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.
Adil Rasheed
Klæbuveien 153
Trondheim, Norway
+47-90291771
adil.rasheed@ntnu.no
PhD. • 2005-2009
Thesis title: Multiscale Modeling of Urban Climate
Master of Technology in Thermal and Fluids Engineering • 2000-2005
Thesis: Numerical Modeling of the scavenging process of a two-stroke internal combustion engine
Bachelor of Technology in Mechanical Engineering • 2000-2005
Thesis title: Experimental and numerical investigation of the heat transfer characteristics of turbulence generators of different shapes in a square channel.
Professor, Bigdata Cybernetics • 2019 - Present
Combining physics based modeling with data driven machine learning algorithms
Professor II, Machine Learning and Artificial Intelligence • 2018 - 2019
Combining physics based modeling with data driven machine learning algorithms
Senior Scientist and Research Manager, CSE Group• 2012 - Present
My roles in the group in addition to research and development are sales & marketing, PhD supervision and consultancy. Look my CV for details.
Visiting Scientist• June - Aug 2014
Established collaboration with the Center of Computational Sciences. The collaboration gives our group access to the high performance computing resource at the center
Research Scientist• 2009 - 2012
IN this role I mostly developed Computational FLuid Dynamics codes and applied them to provide consultancy to Avinor and several architect firms
Associate Engineer• July - Aug 2005
RAPID, Researcher, 2020-2024
Client: RCN, DNVGL, Dr. Techn Olav Olsen
Theme: Hybrid Analysis and Modeling
SFI Autoship, PhD supervision, 2020-2028
Client: RCN, Various industry partners
Theme: Autonomous Ship
EXAIGON, WP Manager and PhD supervision 16MNOK, 2020-2023
Client: RCN, Several industry partners
Theme: Explainable Machine Learning
Autosit, WP Manager and PhD supervision 12MNOK, 2019-2022 Lofoten Airport Citing, Investigator,2018-2019 SOLA Building Design, Researcher, 2018-2020 ATB, Initiator and Researcher, 2018-2020 DIGMOB, Researcher, 2018-2020 Hybrid Analytics, Project Manager, 2MNOK, 2018 POP-SEP MLFunc, Researcher, 150kNOK, 2017 E8, Proposal Writing, 100kNOK, 2017 BIGLEARN, Project Manager, 600kNOK, 2017 AI-CLIMAMOB, Researcher, 350kNOK, 2017 ANALYTICS TECHNOLOGY WATCH, Project Manager, 1mNOK, 2017 ESUSHI, Quality Assurance, 200kNOK, 2016 E3WDM, Quality Assurance, 300kNOK, 2017 OPWIND, Work Package Manager, 7mNOK, 2017-2020 E39, Task Manager, 600kNOK, Jan 2017- FSI-WT (Fluid Structure Interaction for Wind Turbine), Work Package Manager NOWITECH, Task Manager, 4mNOK, 2009-2017 (www.nowitech.no) URBASIM, Project Manager, 1.5mNOK, Jan-Dec 2016 DRIFT TURBULENS, Project Manager, 12mNOK, 2010 – (www.ippc.no) SOLA Special Analysis, Project Manager, 600kNOK, Nov 2016 –
Client: Sømmvågen III AS EXTBUILDFLOW, Project Manager, 1.5mNOK, Jan-Dec 2015 NEST: Thermal Heat Storage, Quality Assurer, 500kNOK, 2014-2015 SESAR (Single European Sky Air traffic management Research), Researcher, 12mNOK, 2010-2016 Avinor FoU, Project Manager, 5.2mNOK, 2010-2013 BUILDSIM, Project Manager, 275kNOK, Jan-March 2015 DAEDALUS, Task Manager, 275kNOK, Jan-Dec 2015 Bodø Special Analysis, Project Manager, 200kNOK, Feb-March 2016
Gimsøya Airport Siting, Project Manager, 300kNOK, Nov 2015-Dec 2016
Sandane Special Analysis, Project Manager, 150kNOK, Dec 2016-
Ørsta Volda Special Analysis, Project Manager, 150kNOK, Dec 2016-
Sola Special Analysis, Project Manager, 300kNOK, Jan-Dec 2015
Flesland Harbour Analysis, Project Manager, 250kNOK, June-Sep 2010
Faroe Island Airport Siting, Project Manager, 250kNOK, 2010-2010
Stokka Airport runway siting, Project Manager, 150kNOK
Kjevik Speial Analysis, Project Manager, 150kNOK
Alta Special Analysis, Project Manager, 150kNOK
Haugesund Special Analysis, Project Manager, 150kNOK
Client: RCN, Kongsberg, DNV GL, Marine Robotics
Theme: Autonomous Ship
Client: Avinor AS
Citing of a new airport in the Lofoten region
Research partners:KVT, MET
Client: Sømmvågen III AS
Building design optimization
Client: Norwegian Research Council HELSEVEL
Aerial Transport of Biological materials between hospitals
Research partners:FFI, OUS, MET
Client: Norwegian Research Council TRANSPORT 2025
Digitalization and mobility: Smart and sustainable transport in urban agglomerations
