Student Research Project Presentations

Time & Date: 9:30 am – 2:00 pm, Thursday, May 7, 2026
Location: Room C-130 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registrations are open now:

Activity Diagram Analysis: https://events.vtools.ieee.org/m/558688
AlgoTrade & XGBoost: https://events.vtools.ieee.org/m/558962
Way-finding Mobile Application: https://events.vtools.ieee.org/m/558690
Line Diagram Analysis: https://events.vtools.ieee.org/m/558692

Join us for an exciting student research presentation!
Come support and learn from students as they share their work, there will be multiple presentations happening throughout the day.
Snacks and lunch will be provided, so bring your curiosity (and your appetite)!
We’d love to see you there!

Agenda:

09:30 am – 10:00 am | Coffee & Networking
10:00 am – 10:10 am | Opening Remarks
10:10 am – 10:40 am | Activity Diagrams Analysis for Visually Impaired Students: Dolcy
10:40 am – 10:45 am | Activity Diagrams Analysis for Visually Impaired Students Q&A: Dolcy
10:45 am – 11:00 am | Snacks & Refreshments
11:00 am – 11:25 am | “Way-finding Mobile Application for Navigation”: Kristina and Dolcy
11:25 am – 11:35 am | “Way-finding Mobile Application for Navigation” Q&A: Kristina and Dolcy
11:35 am – 12:00 pm | Snacks & Refreshments (Lunch)
12:00 pm – 12:30 pm | AlgoTrade & XGBoost: Harsh
12:30 pm – 12:45 pm | AlgoTrade & XGBoost Q&A: Harsh
12:45 pm – 01:00 pm | Snacks & Refreshments
01:00 pm – 01:25 pm | Line Diagram Analysis for Visually Impaired Student: Ajitesh
01:25 pm – 01:40 pm | Line Diagram Analysis for Visually Impaired Student Q&A: Ajitesh
01:40 pm – 02:00 pm | Snacks & Refreshments: Round Table & Open Discussions

Speakers’ Bio:

Dolcy Sareen is a fourth-year Computer Science student at Okanagan College. Dolcy is also the webmaster of the Okanagan College IEEE Student Branch. She is passionate about learning new technologies, with a strong focus on data. Dolcy enjoys connecting with people and continuously learning from new experiences.

Kristina Cormier is a fourth-year Computer Science student at Okanagan College. She has always been curious about science and technology. Recently, she has discovered a passion for data analysis and machine learning algorithms. Kristina is the Chair of the Okanagan College IEEE Student Branch. She coordinates with her team to help organize events and volunteering. Kristina truly enjoys mentoring, meeting new people and discovering new opportunities.

Harsh Saw is a fourth-year Computer Science student with hands-on experience spanning software engineering and AI/ML — from writing production code to deploying real-world systems. He has worked across multiple domains in industry, building technology that goes beyond the classroom. Passionate about turning ideas into working products, Harsh enjoys the full journey from early development to live deployment. He is driven by curiosity about what technology can do when it is built to actually work in the real world.

Ajitesh Parihar is a fourth-year Computer Science student and a Research Assistant at Okanagan College. He is a tech enthusiast who enjoys learning about innovations and technologies and is passionate about software engineering, cybersecurity, and algorithms. He enjoys meeting and working with people with diverse backgrounds.

Photographs

Line Diagram Analysis for Visually Impaired Students

Time & Date: 01:00pm – 02:00pm , Thursday, May 7, 2026
Location: Room C-130 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registration is open now: https://events.vtools.ieee.org/m/558692

Join us for an exciting student research presentation!
Come support and learn from students as they share their work, there will be multiple presentations happening throughout the day.
Snacks and lunch will be provided, so bring your curiosity (and your appetite)!
We’d love to see you there!

Agenda:

1:00 pm – 1:25 pm | Line Diagram Analysis for Visually Impaired Students: Ajitesh
1:25 pm – 1:40 pm | Line Diagram Analysis for Visually Impaired Students Q&A: Ajitesh
1:40 pm – 2:00 pm | Snacks & Refreshments: Round Table & Open Discussions

Abstract:

Line diagrams and mathematical function plots are commonly used in scientific and educational textbooks and articles to convey quantitative relationships. However, their visual nature presents significant challenges and barriers for visually impaired learners. Despite the ongoing efforts to improve accessibility in higher education, the non-text content still remains difficult to interpret non-visually. The existing diagram analysis approaches often focus on chart type classification, require manual user intervention for tasks such as label mapping, or fail to provide accessible end-to-end interaction for non visual analysis. Also, many of these systems do not explicitly consider the requirements of visually impaired users, resulting in workflows and interfaces that are not accessible. We propose an accessibility aware framework for automated analysis of line diagrams from raster images. The proposed end-to-end system performs CNN based curve segmentation, diagram type classification, label and tick extraction using optical character recognition, mapping of curve pixels to the diagram domain, and supports interactive querying along with a user interface compliant with the Web Content Accessibility Guidelines. Evaluation is conducted using a large synthetic dataset of 2D chart images representing a variety of mathematical functions and discrete point line charts, which is used for both model training and system-level assessment.

