Seneca’s Centre for Innovation in AI Technology (CIAIT) is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). CIAIT supports the adoption of artificial intelligence (AI) technologies by small- to medium-sized organizations across multiple sectors to solve challenges, enhance products and reduce costs.
Accelerating the development and adoption of artificial intelligence (AI) solutions by small- and medium-sized enterprises
CIAIT Applied Research Areas of Focus
Business decision support
All companies benefit from quickly recognizing performance gaps, market trends and new revenue opportunities. By using various AI techniques, CIAIT experts can extract actionable information and facilitate data-driven decision-making.
Content analysis and management
AI-based technologies such as natural language processing (NLP), artificial neural networks, computer vision and data mining, can be used to analyze documents, texts, pictures, audio, or video to deliver a broad range of solutions addressing specific business needs or opportunities including automation and enhancement of business processes, building virtual assistants, or content management applications.
Cybersecurity
As cyberattacks become more frequent and complex, traditional safety measures are becoming ineffective. Threats may go unnoticed and can cripple organizational operations. AI-based techniques can be used to analyze and detect threats, minimize risk and enhance security.
CIAIT Applied Research Expertise
Data Analytics
CIAIT supports the collection, transformation and preparation of data for analysis. Computer scientists use a range of techniques from simple statistical analysis to complex machine learning algorithms to extract useful insights and make informed decisions.
Predictive Analytics
CIAIT uses statistical and modelling tools to extract trends and information from historical data to make predictions about the future. This helps address challenges such as variability, risk and optimization.
Machine Learning (ML)
ML uses artificial neural networks and deep learning to mimic human learning, gradually improving the predictive accuracy of a goal or target algorithm. It trains and validates models with large data sets and can either improve on known outcomes in supervised learning or provide insight on questions without a known solution using unsupervised learning.
Natural Language Processing (NLP)
NLP is the ability to understand text and spoken words. It combines computational linguistics — rule-based modeling of human language — with statistical, ML and deep learning models. These technologies enable computers to process human language and understand its full meaning, complete with the intent and sentiment.
Computer Vision
Computer vision enables computers and systems to derive meaningful information from digital images, videos and other visual inputs to take action or make recommendations.
News
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Project Spotlight
Customer Support Chatbot: Enhancing Business Efficiency through AI-Driven Process Optimization
Toronto-based TROES Corp. designs, manufactures and delivers high-performance, cloud-based energy storage systems. It has partnered with Ujjwal Khanna, Professor, School of Computer Programming & Analysis to potentially enhance the company's customer support processes through the development of an AI-driven chatbot. The goal is to investigate how the bot could efficiently manage customer inquiries. By leveraging AI technology, TROES aims to explore opportunities for improving response times and maintaining high levels of customer satisfaction.
Seismic Wave Propagation Using Artificial Intelligence
Toronto-based BFI Energy Group, a geophysical research and development consultancy, is partnering with Amir Moslemi, Professor, School of Software Design & Data Science, on a study using artificial intelligence (AI) to look into how energy waves, also known as seismic waves, move through the earth. The project aims to develop AI models to accurately predict behaviour. Researchers will factor in geological elements such as material properties, fault lines and historical data to better understand how they affect propagation or the way waves spread. The goal is to create AI models for seismic wave propagation prediction and to analyze how that compares with traditional prediction methods.
Automation of model training and evaluation process for predicting equipment downtime
TGT Solutions Inc., based in Stratford, about 150 kilometres west of Toronto, specializes in technology-driven solutions, offering highly specialized products and services. The company wants to develop an AI-based solution for manufacturing companies to analyze large datasets, from sources such as production equipment and quality records, typically generated on production floors. The company is partnering with Dr. Uzair Ahmad, Professor, School of Software Design & Data Science, and a team of student researchers on the project, focusing on predicting equipment downtime. The research team will utilize datasets provided by Memex, TGT's partner company, and a low-code/no-code AI platform called mlOS, provided by Braintoy.