Research partners:TØI
Client: SINTEF Digital
Combining physics based modeling with machine learning algorithms
Client: SINTEF
Predicting the thermoelectric behavior of new chemical molecules using Machine Learning
Client: Statens Vegvesen, Towards Designing Intelligent Transport Solution
Client: SINTEF
Serving as one of the four core members of the BIGLEARN initiative within SINTEF designing a roadmap geared towards digitization.
Client: TØI
Quantify the impact of climate on urban mobility and develop Machine Learning Algorithms to predict number of pedestrians and vehicles on a stretch of road at an instant of time.
Client: Kongsberg
Help the client improve and use their Artificial Intelligence and Machine Learning technology platform as a competitive edge in the new age of digitization and design its future value propositions.
Client: SINTEF ICT
Analysis of the complicated relationship between meteorological data, oceanographic biomarkers (algae, planktons etc.) and fish catch reports (species caught, equipment used etc.)
Clustering of time series based on graph weighted by correlation revealing underlying structure. Automatic anomaly detection in sensor data
Client: Norwegian Research Council, Statoil and Vattenfall
Research Partners: SINTEF Energy, SINTEF DIGITAL, Norwegian Meteorological Institute, NTNU
International Partners: NREL, DTU
Research Area: Real time control system for wind farms, Aerodynamics, Reduced Order Modelling
Client: Norwegian Meteorological Institute, Vegvesen
Research partners: Wind in complex terrain and fjord relevant for bridge designs and road transport.
20mNOK, 2012-2016 (www.fsi-wt.no)
Client: Norwegian Research Council, Statoil, Trønderenergie, WindSIM, Kjeller Vindteknik
Research Partners: SINTEF ICT, NTNU, FFI, Norwegian Meteorological Institute
Research Area: Fluid Structure Interaction, Aerodynamics, Power Forecasting, Ocean-MET interactions, Wake-terrain-turbine-atmosphere interaction modelling
Client: Norwegian Research Council, Statoil, Statkraft, DNV, Dong, CD-ADAPCO
Research Partners: NTNU, SINTEF ICT, SINTEF Energy, MARINTEK, IFE
Research Area: Numerical Methods for Wind Energy, Isogeometric Analysis, Computational Fluid Dynamics, New turbine concepts
Client: Norwegian Research Council
Research Area: Wind Energy in an Urban context, Building refurbishment, Air conditioning, Urban Geometry Modelling, Aerodynamic Buildings, 3D printing and scanning, Urban Climatology
Client: Norwegian Meteorological Institute & Avinor
Research Area: Aviation, Atmospheric turbulence, Microscale wind and turbulence prediction system
Research Area: Building induced turbulence, Aerodynamic buildings, Architecture
Client: Norwegian Research Council
Research Area: Urban Geometry Modelling, Computational Fluid Dynamics, Turbulence Modelling
Client: NEST
Research Area: Thermal heat storage, thermal stresses induced fracture modeling, design optimization
Client: Euro Control
Research Area: Aircraft wakes, Aviation Safety, Vortex Particle Method
Client: Avinor
Research area: Aviation safety, Atmospheric Turbulence Modelling
Client: FG Eiendom
Client: European Space Agency
Research partners: Satavia, Catapult, Avinor ANS, NCAR, UiO
Research area: Atmospheric turbulence, Sensor integration in aircraft
Advanced Machine Learning and AI algorithms are being used to solve problems from a very diverse domain. At the moment we mostly used supervised (Linear and Logistic Regression, Decision Tree and Random Forest, Support Vector Machine, Deep Neural Networks, Convlutional Neural Network, Recurrent Neural Network, Generative Adversarial Networks) and unsupervised (K-neares neighbor, Self Organized Maps, Autoencoders) learning algorithms. Some of the application areas have been wind power forecasting, discovering new chemical compunds with desired properties, predicting pedestrian and bikers behavior in cities and identifying favourable locations for fishing. We have also used anomaly detection algorithms to identify malfunctioning sensors in a system. Recently I conducted a course on "Practical Machine Learning" at the Geilo Winter School. It included interactive python sessions. The school was attended by 80 researchers from industry and academia. I am also designing a 15 minutes weekly online course for SINTEF employees.