Speaker’s Bio:

Ajitesh Parihar is a fourth-year Computer Science student and a Research Assistant at Okanagan College. He is a tech enthusiast who enjoys learning about innovations and technologies and is passionate about software engineering, cybersecurity, and algorithms. He enjoys meeting and working with people with diverse backgrounds.

For further information please contact: Youry Khmelevsky (email: Youry at IEEE.org)
Refreshments will be provided

Algorithmic Trading: XGBoost Machine Learning Model

Time & Date: 12:00pm – 01:00pm , Thursday, May 7, 2026
Location: Room C-130 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registration is open now: https://events.vtools.ieee.org/m/558962

Join us for an exciting student research presentation!
Come support and learn from students as they share their work, there will be multiple presentations happening throughout the day.
Snacks and lunch will be provided, so bring your curiosity (and your appetite)!
We’d love to see you there!

Agenda:

12:00 pm – 12:30 pm | AlgoTrade & XGBoost: Harsh
12:30 pm – 12:45 pm | AlgoTrade & XGBoost Q&A: Harsh
12:45 pm – 1:00 pm | Snacks & Refreshments

Abstract:

Stock markets generate enormous amounts of data every day, yet predicting how a stock will move throughout a trading session remains a difficult problem. This paper presents MarketSight, a system that learns from years of historical market data to forecast how individual stock prices will evolve across an entire trading day — before that day begins. By identifying patterns in past price behavior, the system produces a predicted price path for each stock, giving traders an early picture of likely intraday movement. The system runs automatically each night, updating its predictions with the latest available data. Experiments across approximately 500 US-listed stocks demonstrate meaningful forecasting accuracy, with predictions consistently outperforming naive baselines.

Speaker’s Bio:

Harsh Saw is a fourth-year Computer Science student with hands-on experience spanning software engineering and AI/ML — from writing production code to deploying real-world systems. He has worked across multiple domains in industry, building technology that goes beyond the classroom. Passionate about turning ideas into working products, Harsh enjoys the full journey from early development to live deployment. He is driven by curiosity about what technology can do when it is built to actually work in the real world.

For further information please contact: Youry Khmelevsky (email: Youry at IEEE.org)
Refreshments will be provided

Cross-Platform 2D and 3D Way-finding Mobile Application for Navigation in Industrial Buildings and Parking Lots

Time & Date: 11:00am – 12:00pm , Thursday, May 7, 2026
Location: Room C-130 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registration is open now: https://events.vtools.ieee.org/m/558690

Join us for an exciting student research presentation!
Come support and learn from students as they share their work, there will be multiple presentations happening throughout the day.
Snacks and lunch will be provided, so bring your curiosity (and your appetite)!
We’d love to see you there!

Agenda:

11:00 am – 11:25 am | “Way-finding Mobile Application for Navigation”: Kristina and Dolcy
11:25 am – 11:35 am | “Way-finding Mobile Application for Navigation” Q&A: Kristina and Dolcy
11:35 am – 12:00 pm | Snacks & Refreshments (Lunch)

Abstract:

Finding your way inside large buildings—like campuses, malls, or airports—can be difficult, especially when you’re short on time or in an unfamiliar place. Many existing solutions are expensive or rely on constant internet access. This project presents a mobile app that works both online and offline to help users navigate indoor and outdoor spaces. It uses interactive maps, QR codes for positioning, and a shortest-path algorithm to guide users efficiently. The system is designed to be affordable, easy to maintain, and scalable for different environments.

Speaker’s Bio:

Kristina Cormier is a fourth-year Computer Science student at Okanagan College. She has always been curious about science and technology. Recently, she has discovered a passion for data analysis and machine learning algorithms. Kristina is the Chair of the Okanagan College IEEE Student Branch. She coordinates with her team to help organize events and volunteering. Kristina truly enjoys mentoring, meeting new people and discovering new opportunities.

For further information please contact: Youry Khmelevsky (email: Youry at IEEE.org)
Refreshments will be provided

Activity Diagrams Analysis for Visually Impaired Students

Time & Date: 9:30am – 11:00am , Thursday, May 7, 2026
Location: Room C-130 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registration is open now: VTools Link

Join us for an exciting student research presentation!
Come support and learn from students as they share their work, there will be multiple presentations happening throughout the day.
Snacks and lunch will be provided, so bring your curiosity (and your appetite)!
We’d love to see you there!

