Upcoming Seminars

NO SEMINARS - Winter Closure

Speaker

Dr. Tony Yousefnezhad, Senior Data Scientist at National Bank of Canada, hosted by Dr. Russ Greiner

Title

Orthogonal Contrastive Learning for Multi-Representation fMRI Analysis

Abstract
Functional MRI offers a powerful window into human cognition, yet challenges such as low signal-to-noise ratio, high dimensionality, and limited sample sizes remain major barriers—especially when integrating data across subjects or imaging sites. In this talk, we will introduce Orthogonal Contrastive Learning (OCL), a unified framework for aligning and analyzing multi-subject fMRI data without requiring temporal synchronization or equal time-series lengths.

OCL leverages two identical encoder networks: an online network trained with a contrastive objective that brings same-stimulus responses closer while separating different ones, and a target network that tracks the online model through an exponential moving average for stable learning. Each layer integrates QR decomposition for orthogonal feature extraction, locality-sensitive hashing (LSH) for compact subject-specific signatures, positional encoding for temporal-spatial fusion, and a transformer encoder for generating discriminative neural embeddings. I will also discuss OCL’s unsupervised pretraining on synthetic fMRI-like data and its transfer learning workflow for multi-site applications.

Presenter Bio
Dr. Tony Yousefnezhad is a Senior Data Scientist in the Department of Information Management at the National Bank of Canada, with cross-continental experience spanning Eurasia, East Asia, and North America. In addition to his industry role, he actively contributes to academic research and open-source innovation through his self-founded company, Learning By Machine. His research is at the forefront of pioneering advancements in machine learning, with a focus on deep learning, natural language processing (NLP), and reinforcement learning (RL) methodologies. These developments are designed to analyze a wide range of data modalities, including time series, text, images, audio, and wearable signals.

Website

Tony Muhammad Yousefnezhad

Timing & Location

UComm Seminar Room 2-108

pizza from 11:30, seminar from noon to 1

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Speaker

Alex Ayoub, PhD student at the University of Alberta, supervised by Dr. Csaba Szepesvári & Dr. Dale Schuurmans

Title

Learning to Reason Efficiently with Discounted Reinforcement Learning

Abstract
Large reasoning models (LRMs) often consume excessive tokens, inflating computational cost and latency. We challenge the assumption that longer responses improve accuracy. By penalizing reasoning tokens using a discounted reinforcement learning setup (interpretable as a small token cost) and analyzing Blackwell optimality in restricted policy classes, we encourage concise yet accurate reasoning. Experiments confirm our theoretical results that this approach shortens chains of thought while preserving accuracy.

Presenter Bio
Alex Ayoub is a PhD student at the University of Alberta working on pre and post training large language models to solve reasoning problems like mathematics. 

Timing & Location

UComm Seminar Room 2-108

pizza from 11:30, seminar from noon to 1

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Speaker

Dr. J. Quinn Lee, Amii Fellow & Assistant Professor in Psychology Science at the University of Alberta

Title

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Timing & Location

Amii HQ 2nd floor event space (10065 Jasper Ave)

pizza from 11:30, seminar from noon to 1

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Speaker

Shuai Liu, PhD student, University of Alberta, supervised by Dr. Csaba Szepesvári & Dr. Xiaoqi Tan

Title

Sample Complexity for Zero-Discounted MDP with Linear / Logistic Function Approximation and connections to RLHF

Abstract
I will discuss ideas behind algorithms that achieve nontrivial sample complexity guarantees for 0-discounted MDPs with linear/ logistic function approximation, a.k.a. stochastic contextual linear / logistic contextual bandits, including a deterministic UCB-like algorithm and a computationally efficient Thompson Sampling variant. Finally I’ll discuss if sigmoid function, which is widely used in RLHF, is a good choice for modelling human preference, in this special case.

Presenter Bio
Shuai Liu is a PhD student in the Computing Science department at University of Alberta, co-supervised by Dr. Csaba Szepesvári and Dr. Xiaoqi Tan. His current research interest lies in reinforcement learning theory (policy gradient methods), bandit algorithms and optimization. Before that, he obtained his MSc in Computing Science at University of Alberta under the supervision of Dr. Szepesvári and obtained a Bachelor's degree in Computer Science at the Harbin Institute of Technology.

Website

Shuai Liu

Timing & Location

UComm Seminar Room 2-108

pizza from 11:30, seminar from noon to 1

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Speaker

Dr. Russell Dinnage, Amii Fellow and Assistant Professor in the Department of Biological Sciences at the University of Alberta

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Timing & Location

Amii HQ 2nd floor event space (10065 Jasper Ave)

pizza from 11:30, seminar from noon to 1

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NO SEMINAR - Reading Week

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Speaker

Dr. Tian Tian, Amii Fellow & Professor in the Department of Mechanical Engineering at the University of Alberta

Title

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Timing & Location

Amii HQ 2nd floor event space (10065 Jasper Ave)

pizza from 11:30, seminar from noon to 1

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