In cooperation with the Norwegian Meteorological Institute we have developed a microscale terrain-induced wind and turbulence alert system. The system is operation for 20 Norwegian airports and provide realtime forecast of wind and turbulence. We are also involved in siting of airports, runways and buildings close to the airports. During our involvement in the SESAR project, we also developed a computationally efficient aircraft wake-vortex model which has all the physics required for the initiation, transport and diffusion of the vortices.
My research in this topic dates back to the time when I was working on my PhD. During that time I conceived and implemented the concept of equivalent geometry whihc enables accounting for the complexity in urban geometry in a mesocale model for atmospheric flows. In particular I developed models for simulating wind, solar radiation and occupants behavior to model urban climate more accurately. Although most of the work was at an urban scale it is trivial to apply the concepts and tools to individual buildings.
Over the years my group has developed a set of tools which enable us to simulation highly complicated flows using the national supercomputing infrastructure. In particular, we can simulate incompressible turbulent and multiphase flows dominated by large variety of spatio-temporal scales. We develop solvers from scratch and at the same time we build upon the opensource OpenFoam solvers. The latter enables us to solve industrial scale problems. So far we have applied CFD in the field of meteorology, chemical reactor modeling, wind energy.
I n my personal capacity I have always been using painting, photographs and other such illustration to get a better insight ino the working of "black box" machine learning algorithms. I have also been effectively using art to explain complex topics to general public.
We define "Hybrid Analysis and Modeling" as a modeling technique 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. We have just acquired a project to test out some ideas which are quite promising.
We have been involved in several wind energy prjects and we practically do almost everything that can be numericaly simulated. Using our tools and expertize we can simulate anything starting from the flow aorund a 2D blade to the windfarm-windfarm interactions. Currently, our models are in operational use for forecasting wind energy from an onshore wind farm.
If I am not occpied with work and research, I prefer spending time copying nature. It is only recently I have realized the importance of art in interpreting advanced "black box" machine learning algorithms. We are living in amazing times. Computers have started challenging artists in the area of creative art, a domain exclusively meant for humans to do miracles. Artists will have to learn to use these algorithms to create even more miraculous pieces. Take some time to browse through some of my paintings in four different mediums: acrylic, pastel, watercolor and oil.
Project 1: Ruteplanlegging for autonome farkoster
Background: The ability to route planning is central to autonomous vehicles. Based on a world model, the vessel calculates optimal routes depending on the situation and mission. Routes must be planned both over greater distances, based on map data and other advance information, and in the immediate area based on data from the vehicle sensors combined with the map data. We work with route planning for both ground vehicles and boats. Some issues are relevant for both types of craft, while other issues are more specific.
In order to do multi-level route planning, hierarchical route planning is required to reuse results at a higher level when planning more detailed routes. In addition to avoiding obstacles, it is important to map the terrain so that the vehicle can choose routes through low-risk areas. This work includes automatic analysis of data on the surroundings, such as weather and terrain, to determine how suitable different areas are for different tasks.
