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Department Alumni Fest 2016

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Equity, diversity and inclusion

The Statistics Equity, Diversity and Inclusion Speaker Series   

Are you curious about Equity, Diversity and Inclusion (EDI)? Are you wondering how to incorporate EDI practices into your own work? Do you want to engage with different knowledge systems and increase inclusive dialogues? Well, this speaker series may be just for you. All faculty, staff, post-doctoral fellows and students from the department of Statistics and affiliated units are invited to attend!  

What do the sessions look like? 

Sessions will span from September 2022 to January 2023. There will be a 50-minute talk followed by facilitated a 40-minute small-group discussion with lunch provided. Participants are invited to attend the talk alone or both the talk and discussion. Recordings of each talk will be posted after each session. Participants are invited to turn their cameras off if they do not want to be recorded. Please check the schedule below for more details about the date, time and location of upcoming sessions. 

Who are the speakers?  

This series will highlight academics who are actively working on EDI research, EDI teaching practices, diverse ways of knowing or other EDI initiatives in the fields of Statistics and Data Science. We are inviting speakers from post-secondary institutions from around the globe as well as from right here at UBC. They will be featured on this page prior to each talk, so check back to see which inspiring presenter this series will host next! 

Why is this important?   

This initiative will help create a space to highlight and support EDI work in Statistics and Data Science. The topics presented by the speakers will help participants gain skills and knowledge to advance inclusivity within and outside the classroom. In addition, these seminars will strengthen EDI networks in and outside of UBC, which will support the exchange of ideas and foster research and engagement in equity and inclusion. 



Thursday, September 22nd | Hybrid talk by Dr. Emma Benn from 11am - 11:50am

| Facilitated discussion with lunch in person in room ESB4192 from 11:50am - 12:30pm


Thursday, October 6th | Hybrid talk by Dr. Shandin Pete from 11am - 11:50am

| Facilitated discussion with lunch in person n room ESB4192 from 11:50am - 12:30pm


Thursday, November 17th | Hybrid talk by Dr. Evelyn Asiedu from 11am - 11:50am

| Facilitated discussion with lunch in person in room ESB4192 from 11:50am - 12:30pm


Tuesday, December 6th | Hybrid talk by Dr. Ninareh Mehrabi from 11am - 11:50am

| Facilitated discussion with lunch in person from 11:50am - 12:30pm

Zoom link for talk: 


Registration link for facilitated lunch discussion: 



Towards Trustworthy AI 

With the progressive integration of AI systems in our everyday lives, making those systems safe and trustworthy has become an imperative. This talk focuses on three interrelated aspects of trustworthy AI systems -- fairness, robustness, and interpretability. I will present our work at the intersection of machine learning and natural language processing that address those aspects. First, I will talk about existing harms in AI systems that contribute to unfairness and describe methods to mitigate such effects. Second, I will talk about investigating robustness of machine learning systems against data poisoning attacks that can contribute to unfairness of a model. Third, I will talk about integration of interpretability frameworks as means to design more fair and interpretable AI systems using attention-based mechanisms. 

Ninareh Mehrabi is a postdoctoral scientist at Amazon's Trustworthy Alexa AI team. She received her B.Sc. degree in Computer Science and Engineering from University of Southern California and completed her PhD in Computer Science at University of Southern California's Information Sciences Institute. Her research is on developing trustworthy AI systems with an emphasis on algorithmic fairness in Machine Learning and Natural Language Processing. Her work was published in conferences, such as EMNLP, NAACL, and AAAI. She is a recipient of 2021-2022 Amazon Fellowship from USC+Amazon Center on Secure and Trusted Machine Learning. 

Organizing committee

Melissa Lee, Assistant Professor of Teaching

Dr. Marie Auger-Méthé, Assistant Professor 

Jonathan Agyeman, PhD candidate

Rowenna Gryba, PhD candidate

Sasha McDowell, PhD candidate 

We gratefully acknowledge the financial support from the Equity Enhancement Fund and the Department of Statistics