Latest updates about the event
📢 Event Announcement – Friday, October 3rd
On Friday, October 3rd, students will take part in the official event presentation. During this session, all mentors will be introduced, and each mentor will provide a simplified overview of their challenge. This will give students a clear understanding of the available options before they begin working.
Following the presentations, students will form their groups and select the challenge they wish to pursue. By the end of the day, every student will:
- Be part of a team
- Have an assigned mentor
- Have chosen a challenge to work on
This day marks the official start of the collaborative journey, setting the stage for exciting problem-solving ahead!
🤖 Workshop Announcement – Friday, October 10th
On Friday, October 10th, 2025, students are invited to attend the first session of the JLR Challenge #1 Workshop Series, titled “Introduction to AI Agents (1st Offering)”, led by Salma Aly. This workshop provides a theoretical foundation in the design and evolution of AI agents—autonomous systems that perceive, reason, and act within dynamic environments.
During this session, participants will explore:
- The evolution of AI agents from reactive and rule-based systems to deliberative and cognitive architectures
- How agents integrate reasoning, decision-making, and adaptive learning to achieve autonomy
- Foundational models of agentic intelligence within large language models (LLMs)
By the end of this workshop, attendees will have a strong conceptual understanding of how AI agents think, learn, and act—laying the groundwork for future exploration into autonomous and language-based AI systems.
📅 Date: Friday, October 10, 2025
🕑 Time: 2:00 PM
📍 Location: Lecture Space/Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science Advanced Computing Hub
Prerequisites:
- Basic understanding of artificial intelligence and machine learning concepts
- Interest in the theoretical foundations of autonomous and language-based AI agents
About the Speaker:
Salma Aly is a PhD student in Computer Science at the University of Windsor, specializing in Software Testing, Reinforcement Learning, and Generative AI. She earned her M.Sc. in Computer Science from The American University in Cairo, focusing on AI and Computer Vision. With professional experience as an AI Engineer, Intelligent Automation Consultant, and Data Analyst, she brings a unique blend of academic depth and industry insight to the field of intelligent systems.
This workshop marks the first offering in the JLR Challenge #1 Workshop Series—empowering students to understand the intelligence that drives autonomous agents and their applications in modern AI systems.
🧠 Workshop Announcement – Friday, October 10th
On Friday, October 10th, 2025, students will attend the first technical workshop in the JLR Challenge #2 series, titled “Introduction to DRAMSys (1st Offering)”, led by Steven Rice. This interactive session is designed to provide a hands-on introduction to DRAMSys, a powerful DRAM simulation tool used for analyzing and optimizing memory performance.
During the workshop, participants will:
- Set up the DRAMSys environment and install all necessary requirements
- Explore the key components and structure of DRAMSys
- Learn how to capture real memory traces from their systems and test them within DRAMSys
By the end of this workshop, students will have the foundational skills needed to begin experimenting with DRAMSys and evaluating memory configurations for different performance tasks.
📅 Date: Friday, October 10, 2025
🕘 Time: 9:00 am
📍 Location: Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science Advanced Computing Hub
Prerequisites: Basic familiarity with Linux terminal commands is helpful but not required.
About the Speaker:
Steven Rice’s career centers on leveraging game development technologies to solve industrial challenges. He has pioneered industrial robotics and vision simulation platforms, creating digital twins to validate manufacturing concepts and accelerate client innovation. His current research explores the integration of agentic AI with real-time simulation, driving the future of smart mobility through advanced digital twins and robotics systems.
This workshop marks the start of the JLR Challenge #2 Technical Workshop Series, equipping participants with essential knowledge to excel in upcoming challenge tasks!
⚙️ Workshop Announcement – Friday, October 10th
On Friday, October 10th, 2025, students are invited to join the first session of the JLR Challenge #4 Technical Workshop Series, titled “Introduction to Static Analysis for Predicting Performance Bugs (1st Offering)”, presented by Younes Jahandideh. This workshop explores how static analysis and machine learning can be integrated to detect performance bugs—issues that degrade software efficiency without causing outright failures.
Throughout the session, participants will learn how to:
- Understand what performance bugs are and why they matter
- Use static analysis and Git project history to locate potential issues
- Label buggy and clean code files using automated tools such as SZZ Unleashed
- Train models like Random Forest and XGBoost to predict problem areas in software
- Interpret model accuracy and explore ways to enhance reliability and performance
By the end of this workshop, attendees will gain a clear understanding of how static analysis and machine learning can be used together to prevent performance degradation, improve code quality, and streamline the debugging process.
