Ekta Vats

Assistant Professor in Machine Learning, Docent (Associate Professor) in Computerised Image Processing at the Division of Systems and Control, Department of IT, Uppsala University and a Beijer Researcher at The Beijer Laboratory for Artificial Intelligence Research. Her current projects focus on Multimodal learning, Vision-Language(-Action) Models and Multi-spectral imaging for cultural heritage collections to uncover hidden texts in old manuscripts. She teaches courses: Large Language Models and societal consequences of AI (also course responsible) and Deep Learning at Uppsala University. She previously worked as an AI Scientist at Silo AI (now part of AMD), 2021–2024.

Email: contact@ektavats.se

Robin Hollifeldt

Doctoral student (Sept. 2024–), supervised by Asst. Prof. Ekta Vats and Prof. Thomas Schön.

Thesis topic: Multimodal deep learning and vision-language models.

Robin holds a MSc in Mathematics from Uppsala University and a BSc in Mathematics from Lund University, Sweden. Her PhD studies is funded by Kjell och Märta Beijers Stiftelsen and is affiliated with Wallenberg AI, Autonomous Systems and Software Program (WASP).

Raphaela M. Heil

Affiliated Member, previous Doctoral student (2018-2023),  co-supervised by Ekta Vats at the Department of IT, Uppsala University.

Raphaela defended her thesis on 4 Oct. 2023.

Thesis title: Document Image Processing for Handwritten Stenography Recognition – Deep Learning-based Transliteration of Astrid Lindgren’s Stenographic Manuscripts. She now works as a Software Engineer (Text Recognition)Folkrörelsearkivet för Uppsala län (see Labour’s Memory Project).

PostDoc

We are looking for a PostDoc in Multimodal Deep Learning! (link)

PI: Ekta Vats.

 

Doctoral Student Co-supervision

  • Hong Wang (Sept. 2024–). Thesis topic: Large language models-powered social robots in cybersecurity applications.
  • Raphaela Heil (2018–2023). Thesis title: Document Image Processing for Handwritten Stenography Recognition – Deep Learning-based Transliteration of Astrid Lindgren’s Stenographic Manuscripts

 

M.Sc. Student Supervision

  • Ola Karrar, Masters thesis: Vision-based Deep Learning Approach for Human Fall Detection, UU, 2024.
  • Till Grutschus, Technical University of Munich. Masters in Data Science Project: Human fall detection on untrimmed videos using large foundational video-understanding model, 2023. 
  • Emir Esenov, Masters in Data Science Project: Vision-based fall detection, UU, 2023.
  • Alex Kangas, Masters in Data Science Project: Leveraging Generative AI Models for Handwritten Text Image Synthesis, UU, 2023.
  • Vasiloius toumpanakis, Masters in Data Science Project: Leveraging Generative AI Models for Handwritten Text Image Synthesis, UU, 2023.
  • Liam Tabibzadeh, Masters in Data Science Project: Leveraging Generative AI Models for Handwritten Text Image Synthesis, UU, 2023.
  • Liang Cheng, Masters in Data Science Project: Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition, UU, 2022. Now PhD student at University of Oslo, Norway. 
  • Jonas Frankemölle, Masters in Data Science Project: Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition, UU, 2022. Now ML Engineer at Scaleout.
  • Adam Axelsson, Masters in Data Science Project: Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition, UU, 2022.
  • Dmitrijs Kass, Masters in Data Science Project: AttentionHTR: Handwritten Text Recognition Based on Attention Encoder-Decoder Networks, UU, 2021. Now Machine Learning Engineer at Modulai, Stockholm. 
  • Charalampos Poulikidis Moutsanas, Masters in Data Science Project: Attention-based Handwritten Text Recognition, UU, 2021.
  • Simon Leijon, Hampus Widén, Martin Sundberg, Petter Sigfridsson and Jonathan Kurén, Masters in Data Science Project: Document Image Binarization for Heavily Degraded Swedish Manuscripts, UU, 2021.

 

Subject Reviewer