Investigation into data privacy and confidentiality when using GPT
The CAA Club Group of Companies provides roadside assistance insurance and travel services to more than 2.3 million members of the Canadian Automobile Association (CAA) in Manitoba and the south-central region of Ontario. It’s working with Mark Buchner, Professor, School of Information Technology & Security and researchers to explore how AI programs like ChatGPT could lead to the misuse of private information. The team will examine and test Open AI, the research and commercial AI applications company behind ChatGPT, Microsoft Azure, a cloud computing service and other vendors to understand systems architecture and security measures by analyzing risk and how to mitigate potential problems. The goal is to provide recommendations to safeguard sensitive information and prevent intellectual property leaks and threats. The research is being done with a Natural Science and Engineering Research Council of Canada – Applied Research and Technology Partnership grant.
Development of a Data Lake for Andorix smart building platform
Andorix is a Toronto-based digital infrastructure company that focuses on commercial real estate. It helps customers modernize properties, improve building efficiency, and reduce operating costs. It provides data connectivity solutions for systems such as air quality control, occupancy sensing and smart lighting. It is partnering with Mark Buchner, Professor, School of Information Technology Administration & Security, to develop a data lake, a centralized repository. The ability to store large amounts of information will enable it to run analytics or use machine learning to provide insights into building operations.
Partner with Us
CIAIT supports product, process and service development across various industry sectors with access to expertise and infrastructure at Seneca.
If you are looking for help from Seneca to address a business challenge, please complete our project request form (DOCX) and email it to research@senecapolytechnic.ca. CIAIT will then contact you for a discovery discussion.
Faculty
Viji Angamuthu
Viji Angamuthu is a faculty member at Seneca Polytechnic’s School of Software Design & Data Science. She holds a Master of Computer Science, Master of Computer Engineering and has also completed a Data Science Certification from University of Toronto. She has extensive experience in Machine Learning, Natural Language Processing and recommendation system projects. She is passionate about learning new technologies.
Mark Buchner
Mr. Buchner is a part-time faculty member in Seneca’s School of Information Technology Administration & Security. He holds an honours bachelor of science degree in computer science from the University of Western Ontario and has been working with AI since 1982. Before teaching at Seneca, Mr. Buchner worked at IBM Canada Laboratory in software/compiler development where he led various AI-based projects advancing NLP in partnership with Simon Fraser University and Queen’s University.
Dr. Mariam Daoud
Dr. Daoud is a full-time faculty member in Seneca’s School of Software Design & Data Science with significant experience and expertise in the areas of personalized and contextual information retrieval, semantic data mining and geographic and temporal search, which she refined at Paul Sabatier University (Toulouse III) and York University.
Dr. Reid Kerr
Dr. Kerr is a full-time faculty member and an AI researcher in Seneca’s School of Software Design & Data Science. He completed a PhD in AI at the University of Waterloo and a bachelor’s degree in business administration from Wilfrid Laurier University. He is an expert in the application of AI technologies to solve business problems. He is also the founder of stepForward Innovations, which develops technologies to help students.
Dr. Amit Maraj
Dr. Maraj is a part-time faculty at Seneca Polytechnic’s School of Software Design & Data Science and has been Principal Investigator on several applied research projects. Prior to Seneca, he taught and developed various programs at Durham College including the AI Hub (an AI-focused applied research centre) and an AI graduate certificate. He also works at Google where he creates educational AI material for other engineers. He received his PhD in Natural Language Processing at Ontario Tech University.
Dr. Vida Mohavedi
Dr. Mohavedi is a full-time faculty member in Seneca’s School of Software Design & Data Science and has been Principal Investigator on several applied research projects. Her research experience includes automatic video categorization, pose estimation, image segmentation and video transcoding. She received her PhD in computer science from York University and completed a post-doctoral research fellowship in collaboration with IBM.
Dr. Allan Randall
Dr. Randall is a full-time faculty member in Seneca’s School of Software Design & Data Science with extensive research experience in ML in academic and military contexts. He studied deep learning neural network techniques as a member of the Alberta Centre for Machine Intelligence and Robotics, an interdisciplinary research group at the University of Alberta. He also worked in AI research at Defense Research & Development Canada.
Dr. Mark Shtern
Dr. Shtern is a full-time faculty member in the School of Information Technology Administration & Security, with extensive research experience in computer and data security, ML, big data, software engineering and robotics. He received his PhD in computer science and engineering from York University, where he also completed a post-doctoral research fellowship.
Funding Acknowledgement
We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).
Nous remercions le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG) de son soutien.
