TEAS 2024

Yimeng (Yoyo) Liu is a second-year Computer Science and Chemistry student at Victoria College of the University of Toronto. She is currently researching at the Chemspace Lab, focusing on using machine learning in low-energy conformer generation. She is passionate about AI ethics relating to privacy and information security and captained a CyberTitan Canadian Cyber Defense Competition finalist team. Outside of academics, she loves to read science fiction and make jewelry. Currently, she is co-organizing the first Toronto Ethics in AI Symposium to build a community of students interested in AI ethics at universities around Toronto.

Sierra Wyllie is a fourth-year Engineering Science student at the University of Toronto advised by Nicolas Papernot. Her research focuses on the fairness and security of machine learning systems and their sociotechnical embeddedness. Her most recent paper shows how training ML models on previously ML-generated data leads to increases in the unfairness of models. She is a recipient of the Natural Sciences and Engineering Research Council of Canada undergraduate student research award and a national honorable mention of the National Center for Women in IT in the USA. Currently, she is co-organizing the first Toronto Ethics in AI Symposium to build a community of students interested in AI ethics at universities around Toronto.

Émile P. Torres is a philosopher and historian whose work focuses on the history of thinking about human extinction, the ethics of human extinction, and the philosophical worldviews that have shaped the current race to build artificial general intelligence, or AGI. Their most recent book is "Human Extinction: A History of the Science and Ethics of Annihilation," though much of their current research focuses on what they call, along with Dr. Timnit Gebru, the "TESCREAL bundle" of ideologies--a cluster of overlapping, techno-utopian worldviews that are largely responsible for the AGI race today.

Nicolas Papernot is an Assistant Professor of Computer Engineering and Computer Science at the University of Toronto. He also holds a Canada CIFAR AI Chair at the Vector Institute, and is a faculty affiliate at the Schwartz Reisman Institute. His research interests span the security and privacy of machine learning. Some of his group's recent projects include generative model collapse, cryptographic auditing of ML, private learning, proof-of-learning, and machine unlearning. Nicolas is an Alfred P. Sloan Research Fellow in Computer Science and a Member of the Royal Society of Canada's College of New Scholars. His work on differentially private machine learning was awarded an outstanding paper at ICLR 2022 and a best paper at ICLR 2017. He co-created the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) and is co-chairing its first two editions in 2023 and 2024. He previously served as an associate chair of the IEEE Symposium on Security and Privacy (Oakland), and an area chair of NeurIPS. Nicolas earned his Ph.D. at the Pennsylvania State University, working with Prof. Patrick McDaniel and supported by a Google PhD Fellowship. Upon graduating, he spent a year at Google Brain where he still spends some of his time.

Sneha Talwalkar is an incoming PhD Student advised by Tegan Maharaj at the Faculty of Information, University of Toronto. She holds a Master of Science in Cybersecurity from the Georgia Institute of Technology and a Bachelor of Engineering in Electronics and Telecommunication from Pune University. Her research interests are information security, AI audit and governance.

Rayanna Calaza is a PhD student in Philosophy at York University. Her main research interests are in feminist philosophy, AI ethics, moral responsibility, and decolonialism. She is especially interested in the ways these different areas of research might interact with one another.

Ramaravind is a Ph.D. student in the Faculty of Information at the University of Toronto. He is a graduate fellow at the Data Sciences Institute (DSI) and a graduate affiliate of the Schwartz Reisman Institute for Technology and Society (SRI) at the University of Toronto. He is interested in (a) understanding the various tensions, expectations, and subtle technical assumptions involved in developing ML-based information systems and (b) developing tools to support ML practitioners and other stakeholders in their fairness-related work in practice. His research is interdisciplinary and covers topics in FATE, Machine Learning, Social Networks, and HCI.

I'm a PhD student studying Social Justice Education. While many of my colleagues study how students get educated, I study how artificial intelligence gets educated! Specifically, I study the emotional components of data annotation, a process by which workers tag images, text, etc., so that machine learning models can understand them. My research attempts to develop a holistic model of data annotation that can capture both affect and cognition. I tend to highlight the deeply intimate aspects—romance, faith, emotion, etc.—of computing in order to flesh it out as a set of comprehensive relationships.

Mishall Lallani is a PhD Candidate in the Department of Politics at York University. Her research focuses on the use and applications of artificial intelligence (AI) and machine learning in North American immigration processes. Her research is funded by the Social Sciences and Humanities Research Council (SSHRC) Canada Graduate Scholarship (CGS) and the Provost's Dissertation Scholarship. She is interested in the challenges posed by the proliferation of AI in everyday life, the gendered and racialized effects of automation in immigration processes, and the political economy of techno-scientific innovations.

Yash is involved in trustworthy AI governance across domains. She is currently the inaugural Future of Workers Cross-Sectoral Leadership Fellow, an Expert on the UN's Policy Network on AI Multistakeholder Working Group, and contributes to civil service.