In this two-hour workshop we will explore digital collections of original documents. We will discuss the creation, selection, and preservation of archival records, and how you can read, ask questions of, and understand them in context. We will be exploring 19th and 20th Canadian and American history sources, though with advance notice the workshop can be modified to meet other particular interests. This workshop is for anyone interested in learning more about conducting archival research. (A larger screen, rather than a phone or tablet, is recommended if possible.)

By attending this workshop you will:

• understand how archival documents are preserved, digitized, and shared with the public;
• be able to locate digital archival collections relevant to your research;
• have practiced reading and discussing original documents.

This session will be held online via Zoom. A confirmation with a link to the session will be emailed to you after registration.

Register at: https://libcal.lakeheadu.ca/event/3916862

Zotero is a free and convenient tool that can be used to help you manage information for your research, insert citations into your writing and easily create bibliographies. This session will provide an overview of how Zotero can support your work throughout the research process, with hands-on opportunities to practice using the tool.

After attending this workshop, you will have learned how to use Zotero to:

• save time with the research process
• create better citations and bibliographies
• organise all the books / articles you find during the research process
• use advanced features in Zotero

Note: This session will be held online via Zoom. A confirmation with a link to the session will be emailed to you after registration.

This workshop will introduce you to Lakehead's OMNI search system, and other resource tools which will help fulfill your research needs at Lakehead University. After attending this workshop, you will be able to:

• find books on your topic, and request books from other Ontario universities
• find peer reviewed scholarly articles
• seek further assistance from the library

Note: This session will be held online via Zoom. A confirmation with a link to the session will be emailed to you after registration.

Register at: https://libcal.lakeheadu.ca/event/3925952

For anyone new to researching with primary sources (original documents or objects, including in digital formats). We’ll explore the nature of archival and primary source collections, share tips on locating sources online and in the physical world, and how to read and evaluate historical documents. This session is for anyone interested in doing historical research, regardless of your program.

By attending this workshop you will:

• understand the role of primary sources in research;
• be able to find documents that support your research;
• have practiced reading and discussing original documents.

This session will be held online via Zoom. A confirmation with a link to the session will be emailed to you after registration.

Register at: https://libcal.lakeheadu.ca/event/3916859

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Shreyas Ajit Keelary

Thesis title: Beyond Signal Noise: A Framework for Assessing and Correcting Label Noise in EEG Datasets

Abstract: The reliability of machine learning models in electroencephalography (EEG) research is frequently undermined by label noise. In many cases, trial annotations do not accurately reflect the subject’s true cognitive state due to attentional drift or task switching. This thesis presents a comprehensive, end-to-end framework for identifying and correcting this issue. First, it establishes a robust diagnostic methodology that quantifies the nature and extent of label noise. This is done by integrating an ensemble of outlier detection algorithms with model-based data valuation using Data Shapley. The analyses reveal distinct noise profiles in public datasets. In cognitive tasks, noise is systematic and subjectdriven. In motor imagery paradigms, noise is more randomly distributed, but it remains detrimental. Second, to address these findings, this thesis proposes a novel framework that is universal (agnostic to channel and recording length), subject-adaptive, and extensible. The Mixture-of-Experts (MoE) architecture enables automated label correction and includes a formal task hierarchy for further model expansion. The hierarchical system routes EEG signals through an Activity Detector and a Domain Router. It successfully classifies unseen EEG segments with high accuracy for motor imagery and cognitive tasks. These lead to subject-specific specialized fusion experts that combine geometric, spectral, and temporal features. The MoE architecture provides reliable classification performance, making the system a powerful tool for data auditing. Segment-wise relabeling showed that 95% of cognitive EEG trials contained multiple shifting cognitive states, indicating a significant attentional drift. In contrast, motor imagery trials had a consistent cognitive state, with label noise concentrated at trial onset due to carryover effects. By bridging the gap between label noise identification and correction, this work presents a practical methodology to improve the quality, reliability, and validity of EEG-based research.


Committee Members:
Dr. Garima Bajwa (supervisor, committee chair), Dr. Thiago E Alves de Oliveira, Dr. Vijay Mago (York University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link. Everyone is welcome.

Title: "Predator-prey Interactions Under Thermal Pressure"

Supervisory Committee:
Dr. Adam Algar (supervisor)
Dr. Stephan Hecnar
Dr. Lesley Lancaster
Dr. Luke Mahler (external examiner)

Please join Ryley in ATAC 3004 or contact Heather at biology@lakeheadu.ca for the Zoom link.

Please join the Computer Science department for the upcoming thesis defense:

Presenter: Xiaofan Wang

Thesis title: Feature Extraction Enhances Model Performance

Abstract: Deep learning has emerged as a prominent approach in traditional machine learning paradigms due to its superior capability for deep-level feature extraction. This, in turn, demonstrates that the efficiency, depth, and richness of feature extraction have a profound impact on model performance. Features serve as key characteristics for distinguishing objects and represent dimensionality-reduced representations of data. This paper proposes two effective models applied to EEG emotion recognition and NL2SQL tasks, respectively, which enhance model performance through optimized feature extraction.

In previous models for processing EEG signals, researchers have typically focused on only partial features of EEG while rarely integrating these features comprehensively. To address this limitation, we designed a multi-feature extraction method that improves performance by extracting and combining frequency, spatial, temporal, and global features from EEG signals. We conducted extensive experiments on the SEED and DEAP datasets, generating confusion matrices, t-SNE distributions, and brain region activation heatmaps to demonstrate the effectiveness of our model. Additionally, our method incorporates an adaptive GCN that eliminates the requirement for pre-defined adjacency matrices.

For the NL2SQL task, unlike traditional models that train from scratch, we designed a framework based on fine-tuning pre-trained BERT and conducted experiments on theWikiSQL, Academic, and Spider datasets. The results demonstrate that our model achieves superior performance compared to traditional models in clause prediction and exhibits stronger generalization capabilities, indicating that the prior knowledge embedded in pretrained models also benefits the model’s feature extraction capacity.


Committee members:
Dr. Ruizhong Wei (supervisor, committee chair), Dr. Sabah Mohammed, Dr. Yimin Yang (Western University)

Please contact grad.compsci@lakeheadu.ca for the Zoom link. Everyone is welcome.

The Mathematics Competency Assessment (MCA) is an assessment that all P/J teacher candidates must complete to receive their Bachelor of Education. It tests math content knowledge to ensure that our future teachers have the necessary knowledge and understanding to teach in Ontario schools. It covers the mathematical concepts that make up the Ontario math curriculum (elementary) and requires demonstration of understanding in a range of areas.

Please join us for Hannah Storrs final MA thesis defense. This is a hybrid defense. Should you wish to attend in person it will take place in ATAC 3004.

Title: Understanding Familiarity: Intimacy’s Role in Public Stigma towardsMental Illness

Supervisor: Dr. Amanda Maranzan

Second Reader: Dr. Rupert Klein

External Examiner: Dr. Josephine Tan

GSC Rep: Dr. Dwight Mazmanian

Please contact admin.psych@lakeheadu.ca for Zoom link and passcode

Do you have a quick question or need help, and can't wait for an appointment? Join the weekly drop-in sessions on Wednesday afternoon to speak to any of your International Student Advisors. This session is offered in person and online via Zoom (first come, first served). Zoom link: https://lakeheadu.zoom.us/j/98736320404