Arpit Garg

Dr Arpit Garg

Research Fellow

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

My research focuses on developing robust and responsible AI systems that can operate reliably in real-world environments with imperfect data. I specialize in instance-dependent noisy label learning, where I've pioneered novel graphical model approaches and adaptive estimation techniques that significantly improve model performance when training labels are corrupted or unreliable. Through my work at TikTok, I advanced multimodal large language models (MLLMs) for Trust & Safety applications, achieving substantial AUC improvements by integrating diverse vision backbones with state-of-the-art language models.

Currently, as a Grant-Funded Research Fellow at the Responsible AI Research Centre, I'm developing privacy-preserving machine learning frameworks for Australian data sovereignty and AI safety protocols for critical infrastructure. My research bridges theoretical advances with practical applications, from contributing ML innovations to major film productions (Mad Max: Furiosa, Wolverine & Deadpool) to architecting scalable AI systems with multi-million dollar revenue impact. I maintain active collaborations with leading international institutions and contribute regularly to top-tier conferences including ECCV, WACV, and ICCV.

  • Education
    Date Institution name Country Title
    2021 - 2025 University of Adelaide Australia PhD
    2019 - 2021 University of Adelaide Australia Master of Data Science
    2015 - 2019 Rajasthan Technical University India Bachelors of Technology in Computer Science

My research on Noisy label was supported by prestigious international funding including the Australian Research Council grants DP180103232 and FT190100525, which fund my collaborative work on robust machine learning and computer vision applications. Additionally, I'm supported by the UK's Engineering and Physical Sciences Research Council (EPSRC) grant EP/Y018036/1, enabling cross-institutional research with the University of Oxford and University of Surrey.
This comprehensive funding support has enabled me to focus on high-impact research while collaborating with leading institutions across Australia, the UK, and internationally, positioning my work at the forefront of responsible AI development.

Lecturer for COMP SCI 7306 - Mining Big Data (Tri 1, 2022)

As a Research Fellow at the Australian Institute for Machine Learning, I actively mentor next-generation Australian AI researchers, providing guidance on responsible AI development and advanced machine learning techniques. My teaching philosophy emphasizes hands-on learning through real-world applications, drawing from my diverse industry experience at TikTok, Rising Sun Pictures, and defense research organizations.

I contribute to the academic community by serving as a reviewer for premier machine learning conferences including ICCV, WACV, ECCV, and ICLR, helping maintain high standards in AI research publication. My mentoring extends beyond formal academic settings, as I've guided team members across cross-functional industry collaborations and contributed to open-source projects that have received over 140,000 GitHub visits, demonstrating my commitment to knowledge sharing and community education in the AI field.

  • Other Supervision Activities
    Date Role Research Topic Location Program Supervision Type Student Load Student Name
    2025 - 2023 Principal Supervisor Live AI Art Gallery Australian Institute for Machine Learning Bachelor of IT Other Full Time Abhijith Sreekumar
    2023 - 2024 Principal Supervisor Live AI ART Gallery Australian Institute for Machine Learning Master of Data Science Master Full Time Divye Maggo
  • Position: Research Fellow
  • Phone: 83135615
  • Email: arpit.garg@adelaide.edu.au
  • Campus: Lot 14
  • Building: Australian Institute for Machine Learning Building, floor Ground Floor
  • Room: G.05.01
  • Org Unit: Australian Institute for Machine Learning
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