Proudly Sponsored By
Presenter: Amit Kesarwani
Director of Solution Engineering
lakeFS by Treeverse
Quest into the realm of Generative AI. An open exploration with bright minds and bold ideas.
From academia to industry, startups to established enterprises, we look to explore the next wave of integrated Gen AI, and explore the advances around it’s deployment in production environments.
See groundbreaking innovations and meet the innovators pushing technological boundaries in Gen-AI.
Machine Learning Researcher, Protect AI
Talk: Attack on Machine Learning Models- Scan and Secure ML Models
Director, Data Ethics and Use, Scotiabank
Talk: Operationalizing Data Ethics
Business Leaders: C-Level Executives, Project Managers, and Product Owners will get to explore best practices, methodologies, principles, and practices for achieving ROI.
Engineers, Researchers, Data Practitioners: Will get a better understanding of the challenges, solutions, and ideas being offered via breakouts & workshops on Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML, and more.
Job Seekers: Will have the opportunity to network virtually and meet over 60 Top Al Start-ups and companies during the EXPO & Career Fair.
TMLS is a community response addressing the need to unite academic research, industry opportunities and business strategy in an environment that is safe, welcoming and constructive for those working in the fields of ML/AI.
See our team and learn more about the Toronto Machine Learning Society here.
Email for Brochure: faraz@torontomachinelearning.com
Presenter:
Mehrin Kiani, Machine Learning Researcher, Protect AI
About the Speaker:
Dr. Mehrin Kiani is a highly accomplished researcher in the field of machine learning with a strong focus on secure, ethical, and explainable machine learning. Her work has been published in prestigious journals such as IEEE Transactions on Artificial Intelligence and Nature Communications Biology, highlighting the significance and quality of her research.
In the domain of explainable machine learning, Dr. Kiani’s contributions are valuable for enhancing our understanding of complex machine learning models. Explainability is an essential aspect of ML, particularly in applications where transparency, accountability, and interpretability are crucial, such as healthcare, finance, and legal fields.
Beyond academia, Dr. Kiani is an active advocate for open-source initiatives, believing in the power of collaboration and knowledge-sharing. Dr Kiani and her team at Protect AI have released several cutting-edge tools and libraries that empower fellow researchers and developers to harness the potential of secure ML (ModelScan and NB Defense- available on GitHub). These tools could prove instrumental in advancing the field, as secure and transparent machine learning is vital for addressing real-world challenges and concerns related to privacy, fairness, and safety.
Talk Abstract:
How can attackers use Machine Learning (ML) models as an effective attack vector on ML systems? Using model serialization attacks (MSA)! MSA is where malicious code is added to the contents of a ML model during serialization (saving). The motivation for the session is to inform ML practitioners about MSA and to encourage them to scan ML models before use.
Presenter:
Mona B. Abadi, Director, Data Ethics and Use, Scotiabank
About the Speaker:
Mona has worked as an economist at the provincial Treasury Board Secretariat and Ministry of Finance. In her last position before joining Scotiabank, she acted as an internal consultant in the Behavioural Insights unit where she helped to increase the uptake and efficacy of government programs and services, using principles from psychology and economics.
Currently, Mona leads the Data Ethics & Use Team at Scotiabank.
Mona holds an undergraduate degree in Psychology and Economics, and a Masters in Economics
Talk Abstract:
The rise of data and analytics, particularly the use of Big Data, has led to growing concerns about the ethics of how data is being managed and used. As companies collect, share, manage and use data, organizations are becoming more thoughtful about the unintended consequences that can arise when the ethical use of the data is not a key consideration. In this talk, Mona Balesh Abadi will discuss how building a Data Ethics practice at an organization can help practitioners take a proactive approach to incorporating ethical considerations into their projects, and minimize the risk of unintended harm.
Scotiabank is one of the first organizations in the financial industry to tackle these challenges by creating a dedicated Data Ethics team. During the session, Mona will underscore the importance of Data Ethics and share her insights on the Bank’s journey to operationalize it.
Business Leaders: C-Level Executives, Project Managers, and Product Owners will get to explore best practices, methodologies, principles, and practices for achieving ROI.
Engineers, Researchers, Data Practitioners: Will get a better understanding of the challenges, solutions, and ideas being offered via breakouts & workshops on Natural Language Processing, Neural Nets, Reinforcement Learning, Generative Adversarial Networks (GANs), Evolution Strategies, AutoML, and more.
Job Seekers: Will have the opportunity to network virtually and meet over 60 Top Al Start-ups and companies during the EXPO & Career Fair.
Ignite what is an Ignite Talk?
Ignite is an innovative and fast-paced style used to deliver a concise presentation.
During an Ignite Talk, presenters discuss their research using 20 image-centric slides which automatically advance every 15 seconds.
The result is a fun and engaging five-minute presentation.
You can see all our speakers and full agenda here