Women in Machine Learning

19:00-22:00, Monday 10 September 2018
Venue: STIAS (Stellenbosch Institute for Advanced Study, South Africa)

Kindly hosted by Microsoft

Join our fantastic speakers at an event to encourage, support and unite women in machine learning, while highlighting diverse career paths: from academia, to industrial research, to applied machine learning, and start-ups. Our panellists will each describe their personal career journey, and their experiences as a woman in machine learning, followed by a panel discussion, Q&A from the audience and a chance to network. Free and open to all conference attendees.

Speakers

Sarah Brown

Brown University

Dr. Sarah Brown is a Postdoctoral Research Associate in the Data Science Initiative at Brown University (USA) affiliated to the Division of Applied Mathematics. Dr. Brown received her BS, MS, and PhD degrees in Electrical Engineering from Northeastern University. Dr Brown builds machine learning tools that bridge from data-agnostic methods to systems that fuel data driven discovery in historically qualitative domains. Her work approaches this from two fronts: building interfaces that enable my algorithms to leverage domain scientists' qualitative expertise and developing model-based machine learning solutions through close collaboration with domain scientists. Sarah has been an instructor with The Carpentries since November 2017 serves as a member of the Lesson Infrastructure Committee. Currently she serves as treasurer and previously as a workshop organizer for Women In Machine Learning, Inc. Previously, she has served as general co-chair of the Broadening Participation in data mining Program, a founding member of the Black in AI organizing committee, and in various leadership roles in the National Society of Black Engineers.

Kathleen Siminyu

Africa's Talking

Kathleen is a data scientist who enjoys building and maintaining data infrastructure as well as discovering patterns that uncover insights from data. She is the Head of Data Science at Africa's Talking (Kenya). Passionate about the democratization of machine learning, she co-founded and organises a data science and machine learning community which focuses on encouraging individuals, with a special focus on women, to get into the field. With over 1500 members, the community is designed to help individuals interested in growing their skills by supporting their learning journeys, connecting them with peers for collaboration as well as connecting them to opportunities for work.

Konstantina Palla

Microsoft Research

Konstantina is a Machine Learning Researcher in the Healthcare ML Division at Microsoft Research Cambridge (UK). Her research is focusing on the construction and application of Bayesian probabilistic models for discovering latent structure in data. Recently, she has been particularly interested in the application of probabilistic modelling in the Healthcare domain as a means to understand disease subtypes and patients’ subgroups. In her PhD, she developed nonparametric models for relational data with a focus on time evolving settings.

Muthoni Wanyoike

Instadeep

Muthoni Wanyoike, is the team lead at Instadeep in Kenya. She is passionate about bridging the skills gap in AI in Africa and does this by co-organizing the Nairobi Women in Machine Learning community. Through the community, we are able to provide learning, mentorship, networking and job opportunities for people with interests and working in AI. She is experienced in Research, Data Analytics, community and project management and community growth hacking.

Tempest van Schaik

Microsoft Commercial Software Engineering

Tempest is a multi-disciplinary engineer, designer and researcher. She has degrees in Biomedical Engineering, and Electrical (Information) Engineering from University of the Witwatersrand (South Africa), and a PhD in Bioengineering from Imperial College London. Her art and design has been awarded and exhibited internationally, including Milan Design Week, and her engineering expertise is complemented by experience in UX and product development. She has developed sensors to study the progression of cancer, low cost soil-nutrient tests for farmers in developing countries, phone & watch games which measure cognitive function in people with depression, and low-cost device to measure ocean ecology. In her current role as a software engineer at Microsoft (UK), she applies machine learning to solve real problems in business and government, with a focus on healthcare.