📅 Date: Friday, October 10, 2025
🕒 Time: 3:00 PM
📍 Location: Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
Prerequisites:
- Basic understanding of programming, software engineering, and machine learning
About the Speaker:
Younes Jahandideh is a Ph.D. student and research assistant at the School of Computer Science, University of Windsor, where he began his doctoral studies in Fall 2023. His research focuses on learning-based optimization, a field dedicated to developing self-optimizing systems in cloud computing. His expertise lies in applying data-driven techniques to enhance system performance, scalability, and efficiency.
This workshop marks the beginning of the JLR Challenge #4 Technical Workshop Series, empowering students to explore intelligent methods for improving software reliability and performance through static analysis and machine learning.
🧩 Workshop Announcement – Monday, October 20th
On Monday, October 20th, 2025, students are invited to attend the first session of the JLR Challenge #3 Workshop Series, titled “EvoNorms: Revolutionizing Neural Networks with Unified Normalization-Activation Layers (1st Offering)”, led by Reem Al-Saidi. This workshop delves into the evolution of normalization and activation layers in deep neural networks, highlighting how EvoNorms transform conventional architectures into more efficient and unified computation models.
Throughout the session, participants will explore:
- Fundamentals of CNN architectures and the role of normalization and activation layers
- Traditional techniques such as BatchNorm, GroupNorm, and activation functions like ReLU and Swish
- The transition from sequential normalization-activation patterns to unified EvoNorm operations
- Variants of EvoNorms, including EvoNorm-B0 (batch-dependent) and EvoNorm-S0 (sample-based)
- Hands-on implementation and real-world use cases
By the end of this workshop, attendees will understand how EvoNorms were discovered through evolutionary search and why they outperform traditional methods—particularly in challenging scenarios like small-batch training. Participants will also gain the skills to implement EvoNorm layers in their own deep learning models using Python and PyTorch.
📅 Date: Monday, October 20, 2025
🕙 Time: 10:00 AM
📍 Location: Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science Advanced Computing Hub
Prerequisites:
- Basic understanding of deep learning concepts
- Familiarity with neural network architectures, especially CNNs
- Working knowledge of Python and PyTorch
- Experience implementing and training neural networks
About the Speaker:
Reem Al-Saidi is a PhD student in Computer Science at the University of Windsor. Her research centers on privacy-preserving machine learning, with a focus on applying large language models (LLMs) to health and genomic data within secure cloud environments. She explores deep learning-based synthetic data generation for safe and reliable data sharing. Reem’s work bridges cutting-edge AI research with real-world applications in data privacy and healthcare innovation.
This workshop marks the first offering in the JLR Challenge #3 Workshop Series, equipping participants with modern tools and insights to revolutionize their deep learning architectures through unified normalization-activation design.
🧠 Workshop Announcement – Monday, October 20th
On Monday, October 20th, 2025, students are invited to join the second session of the JLR Challenge #2 Workshop Series, titled “Introduction to DRAMSys (2nd Offering)”, presented by Steven Rice. This workshop provides a hands-on introduction to DRAMSys, an advanced DRAM simulation framework designed to analyze and optimize memory performance in computing systems.
During this session, participants will learn how to:
- Set up the DRAMSys environment and install all required dependencies
- Understand and explore the major components of DRAMSys
- Capture real memory traces from their systems and test them within the DRAMSys simulator
By the end of this workshop, attendees will have the practical knowledge to begin experimenting with DRAMSys, enabling them to assess which memory configurations yield the best performance for various computational tasks.
📅 Date: Monday, October 20, 2025
🕦 Time: 11:30 AM
📍 Location: Lecture Space/Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science Advanced Computing Hub
Prerequisites:
- Familiarity with Linux terminal commands is helpful but not required
About the Speaker:
Steven Rice’s career focuses on applying game development technologies to address complex industrial challenges. He has led the creation of advanced robotics and vision simulation platforms, developing digital twins to validate manufacturing concepts and speed up client development cycles. His ongoing research explores the integration of agentic AI with digital twins and robotic systems—driving innovation at the intersection of artificial intelligence and real-time simulation to shape the future of smart mobility.
This session marks the second offering of the “Introduction to DRAMSys” workshop, giving students another opportunity to engage with this powerful simulation tool as part of the JLR Challenge #2 Technical Workshop Series.
