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 the course (also course responsible): Large Language Models and societal consequences of AI at Uppsala University. 

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.

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

 

Incoming PostDoc in Deep Learning with a focus on Vision-Language Models.

PI: Ekta Vats.

Project start date: Nov. 2024.

 

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