Professional background

PhD from Western Norway University of Applied Sciences. My research activities primarily revolved around machine learning and medical image analysis, with a particular focus on design methodologies in deep learning for efficient use of data.

Currently, I am employed as a Data Scientist at Lerøy Seafood, a global leader in the seafood industry. My primary responsibilities include implementing MLOps and incorporating large language models into our operational strategies, aiming to improve business efficiency and decision-making. You can read more about how we are working on implementing ML models and exploring opportunities within generative AI here.

Publications

1. fastMONAI: a low-code deep learning library for medical image analysis
   • S. Kaliyugarasan, A.S Lundervold, Software Impacts, 2023
   • Source code (92 stars, 16 forks)
2. Multi-Center CNN-based spine segmentation from T2w MRI using small amounts of data
   • S. Kaliyugarasan, A.S Lundervold and others, 20th IEEE International Symposium on Biomedical Imaging (ISBI)
3. LAB-Net: Lidar and aerial image-based building segmentation using U-Nets
   • S. Kaliyugarasan, A.S Lundervold, Nordic Machine Intelligence, second place 2022 NORA MapAI competition
4. Fully automatic whole-volume tumor segmentation in cervical cancer
   • E. Hodneland, S. Kaliyugarasan, and others, Cancers, 2022
5. Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI
   • S. Kaliyugarasan, A. Lundervold, A.S Lundervold, IJIMAI, 2021
6. 2D and 3D U-Nets for skull stripping in a large and heterogeneous set of head MRI using fastai
   • S. Kaliyugarasan, M. Kocinski, A. Lundervold, A.S Lundervold, NIK2020, no.1, 2020

Conference posters

1. fastMONAI: a low-code deep learning library for medical image analysis
   • with A.S Lundervold, MMIV Conference 2022, Bergen, Norway, Dec. 2022
2. Brain age versus chronological age: A large scale MRI and deep learning investigation
   • with A. Lundervold and A.S Lundervold, EPOS scientific poster at ECR 2020, online, Jul. 2020
3. Transfer learning for medical images: a case study
   • with A.S Lundervold, GTC Europe 2018, Munich, Germany, Oct. 2018

Teaching Experience

Courses

• DAT158: Machine learning engineering and advanced algorithms (Fall 2021)
  • Course website

Supervision

• MSc Projects (co-supervision)
  • Jostein Digernes and Carsten Ditlev-Simonsen (2020–2022): A workflow-integrated brain tumor segmentation system based on fastai and MONAI
• BSc Projects (co-supervision)
  • Preben Andersen and Andrea Svendheim (2024): Large Language Models (LLMs) and Fish Health

Talks and conferences

• Inspirasjonsdag for realfagselever: “Lerøy og kunstig intelligens”
  • Western Norway University of Applied Sciences, Bergen, Norway, November 4, 2024
• Hvordan jobber vi med kunstig intelligens?
  • Springbrett, Grieghallen, Bergen, Norway, September 24, 2024
• Lerøy’s motivational gathering: “Lerøy og kunstig intelligens”
  • Scandic Flesland, Bergen, Norway, September 7, 2023
• Kunstig intelligens i hverdagen
  • Tekna, Bergen Vitensenter, Bergen, Norway, April 29, 2024
• Workshop i Executive MBA-programmet innen strategisk ledelse
  • NHH, Gardermoen, Norway, April 10, 2024
• RAD230 lecture and lab: “Kunstig intelligens og framtidens radiografer”
  • Western Norway University of Applied Sciences, Bergen, Norway, May 8, 2023
• ISBI 2023: “Multi-Center CNN-based spine segmentation from T2w MRI using small amounts of data”
  • Cartagena de Indias, Colombia, April 18-21, 2023
• Bouvet deler: “Kunstig intelligens i medisinsk bildebehandling”
  • Litteraturhuset i Bergen, Norway, January 19, 2023
• RSNA 2022: “Fully automatic whole-volume tumor segmentation in cervical cancer”
  • McCormick Place, Chicago, IL, USA, December 27 - December 1, 2022
• RAD230 lecture and lab: “Hva er dyplæring?”
  • Western Norway University of Applied Sciences, Bergen, Norway, May 13, 2022
• DAT255 guest lecture: “How to use fastai for medical image analysis”
  • Western Norway University of Applied Sciences, Bergen, Norway, March 2, 2022
• CurieLecture: “Praktisk dyp læring i medisinsk bildeanalyse”
  • Eitri Medical Incubator, Bergen, Norway, February 2, 2022
• NordicAIMeet 2021 – Nordic Young Researchers Symposium: “How old is your brain? A Large Scale MRI and Deep Learning Investigation”
  • Oslo Kongressenter, Norway, November 1-2, 2021
• Seminar in Engineering Computing: “Practical deep learning in medical image analysis”
  • Western Norway University of Applied Sciences, Bergen, Norway, May 21, 2021
• Medisinsk bildebehandling og maskinlæring: “Kunstig intelligens ved MMIV, Radiologisk avdeling, Haukeland universitetssykehus”
  • Streaming, October 12, 2020
• Seminar, Bergen gynekologisk kreft - Voss 2020: “Artificial intelligence in image diagnostics: design methodologies for efficient use of data and radiologist’s expertise”
  • Scandic Voss, Norway, March 5-6, 2020
• Bergen Data Science Meetup: “Artificial intelligence in image diagnostics: design methodologies for efficient use of data”
  • Bouvet ASA, Bergen, Norway, November 19, 2019
• TekPRAT Førde - Maskinlæring og kunstig intelligens: “MMIV@HUS: Kunstig intelligens ved radiologisk avdeling”
  • Førde Sentralsjukehus, Norway, September 23, 2019
• MMIV Seminar September 2019: “Artificial intelligence in image diagnostics – transfer learning and active learning for efficient use of data and radiologist’s expertise”
  • Haraldsplass Diakonale Sykehus, Bergen, Norway, September 20, 2019
• NordBioMedNet Summer School 2019 in Computational Biomedicine - Imaging, machine learning and precision medicine: “Deep Learning in medical image analysis”
  • Seili, Finland, August 11-16, 2019
• Exhibition at Christiekonferansen 2019: “Mohn Medical Imaging and Visualization Center”
  • Universitetsaulaen i Bergen, Norway, April 29, 2019
• Bergen AI & Machine Learning Symposium 2019: “Deep transfer learning: a case study”
  • Solstrand Hotel, Bergen, Norway, March 25-26, 2019
• Exhibition at EHiN 2018
  • Oslo Spektrum, Norway, November 14, 2018
• Machine learning seminar: A machine learning mini-conference: “Deep transfer learning: Can a network trained to do a task be reused for other tasks?”
  • Haukeland universitetssjukehus, Bergen, Norway, October 17, 2018
• Poster at GTC Europe 2018: “Transfer learning for medical images: a case study”
  • Munich, Germany, October 9-11, 2018