Mr. Anders Austlid Taskén | Intelligence | Best Researcher Award
Profile Summary
Anders Austlid Taskén, along with his collaborators, focuses on leveraging artificial intelligence (AI) for automated estimation and monitoring of cardiac function using transesophageal echocardiography (TEE). Their research includes the development of AI-based algorithms for the automatic estimation of mitral annular plane systolic excursion (MAPSE) and global left ventricular systolic function. They have also worked on automatic detection and tracking of anatomical landmarks in TEE images for quantifying left ventricular function. Their efforts aim to enhance clinical practice by providing efficient and accurate tools for cardiac function assessment and monitoring, potentially improving patient outcomes in intensive care settings.
Professional Profiles
🎓 Education
PhD Candidate in Computer Science, Norwegian University of Science and Technology, Trondheim, Norway (2021–2024) Project Description: Functioning and robust non-harming monitoring of LV cardiac function in a perioperative setting by automatic predictions of cardiac contraction and deformation based on machine learning, and estimation of cardiac parameters (i.e. MAPSE, strain, EF). Tentative Thesis Title: Computerized Artificial Intelligence for Automated Monitoring of Left Ventricular Function by Ultrasound. Research Stay Abroad, CREATIS, INSA, Lyon, France (2023–2023) Project Description: Generation of realistic synthetic ultrasound data for the purpose of training and validating segmentation and motion tracking algorithms for diagnostic support. Transesophageal data was generated by adopting a simulation pipeline developed within CREATIS, INSA Lyon. Synthetic data was used to train state-of-the-art machine learning-based methods for segmentation and motion tracking. Master of Science in Cybernetics and Robotics, Norwegian University of Science and Technology, Trondheim, Norway (2016–2021) Field of Study: Biomedical Cybernetics. Thesis: Deep Learning Based Tracking of Anatomic Structures in Intra-operative Cardiac Volumes. Average Grade: B (5-year), A (master) Student Exchange (Erasmus), University of Trento, Trento, Italy (2019–2020) Exchange student, taking the fourth year as part of the Master on a collaborative programme between NTNU and University of Trento. Upper Secondary School, Valler High School, Bærum, Norway (2012–2015) Average Grade: 5.3
💼 Experience
Researcher, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway (2023–2024) Adjunct appointment 20%, temporary position. Researcher, St. Olavs hospital, Trondheim University Hospital, Clinic of Cardiology, Trondheim, Norway (2021–2023) Adjunct appointment 20%, temporary position. Summer Intern, Hy5 Pro AS, Oslo, Norway (2020) Developed a mobile platform for a myoelectric hand prosthetic. Summer Intern, Computas AS, Oslo, Norway (2019) Developed an emergency preparedness platform. Senior Research Developer, Ntention (former Arveng Technologies), Trondheim, Norway (2017–2019) Developed a highly technological glove equipped with sensors to capture the motion of the human hand. Summer Intern, Nordic Semiconductor, Trondheim, Norway (2018) Developed a complete app to control drones with the Ntention glove. Voluntary Experience Software Developer, NTNU, Operating Room of the Future, Trondheim, Norway (2018–2019) Visualized radiological imaging on Microsoft HoloLens by Volume Rendering.
Qualifications
Lecture Presentation, Computer Assisted Radiology and Surgery (CARS) Congress 2024 (2023) Journal Reviewer Scientific Reports, Nature Portfolio International Journal of Clinical Practice (2023) Co-supervisor of Master Thesis for Vilde Wøien M.Sc. in Engineering and ICT. Thesis Title: Supervised Deep Learning for Perioperative Cardiovascular Monitoring (2022) Co-supervisor of Master Thesis for Kåre Fosli Obrestad M.Sc. in Computer Science. Thesis Title: Aortic Valve Localization in 3D Transesophageal Echocardiography Volumes using Deep Learning (2022) Attendee at MICCAI 2022 Attended the international conference on Medical Image Computing and Computer Assisted Intervention (2022) Exhibitor at CES 2018 Las Vegas Exhibited at the world’s largest consumer electronics technology conference with Ntention (2018) Exhibitor at Startup Launchpad 2018 Exhibited at Asia’s largest retail startup fair in Hong Kong with Ntention (2018) Attendee at Sino Track 2018 Attended an international accelerator program run by COMB+ with Ntention in Beijing (2018)
Computer Skills
- Python, PyTorch, OpenCV, MATLAB, C++, C, git, Latex, Unity, Hololens, Swift, Julia, Elixir
Research Focus:
The research focus of N. Belbachir centers on the optimal integration of renewable energy sources, particularly photovoltaic distributed generation (PVDG), into electrical distribution systems. Employing metaheuristic optimization algorithms and considering various factors such as seasonal uncertainties, load demand, and overcurrent relay characteristics, Belbachir investigates the efficient allocation and sizing of PVDG and related systems like battery energy storage and distribution static var compensators. By addressing these challenges, Belbachir’s work contributes to enhancing the reliability, efficiency, and sustainability of distribution networks, ultimately facilitating the transition towards a greener and more resilient energy infrastructure.
- All Time:
- Citations: 5 📖
- h-index: 1 📊
- i10-index: 0 🔍
- Since 2018:
- Citations: 5 📖
- h-index: 1 📊
- i10-index: 0 🔍