Hanan Gani

About Me

I am currently a Research Associate at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) and have recently obtained my Masters degree in machine learning from the same institution. At MBZUAI, I am affliated with the Intelligent Visual Analytics Lab (IVAL) where I am primarily advised by Dr. Salman Khan and have a close collaboration with Dr. Muzammal Naseer. My secondary advisor is Dr. Fahad Khan. I have also worked as a visiting student with Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST).

I received my undergraduate degree in Electronics and Communication Engineering with a minor in Computer Science from the National Institute of Technology (NIT) Srinagar, India. After graduation, I worked as a Machine Learning engineer at Harman International (Samsung) before transitioning into academia in 2021 and joining MBZUAI as a Research Assistant under Dr. Mohammad Yaqub

Research Interests

I am broadly interested in Multi-modal Generative and Discriminative learning in the realm of Label-efficiency with applications to problems in 1. Test-time out-of-distribution generalization, 2. Unifying generative and discriminative spaces. 3. World-centric evaluation of Multi-modal models and 4. Self-supervised learning.

News

• [June. 2024] Our paper “MedContext: Learning Contextual Cues for Efficient Volumetric Medical Segmentation” got accepted at MICCAI 2024.
• [June. 2024] One paper accepted at IEEE ICIP 2024.
• [March. 2024] Awarded ICLR 2024 Travel Grant.
• [Jan. 2024] Our paper “LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts” got accepted at ICLR 2024.
• [Oct. 2023] Awarded NeurIPS 2023 Travel Grant.
• [Sept. 2023] Our paper “Align your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization” got accepted at NeurIPS 2023.
• [June. 2023] Joined Visual Computing Center (VCC) at KAUST as a Visiting Student under Professor Peter Wonka.
• [Jan. 2023] Our US patent about training Vision Transformers on small-scale datasets got approved for filing.
• [Sep. 2022] I started working with Dr. Salman as a part of Intelligent Visual Analytics Lab (IVAL).
• [Sep. 2022] I joined MBZUAI as a grad student in Machine Learning with full scholarship.
• [Oct. 2022] Our paper about training Vision Transformers on small-scale datasets got accepted to BMVC 2022.
• [Oct. 2021] I joined BiomedIA Lab at MBZUAI as a Research Assistant.
• [March. 2021] I started as a part-time Predoctoral fellow at Fatima Fellowship working under Dr. Abubakr Abid.
• [Oct. 2018] I joined as a Machine Learning Engineer at Harman International Inc. (Samsung).
• [July. 2018] Our paper about supervised methodology for disguised facial recognition in wild got accepted to ACM ICRCV 2018.
• [August. 2018] Our paper about disguised facial recognition got accepted to IEEE ICSIP 2018.
• [June. 2018] I completed my undergraduate studies in Electronics and Communications Engineering from the National Institute of Technology (NIT) Srinagar.
• [Dec. 2017] I joined as a Research Intern in Computational Intelligence and UAV Lab at the Indian Institute of Science, Bangalore

Publications

Services

Conference Reviewing

• Served as a Reviewer at NeurIPS'23, ICLR'24, CVPR'24, ECCV'24 and ICML'24

Academic Services

Teaching Assistant

• Deep Learning (AI702) course - Spring 2024
• Vision-Language Foundational Models (CV806) course - Spring 2024
• Statistical Inference and Causality (ML703) course - Fall 2023
• Machine Learning (ML701) course - Fall 2023

Awards and Merits

• Awarded ICLR 2024 Travel Grant.
• Awarded NeurIPS 2023 Travel Grant.
• Full scholarship to study Masters in Machine Learning at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI).
• Selected as Predoctoral fellow at Fatima Fellowship under Dr. Abubakr Abid.
• Harman Star Excellence award from the Harman International India (Regional) Head.
• Merit Based Scholarship granted for undergraduate studies by Ministry of Minority Affairs, India.
• Secured 80th state rank in IIT-JEE Mains 2014 (among top 1% of 1.5 million students across the country).

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