About us

Adil Rasheed

I am the Professor of Bigdata Cybernetics at the Department of Engineering Cybernetics, Norwegian University of Science and Technology. In the role I am developing new methods for combining the physics based modeling with the data driven modeling. I also hold a part time position as a Senior Scientist at the Mathematics and Cybernetics Department of SINTEF Digital.


Email: adil.rasheed[@]ntnu.no
Mobile: +47-90291771

Ph.D.

It will be impossible for me to pursue my very wide array of interest without the help of some of my brilliant group members 🙂

Oluwaleke Umar Yusuf

2023-2026 (Sup)

Future mobility solution

Mehmet Cagri Altindal

2023-2026 (Sup)

Hybrid and Machine Learning method development for hole cleaning monitoring

Karl Johan Haarberg

2022-2025 (Sup)

Digitalization of Bussiness Processses

Daniel Menges

2022-2025 (Sup)

Digital Twin for Autonomous Ships

Alberto Mino

2021-2024 (Sup)

Explainable Artificial Intelligence

Florian Stadman

2021-2024 (Sup)

Enabling Technologies for Digital Twins

Thomas Nakken Larsen

2020-2024 (Sup)

Learning in a Digital Twin Environment

Mohammed Ayalew Belay

2021-2024 (Co-Sup)

Anomaly detection in timeseries data

Even Klemsdal

2021-2024 (Co-Sup) 

Multiagent reinforcement learning

Håvard Bjørgan Bjørkøy

2020-2024 (Co-sup)

Big data cybernetics for the process industry

Roya Doshmanziari

2020-2023 (Co-sup)

Biofeedback

Hans A. Engmark

2021-2023 (Co-sup)

Big data cybernetics for the process industry

Erlend Lundby 

2019-2023 (Co-sup)

Hybrid Modeling in Process Industry

Haakon Robinson

2019-2023 (Sup)

Hybrid Analysis and Modeling

Wanwan Zhang

2021-2024 (Co-sup)

Predictive Maintenence

Abdallah Alshantti

2019-2022 (Co-sup)

AI in financeMoney

Prateek Gupta

2018-2022 (Co-sup)

Ship performance monitoring using in-service measurement data 

Zakari Midjiyawa

2018-2022 (Co-sup)

Turbulence characterization in complex fjord using measurement and numerical modeling for bridge design. 

Muhammad Salman Siddiqui

2015-2019 (Co-sup)

High fidelity simulation and reduced order modeling of wind turbines

MSc

I have had the privilendge to work with some of the best masters students. Here are topics of their thesis 

Anne Willkommen Eiken
(2022)

Job Title

 Position Alignment and Geographical Location Determination of Railway Track Condition Monitoring Data

Annfrid Hopland Myklebust
(2022)

Job Title

Building a digital twin of the thermodynamic behaviour of a building using hybrid modeling

Elias Mohammed Elfarri
(2022)

Job Title

Digital Twin of a Building Powered by Artificial Intelligence and Demonstrated in Virtual Reality

Kristian Brudeli
(2022)

Job Title

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Lars Gjardar Musæus
(2022)

Job Title

Fractal Analysis and Its Application on Time-Series Data – An Innovative Method for Condition Monitoring of Hole Cleaning Operations

Ludvig Løken Sundøen
(2022)

Job Title

Path Following for Quadcopters using Deep Reinforcement Learning

Marcus Skagemo
(2022)

Job Title

Improved market entry of long-term time horizon trading signals using short-term residual reversal

Simon Mork Sætre
(2022)

Job Title

 Laying The Foundation For an Artificial Intelligence-Powered Extendable Digital Twin Framework For Autonomous Sea Vessels

Sondre Sorbø
(2022)

Job Title

Corrective Source Term Approach for improving Erroneous Physics-Based Models

Katarina Charlotte Guderud
(2021)

Raa Labs

Predicting feeding patterns in aquaculture

Tiril Sundby
(2021)

 Bouvet ASA

Towards Geometric Change Detection in Digital Twins using Dynamic Mode Decomposition, Object Detection and 3D Machine Learning

Julia Graham
(2021)

Cognite

Geometric change detection in digital twins

Olav Pedersen
(2021)

Prediktor

A proof-of-concept Digital Twin implementation for monitoring patients through the Clinical Pathway for Prostate Cancer in the Norwegian Health and Care Service

Bendik Austnes
(2021)

