Tejumade Afonja is a Graduate Student at Saarland University studying Computer Science. Previously, she worked as an AI Software Engineer at InstaDeep Nigeria. She holds a B.Tech in Mechanical Engineering from Ladoke Akintola University of Technology (2015) . She’s currently a remote research intern at Vector Institute where she is conducting research in the areas of privacy, security and machine learning.

Tejumade is the co-founder of AI Saturdays Lagos, an AI community in Lagos, Nigeria focused on conducting research and teaching machine learning related subjects to Nigerian youths. Tejumade is one of the 2020 Google EMEA Women Techmakers Scholar.

Tejumade was a co-organizer for ML4D 2019 NeurIPS workshop and she is serving as the lead organiser this year. She is affiliated to several other workshops like BIA, WIML, ICLR, Deep Learning Indaba, AI4D and DSA where she occasionally serves as a volunteer or mentor.

Konstantin Klemmer is a PhD student at the University of Warwick and currently visiting the Machine Learning for Good (ML4G) lab at New York University. He has also spent part of his PhD at the Alan Turing Institute in London. Konstantin’s research focuses on machine learning methods for geospatial data and applications in computational sustainability. He holds an undergraduate degree from the University of Freiburg, Germany and a Masters from Imperial College London and UCL.

Konstantin is one of the co-organizers of the “AI & Cities” track series at the Applied Machine Learning Days (AMLD) at EPFL, Switzerland. He has also helped to organize the “Tackling Climate Change with Machine Learning” workshop at ICLR 2020, Addis Ababa, Ethiopia.

Aya Salama is Co-founder and Machine learning engineer at Aigorithm tech a Cairo-based data services and intelligent automation startup. She is also a research assistant at the DataScience Hub, the American University in Cairo.

Aya has worked on several community based initiatives that are aimed at using AI for good, the latest of which is her engagement with Omdena an international platform for connecting NGOs with technology enthusiasts from around the world to tackle social problems with technology and data science. She is also the founder of IndabaXEgypt A subsidiary of the Deep learning Indaba, an organization aimed at connecting and building capacity in ML across Africa. IndabaXEgypt is committed to strengthening the ML/DL community in Egypt and has held its inaugural 2 day conference in 2019.

In 2018, Aya attained an Mphil degree in Advanced Computer Science from the University of Cambridge with a thesis project at the intersection of genomics and artificial neural networks.

Paula Rodriguez Diaz is a MSc student at Universidad de Los Andes and she holds a B.Sc. in Mathematics and a B.Sc. in Industrial Engineering from Universidad de Los Andes. Currently, she is a Data Science for Social Good Fellow at the Alan Turing Institute and the University of Warwick and a Junior Data Science Researcher at Quantil | Applied Mathematics. She has also worked at Universidad de Los Andes as a Teaching Assistant for the Economics Faculty and a Machine Learning Assistant Professor for the Continuing Education Program. Prior to Quantil, she worked as a Research Assistant at Cornell University working on functional data analysis. She is also a Women in Data Science (WiDS) ambassador for Bogota and has previously worked as a Data Science Trainee at Data Pop Alliance where she developed machine learning tutorials for the national statistic departments from Colombia, Mexico and Dominican Republic.

Niveditha Kalavakonda is a Ph.D. student at the University of Washington, Seattle. She is a part of the BioRobotics Lab, working with Prof. Blake Hannaford and Dr. Laligam Sekhar on surgical robotics. She holds a Bachelor’s degree from Amrita School of Engineering in India and has worked as a Project Associate at the Indian Institute of Technology, Madras. Her research is at the intersection of Human-Robot Interaction(HRI) and computer vision with applications in the field of medicine. She is also a part of the Science, Technology, and Society Studies Department, where she works with Prof. Ryan Calo on Tech Policy. Niveditha has co-organized the 2020 Women in Data Science - Puget Sound conference, and Hopperx1 Seattle 2017 and 2019.

Oluwafemi Azeez is a Research Engineer at Instadeep where he works on Reinforcement Learning projects. He is a recent masters graduate of Carnegie Mellon University, He spent some time at the African campus in Kigali and in the Pittsburgh Campus where He studied Electrical and Computer Engineering. His research focused on unsupervised domain adaptation in image segmentation, which He did with Yang Zou under the supervision of VijayaKumar Bhagavatula, and speech separation with Yuichiro Koyama under the supervision of Bhiksha Raj. He also co founded AI Saturdays Lagos and Kigali with the focus of helping others learn AI through community study groups, free online resources and peer motivation.

Maria De-Arteaga is an Assistant Professor at the Information, Risk and Operation Management Department at the University of Texas at Austin. She received her PhD in Machine Learning and Public Policy from Carnegie Mellon University. She holds a M.Sc. in Machine Learning from Carnegie Mellon University and a B.Sc. in Mathematics from Universidad Nacional de Colombia. Her research focuses on the risks and opportunities of using machine learning for decision support in high-stakes settings. Her work has been awarded the Best Thematic Paper Award at NAACL’19, the Innovation Award on Data Science at Data for Policy’16, and has been featured by UN Women and Global Pulse in their report Gender Equality and Big Data: Making Gender Data Visible. She is a recipient of a 2018 Microsoft Research Dissertation Grant, and was named an EECS 2019 Rising Star. In 2017 she co-founded the Machine Learning for the Developing World (ML4D) Workshop series.

Amanda Coston is a joint PhD student in Machine Learning and Public Policy at Carnegie Mellon University. She received her BSE from Princeton (2013) where she studied computer science and public policy. After graduating, she worked as a program manager at Microsoft, a data scientist at Teneo, and a data consultant at Hivisasa, a journalism startup in Nairobi. She is broadly interested in how machine learning can solve problems of societal interest, and her research areas include algorithmic fairness and causal inference. Her work on counterfactual risk assessments for child welfare screening was awarded the Suresh Konda First Ph.D. Research Paper Award. She is a recipient of the NSF Graduate Research Fellowship and the Tata Consultancy Services (TCS) Presidential Fellowship.

Artur Dubrawski is a Research Professor at Carnegie Mellon University School of Computer Science where he directs the Auton Lab, a large research group focusing on fundamental and applied artificial intelligence. He investigates intelligent systems that can be useful and impactful, and deploys them in practice. His primary application areas include public health, food safety, healthcare, countering human trafficking, predictive maintenance of equipment, radiation safety, and all other fields where AI can be used for societal benefit. Dr. Dubrawski combines academic and real-world experience having served in executive and research lead roles in the high-tech industry. He has helped start and organize the series of ML4D workshops.

Sriganesh Lokanathan is currently the Data Innovation & Policy Lead at Pulse Lab Jakarta, a joint initiative of the UN and the Government of Indonesia. He is a practitioner and interlocutor at the nexus of data, algorithms, policy, and development across emerging Asia Pacific. He previously co-established and led the big data for development research practise at LIRNEasia, a regional think tank in Sri Lanka.

He holds a Bachelor's degree in Computer Science, from the Massachusetts Institute of Technology, and a Master’s degree in Public Policy from the Lee Kuan Yew School of Public Policy of the National University of Singapore.

Ernest Mwebaze is a research scientist at Google. He is passionate about finding and implementing better, simpler and cost-effective solutions to address some of the prevailing problems in developing countries. Particularly he is interested in problems that can be solved by the application of computational techniques. As such his passion is research, particularly in the varied fields of Artificial Intelligence - specifically machine learning and computer vision and how to apply these in such a way as to obtain an optimal solution to real-world problems in developing countries.