AIQ is the leading Industrial AI solution provider in the MENA region with a $1.4B valuation.

I am leading the development of an ML-based optimization solution for oil and gas production - RoboWell. The solution is deployed across 5 oil and gas fields with over 500 well counts.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅ Identified 3 ML/MLOps sub-products and improved DS task delivery time by 20% by developing 
a roadmap, prioritizing tasks, and optimizing resource allocation for the solution scale-up phase. 
The sub-products include ML Models Monitoring Report, ML Proxy Model, and Data Quality Control.

✅ Saved $3M/year by developing end-to-end ML Proxy Modeling and Monitoring Pipelines that eliminated the cost of sub-contractors and reduced software license costs used for proxy model generation.

✅ Improved by 22% the ML solution MAPE error by designing model validation, data cleaning, and hyperparameter tuning strategies and supervising their implementation by the team.

Principal Data Scientist

Conundrum

Conundrum is an Industrial AI company that develops a SaaS platform for optimal control of metals and mining assets.

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀:
🟢 Led a team of 7 data scientists in the development of machine learning modeling and optimization solutions.
🟢 Covered client communication, team management, problem formulation, model development, and solution deployment of complex ML / optimization solutions.
🟢 Data Science team hiring, growth, and development.
🟢 Support of the sales and business development from the AI-product strategy and pre-sale perspectives.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅  Increased by $60M+ customer revenue by delivering 4 real-time end-to-end ML solutions that optimized ore processing and smelting plants. The solutions showed 2.5%+ increase of plants throughput and stabilized by 25%+ of the product variability.

✅  Identified potential $50M customer revenue improvement by completing 5 bottleneck analysis projects for Tier-1 metals and mining companies, resulting in new projects with them.

✅ Grew and structured a DS team from 5 to 20 data scientists who formed 4 internal teams responsible for specific solution directions.

✅ Supported closing of 3 multi-million contracts by providing technical support to the sales and business development teams.

✅ Reduced by 400%+ the time of development and deployment of ML applications by creating a machine learning pipeline template based on Kedro framework.

✅ Decreased from 15 to 3 days the data quality assessment time by creating a standard library that generated consistent data quality reports for new client data.

✅  Improved by 70% the DS team task completion rate within a sprint by introducing and adapting Scrum practices.

𝗨𝘀𝗲𝗱 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
Git | GitLab | Kedro | Great Expectations | MLFlow | Scikit-Learn | XGBoost | CatBoost | PyTorch | Keras | Numpy | Pandas | SHAP | Plotly | Matpotlib | Seaborn | Scipy

Ctrl2GO Global

Ctrl2Go is one of the major AI solution providers for the railway and energy industries in the RCIS region.

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀:
🟢 Led a team of 5 data scientists in the development of predictive monitoring machine learning models for various industrial equipment. 
🟢 Structured and optimized the company's business to enhance the efficiency of the data science team's contributions.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅  Increased by 2.3% smelting plant production by supervising the solution development for optimal ore blending using MINLP formulation in Pyomo.

✅ Created a comprehensive model database/library for quicker deployment of the existing solutions in new projects.

✅ Formulated product development directions to the C-level management of the company based on the existing clients and partners and the data science team's expertise. It allowed the company management to prioritize development directions, understand risks, and estimate costs.

𝗨𝘀𝗲𝗱 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
Git | GitLab | Kedro | Great Expectations | MLFlow | Scikit-Learn | XGBoost | CatBoost | PyTorch | Keras | Numpy | Pandas | SHAP | Plotly | Matpotlib | Seaborn | Scipy | Pyomo

Head of Data Science

Zyfra Company

Zyfra is one of the major Industrial AI solution providers in the RCIS region.

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀:
🟢 Led a team of 3 data scientists in the development of ML solutions for problems in the petroleum industry including process modeling, optimization, and anomaly detection.
🟢 Covered Industrial AI-based product strategy formulation, management, development, and sales.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅ Increased by 1.2% oil and gas production for 2 clients by leading the from-scratch development of the 
”AI Oil Optimization Assistant” solution. This is web-based ML software for accurate prediction and optimization of oil and gas production in real-time by using machine learning and advanced dynamic optimization algorithms.

✅ Increased by 5% the drilling speed and reduced by 50% the stuck pipe occurrence on a pilot test by developing a real-time ML solution to detect drilling pipe sticking events.