🧠 Workshop Announcement – Tuesday, October 21st
On Tuesday, October 21st, 2025, students are invited to attend the second offering of the JLR Challenge #3 Workshop Series, titled “EvoNorms: Revolutionizing Neural Networks with Unified Normalization-Activation Layers”, led by Reem Al-Saidi. This session continues to explore the transformative concept of EvoNorms—a unified approach that merges normalization and activation layers into a single operation to enhance deep learning performance and efficiency.
During this workshop, participants will dive into:
- The fundamentals of CNN architectures and their normalization-activation layers
- Traditional normalization techniques like BatchNorm and GroupNorm
- Common activation functions such as ReLU and Swish
- The evolution from sequential to unified normalization-activation using EvoNorms
- The differences between EvoNorm-B0 (batch-dependent) and EvoNorm-S0 (sample-based)
- Hands-on examples of implementing EvoNorms in neural networks
By the end of this session, attendees will understand how EvoNorms were developed through evolutionary search, why they outperform traditional methods—especially under small-batch conditions—and how to apply them in practical Python and PyTorch projects.
📅 Date: Tuesday, October 21, 2025
🕙 Time: 10:00 AM
📍 Location: Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
Prerequisites:
- Basic understanding of deep learning concepts
- Familiarity with neural network architectures (especially CNNs)
- Working knowledge of Python and PyTorch
- Experience with implementing and training neural networks
About the Speaker:
Reem Al-Saidi is a PhD student in Computer Science at the University of Windsor. Her research focuses on privacy-preserving machine learning, emphasizing the application of large language models (LLMs) to health and genomic data within secure cloud environments. Her work explores deep learning–based synthetic data generation for secure data sharing and publication, combining innovation in AI with a strong commitment to data ethics and privacy.
This session marks the second offering of the “EvoNorms” workshop in the JLR Challenge #3 Series, giving students another opportunity to deepen their understanding of unified normalization-activation techniques and apply them to real-world neural network architectures.
🧠 Workshop Announcement – Tuesday, October 21st
On Tuesday, October 21st, 2025, students are invited to attend the first session of the JLR Challenge #3 Technical Workshop Series, titled “Convolutional Neural Networks Explained (1st Offering)”, presented by Ali Forooghi. This workshop offers an accessible yet detailed exploration of Convolutional Neural Networks (CNNs), the cornerstone of modern computer vision systems.
Throughout this session, participants will uncover how CNNs enable machines to see, recognize, and interpret visual information by learning patterns directly from images. The workshop will break down both the theoretical and practical aspects of CNNs, helping students grasp the architecture and intuition behind these powerful models.
Participants will learn about:
- The fundamentals of deep learning and neural networks
- Core components of CNNs including convolutional layers, pooling, activation functions, and feature maps
- Designing and training CNN architectures
- Visualization and interpretation of learned features
- Hands-on demonstrations using Python and TensorFlow/PyTorch
By the end of the workshop, attendees will have a solid understanding of how CNNs function and how to implement them for visual recognition tasks, bridging theory with real-world applications.
📅 Date: Tuesday, October 21, 2025
🕛 Time: 12:00 PM
📍 Location: Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
Prerequisites:
- Basic understanding of Python programming
- Familiarity with fundamental machine learning concepts (no prior deep learning experience required)
About the Speaker:
Ali Forooghi is a Ph.D. student in Computer Science at the University of Windsor with a research focus in Natural Language Processing (NLP). His academic and professional interests extend into deep learning and artificial intelligence, where he explores the intersection of language and vision technologies. (foroogh@uwindsor.ca)
This session marks the first offering in the JLR Challenge #3 Technical Workshop Series, providing students with a foundational understanding of CNNs—the building blocks behind many of today’s AI-driven image recognition systems.
🤖 Workshop Announcement – Wednesday, October 22nd
On Wednesday, October 22nd, 2025, students are invited to attend the second offering of the JLR Challenge #1 Workshop Series, titled “Introduction to AI Agents”, led by Salma Aly. This session continues the exploration of AI agents—autonomous systems capable of perceiving, reasoning, and acting intelligently within complex and dynamic environments.
During this workshop, participants will delve into:
- The evolution of AI agents, from simple reactive models to advanced deliberative and cognitive architectures
- How agents integrate reasoning, decision-making, and adaptive learning to achieve autonomy
- Theoretical foundations of agentic intelligence within large language models (LLMs)
- How modern AI frameworks combine reasoning and action to simulate intelligent behavior
By the end of this session, attendees will have a deep conceptual understanding of how agent-based systems operate and how they underpin many of today’s autonomous and language-driven AI applications.
