Portrait of Samira Ebrahimi Kahou

Samira Ebrahimi Kahou

Affiliate Member
Canada CIFAR AI Chair
Assistant Professor, University of Calgary, Deparment of Electrical and Software Engineering
Adjunct Professor, École de technologie suprérieure, School of Computer Science
Adjunct Professor, McGill University, School of Computer Science
Research Topics
Computer Vision
Deep Learning
Medical Machine Learning
Multimodal Learning
Natural Language Processing
Reinforcement Learning
Representation Learning

Biography

I am an Assistant Professor at the Schulich School of Engineering's Department of Electrical and Software Engineering at the University of Calgary. I am also an adjunct professor at the Department of Computer Engineering and Information Technology of ÉTS and an adjunct professor at the Computer School of McGill. Before joining ÉTS, I was a postdoctoral fellow working with Professor Doina Precup at McGill/Mila. Before my postdoc, I was a researcher at Microsoft Research Montréal.

I received my Ph.D. from Polytechnique Montréal/Mila in 2016 under the supervision of Professor Chris Pal. During my Ph.D. studies, I worked on computer vision and deep learning applied to emotion recognition, object tracking and knowledge distillation.

Current Students

Master's Research - École de technologie suprérieure
PhD - École de technologie suprérieure
PhD - Université de Montréal
Principal supervisor :
Collaborating researcher - McGill University
Co-supervisor :
Professional Master's - Université de Montréal
Master's Research - École de technologie suprérieure
Principal supervisor :
Master's Research - École de technologie suprérieure
PhD - École de technologie suprérieure
Principal supervisor :
PhD - McGill University
Co-supervisor :
Master's Research - École de technologie suprérieure
PhD - McGill University
Principal supervisor :
Master's Research - McGill University
Principal supervisor :

