting-edge research (publications, IP-patents, products) in AI-machine learning-big data agile Eco-system of physical and data-driven models for better decision and future forecasting with KPI business strategies. - Developing cutting-edge and break-through big data analytics with machine learning-AI ((NLP-semantic knowledge; graph theory; data mining, and large-scale optimization-statistics innovative algorithms.

As a head of data scientist; I lead a team that is focusing on developing and deploying state of the art AI and machine learning with Big Data Analytics through IOT (Innovative digital cutting-edge strategic agile Eco-system platform) and big data platform: In Healthcare: ======== Lead externally-funded efforts developing novel innovative R&D AI -Data Driven for healthcare production, enhance personalized medicine through Genomics development, disease association and protection: Solutions dedicated Oncology and MRI systems with existing clinical EMR/MRI scanner. Play program with missions to reduce cost and develop machine learning models using artificial intelligence to provide high quality treatments and digital receiver components. Oil and gas full stream challenges: ====================== Lead team to integrate multiple sources of data (logs/seismic/core/sensor devices...etc) to develop customized predictive models to enhance oil recovery (EOR) using hydraulic fracture and High-end Oil and Gas Operation Analytics through Deep Learning, Oil and Gas TechVision Opportunity Engine (TOE) , PVT and reservoir surveillance (innovative Production Operations Surveillance Hub (POSH)) system to provide advanced alerts to production engineers in the oilfield; identify the promising areas for future drilling, and improve upstream and downstream outcomes; intelligence oilfield (IOF), scheduling and planning, and Planet Operations Advisory that connect wells to are to have real-time access to common machine and operational data sets across all wells to improve efficiency, prevent failures and minimize downtime. POSH will track and coordinate field activities with most of the existing data-based enterprise solution that integrates and streamlines business processes for oil-well and oilfield services (Well and Field Automation (WAFA); which will improve the business planning process and enhance completion task-efficiency and maximize the utilization of resources.

o Lead innovative R&D AI-machine learning and computer vision for healthcare and biomedicine for he need of modeling algorithms to analyze big biomedical (EMR, Molecules, Genomics, and others) data. Lead a data science team to deliver innovative AI-data driven solutions for healthcare and biomedicine using EMR and clinical data to improve healthcare outcomes, save lives, while reducing cost and minimize sever risks. o Develop deep learning algorithms for digital personalized medicine and disease intervention and therapeutics for chronic diseases, such as, diabetes, cancer, cardiovascular, asthma through integration of EMR and Genomics data. o Mining and analyzing whole genome sequencing and identify rare variants with comprehensive variant analysis of individuals, identify new mutations using whole genome sequences and identify rare variants and SNP selections based on genomic wide association studies (GWAs) data. o Lead the future innovative R&D in AI-digital solutions for the future of digital therapeutics and develop novel predictive modeling algorithms to analyze big biomedical (EMR, Molecules, Genomics, and others) data: Highlight the potentialities of AI-Deep reinforcement learning and knowledge graph automated reasoning NLP to accelerate scientific drug discovery and development, clinical trials phases, and robot process automation (RPA); identify novel therapeutics; and identify drug targets.

Develop big data analytic with IOT solutions and build innovative predictive modeling and big data analytics paradigm with real-time monitoring systems for Healthcare and Biomedicine, finance, retails, and energy ...etc. - Healthcare, Genomics Analysis and Biomedicine -------------------------------------------------- Drive individual medicine using whole genome sequencing and disease related: (i) drive down hospital readmissions to improve the quality of hospitals and overcome the Medicare penalties for re-admission; (ii) develop with a proper customization Fraud Prediction Scorecard to predict the probability of fraud and avoid the mistakes that we had within Fraud Audit Rules. We focus on: - Developing clinical core of risk: healthcare quality and chronic management and reduce treatment cost based on clinical and genomic data. - Handling the future big Omics-Clinical data; then provide basic genome information to clarify the relationship between genetic variations and cancer, human diseases, and drug response. Mining and analyzing whole genome sequencing (WGS) and identify rare variants with comprehensive variant analysis of individuals. Identify new mutations using WGS and rare variants and SNP selections based on GWAs data. - Oil & Gas: -------------------------- Reservoir characteristics, unconventional gas development technology: Permeability and porosity prediction from well logs, PVT Properties, hydrocarbon and rock mechanics, predict, control, and optimize the reservoir and enhance the oil recovery (EOR) using historical (seismic, core, logs) data. - Focus on (i) Reservoir management/simulation to enhance/optimize oil recovery; (ii) Study unconventional reservoir and shale rocks and develop advanced 3D imaging technology CT scan digital rock physics (Shale and Hydrocarbon). Allocate future wells drilling with the highest oil production; (ii) identifying zones worth further exploration using the tremendous volume of available data.

