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WiML Social @ ICLR 2023

Kigali, Rwanda

May 3, 2023

3:00 pm - 5:00 pm

Date: May 3, 2023

Time: 3:00-5:00pm

Loc: Larder Terrace (in the Hotel of the conference center)

The topic of the panel will be AI moving forward, leaving no one behind. We hope to get a conversation going regarding ChatGPT, international collaborations, copyright issues of foundational models etc.

The program is:

3:00 pm - 3.45 pm Introduction and networking

3.45 pm - 4.30 pm panel discussion on “AI moving forward, leaving no one behind”

4.30 pm – 5:00 pm Networking roundtables


Panelist Bios

Panelist: Tegan Maharaj

Tegan is an Assistant Professor in the Faculty of Information at the University of Toronto, an affiliate of the Vector Institute and Schwartz-Reisman Institute for Technology and Society, and a visiting researcher at the Centre for the Study of Existential Risk at Cambridge University. She is also a managing editor at the Journal of Machine Learning Research (JMLR), the top scholarly journal in machine learning, and co-founding member of Climate Change AI (CCAI), an organization which catalyzes impactful work applying machine learning to problems of climate change. Prior to joining the iSchool, Tegan did her Ph.D. at Mila and Polytechnique Montreal, where she was an NSERC and IVADO-awarded scholar with Chris Pal. Her recent research has two themes (1) Real-world generalization, learning theory, and practical auditing tools (e.g. unit tests, sandboxes) to empirically evaluate learning behavior or simulate deployment of an AI system (2) Deep representation learning & predictive methods in ecological dynamical systems for impact assessment, policy analysis, and risk mitigation, especially for climate and common-good problems.


Panelist: Kathleen Siminyu 

Kathleen Siminyu is an AI Researcher focused on Natural Language Processing(NLP) for African Languages. She works at Mozilla Foundation as a Machine Learning Fellow to support the development of a Kiswahili Speech Recognition dataset and to build transcription models for end use cases in the agricultural and financial domains. In this role, she is keen to ensure the diversity of Kiswahili speakers, in terms of age, gender, accent and language variant/dialect, is catered for in the dataset and models created. She would welcome opportunities exploring the application of speech technologies in education. 

Kathleen is also currently part of a committee constituted by the African Union to develop an Artificial Intelligence continental strategy for Africa. 

Before joining Mozilla, Kathleen was Regional Coordinator of AI4D Africa, where she worked with ML and AI communities in Africa to run research programs. One of these, a fellowship for African language dataset creation, led to the creation of over 9 African language datasets. For this work, Kathleen was listed as one of the MIT Technology Review 35 Innovators under 35 for 2022. She has vast experience as a community organiser having co-organised the Nairobi Women in Machine Learning and Data Science community for three years and she continues to organise as part of the committees of the Deep Learning Indaba and the Masakhane Research Foundation


Supervolunteer: Hewitt Tusiime

Hewitt Tusiime is a research assistant at the Makerere Artificial Intelligence Research lab at Makerere University with a particular interest in data science for finance and AI governance and ethics. She graduated from Makerere University with a Bachelor of Science in Software Engineering. She has worked on several research projects related to the application of machine learning algorithms in agriculture, natural language processing, and finance. She also has extensive experience designing and developing protocols for system deployment, ensuring effectiveness, and a smooth user adoption process. She is passionate about the ethical and social implications of AI on different groups of people. She also oversees community engagement while working to achieve research project objectives and strengthen bonds of trust between communities and the project teams.


Organiser: Caroline Weis

Dr. Caroline Weis is currently an AI/ML Engineer at, working on clinical machine learning applications. Centered around multi-omics and multimodal clinical data, her projects leverage approaches from biomarker discovery, model interpretability and personalised healthcare.

Caroline joined after obtaining a PhD in Machine Learning for Healthcare from ETH Zurich, where she developed early clinical machine learning applications for antimicrobial resistance prediction, through developing new kernel methods, adversarial domain adaptation and representation learning. Her research was elected for the Remarkable Outputs award of 2021 by the Swiss Institute of Bioinformatics.

Prior to that, she has built an academic background bridging microbiology, biomathematics, and biophysics. She worked on protein X-ray scattering at Lawrence Berkeley National Laboratory and gained experience developing machine learning applications for screening images at Genedata in Basel. She holds an MSc in Biotechnology and a BSc in Integrated Life Sciences.

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