For some types of missions it is necessary to move in hiding, other times you will move quickly or observe certain areas. Different terrain characteristics are preferred for the different tasks, and we want to develop methods for calculating this automatically. There is also a need to further develop existing optimization methods to search for routes.
There is room for several tasks within this theme. All the thesis is intended as project assignments of 15 credits, and is suitable for extension to a master's thesis. We note that FFI requires the grade B or better on average to write a master's thesis for us.
Problem Statement: Dynamic environment: The environment in which an autonomous vehicle moves will often be dynamic, so the cost of a move will depend on when the move occurs. Examples of this may be dynamic obstacles, phenomena such as queuing, need to stay close to another unit along the way, or varying weather conditions. In many cases, it is possible to estimate future changes, so that this can be taken into account in route planning. This is a challenge for traditional methods such as Dijkstra, and it is no longer certain that existing methods provide optimal routes. The task is intended to be based on graph-based route planning algorithms, such as Dijkstra, A * / D * or other. However, the student is free to come up with their own suggestions for solutions.
Contact at NTNU: Adil Rasheed (adil.rasheed@ntnu.no)
Contacts at FFI: Martin Syre Wiig (martin-syre.wiig@ffi.no)
Project 2: Medical Diagnosis
How can we give artificial intelligence systems used for medical diagnosis the ability to say... "I have never seen this before. Don't trust me on this one." ?
Problem Description: Medical diagnosis is difficult because not every case is a typical case. Sometimes patients have signs and symptoms that are unusual. In extreme cases, due to genetic mutation, the patient is the only person in the world with the disease and its associated symptoms.
When a doctor comes across an unusual case she may say "I've never seen this before." and proceed carefully. An AI system that diagnoses patients should also have the ability to say... "I have never seen this before. Don't trust me on this one." Assuming the AI system is based on supervised learning, how can we add capability to the AI system so that it tells users to what extent its training dataset is representative of the current case? Maybe this requires a secondary AI algorithm built with unsupervised learning?
We can attempt to tackle this problem by looking at a system which performs automatic analysis of lung sounds. Such a system could perform better in the real-world if it detected input data which will perform poorly due to either the input data not being representative of the dataset or the data is of poor quality. The excluded data can then be assessed through traditional means such as review by a doctor.
Company dedeX is a med tech startup. dedeX is looking to solve the problem: how do we get the data needed to build AI algorithms that can empower medical history taking and physical examination? dedeX understands both real-world healthcare and AI and can give the student feedback from this unique perspective.
Data: Two separate datasets of annotated lung sounds with different bias and variance should be used. One is the ‘development’ dataset and the other is a ‘real-world’ dataset. The development dataset will be used to create a ‘primary’ supervised machine learning algorithm and a ‘secondary’ unsupervised machine learning algorithm.
The primary algorithm will be used to classify lung sounds in both datasets. This algorithm can be based on existing work by other researchers.
The secondary algorithm will identify data from the real-world dataset that will perform poorly when used in the primary algorithm. In simplified terms it is an unsupervised learning algorithm which looks for the data that is the least similar to data in the development dataset. When this data is excluded from the real-world dataset then the primary algorithm should achieve higher accuracy on the real-world dataset.
We have identified four sources of annotated lung sound data. The student can pick two of these for this project or alternatively find other data sources if more convenient.
Task
Thesis Information
Timeframe: 6 or 12 months
Contact at NTNU: Adil Rasheed (adil.rasheed@ntnu.no)
Contacts at dedeX: Jon Bekker(jon.floystad@dedex.ai)
Project 3: Alternate representation of Deep Neural Networks (Affiliated to the Explainable AI project EXAIGON)
Neural networks have been applied to a vast array of problems, many of them safety critical. Despite their usefulness, there are many unanswered questions regarding their robustness, stability, and overall safety. This is problematic, as they are increasingly being applied to robotics and control problems in research.