Focusing on providing innovative and cutting-edge research to save lives; while reducing cost and identify patients with severe risks based on big data science and predictive analytics: Enabling personalized medicine for high-quality care, better outcomes. With the cooperation of outstanding and talent capable team of expertise (scientist, physicians, clinicians, and developers): Towards Clinical Care Research: - Having deep insights of healthcare data to discover the hidden patterns and forecast healthcare outcomes using novel predictive modeling algorithms using multiple sources of patients’ data from Hospital EMR with socio-demographic and other hospital data. - Leveraging modern technology to address healthcare challenges, provide quality; while reducing cost. Towards Precision Medicine and Gene Mutations in Clinical Practice at Bedside: With talent team of scientists to solve the challenging problems in biomedical informatics; then handling the future big Omics-Clinical data; then provide basic genome information to clarify the relationship between genetic variations and cancer, human diseases, and drug response. - The specification of the research and development include the mining and analyzing whole genome sequencing and identify rare variants with comprehensive variant analysis of individuals. In addition, identify new mutations using whole genome sequences and identify rare variants and SNP selections based on GWAs data. - Combine whole genome sequencing (WGS); whole exome sequencing (WES), and RNA-Seq, and gnomic wide association data, carry out fixed effect of meta-analysis using statistics and machine learning with conservative stepwise procedure for reporting loci from single-variant analysis and validate the associations of rare variants; and then make available an extensive resource, including individual-level genetic and phenotypic data-web-based to facilitate the exploration of association results.

Carrying both teaching and research in machine learning and data mining with advanced artificial intelligence and pattern recognition in oil and gas with wind turbine, sensor data and smart grid; energy demand and supply . With Petroleum and and Industrial Engineering developed proposals for a graduate program in computational intelligence and its applications in oil and gas industry. We carry both basic and applied research in artificial intelligence with data mining and machine learning; optimization, ensemble learning and open-standard cloud computing; soft-computing breakthrough innovative techniques and image recognition with hybrid notion of ensemble learning computational intelligence predictive modeling with reasoning about knowledge, ontology and multi-agent systems and stochastic processes in numerous of oil and gas: Exploration and Production: (upstream and downstream), reservoir characteristics, unconventional gas development technology. I with outstanding colleagues and scientists at KFUPM petroleum Institute utilized both data mining predictive modeling research in numerous of Saudi Aramco oil and gas projects; such as, reservoir characteristics, permeability and porosity prediction from well logs and hydraulic flow units of crude oil Systems, history matching and reservoir simulations, risk analysis management, PVT Properties, hydrocarbon and rock mechanics,classification, identifying flow regimes and liquid-holdup in horizontal/vertical multiphase flow, seismic analysis, and estimating rock mechanics properties of reservoir to enhance its oil and gas recovery. I with my research team build NORMA (Numerical Optimization of Reservoir Management Algorithms) software to manage, predict, control, and optimize the reservoir characteristics and enhance the oil recovery (EOR) based on artificial intelligence/data mining/predictive modeling algorithms based on historical (seismic, core, wireline logs) data through the daily life.

