Prakash Chandra Chhipa
RISE Research Institutes of Sweden
Research areas: Computer Science · Artificial Intelligence · Domain Adaptation and Few-Shot Learning · Advanced Neural Network Applications · EEG and Brain-Computer Interfaces
0000-0002-6903-7552
About
Prakash is a Research Scientist and AI professional working at the intersection of foundation models, multimodal AI, and computer vision, with over 13 years of experience in machine learning and a strong focus on translating advanced research into real-world impact.
He completed his PhD in Machine Learning from Luleå University of Technology, Sweden, in 2025, advised by Prof. Marcus Liwicki. His research focused on self-supervised learning, robustness, and multimodal systems, applied to real-world tasks such as object detection, crowd analytics, and person re-identification. It further extended to reinforcement learning–driven adversarial attacks and the adaptation of self-supervised methods across domains, ranging from industrial mines to medical imaging. During his PhD, he was a visiting researcher at the Center for Research in Computer Vision (CRCV), University of Central Florida, USA, collaborating with Prof. Mubarak Shah, a prominent and widely recognized leader in the field of computer vision. He was also a recipient of the DAAD AI-NET Fellowship.
His work has been published at top-tier venues including NeurIPS, ICLR, ECCV, CVPR, and WACV, and he has presented his research internationally through invited talks at institutions such as MBZUAI (UAE) and EMBL (Cambridge), as well as at leading conferences. In addition, he actively contributes to the research community as a reviewer for major conferences including CVPR, NeurIPS, ICLR, ICML, and ICCV. He is also a member of the European Laboratory for Learning and Intelligent Systems (ELLIS).
Prior to his PhD, Prakash spent over a decade in industrial R&D, building and deploying large-scale AI systems across domains. At Samsung Research (2012–2018), he developed and scaled audio-visual recognition and recommendation systems used by millions of devices, while contributing to innovation through multiple international patents with citations from major technology companies. He later joined ARKRAY R&D (2018–2020), where he led the development of AI-driven medical imaging systems, resulting in the commercialization of the Aution Eye AI 4510 for urine sediment analysis.
Currently, Prakash works as an AI Expert at RISE, contributing to the MIMER AIF. The Mimer AI Factory is part of an EU program that builds a network of supercomputing-powered hubs across Europe to accelerate the development of trustworthy, cutting-edge AI models and support innovation for researchers, startups, and industry. Prakash’s work focuses on enabling AI transformation across industry, the public sector, and research by designing, adapting, and deploying foundation models and multimodal systems on high-performance computing infrastructure. He actively bridges the gap between cutting-edge research and scalable, production-ready AI solutions.
He is always open to discussing new ideas, identifying critical gaps, and collaborating on building impactful solutions that can evolve into tomorrow’s products and companies
Employment
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RISE Research Institutes of Sweden Resercher2026 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (22)
- LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective Distortion Save
- Möbius Transform for Mitigating Perspective Distortions in Representation Learning Save
- Open-Vocabulary Object Detectors: Robustness Challenges Under Distribution Shifts Save
- An EEG Analysis Framework for Brain Disorder Classification Using Convolved Connectivity Features Save
- LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective Distortion Save
- Möbius Transform for Mitigating Perspective Distortions in Representation Learning Save
- A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions Save
- Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions? Save
- Depth Contrast: Self-supervised Pretraining on 3DPM Images for Mining Material Classification Save
- Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery Save
- Emotions Classification Using EEG in Health Care Save
- Emotions Classification Using EEG in Health Care Save
- Functional Knowledge Transfer with Self-supervised Representation Learning Save
- Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images Save
- Magnification prior: a self-supervised method for learning representations on breast cancer histopathological images Save
- Multi-Task Meta Learning: learn how to adapt to unseen tasks Save
- SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems Save
- Deep Neural Network approaches for Analysing Videos of Music Performances Save
- Depth Contrast: Self-supervised Pretraining on 3DPM Images for Mining Material Classification Save
- Detecting COVID-19 from Audio Recording of Coughs Using Random Forests and Support Vector Machines. Save