Prakash Chandra Chhipa

Wallenberg WASP Postdoctoral Research Scientist, Luleå Universtiy of Technology, Sweden

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A35374, Luleå Universtiy of Technology, Sweden

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Click here for updated CV!

Prakash brings over 13 years of machine learning R&D experience spanning computer vision, self-supervised learning, foundation models, multimodality, and generative AI. From training large-scale models to leading teams and translating ideas into publications, patents, and products—he bridges scientific depth with real-world impact.

He is a researcher and applied scientist, currently a WASP Fellow and ELLIS Member, holding a PhD in Machine Learning from Luleå University of Technology, Sweden (2025). His research advances robust, domain-aware self-supervised learning—starting from modeling fundamental yet natural phenomena of scene distortion caused by camera perspective, leading to improved robustness in real-world tasks such as object detection, crowd analytics, and person re-identification; extending to reinforcement learning–driven adversarial attacks, and adapting self-supervised methods across domains—from industrial mines to medical imaging. He has published in ICLR, ECCV, ACCV, WACV, and served as reviewer, invited talk speaker, and visiting researcher at UCF, received multiple grants and fellowships, and collaborated with leading researchers worldwide. He is currently working on multimodal foundation models, aiming to make them more robust, adaptable, and practically useful for real-world applications.

Before academia, he spent a decade in industrial R&D. At Samsung R&D (2012–2018), he built large-scale vision systems and ad-recommendation models, leading to 11 international patents (5 granted) cited by major tech companies. Later at Arkray R&D (2018–2020), he led the ML team developing AI-driven diagnostics (Aution Eye AI 4510), merging healthcare and machine learning innovation.

Open for Research Scientist roles and discussions. Please check his updated CV here.

AskMeAnythingInCharts – Qwen2.5-VL-7B (LoRA) for Chart QA - get your question answered on charts and plots. [Opensourced and released on Oct 2025] - Open-source model on Hugging Face Try the live demo Training code

AskMeAnythingInCharts Demo

Möbius Perspective Distortion (MPD) augmentation for PyTorch & Albumentations [Opensourced and released on June 2025] - visit PyPI for pip install


Academic Research Highlights

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Currently Posdoctoral Researcher funded by Wallenberg AI, Autonomous Systems, and Software Program (WASP). Recieved PhD in Machine Learning from Luleå tekniska universitet, Sweden. Research focused on robustness and domain-aware self-supervised representation learning. Published rsearch works in top conferences including ICLR, ECCV, WACV, ICIP and ICCV/ECCV workshops. Have been a reviewer at CVPR, NeurIPS, ICLR, and other conferences, industrial research with Swedish companies, and secured multiple grants.

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Visiting Researcher at Center for Research in Computer Vision (CRCV), University of Central Florida, USA. Guided by host Prof. Mubarak Shah. Working on robust representation learning and also focusing Geo-localization domain. (Mar-June 2024)

Industrial R&D Work Tenure

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Built a team and led computer vision research and development in the microscopic histopathology visual domain at Arkray R&D. Proudly contributed to achieving novel sediment recognition method on low-resolution histopathology for successful commercialization of Urinary Sediment Analyzer AUTION EYE AI-4510. Have been inventor for multiple patent applications (2018-2020).

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Machine learning at AI & Data Intelligence Group (Now Advanced Research - Vision AI), Samsung R&D Institute for 6+ years (2012-2018) in different roles. Have been in forefornt for long-term R&D of successful large-scale products Samsung ACR and Samsung Ads focusing majorly on computer vision and reinforcement learning. Have been inventor for multiple granted patents/IP applications and awardee for Innovator of the Year-2017 for multiple IPs.

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Worked with HCL Technologies for two years (2010-2012) and have been a contributor for enterprise-scale development of a platform for leading Telcom service provider T-Mobile.