I was a primary instructor with sole responsibility for teaching, designed syllabi and overall class structure. I prepared and delivered lectures, developed handouts, assignments, projects and exams. I worked as a primary instructor for the courses: Introduction to Computer Technology; Operating Systems; Computer Science with C++/Java; Database Management Systems; and Artificial Intelligence with Pattern Recognition. I have been involved in the both research and undergraduate committees of both computer science department and College of Arts and Science. I developed with a team of faculty Business School’s, proposal for “Enhancement for increasing the enrollment in the graduate Program at KFUPM”. Form a research team to utilize both data mining predictive modeling and the state of the art cutting-edge data mining technology and computational Intelligence research in numerous of client projects in bioinformatics and medicine within King Abdulaziz City of Science and Technology (KACST): The utilization of the cutting-edge machine learning and data mining technology in modeling biomedical databases, DNA structure and genomic sequences. Furthermore, I developed, with different team members of faculty from information systems and computer science department, proposals for a new curriculum according to Deanship of Academic Development Program. We have published numerous research articles in referred journal and prestigious conference proceedings in numerous multidisciplinary research society, such as, IEEE neural networks transactions, Expert systems with applications, Computer and simulations, Bioinformatics, Petroleum science and engineering, geosciences journals, and society of petroleum engineering.

The main job is to design and implement the Architectural Studio Software for 3D Computer Vision using machine learning and data mining with Java Development and XML document. Design the Graphical User Interface (GUI), Collaboration Work, and Cryptography Algorithms for Network Dynamical Security. The solution methodologies include: linear and non-linear programming, geometric programming, and interior point primal-dual algorithms.

Corning offers a comprehensive range of innovative, high-quality tools and solutions for life science research and bioproduction. The main goal is to classify and predict protein and genomics, analyzing DNA alignments’, and Bioinformatics applications using Data mining and machine learning algorithm. I worked with a twenty research group to invent and develop most of this software, which is called in-house Express Array Technology, and analyze its output for Quality Control in the Data Base Systems. Generally, we used the computational biology techniques for design an automatic quality control system, using C++/C#, java, Visual Basic, Oracle and MATLAB.

I pursued my graduate studies towards my Ph.D. at Cornell University. I have engaged in teaching undergraduate laboratories and theory sections in Mathematics and Statistics. I was teaching assistance for the following courses: Linear Algebra, Differential Equations with Matlab and C++, Multivariate Calculus and Differentiation, Probability and statistics, and Multiple Regression Analysis, and Point Estimation. My role was to carry out discussion of class materials in recitation sections and during office hours; preparation; grading both exams and assignments. During my graduate study in Cornell University, I took several graduate courses in Computer Science, Statistic, and Mathematics.

During my Ph.D. work, I came with a new computational intelligence framework called “Functional Networks Predictive Models Paradigm Scheme for Pattern Classification”. This new paradigm is a process of finding hidden patterns, interpreting the results to create knowledge and trends in distinct industry databases. This new framework takes into consideration both data domain and knowledge expertise during its implementation processes, Functional networks can be considered as a new computational intelligence data mining predictive modeling strategy that provides both sufficient and reliable modeling schemes with continuous protected process and accurate solutions for numerous industry applications, such as, business/economics/marketing, energy/renewable energy and environmental sectors, oil and gas exploration and production, software engineering and intrusion detection systems, security and E-commerce, biomedicine and pharmaceutical with bioinformatics and healthcare industries.

• Through the period of 1994-1997, I worked full time as an assistant lecturer of Mathematics and Computational Science in the Department of Mathematics and Computational Science, Faculty of Science, Mansoura University, Egypt. I taught undergraduate level courses including both laboratories and small sections comprising general courses in both Computer Science and Mathematics. I taught to undergraduate students in Mathematics, Computer Science, and engineering school’s; his role is to do discussion of class materials in recitation sections and during office hours; preparation. I worked on his Master thesis research in the area of Applied Mathematics and Computer Science. He designed a new training algorithm for computing the torsion of non-homogeneous anisotropic bars, and its rigidity coefficients.

I was ranked the first within a class of 350 students: I got the bachelor degree in Mathematics with Computational Sciences with a distinction with honor degree (94.1% Top of the entire class of 350 students) (GPA: 4.0). • My graduation Project: “Cooley-Tukey Algorithm for Fast Fourier Transformations”.