𝗨𝘀𝗲𝗱 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
Git | GitLab | Kedro | Great Expectations | MLFlow | Scikit-Learn | XGBoost | CatBoost | PyTorch | Keras | Numpy | Pandas | SHAP | Plotly | Matpotlib | Seaborn | Scipy | Casadi

Senior Data Scientist

Norwegian University of Science and Technology (NTNU)

I was developing advanced industrial ML approaches for combining machine learning with process engineering physics for multiphase flow and reservoir modeling and equipment degradation monitoring.

𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
✅ Reached TOP 6 most cited papers in Computers and Chemicals Engineering Journal (Q1) within
the last 3 years based on the Scopus citation system.
✅ Reached TOP 3 in the ranking of most downloaded papers for 2 publications in 2 Q1 journals.
✅ Reached over 300 citations within 3 years of active publishing.

𝗨𝘀𝗲𝗱 𝘁𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀:
Scikit-Learn | XGBoost | PyTorch | Keras | Numpy | Pandas | Plotly | Matpotlib | Seaborn | Linear Regression | Bayesian Neural Networks | Feed-Forward NN | Gradient Boosting | Random Forest

Researcher in Industrial Machine Learning

PSRE Co (NTP Truboprovod)

✅ Developed a model to predict severe slugging occurrence in pipeline networks and stability map representation.
✅ Developed a model to calculate transport of solids (flow regime and pressure drop) in single and multiphase steady-state flows for different flow patterns.
✅ Adopted industrial standards to calculate erosion rate in pipelines with single and multiphase flows.
✅ Developed a model of reciprocating compressors for equipment selection and performance evaluation purposes.

Multiphase Flow Models Developer

HOERBIGER

✅ Developed a calculation spreadsheet for the estimation of an oil production increase by wellhead compression technology.
✅ In a team of 2 application and 5 service engineers, completed 6 projects on supplying reciprocating compressor parts according to the customers’ needs.
✅ Assisted my colleague in a project of reciprocating compressor upgrade and revamp by developing a 3D compressor model in SolidWorks software.

Application Engineer

✅ Developed a dynamic model of a reciprocating compressor for CO2  compression in Modelica.

Visiting Researcher

Principal Data Scientist with over 8 years of experience in the full lifecycle of Machine Learning solutions, including strategy development, value discovery, code implementation, deployment, and monitoring. 

My expertise in ML and Optimization enables the creation of robust, accurate solutions, driving $3-10M in additional annual revenue per project.

As a Data Science Manager, I have 4 years of experience in leading, hiring, and managing data science teams. I am capable of organizing team work under harsh deadlines and complex project scopes. 

𝐈 𝐚𝐥𝐬𝐨 𝐞𝐧𝐣𝐨𝐲 𝐡𝐞𝐥𝐩𝐢𝐧𝐠 𝐨𝐭𝐡𝐞𝐫 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 𝐠𝐫𝐨𝐰 𝐢𝐧 𝐭𝐡𝐞𝐢𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐛𝐲:
- Upgrading their CVs/LinkedIn
- Providing 1:1 Career Mentoring
- Sharing Free Practical ML Materials to support their technical growth

𝐌𝐲 𝐚𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭𝐬 𝐨𝐯𝐞𝐫 𝐭𝐡𝐞 𝐩𝐚𝐬𝐭 𝐬𝐞𝐯𝐞𝐫𝐚𝐥 𝐲𝐞𝐚𝐫𝐬:

✅ Increased by $60M+ customer revenue by delivering 4 real-time end-to-end ML solutions that optimized Metals and Mining assets. During the projects, successfully managing the team of 7 Data Scientists. The solutions showed 2.5%+ increase of plants throughput and stabilized by 25%+ of the product variability.

✅ Identified potential $50M customer revenue improvement by completing 5 bottleneck analysis projects for Tier-1 metals and mining companies, resulting in new projects with them.

✅ Increased by 1.2% oil and gas production for 2 clients by leading the from-scratch development of the "AI Oil Optimization Assistant" solution. This is an ML software for accurate prediction and optimization of oil and gas production in real-time by using advanced ML and Optimization algorithms.

✅ Grew and structured a DS team from 5 to 20 data scientists who formed 4 internal teams responsible for specific solution directions.

► Tech Skills: Machine Learning | Data Science | AI | Data Analytics | Predictive Analytics | MLOps | Time Series | Anomaly Detection | Deployment  | Optimization | Git | Kedro | MLFlow | Scikit-Learn | XGBoost | Numpy | Pandas | SHAP |  Scipy | Docker

► Soft Skills: Team Management | Leadership | Empathy | Project Management | Presentation Skills | Problem Solving | Stakeholder Management

Timur Bikmukhametov, PhD

Carrier

Technical Fellow - Product analytics

Honeywell

Lead control advances in building management systems business 
Lead edge and cloud based analytics efforts for smart building systems and system of systems
Liaising with research and engineering team for new product/service/feature introduction

Lead control architect

Developed algorithms for improved temperature control using mini splits for cooling/heating the space for D6 Pro Wi-Fi Ductless Controller. 

