Women in AI Virtual Evening schedule

As Principal Analytical Lead at Google Ashley helps her clients draw the connections between their investment in Google and their objectives. In addition, she leads projects to leverage her clients' tech infrastructure to drive their business. She has a strong background in statistics and analytics through previous work as a Statistician at The Applied Health Research Centre at St. Michael's Hospital & The Hospital for Sick Children where she has partnered with academic researchers across North America.

Diversity's Critical Role in AI and Innovation

Artificial intelligence and Machine Learning models are heavily reliant on the data that feed them. While AI can improve on human decision making; however, since data can be biased based on human decisions made in the past, AI output may inherit or even amplify biases. There are different solutions that could help mitigate bias, such as interrogating the data to better understand any inherent bias beforehand or conducting fairness tests to check if the model output may unfairly discriminate against protected groups. One way to provide additional perspectives and mitigate bias that perhaps we don't often talk about is having a more diverse, multi-disciplinary workforce who works in AI. Several sources estimate only 10% to 20% of those who work in AI are women, this percentage being stagnant over the years. Education, mentorship, role models, sponsorship, and recruitment practices can play critical roles to bring more diversity into AI

Jane leads a Data & Analytics team within our Innovation, Technology and Shared Services (ITSS) group. She has been with TD for five years and has led Analytics teams for customer facing contact centre and collections businesses. Prior to TD, Jane held increasingly senior roles in Marketing and Analytics in Telecommunications, Retail and Insurance.

Jane is the Co-Chair of Women in Data & Analytics at TD, and is also President of the Queen's University Smith Analytics & AI Alumni Club. Jane holds a Master of Management Analytics from Queen's University Smith Business School, an MBA from IMD in Switzerland, and HBA from the Ivey School of Business at Western University.

Action Recognition for Behaviour Understanding from Video in 2020

Automated Behavior Understanding is presently an actively researched area of Computer Vision. It is of interest for applications such as autonomous vehicles, health care, HCI, video summarization, etc. We may break down the behavior understanding task into several levels to make it simpler. At the first level, basic motions, poses, gaze may be recognized. At the next level, we may consider activity recognition approaches. Finally, we may further assign associations for group dynamics, predict crowd motion, interpret scene contexts or even intent from visual cues, essentially unfolding a higher degree of semantics along with temporal information. This talk will explore the latest developments and challenges in this area of research, with a focus on action recognition.

Tanmana Sadhu is a Computer Vision Engineer at Huawei Canada's Vancouver Research Center where she conducts research and development in video understanding. Tanmana graduated from University of Victoria, located in beautiful British Columbia, Canada, with a Masters in Electrical Engineering in 2016. She has since then worked in diverse domains of Machine Learning and Data Science spanning ocean research, banking and smart devices at various organizations.

Roundtable Discussions include:

• AI in the Post COVID19 World and Mentorship for Women in AI Hosted by Jane Ho, TD Bank

• The Importance of Diversity - Hosted by Catherine Paradis-Therrien, TD Bank

• Solving Human Problems with AI in 2020 - Hosted by Tanmana Sadhu, Huawei Canada

You are free to come in and out of all sessions to ask your questions, share your thoughts and learn more from the speakers and other attendees

Catherine Paradis-Therrien, Senior Manager, Analytics & Insights, TD Bank is a passionate leader with over 10+ years of experience in Predictive Analytics. She joined the bank in 2007 having held many progressive roles within the organization supporting both the insurance and personal banking analytics strategy while leading projects involving Machine Learning, GLM, Sampling, and Client Segmentation. Catherine holds a Bachelor of Mathematics and a Masters in Statistics from the University of Montreal.

Inmar Givoni is a Senior Autonomy Engineering Manager at Uber Advanced Technology Group, Toronto, where she leads a team whose mission is to bring from research and into production cutting-edge deep-learning models for self-driving vehicles. She received her PhD (Computer Science) in 2011 from the University of Toronto, specializing in machine learning, and was a visiting scholar at the University of Cambridge. She worked at Microsoft Research, Altera (now Intel), Kobo, and Kindred at roles ranging from research scientist to VP, applying machine learning techniques to various problem domains and taking concepts from research to production systems. She is an inventor of several patents and has authored numerous top-tier academic publications in the areas of machine learning, computer vision, and computational biology. She is a regular speaker at AI events, and is particularly interested in outreach activities for young women, encouraging them to choose technical career paths. For her volunteering efforts she has received the 2017 Arbor Award from UofT. In 2018 she was recognized as one of Canada’s 50 inspiring women in STEM.

Sedef Akinli Kocak is the Project Manager at Vector Institute for artificial intelligence engaging Vector sponsors on AI projects. Currently she leads several multi-industrial participant projects. She holds a PhD degree from the Data Science Lab at Ryerson University, Canada and earned master’s degrees in both Chemical Engineering and Business of Administration. She worked in data intensive R&D project development and academic industry partnerships in the area of AI/ML at SOSCIP, the Southern Ontario Smart Computing for Innovation Platform. She is also an experienced and accomplished researcher in the area of ICT for sustainability and sustainability design in software intensive systems and a part time Data Science and Analytics lecturer at Ryerson University since 2014. She served as a member of the Compute Ontario Board Advisory Committee and AI program development advisor at the Continuing Education, University of Toronto.

Hakimeh Purmehdi is a senior data scientist at Ericsson Global Artificial Intelligence Accelerator, where leads innovative AI/ML solutions for future wireless communication networks. She received her Ph.D. degree in electrical engineering from the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. She finished her postdoc in AI and image processing at the Radiology Department, University of Alberta. Before joining Ericsson, she was the co-founder of Corowave, a startup to develop non-contact bio-signal monitoring, and she was with Microsoft Research (MSR) as a research engineer. Her research focus is basically on the intersection of wireless communication (5G and beyond), AI solutions (such as online learning, federated learning, reinforcement learning, deep learning), and biotech.

Join a 1-to-1 Speed Networking session to be randomly paired with others with a similar interest for short video calls to expand your network and connect with others.