A surprising fact about neural networks is that if they only use piecewise linear activation functions (for example, ReLU), the network as a whole will be piecewise linear There is a vast literature on this class of functions, which can be leveraged in the study of neural networks. A central challenge with this approach is that the functions are extremely complicated, with huge numbers of linear regions. Despite this, these ideas have been used to:
This is a cutting-edge field within deep learning, with many possible applications and future research directions. The following research projects are available as thesis topics:
Desired background:
Relevant bibliography:
Contact at NTNU: Adil Rasheed (adil.rasheed@ntnu.no)
Co-supervisor: Haakon Robinson (haakon.robinson@ntnu.no)
Project 4: Machine Learning Controllers
Problem Description: There exists a multitude of traditional controllers to determine the control inputs (actuators) to achieve the desired behavior of a dynamic system, e.g. feedback linearizing, backstepping, sliding mode, adaptive control etc. However, these controllers are often based on a dynamic model of the system, numeric values for system parameters and several assumptions which are not always accurate. By applying machine learning, it is possible to develop controllers that are not based on a model or assumptions. Specifically, Machine Learning Control (MLC) aims to learn an effective control law b = K(s) that maps the system output (sensors s) to the system input (actuators b) and can solve problems involving complex control tasks where it may be difficult or impossible to model the system and develop a useful control law. The pre-project phase of the project will involve:
The pre-project will lead to a masters project where the major steps will be as follows:
Supervisor: Adil Rasheed (adil.rasheed@ntnu.no), NTNU
Co-supervisor: Haakon Robinson, NTNU (haakon.robinson@ntnu.no)
Reference
Project 5: Evaluating the hydrostatic characteristics of a ferry using high fidelity simulations and dimensionality reduction algorithms
Project Description:
Airflow around a typical ferry is dominated by a large variety of spatio-temporal scales and complex flow structures. State-of-the art Computational Fluid Dynamics simulations have been used to simulate the drag, yaw and pitch characteristics of such ferries. A solid body like a ferry has intricate designs all of which are not relevent for evaluating its hydrodynamic characteristics. To this end, in this project the student will evaluate and quantify the impact of geometric simplifications of a ferry on its hydrodynamic characterists. The flow simulator based on OpenFoam will be utilized to generate the 3D wind and turbulence field around a moving ferry. The results will be postprocessed using advaced dimensionality reduction algorithms like Proper Orthogonal Decomposition and AutoEncoders. The project will include the following tasks:
Supervisor: Adil Rasheed, NTNU
Co-supervisor: Mandar Tabib, SINTEF Digital
Project 6: Explaining turbulence using Deep Learning (Affiliated to the Explainable AI project EXAIGON)
Turbulent flows are dominated by a large variety of spatio-temporal scales. So far tools like Fast Fourier Transform and Wavelet Transform have proven to be the workhorse for analyzing these scales. With the recent revolution in Deep Learning and more particularly in Convolutional Neural Network (CNN) and Auto Encoders (AE) we are witnessing huge improvements in classification and data compression tasks. It is foreseen that the automatic pattern identification properties of CNN and important feature identification properties of AE can be utilized to improve our understanding of physical phenomena governed by multiple spatio-temporal scales like turbulence. In this project the focus will be to better describe flow structures observed in turbulent flows. The pre-project will involve the following tasks:
The pre-project will lead to a Masters project which will have the following steps:
Supervisor: Adil Rasheed, NTNU
Co-supervisor: Mandar Tabib and Kjetil Andre Johannessen, SINTEF Digital
Learning outcome: Computational Fluid Dynamics, Turbulence Modeling, Deep Learning, Dimensionality Reduction
Project 7: Hybrid Analysis and Modeling as an enabler for Big Data Cybernetics
Project Description: Most of the modeling approaches lie in either of the two categories:
Physics based modelling:strong> So far, the engineering community has been driven mostly by a physics-based modeling approach. This approach consists of observing a physical phenomenon of interest, developing a partial understanding of it, putting the understanding in the form of mathematical equations and ultimately solving them. Due to partial understanding and numerous assumptions along the line from observation of a phenomena to solution of the equations, one ends up ignoring a big chunk of the physics. High-performing simulators capable of handling a billion degrees of freedom are opening new vistas in simulation-based science and engineering and combined with multiscale modeling techniques have improved significantly the predictive capabilities. Thus, the dream of establishing numerical laboratories (e.g. numerical wind tunnels) can now be realized for many interesting real world systems. However, despite their immense success, the use of high-fidelity simulator has so far been limited to the design phase. Some of the advantages of physics based models are that they have robust foundation from first principle, are trustworthy, are generalizable to new problems and robust theory exists for uncertainty and error analysis of the model results. However, these models are generally computationally expensive and not adapting to new scenarios automatically.
