Aamer Abdul

I am a Masters student at Mila Quebec where I am advised by Professor Samira Ebrahimi Kahou. Currently, my research interests lie at the intersection of generalization, explainabiltiy and robustness, applied towards problems in climate and healthcare. I am also interested offline reinforcement learning and vision-language models.

Previously, I was a ML research intern at the University of Toronto, working under the supervision of Professor Chi-Guhn Lee, where I worked on adversarial domain adaptation for data-sparse object detection. My undergrad was in Electronics and Communication Engineering from BITS Pilani, during which I mainly worked on computer vision and machine listening.

I'm actively seeking internship opportunities and am open to collaborations. Don't hesitate to get in touch!

Email / Resume / Publications / Google Scholar / Twitter / LinkedIn

News

Aug 2024: Paper @ AGU 2024 - Our work Efficient Fine-tuning of Atmospheric Foundation Models for Wildfire Modeling was accepted at AGU 2024.
July 2024: Summer School - Attended the 2024 International Computer Vision Summer School in Sicily.
June 2024: Paper @ Preprint release - Preprint release of our work Empowering Clinicians with Medical Decision Transformers.
May 2024: Amii Talent Bursary - Received award worth $1500 to attend UpperBound 2024.
Oct 2023: Spotlight Paper (Top 10%) @ NeurIPS Workshop - Our work Empowering Clinicians with MeDT: A Framework for Sepsis Treatment was accepted at NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning.
July 2023: Paper @ Preprint release - Preprint release of our work Transformers in Reinforcement Learning: A Survey.
May 2023: Reviewer - Served as reviewer for ICCV.
Nov 2022: Paper @ NeurIPS Workshop - Our work Learning from Uncertain Concepts via Test Time Interventions was accepted at NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning.
Nov 2022: Paper @ NeurIPS Workshop - Our work Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning was accepted at ICBINB NeurIPS 2022 Workshop .
June 2022: Paper @ CVPR - Our work Revisiting learnable affines for batch norm in few-shot transfer learning was accepted at CVPR 2022.
May 2022: Position - Started Masters by Research at Mila, in collaboration with CHU Sainte-Justine.
Dec 2021: Paper @ NeurIPS Workshop - Our work Shift and Scale is Detrimental To Few-Shot Transfer was accepted at NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications.
July 2021: Position - Started Research Internship at Mila.
Dec 2020: Position - Started Research Internship at University of Toronto.

Publications

	Empowering Clinicians with MeDT: A Framework for Sepsis Treatment Aamer Abdul Rahman, Pranav Agarwal, Rita Noumeir, Philippe Jouvet, Vincent Michalski, Samira Ebrahimi Kahou Submitted to TMLR 2024 NeurIPS 2023 Goal-Conditioned Reinforcement Learning Workshop (Spotlight) Project page / Paper / Code Offline reinforcement learning is promising for safety-critical tasks like clinical decision support, but faces challenges of interpretability and clinician interactivity. To overcome these, the proposed Medical Decision Transformer (MeDT) utilizes a goal-conditioned RL paradigm for sepsis treatment recommendations. MeDT employs the decision transformer architecture, considering factors like treatment outcomes, patient acuity scores, dosages, and current/past medical states to provide a holistic view of the patient's history. This enhances decision-making by allowing MeDT to generate actions based on user-specified goals, ensuring clinician interactability and addressing sparse rewards. Results from the MIMIC-III dataset demonstrate MeDT's effectiveness in producing interventions that either outperform or compete with existing methods, offering a more interpretable, personalized, and clinician-directed approach.
	Transformers in Reinforcement Learning: A Survey Pranav Agarwal, Aamer Abdul Rahman, Pierre-Luc St-Charles, Simon J.D. Prince, Samira Ebrahimi Kahou Arxiv Preprint, 2023 Paper We present a comprehensive survey on the emergence of transformers in reinforcement learning.
	Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning Moslem Yazdanpanah, Aamer Abdul Rahman, Muawiz Chaudhary, Christian Desrosiers, Mohammad Havaei, Eugene Belilovsky, Samira Ebrahimi Kahou CVPR, 2022 Paper / Code We introduce a novel normalization strategy for few-shot transfer learning, bringing performance improvements upto 5% in accuracy with neglible computational overhead.
	Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning Ivaxi Sheth, Aamer Abdul Rahman, Mohammad Havaei, Samira Ebrahimi Kahou NeurIPS, 2022, ICBINB Workshop arxiv We demonstrate that using conditional batch normalization for contextual multimodal tasks leads to shortcut learning. Our experiments reveal that the network learns close to no visual features and relies solely on contextual auxiliary data to make predictions.
	Learning from Uncertain Concepts via Test Time Interventions Ivaxi Sheth, Aamer Abdul Rahman, Laya Rafiee, Mohammad Havaei, Samira Ebrahimi Kahou NeurIPS, 2022, Trustworthy and Socially Responsible Machine Learning Workshop arxiv We propose a novel uncertainty based strategy to select the interventions in Concept Bottleneck Models. We demonstrate the proposed method can improve human-model interaction via more meaningful corrections, while reducing information leakage.
	Robust Segmentation of Vascular Network using Deeply Cascaded AReN-UNet Aamer Abdul Rahman, Birendra Biswal, Geetha Pavani, Shazia Hasan Biomedical Signal Processing and Control, 2021 We present a U-Net segmentation architecture based on cascading and attention modules for improved performance on retinal blood vessel segmentation.
	Classification of Urbansound8k: A Study using Convolutional Neural Networks and Multiple Data Augmentation Techniques Aamer Abdul Rahman, Angel Arul Jyothi International Conference on Soft Computing and its Engineering Applications, 2020 A study on effectiveness of data augmentation strategies on downstream performance for urban sound classification with CNNs.
	Early Detection of Locust Swarms using Deep Learning Karthika Suresh, Aamer Abdul Rahman Advances in Machine Learning and Computational Intelligence, 2020 An object detection approach with Faster RCNN's to detect and track locust insects.

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