Audio (non-invasive sensing) based equipment health monitoring using a system of AI and crowd sourcing 
(1) Self learning system using audio with AI is built as opposed to invasive vibration and electric voltage based approaches that use empirical thresholds for fault detection 
(2) Audio descriptive statistics in time and frequency are used as features
(3) Anomaly detection using unsupervised ML 
(4) Crowd sourcing to categorize the anomalies and using supervised ML algorithms to flag an event/fault 
(5) Providing insights for 25 machines at customer sites

Edge analytics framework to orchestrate analytics flows in smart edge devices in building systems
(1)  Analytics flow configuration with the ease of drag and drop from functional blocks libraries by low/medium/high competency personnel
(2) Light weight run time environment to run analytics at edge
(3) Visualization to demonstrate value and for integration into customized dashboards
(4) Demonstrated and deploying orchestration of analytics flows in Raspberry Pi and Honeywell BMS controllers

Senior control architect

Folk AS / Folk Varig AS

1. Draugen, Shell, Norway: Produced water re-injection system controllers tuning after re-commissioning chokes.
The main tasks are (i) to facilitate water re-injection system to operate in the 7 possible modes (ii)  staying inline with the overall control objectives of produced water re-injection system.

2. Ormen Lange, Shell, Norway : Control engineering design for liquid management system

This project has multiple tasks, first task is to identify challenges in liquid management system and suggesting alternatives to overcome these challenges. secondly, evaluating the suggested alternatives via engineering analysis tool to recommend a new control structure in liquid management system. The engineering analysis tool is to evaluate how well the suggested multiple control structures fare in meeting the overall control objective with the help of a benchmarked engineering simulator in the presence of reported worst case disturbances. Finally, implementing the recommended control structure, tuning the controllers and training the operators on the changes at Ormen Lange.  Benefits to customer are documented and concluded the project.

Overall this project involved engineering analysis tool development, benchmarking, evaluation of 4 control structures, 2 worst case disturbance (slugs and compressor trip) scenarios and interactions in liquid management system and changing control logic and tuning for 10 controllers.
3. Evaluated ABB product offering for Sleipner and Goliat.
4. Slug management - Tuning support tool for ABB slug management product is developed as part of this project. The multiple controllers interaction is analyzed.
5. Developed courses (i) to evaluate the understanding of process control diagram of a process/control/automation engineer (ii) to introduce the generic workflow and key issues in tuning a PID controller. The key focus in tuning course is to ensure good dynamic process behavior which is the end goal of all process control activities.

Senior consultant for ABB

SINTEF

SOLVit Project: A research and development program to produce cost-effective amines 
for CO2 capture processes (Jan 2012 – April 2013) 

1. Full life cycle management of amine based solvents in CO2 capture processes. This involves prescreening of candidate amines, lab scale experiments to evaluate absorption selectivity, maximum possible loading, and ease in desorption. 
2. Modeling and evaluation of the prospective amines in a medium scale test pilot plant. 
3. Optimization to minimize the energy requirement while capturing 90% CO2.

Researcher

NTNU

The appropriate selection of controlled variables is important to operate a process optimally in the presence of disturbances. Self-optimizing control provides a mathematical framework for selecting the controlled variables as combinations of measurements, c = Hy, with the aim of minimizing the steady state loss from optimal operation in the presence of disturbances. In this study, we present 

1. a convex formulation to ﬁnd the optimal combination matrix H for a given measurement set.
2. a Mixed-Integer Quadratic Programming (MIQP) methodology to select optimal measurement subsets that result in minimal loss. 
3. proposed convex approximation methods to find controlled variables with structural constraints (such as disjoint measurements or any specified structure) for optimal operation.
4. Extended the self-optimizing control ideas to regulatory layer to arrive at minimal number of regulatory layer control loops. Here the objective function is to minimize the state drift from the optimal steady state in the presence of disturbances.

Research fellow sponsored by Statoil Gassmaks

Honeywell Process Solutions

Worked extensively on Model Predictive Control, Simulator Development, Developing Batch Process Monitoring methods and on iterative control for diabetics.