news

Oct 20, 2025 🧠 Released opensource A new LoRA-fine-tuned version of Qwen2.5-7B model ‘AskAnythingInCharts-Qwen2.5-7B’ on Hugging Face for robust and improved Q&A capability on charts and plots. Try demo at ‘HF Spaces’.
Jun 29, 2025 📚 Released opensource library ‘Möbius Perspective Distortion (MPD) augmentation for PyTorch & Albumentations’ for synthesizing perspective distortion based on work on Möbius transform in ECCV2024.
Apr 22, 2025 🎤 Gave invited research talk titled ‘Towards Robust Self-Supervised Representation Learning’ at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. 🇦🇪
Apr 08, 2025 🎓 Sucessfully defended PhD with thesis titled ‘Towards Robust and Domain-aware Self-supervised Representation Learning’). 🇸🇪
Jan 22, 2025 🎉 Paper titled ‘ASTrA: Adversarial Self-supervised Training with Adaptive-Attacks’ accepted in ICLR 2025. 🎉
Nov 02, 2024 🥇 Featured in Swedish Trans-Atlantic Researchers and Scholars Network (STARS) for recent work. 🇸🇪 🇺🇸
Oct 29, 2024 🎓 Received DAAD AI-Net Fellow 2024 fellowship to visit and collaborate with host German research institute on Artificial Intelligence. 🇩🇪
Sep 20, 2024 🎉 Paper titled ‘LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective Distortion’ accepted in ACCV 2024. 🎉
Aug 15, 2024 🎉 Paper titled ‘Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts’ accepted in ECCV OOD-CV workshop 2024. 🎉
Jul 03, 2024 🎉 Paper titled ‘Möbius Transform for Mitigating Perspective Distortions in Representation Learning’ accepted in ECCV 2024. 🎉
Jun 20, 2024 📰 Visiting research experience at CRCV, University of Central Florida, USA featured in The Swedish Society for Automated Image Analysis (SSBA) ‘Magazine (June 2024 edition)’. 🇸🇪
Mar 10, 2024 ✈️ Started research visit at Center for Research in Computer Vision (‘CRCV’), University of Central Florida, hosted by Prof. Mubarak Shah. 🇺🇸
Oct 08, 2023 🎉 Paper titled ‘Functional knowledge transfer with self-supervised representation learning’ accepted in ICIP 2023. 🎉
Mar 17, 2023 🎓 Sucessfully defended Licentiate thesis titled ‘Self-supervised Representation Learning for Visual Domains Beyond Natural Scenes’. 🇸🇪
Jan 03, 2023 🎉 Paper titled ‘Magnification Prior A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Image’ accepted in WACV 2023. 🎉
Nov 24, 2022 🎤 Invited to present my research titled ‘Learning Self-supervised Representations on Histopathological Images - Why and How?’ at European Molecular Biology Lab European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom. 🇬🇧
Nov 06, 2022 🎤 Invited to present my research titled ‘Self-supervised Representation Learning on Beyond the Natural Visual Concepts’ during MIRAI2.0 R&D Week at Kyushu University, Fukuoka, Japan. 🇯🇵
Oct 23, 2022 🎉 Paper titled ‘Depth Contrast Self-Supervised Pretraining on 3DPM Images for Mining Material Classification’ accepted in ECCV Workshops 2022. 🎉

selected publications

  1. astra.JPG
    ASTrA: Adversarial Self-supervised Training with Adaptive-Attacks
    Prakash Chandra Chhipa, Gautam Vashishtha, Jithamanyu Settur, Rajkumar Saini, Mubarak Shah, and Marcus Liwicki
    International Conference on Learning Representations, 2025
  2. mpd.JPG
    Möbius Transform for Mitigating Perspective Distortions in Representation Learning
    Prakash Chandra Chhipa, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Marcus Liwicki, and Mubarak Shah
    European Conference on Computer Vision, 2024
  3. lcm.PNG
    LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective Distortion
    Meenakshi Subhash Chippa, Prakash Chandra Chhipa, Kanjar De, Marcus Liwicki, and Rajkumar Saini
    Asian Conference on Computer Vision, 2024
  4. ovod.png
    Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts
    Prakash Chandra Chhipa, Kanjar De, Meenakshi Subhash Chippa, Rajkumar Saini, and Marcus Liwicki
    European Conference on Computer Vision Workshops, 2024
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    Magnification prior: a self-supervised method for learning representations on breast cancer histopathological images
    Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren, Rajkumar Saini, Seiichi Uchida, and Marcus Liwicki
    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023
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    Depth contrast: Self-supervised pretraining on 3dpm images for mining material classification
    Prakash Chandra Chhipa, Richa Upadhyay, Rajkumar Saini, Lars Lindqvist, Richard Nordenskjold, Seiichi Uchida, and Marcus Liwicki
    In European Conference on Computer Vision Workshops , 2022
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    Can Self-Supervised Representation Learning MethodsWithstand Distribution Shifts and Corruptions?
    Prakash Chandra Chhipa, Johan Rodahl Holmgren, Kanjar De, Rajkumar Saini, and Marcus Liwicki
    In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2023
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    Functional Knowledge Transfer with Self-supervised Representation Learning
    Prakash Chandra Chhipa, Muskaan Chopra, Gopal Mengi, Varun Gupta, Richa Upadhyay, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Seiichi Uchida, and Marcus Liwicki
    In IEEE International Conference on Image Processing (ICIP) , 2023
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    Domain adaptable self-supervised representation learning on remote sensing satellite imagery
    Muskaan Chopra, Prakash Chandra Chhipa, Gopal Mengi, Varun Gupta, and Marcus Liwicki
    In International Joint Conference on Neural Networks (IJCNN) , 2023
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    Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images
    Ekta Gupta, Varun Gupta, Muskaan Chopra, Prakash Chandra Chhipa, and Marcus Liwicki
    In International Joint Conference on Neural Networks (IJCNN) , 2023
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    Multi-task meta learning: learn how to adapt to unseen tasks
    Richa Upadhyay, Prakash Chandra Chhipa, Ronald Phlypo, Rajkumar Saini, and Marcus Liwicki
    In International Joint Conference on Neural Networks (IJCNN) , 2023