NoA Ignite

Increasing Validity and Uncovering Utility in Machine Learning Studies: An Illustrative Approach to Essential Concepts and Procedures in Model Development and Assessment

Sindre Stenene Blakseth
(2021)

SINTEF Energy

Introducing CoSTA: A Deep Neural Network Enabled Approach to Improving Physics-Based Numerical Simulations

Viljar Ness
(2021)

Sopra Steira

Simulating Differential Equations using Physics Guided Machine Learning

Ole Jørgen Hennestad
(2021)

Firda

Securing trust in AI systems through increased explainability: linking Norwegian organizations’ challenges in regards to eXplainable Artificial Intelligence (XAI) with a 2021 view on relevant techniques and methods 

Andrine Elsetrønning
(2021)

Sopra Steira

Generalized workflow with uncertainty quantification for detecting abnormalities in lung sounds 

Marie Skatvedt 
(2021)

Norkart AS

Machine Learning for bottom-detection in Doppler Velocity Logs

Fredrik Pettersen Sandvik
(2021)

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Applications of Data-Driven Equation Discovery to Synthetic and Experimental Data

Torkel Laache
(2021)

Job Title

Physics Guided Machine Learning: Injecting neural networks with simplified theories 

Vebjørn Malmin
(2021)

Bredvid AS

Reinforcement Learning and Predictive Safety Filtering for Floating Offshore Wind Turbine Control

Halvor Ødegård Teigen
(2021)

Sopra Steria

Reinforcement Learning and Predictive Safety Filtering for Floating Offshore Wind Turbine Control

Simen Havenstrom
(2020)

Equinor

From Beginner to Expert: Deep Reinforcement Learning Controller for 3D Path-Following and Collision Avoidance by Autonomous Underwater Vehicles

Thomas Nakken Larsen
(2020)

PhD student at NTNU

The applicability of perceptually driven generative-adversarial networks for super-resolution of wind fields in complex terrain

Peter Herman Stavelin
(2020)

Penetrace

Object Detection Applied to Marine Data for Utilizing Optic-Acoustic Optimization

Eirik Ekjord Vesterkjær
(2020)

Kongsberg Digital

Combining Grid-based Uncertainty Propagation and Neural Networks with Uncertainty Estimation

Amalie Heiberg
(2020)

Equinor

COLREG-Compliance for Autonomous Surface Vehicles using Deep Reinforcement Learning

Duy Tan Tran
(2020)

Netlight

Convolutional Neural Network and Generative Adversarial Networks Enabled Resolution Enhancement of Numerical Simulations

Eivind Meyer
(2020)

PhD Student at TUM

On Course Towards Model-Free Guidance: A Self-Learning Approach To Dynamic Collision Avoidance for Autonomous Surface Vehicles

Daniel Nakken

Job Title

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Haakon Robinson

Job Title

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Research

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Digital Twins

A digital twin is defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making.

Hybrid Analysis and Modelling

HAM / HM / HA is a new paradigm in modeling that combines the interpretability, robust foundation and understanding of a physics-based approach with the accuracy, efficiency, and automatic pattern-identification capabilities of advanced data-driven machine learning and artificial intelligence algorithms to produce less uncertain results.

Physics-based modeling

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Machine Learning and Artificial Intelligence

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Big Data Cybernetics

Controlling an asset with big data after processing in real time

Design of Experiment

Desgning experimental set-up to test methods, design of experiments to collect data with minimum effort

Application

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Wind Energy

Projects: NorthWind, OPWIND, FSI-WT
Theme: Computational fluid dynamics, Solid mechanics, fluid-structure interaction, high performance computing, reduced order modeling, hybrid analysis and modeling, Digital Twin, Asset Management

Built Environment

Project: Multiscale modeling of urnal climate
Theme: Atmospheric modeling, Building physics, Digital twin, Sensor data analysis

Autonomous ships

Projects: SFI Autoship, Aitosit
Theme: Situational awareness, digital twin
Methods: Reinforcement learning, Computer vision, DIgital twin

Oil and Gas

Project: Hole Cleaning Monitoring in drilling with distributed sensors and hybrid methods
Theme: Drilling, Hybrid modeling

Process Industry

Project: Towards autonomy in process industries
Theme: Hybrid Modeling, Sparse data modeling, Machine Learning and Artificial Intelligence, Aluminium extraction

Finance

Project: Money Laundering, Optimal investment, bussiness processes

Paintings

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Acrylic painting

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Pastel Painting

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Oil painting

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Watercolor painting

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