MD4SG

Colloquium Series

We're excited to announce the speakers for our Spring 2021 Colloquium Series below. The Colloquium Series serves to highlight exemplary work on improving access to opportunity for disadvantaged and marginalized groups through a monthly online talk series. You can find here a list of the past talks in our colloquium series and their recordings on our youtube channel and you can subscribe to our calendar.
 

Please reach out to the organizers at organizers@md4sg.com if you have any questions or suggestions for future colloquium speakers.

 

Upcoming Talks



 

Vukosi Marivate, University of Pretoria, South Africa


Date: Friday, April 9th, 11:00 - 12:15 PM ET / 5-6:15 PM CET/WAT)

 

Vukosi Marivate holds a PhD in Computer Science (Rutgers University, as Fulbright Science and Technology Fellow) and MSc & BSc in Electrical Engineering (Wits University). Dr Marivate is based at the University of Pretoria as the UP ABSA Chair of Data Science. He works on developing Machine Learning/Artificial Intelligence methods to extract insights from data. A large part of his work over the last few years has been in the intersection of Machine Learning and Natural Language Processing (NLP). This has led to research outputs focused on how we can better improve low resource language tools, especially for African Languages. This has included creating new software libraries, new research approaches for robust NLP and encouraging the development of datasets for African languages. As part of his vision for the ABSA Data Science chair, Vukosi is interested in Data Science for Social Impact (https://dsfsi.github.io/), using local challenges as a springboard for research. In this area, Vukosi has worked on projects in science, energy, public safety and utilities. Vukosi is cofounder of the Deep Learning Indaba, the largest Machine Learning/Artificial Intelligence workshop on the African continent, aiming to strengthen African Machine Learning.

Coming to grips with the reality of Data Science - It's people all the way down

As practising Data Science researchers and practitioners, the COVID-19 pandemic has highlighed both the need for data driven decision making and the reality of what it really takes to get to that point. It is not only about throwing data + model at a problem. It is about understanding the environment that one is in and then strategising on what might best work for that environment. In this talk I look back at some of the work we have done within responding to different challenges within both Data Science and Natural Language Processing. I place at the center people and how they are the important piece in our practice.




 

Maria Rodriguez, University at Buffalo


Date: May, TBA

 

Dr. Rodriguez is an Assistant Professor at the University at Buffalo's School of Social Work (SUNY). She is also a faculty associate at the BerkmanKlein Center for Internet and Society, a faculty fellow at the Center for Democracy and Technology, and a member of the Twitter Academic Research Advisory Board. Her research is at the intersection of applied demography, computational social science, and social policy. The first line of research examines the ethical implications of algorithmic decision-making in human services, child welfare in particular. The second line of research looks at the lived experience of marginalized communities as self-reported on social media. The through line between the two concerns the methods involved: she identifies as a methodologist in social work spaces, in as far as her substantive focus is how computational methods can support using large, unstructured data to scale social work interventions. For that reason Rodriguez founded the caretLAB, the first lab housed in a school of social work to her knowledge dedicated to computational research methods in the public interest. Her lab works with technologists across fields of inquiry to leverage cutting edge, innovative technologies to develop, implement, and evaluate scaled interventions.