📅 Date: Wednesday, October 22, 2025
🕙 Time: 10:00 AM
📍 Location: Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
Prerequisites:
- Basic understanding of artificial intelligence and machine learning concepts
- Interest in the theoretical foundations of autonomous and language-based AI agents
About the Speaker:
Salma Aly is a PhD student in Computer Science at the University of Windsor. Her research focuses on Software Testing, Reinforcement Learning, and Generative AI. She holds an M.Sc. in Computer Science from The American University in Cairo, where her work emphasized artificial intelligence and computer vision. Professionally, she has served as an AI Engineer, Intelligent Automation Consultant, and Data Analyst, blending academic insight with hands-on industry experience.
This session marks the second offering of the “Introduction to AI Agents” workshop in the JLR Challenge #1 Series, providing students with another opportunity to deepen their understanding of autonomous AI systems and the agentic intelligence behind large language models.
💡 Workshop Announcement – Wednesday, October 22nd
On Wednesday, October 22nd, 2025, students are invited to participate in the first offering of the JLR Challenge #2 Technical Workshop Series, titled “Applying Evolutionary Models to DRAM Design”, presented by Steven Rice. This workshop builds on concepts introduced in previous DRAMSys sessions, exploring how evolutionary algorithms can be leveraged to optimize DRAM architectures for enhanced performance and efficiency.
During this session, participants will:
- Gain an overview of common evolutionary methods used in optimization and machine learning
- Discuss practical applications of evolutionary modeling in DRAM design using DRAMSys
- Explore a sample implementation that demonstrates how evolutionary models can dynamically identify optimal DRAM configurations
By the end of this workshop, attendees will understand how to integrate evolutionary modeling with DRAMSys to develop adaptive, data-driven strategies for improving DRAM performance in real-world systems.
📅 Date: Wednesday, October 22, 2025
🕘 Time: 9:00 AM
📍 Location: Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science Advanced Computing Hub
Prerequisites:
- Familiarity with DRAMSys (recommended to have attended the “Introduction to DRAMSys” workshop)
About the Speaker:
Steven Rice’s career focuses on applying technologies from the world of game development to tackle complex industrial challenges. He has led the creation of industrial robotics and vision simulation platforms, developing digital twins that validate manufacturing concepts and accelerate innovation cycles. His current research explores combining agentic AI with real-time simulation to pioneer the next generation of smart mobility systems. Steven is passionate about merging creativity and computation to redefine how intelligent systems evolve and adapt.
This session marks an exciting step in the JLR Challenge #2 Technical Workshop Series, empowering students to apply evolutionary computation principles to memory system design through DRAMSys.
🤖 Workshop Announcement – Wednesday, October 22nd
On Wednesday, October 22nd, 2025, students are invited to attend the second offering of the JLR Challenge #1 Workshop Series, titled “Introduction to AI Agents (2nd Offering)”, presented by Salma Aly. This session provides a deep theoretical understanding of AI agents—autonomous systems capable of perceiving their surroundings, reasoning through complex situations, and making decisions to act intelligently within dynamic environments.
During this workshop, participants will explore:
- The evolution of AI agents—from reactive and rule-based systems to advanced deliberative and cognitive architectures
- How agents integrate reasoning, decision-making, and adaptive learning to achieve autonomy
- Key principles of intelligent behavior such as perception, goal orientation, and self-improvement
- Foundational models of agentic intelligence in large language models (LLMs) and how modern frameworks combine reasoning and action
By the end of this session, attendees will have a strong conceptual understanding of agent-based intelligence and its applications in autonomous and language-driven AI systems.
📅 Date: Wednesday, October 22, 2025
🕙 Time: 10:00 AM
📍 Location: Workshop Space, 4th Floor – 300 Ouellette Ave., School of Computer Science, Advanced Computing Hub
Prerequisites:
- Basic understanding of artificial intelligence and machine learning concepts
- Interest in the theoretical foundations of autonomous and language-based AI agents
About the Speaker:
Salma Aly is a PhD student in Computer Science at the University of Windsor. Her research focuses on Software Testing, Reinforcement Learning, and Generative AI. She earned her M.Sc. in Computer Science from The American University in Cairo, Egypt, where her work concentrated on artificial intelligence and computer vision. Professionally, she has served in multiple roles including AI Engineer, Intelligent Automation Consultant, and Data Analyst, bringing both academic expertise and practical experience in AI-driven systems.
This workshop marks the second offering of “Introduction to AI Agents” in the JLR Challenge #1 Workshop Series, offering students another opportunity to engage with the core theories and principles behind agentic AI and its emerging role in intelligent autonomous systems.