Agenda:

9:30 am – 10:00 am | Coffee & Networking
10:00 am – 10:10 am | Opening Remarks
10:10 am – 10:40 am | Activity Diagrams Analysis for Visually Impaired Students: Dolcy
10:40 am – 10:45 am | Activity Diagrams Analysis for Visually Impaired Students Q&A: Dolcy
10:45 am – 11:00 am | Snacks & Refreshments

Abstract:

Diagrams are a very important part of education,learning and understanding , allowing the transmission of complex ideas in a visual format. However, current accessibility technologies introduce substantial limitations for visually impaired students in accessing these materials. Traditional approaches to fix this problem, such as audio descriptions and tactile graphics, either lack detail or are prohibitively expensive to scale. This review will focus on the current  status of accessible diagram technologies, such as tactile graphics, 3D printing, AI-driven recognition applications and methods proposed till now . We identify the major technical challenges and propose an integrated approach using multi-modal interaction by combining audio, and AI-enhanced diagram interpretation. The approach offers equal access to STEM visual content as part of the road to inclusive education.

Speaker’s Bio:

Dolcy Sareen is a fourth-year Computer Science student at Okanagan College. Dolcy is also the webmaster of the Okanagan College IEEE Student Branch. She is passionate about learning new technologies, with a strong focus on data. Dolcy enjoys connecting with people and continuously learning from new experiences.

For further information please contact: Youry Khmelevsky (email: Youry at IEEE.org)
Refreshments will be provided

Data Collection and Staging Process Automation Precision, Speed and Scalability for Machine Learning Modelling of Algorithmic Trading Stocks-Price Prediction

Time & Date: 9:00am – 11:00am , Wednesday, April 22, 2026
Location: Room E-301 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registration is open now: https://events.vtools.ieee.org/m/555692

This project brings together three teams: Data Collection, Data Warehousing, and Machine Learning into a unified, end-to-end system for high-frequency stock price prediction.
Learn how we designed a scalable pipeline using distributed computing and XGBoost, covering system architecture, data engineering, and real-world ML applications in algorithmic trading.
This work also establishes a foundation for ongoing research and extended large-scale evaluation.
Open to students, faculty, and anyone interested in machine learning, data systems, or fintech.

Abstract:

This presentation discusses an automated data collection and staging pipeline for high-frequency stock price prediction using machine learning. The system integrates scalable ELT processes, data deduplication, and distributed training with XGBoost on high-performance computing infrastructure. Designed for precision, speed, and scalability, the framework enables efficient handling of large financial time-series datasets while maintaining robust predictive performance and optimized resource utilization.

Contributors:

This project was developed through a collaborative effort across three specialized teams:

Data Collection Team

Responsible for sourcing, aggregating, and preprocessing raw financial and market data.

• Andrew Johnson
• Emilio Iturbide
• Reilly Mager
• Lian Heckrodt
• Cade Dempsey
• Kristina Cormier

Data Warehouse Team

Designed and implemented the data storage architecture, ETL/ELT pipelines, and database systems.

• Alex Anthony
• Hayden Nikkel
• Daemon Lewis
• John Cortez
• Jackson Rosco

Machine Learning Team (XGBoost)

Developed, trained, and evaluated machine learning models for predictive analytics and trading strategies.

• Harsh Saw
• Zane Tessmer
• Kavaljeet Singh
• Dante Bertolutti
• Guntash Brar
• Parag Jindal

Acknowledgements

We thank all contributors and collaborators who supported the development, testing, and deployment of this system.

Photographs

For further information please contact: Youry Khmelevsky (email: Youry at IEEE.org)
Refreshments will be provided

Data Warehouse for Algorithmic Trading Stocks-Price Forecasting on Digital Research Infrastructure using Machine Learning Modelling

Time & Date: 9:00am – 10:00am , Wednesday, April 21, 2026
Location: Room HS107 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registration is open now: https://events.vtools.ieee.org/m/555684

Join this presentation to explore how a Data Warehouse system is built to manage and process large-scale stock market data. The project demonstrates how raw financial data is transformed into structured formats that can be efficiently queried and used for predictive modelling.

You’ll learn:

• How Data Warehouses store and organize large datasets
• Star schema design in a real system
• How data pipelines automate transformation of financial data
• How machine learning models use prepared datasets for predictions
• How large datasets are processed efficiently

Abstract:
This presentation explains how large volumes of stock market data are organized and processed using a structured database system. The system takes raw financial data and converts it into a format that is easier to store, search, and analyze.

The data is processed through an automated pipeline that calculates key financial values such as price changes and trading volume summaries. The structured data is then stored in an optimized system that allows fast access and efficient analysis.

This approach helps make large financial datasets more usable and easier to work with.

Speakers Bio:
Emilio Iturbide Gonzalez is a graduating Computer Science student at Okanagan College with interests in data systems, software engineering, and artificial intelligence.
He has worked on building a Data Warehouse system designed to manage and process large volumes of stock market data. His work focuses on creating efficient data pipelines, organizing complex datasets, and supporting data-driven applications.
He is interested in building practical systems that connect software engineering, data processing, and real-world applications in technology.

Photographs:

For further information please contact: Youry Khmelevsky (email: Youry at IEEE.org)
Refreshments will be provided