Women in Machine Learning

Kindly hosted by:

19:00-22:00, Monday 10 September 2018

Venue: STIAS (Stellenbosch Institute for Advanced Study, South Africa)

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.

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

Data Scientist Africa's Talking; Co-organiser, Nairobi Machine Learning and Data Science

Kathleen is a data scientist and machine learning engineer who is Regional Coordinator for the Artificial Intelligence for Development – Africa Network. She is Co-Founder and Co-Organiser of the Nairobi Women in Machine Learning and Data Science community as well as part of the Deep Learning Indaba leadership.

Kathleen is also currently a Masters student at the Georgia Institute of Technology undertaking the Online Masters in Computer Science with a specialization in Computational Perception and Robotics. She is keen on investing time and effort in ventures that involve natural language processing for African languages as well as low-cost hardware robotics.

She can be reached on twitter @siminyu_kat and on LinkedIn.

Konstantina Palla

Researcher in the Healthcare ML Division at Microsoft Research Cambridge

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

Code for Africa; and Co-organiser Nairobi Machine Learning and Data Science

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 van Schaik is a multi-disciplinary engineer with experience in the end-to-end development of health technology, from wet-lab research, to medical devices, to clinical UX, and medical data science. She currently works as a machine learning engineer at Microsoft, London, focusing on healthcare & biosciences. She puts machine learning into practice in close collaboration with clinical & pharmaceutical researchers to solve diverse, real-world problems. Recently she has used ML to better understand the physiotherapy of Cystic Fibrosis. 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. She is an Ambassador for Diversity & Inclusion for her team at Microsoft (Commercial Software Engineering).

You can find her on Twitter: @Dr_Tempest