Data-driven modeling: While physics based models are the workhorse at the design phase, with the abundant supply of big data, opensource cutting edge and easy-to-use libraries (tensorflow, torch, openAI), cheap computational infrastructure (CPU, GPU and TPU) and high quality, readily available training resources, data-driven modeling is becoming very popular. Compared to the physics based modeling approach, this approach is based on the assumption that since data is a manifestation of both known and unknown physics, by developing a data-driven model, one can account for the full physics. The data-driven approach in general and deep learning in particular has started achieving human level performance in several tasks which were until recently considered impossible for computers. Notable among these are image classification, creative art, anomaly detection. SOme of the advantages of these models are that they can learn in real time from the data, can be highly accurate and are generally very computationally efficient making them idle for real time control applications. However, owing to their blackbox nature, poor generalizability and no robust theory for uncertainty and error quantification, their acceptability in safety critical autonomous systems is limited. Thus for a dream model we have identified some characteristics which are as follows:
The four desired model characteristics mentioned above can be addressed by developing a new breed of modelling approach that will combine 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. We call this new approach as Hybrid Analysis and Modeling.
Supervisor: Adil Rasheed, NTNU
Co-supervisor: Damiano Varagnolo, Trond Kvamsdal, NTNU
Learning outcome: Hybrid Analysis and Modeling paradigm in modeling
Reference
Project 8: Digital Twin Laboratory
Project Description: Digital twin can be 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. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In the projects related to DT lab, the students are expected to work towards developing a digital twin of a room. The digital twin should be indistinguishable from its physical counterpart. The projects will be broadly of two kinds: (1) experimental and hardware related and (2) algorithm development and software related for bringing physical realism in the DT.
Some tentative topics which can be adapted to the interest of the students.
Supervisor: Adil Rasheed, NTNU
Co-supervisors: Damiano Varagnolo, Annette Stahl NTNU
Learning outcome: Digital Twin technologies, Machine Learning
Reference:
Project 9: 3D Machine Learning
Project Description: The project will focus on combining the two distinct fields of mesha generation and machine learning. The project will be conduected in collaboration with the Mathematics and Cybernetics Department of SINTEF Digital.
The pre-project will involve the following tasks:
The pre-project will lead to a Masters project which can have the following applications
Supervisor: Adil Rasheed, NTNU
Co-supervisor: Kjetil Andre Johannessen and George Muntingh, SINTEF Digital
Learning outcome: 3D Machine Learning, CAD modeling, Mesh Generation
Reference
Project 10: Dynamic Mode Decomposition informed deep neural network for object detection and classification (Affiliated to the Explainable AI project EXAIGON)
Project description: Dynamic Mode Decomposition method originated in the fluid dynamics community tas a method to decompose complex flow fiels into a simple representation based on spatiotemporal coherent structures. More recently Grosek and Kutz, provides a novel application of the DMD atechnique and its dynamicsl decomsposition for state-of-teh art video processing. DMD modes with Fourier frequencies near the origin (zero frequencies) are interpreted as bacl-ground portions of the given video frames, and the modes with Foureir frequencies bounded away from the origin constitute their sparse counterparts. An approximate low-rank / sparse separation is achieved at the computational cost of just one SVD and one linear equatin solve.The DMD method can operate in real time with personal lamtop-class computing power and without any parameter tuning. This project will involve combining the strenghts of DMD to identify zones in a video stream where objects are moving with the power of convolutional neural network to only classify the image.