(1) Multivariate statistical methods for Batch Process and Performance monitoring ---------------  Dec 2006 – Dec 2008 
The batch processes pose challenges in process and performance monitoring as they have inherent time varying nature and varying batch lengths. These do not allow the direct usage of multivariate statistical methods (PCA, PLS, FDA). To account for the challenges and to parsimoniously represent the system with a fewer coefficients, we used the wavelet based functional approximation methods. Using multivariate statistical and wavelet approximation methods, we classified batches based on the batch length (i.e. fast, medium, slow) and on the yield (i.e. high, medium, low) and the performance of a new batch was evaluated. The tools developed provide a prognostic process and performance monitoring for the batch processes to make a decision on batch propagation or termination. 

(2) Iterative learning control for Health care systems (Project Focal) ---June 2007 – Dec 2008 
The advances reported in automated drug delivery for Type 1 diabetics motivated us to look at possible iterative learning 
control for health care systems (focus on blood glucose/blood pressure regulation, and geriatrics). We used a physiological model of a Type 1 diabetic patient and evaluated iterative learning control algorithms for their efficiency in keeping the glucose levels within the glycemic range (70 – 100 mg/dl). The results show that iterative learning control can result in good customized blood glucose regulation within 3 – 4 iterations. 
Outcome: Prototype is developed and its robustness is evaluated for 570 different perturbed patient cases by considering ±40 % parametric uncertainty.

Senior Research Scientist

(1) Model maintenance for Advanced Process Control (APC) June 2004 – May 2006
In chemical process industries, model fidelity used in model-based control strategies plays a vital role. Usage of same models in the model-based controllers will result in poor performance over time due to the possible changes in process parameters (Ex: catalytic reactivity, feed composition change, changes in operating condition etc). Hence, we developed a conditional performance monitoring tool that quantifies the degraded performance to trigger either re-identification of 
models or retuning of the controller. As the models may not be capturing the system dynamics effectively over time, reidentification seems to be a wise choice. In order to perform this re-identification, closed-loop identification is preferred over open-loop identification as the open loop testing can take several weeks based on the plant dynamics and settling times. So the objective of present study is to evaluate the feasibility of integrated approach of performance monitoring and closed-loop re-identification methods for APC to guarantee sustainable APC performance. 
(2) Development and Integration of the Profit SensorPro in Honeywell Profit Design Suite (Project Focal)  ----- June 2006 – Nov 2006 
An infrequent measurement of dependent process variables motivates the development of inferential models. Inferential modeling tool develops “soft sensor” relationships among process variables. Given the frequently measured independent process variables, the inferential model predicts the dependent process variables. These relationships provide the user a better understanding of the process operations for real-time quality control, monitoring, control and optimization. The user-entered-structure (UES) feature is added to ProfitSensorPro. These models can either be linear or nonlinear and are used off-line/on-line. The model accuracy is maintained through periodic laboratory or on-line analyzer measurement.

Senior Engineer

National University of Singapore

Advanced Process Control for automated drug delivery in Type I Diabetics –
Administering ‘optimal’ amount of insulin to maintain the blood glucose concentration in the normoglycemic range is vital in Type 1 diabetics. Significant inter, intra patient variability among the diabetic patients needs to be considered in the development of a controller that specifies right insulin dose based on the glucose measurement. In this project, IMC (Internal Model Controller), EIMC (Enhanced Internal Model Controller), several Proportional-Integral-Derivative (PID) controllers are designed using classical and latest tuning techniques for a diabetic and are evaluated for their robustness.

Research scientist

I am a Technical Fellow in product analytics at Carrier, a global leader in energy and climate building solutions. I have a PhD in Process Control and Chemical Engineering from the Norwegian University of Science and Technology, and over 20 years of experience in applying advanced machine learning, signal processing, mathematical optimization, applied statistics, modeling, control and optimization methods to realize business value.

My mission is to build intelligent systems that enhance the performance, efficiency, and sustainability of buildings and process plants. I have led multiple differentiated feature insertions for new product introductions, successful proof-of-concepts execution with four new products in research and development, realized a return on investment of three months on USD 0.5 million investments in consulting roles. In my current role, I started a new team of 15 people from scratch, positioned the team on par with other Carrier analytics teams, successfully monetized analytics for connected chillers for aftermarket revenue growth (~USD 1 million  + USD 15 million in pipeline) from my team and developed CBM for 30 % productivity gain in chillers service contracts. I secured in 10 patents and 16 peer-reviewed publications while working for the PoCs, new product features, new products and services in the process systems engineering area. I am passionate about working on challenging problems with an objective-oriented, structured approach for consistent results.

taplio

The Battle

Timur Bikmukhametov, PhD

Ramprasad Yelchuru, PhD

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