Publications

GradTune: Last-layer Fine-tuning for Group Robustness Without Group Annotation
Patrik Joslin Kenfack
This work addresses the limitations of deep neural networks (DNNs) in generalizing beyond training data due to spurious correlations. Recent… (see more) research has demonstrated that models trained with empirical risk minimization learn both core and spurious features, often upweighting spurious ones in the final classification, which can frequently lead to poor performance on minority groups. Deep Feature Reweighting alleviates this issue by retraining the model's last classification layer using a group-balanced held-out validation set. However, relying on spurious feature labels during training or validation limits practical application, as spurious features are not always known or costly to annotate. Our preliminary experiments reveal that ERM-trained models exhibit higher gradient norms on minority group samples in the hold-out dataset. Leveraging these insights, we propose an alternative approach called GradTune, which fine-tunes the last classification layer using high-gradient norm samples. Our results on four well-established benchmarks demonstrate that the proposed method can achieve competitive performance compared to existing methods without requiring group labels during training or validation.
GradTune: Last-layer Fine-tuning for Group Robustness Without Group Annotation
Patrik Joslin Kenfack
This work addresses the limitations of deep neural networks (DNNs) in generalizing beyond training data due to spurious correlations. Recent… (see more) research has demonstrated that models trained with empirical risk minimization learn both core and spurious features, often upweighting spurious ones in the final classification, which can frequently lead to poor performance on minority groups. Deep Feature Reweighting alleviates this issue by retraining the model's last classification layer using a group-balanced held-out validation set. However, relying on spurious feature labels during training or validation limits practical application, as spurious features are not always known or costly to annotate. Our preliminary experiments reveal that ERM-trained models exhibit higher gradient norms on minority group samples in the hold-out dataset. Leveraging these insights, we propose an alternative approach called GradTune, which fine-tunes the last classification layer using high-gradient norm samples. Our results on four well-established benchmarks demonstrate that the proposed method can achieve competitive performance compared to existing methods without requiring group labels during training or validation.
Towards personalized healthcare without harm via bias modulation
Frank Ngaha
Patrik Joslin Kenfack
Clinical prediction models are often personalized to target heterogeneous sub-groups by using demographic attributes such as race and gender… (see more) to train the model. Traditional personalization approaches involve using demographic attributes in input features or training multiple sub-models for different population subgroups (decoupling model). However, these methods often harm the performance at the subgroup level compared to non-personalized models. This paper presents a novel personalization method to improve model performance at the sub-group level. Our method involves a two-step process: first, we train a model to predict group attributes, and then we use this model to learn data-dependent biases to modulate a second model for diagnosis prediction. Our results demonstrate that this joint architecture achieves consistent performance gains across all sub-groups in the Heart dataset. Furthermore, in the mortality dataset, it improves performance in two of the four sub-groups. A comparison of our method with the traditional decoupled personalization method demonstrated a greater performance gain in the sub-groups with less harm. This approach offers a more effective and scalable solution for personalized models, which could have a positive impact in healthcare and other areas that require predictive models that take sub-group information into account.
Towards personalized healthcare without harm via bias modulation
Frank Ngaha
Patrik Joslin Kenfack
Personalized machine learning models have gained significant importance in various domains, including healthcare. However, designing efficie… (see more)nt personalized models remains a challenge. Traditional approaches often involve training multiple sub-models for different population sub-groups, which can be costly and does not always guarantee improved performance across all sub-groups. This paper presents a novel approach to improving model performance at the sub-group level by leveraging bias and training a joint model. Our method involves a two-step process: first, we train a model to predict group attributes, and then we use this model to learn data-dependent biases to modulate a second model for diagnosis prediction. Our results demonstrate that this joint architecture achieves consistent performance gains across all sub-groups in the Heart dataset. Furthermore, in the mortality dataset, it improves performance in two of the four sub-groups. A comparison of our method with the traditional decoupled personalization method demonstrated a greater performance gain in the sub-groups with less harm. This approach offers a more effective and scalable solution for personalization of models, which could have positive impact in healthcare and other areas that require predictive models which take sub-group information into account.
Learning Multi-agent Multi-machine Tending by Mobile Robots
Abdalwhab Abdalwhab
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (see more)ive robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions.
Handling Delay in Real-Time Reinforcement Learning
Ivan Anokhin
Rishav
Matthew D Riemer
Stephen Chung
Handling Delay in Real-Time Reinforcement Learning
Ivan Anokhin
Rishav
Matthew D Riemer
Stephen Chung
Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second… (see more) due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
Rambod Azimi
Rishav
Marek Teichmann
Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
Riadh Azzaz
Valentin Hurel
Patrice Ménard
M. Jahazi
Elmira Moosavi-Khoonsari
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
Aamer Abdul Rahman
Pranav Agarwal
Rita Noumeir
Philippe Jouvet
Vincent Michalski
Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its appli… (see more)cation, however, has been limited by the lack of interpretability and interactivity for clinicians. To address these challenges, we propose the medical decision transformer (MeDT), a novel and versatile framework based on the goal-conditioned reinforcement learning paradigm for sepsis treatment recommendation. MeDT uses the decision transformer architecture to learn a policy for drug dosage recommendation. During offline training, MeDT utilizes collected treatment trajectories to predict administered treatments for each time step, incorporating known treatment outcomes, target acuity scores, past treatment decisions, and current and past medical states. This analysis enables MeDT to capture complex dependencies among a patient's medical history, treatment decisions, outcomes, and short-term effects on stability. Our proposed conditioning uses acuity scores to address sparse reward issues and to facilitate clinician-model interactions, enhancing decision-making. Following training, MeDT can generate tailored treatment recommendations by conditioning on the desired positive outcome (survival) and user-specified short-term stability improvements. We carry out rigorous experiments on data from the MIMIC-III dataset and use off-policy evaluation to demonstrate that MeDT recommends interventions that outperform or are competitive with existing offline reinforcement learning methods while enabling a more interpretable, personalized and clinician-directed approach.
Reinforcement Learning for Sequence Design Leveraging Protein Language Models
Jithendaraa Subramanian
Shiva Kanth Sujit
Niloy Irtisam
Umong Sain
Riashat Islam
Handling Delay in Reinforcement Learning Caused by Parallel Computations of Neurons
Ivan Anokhin
Rishav
Stephen Chung
Biological neural networks operate in parallel, a feature that sets them apart from artificial neural networks and can significantly enhance… (see more) inference speed. However, this parallelism introduces challenges: when each neuron operates asynchronously with a fixed execution time, an