The project will involve the following tasks:
Supervisor: Adil Rasheed, NTNU
Co-supervisor: Anastasios Lekkes, NTNU
Reference
Project 11:Data-driven physics discovery
Proposals from Harald Martens, Idletechs AS, sorted by priority.
Idletechs may ask for a standard report confidentiality period.
Project 12:COLREG-compliant path following and collision avoidance with moving obstacles in the Trondheim fjord (Affiliated to the Explainable AI project AUTOSIT)
Project description: This project concerns the problem of anticipating the intentions of another ship on time intervals in the order of minutes. Over such time intervals, one can typically expect the ship to perform certain manoeuvres (e.g., go straight, approach its destination port, make a complete evasive manoeuvre), but all such predictions will be highly uncertain. The goal of this project is to develop methods that are able to combine relevant sources of information to make predictions over such time intervals. The project will build upon the work by Meyer et al and will be realized through the following potential tasks.
Supervisor: Adil Rasheed (adil.rasheed@ntnu.no)
Co-supervisor: Edmund Brekke (Edmund.brekke@ntnu.no), Morten Breivik (morten.breivik@ntnu.no)
Project 13:Creating a bussiness model in the cloud using Digital Twin
Contact me to discuss the idea and how to proceed in this intersting direction
Project 14: Digital Twins in precision production of healthcare services: Using case clinical pathways for colon and rectal cancer
Background: This project is a continuation of a project and consequent master thesis performed in 2019 / 2020 comprising a collaboration between Prediktor and the Østfold Hospital Trust (ØHT). The project will consider the following setup: as a patient moves through her/his medical pathway (i.e., departments, visits, hospitals, etc.) he/she will produce a series of data relative to her/his medical/clinical, logistics and economical aspects. Importantly, this data can collected from different sensors/instruments into centralized databases. This information can then be used to both model important medical aspects of that single patient, but also have a better overview of the medical healthcare system as a whole.Thomas Nakken (Sup)
Department of Engineering Cybernetics, NTNU
Topic: Reinforcement Learning for Drones
Domain: Bigdata Cybernetics
Homepage: Click here
Haakon Robinson (Sup)
Department of Engineering Cybernetics, NTNU
Topic: Hybrid Modeling
Domain: Bigdata Cybernetics
Homepage: Click here
Håvard Bjørkøy (Co-sup with Damiano Varagnolo)
Department of Engineering Cybernetics, NTNU
Topic: Combining data-driven and physics driven models
Domain: Bigdata Cybernetics
Homepage: Click here
Hans A. Engmark (Co-sup with Damiano Varagnolo)
Department of Engineering Cybernetics, NTNU
Topic: Combining data-driven and physics driven models
Domain: Bigdata Cybernetics
Homepage: Click here
Roya Doshmanziari (Co-sup with Damiano Varagnolo)
Department of Engineering Cybernetics, NTNU
Topic: Combining data-driven and physics driven models
Domain: Biofeedback
Homepage: Click here
Erlend Lundby (Co-sup with Jan Tommy Gravdahl)
Department of Engineering Cybernetics, NTNU
Topic: Big Data in the process industry
Domain: Bigdata Cybernetics
Homepage: Click here
Abdallah Alshantti (Co-sup with Frank Westad and Damiano Varagnolo)
Department of Engineering Cybernetics, NTNU
Topic: Finance and Banking
Domain: Data-driven modeling
Homepage: Click here
Prateek Gupta
(Co-sup with Sverre Steen)
Department of Marine Technology, NTNU
Topic: Ship performance monitoring and optimization using in-service measurements and big data analysis methods
Domain: Marine Hydrodynamics
Homepage: Click here
Status: Ongoing
Aoudou Midjiyawa Zakari
(Co-sup with Trond Kvamsdal)
Department of Mathematical Sciences, NTNU
Topic: Wind modeling in fjord
Domain: Atmospheric Modeling
Homepage: Click here
Status: Ongoing
Muhammad Salman Siddiqui
(Co-sup with Trond Kvamsdal)
Department of Mathematical Sciences, NTNU
Topic: Reduced Order Modeling of Wind Turbine
Domain: Wind Energy
Homepage: Click here
Status: Complete
Simen Havenstrøm