Meet the 2018 Speakers
Plenary Speakers
I have recently moved as an Assistant Professor to Ruhr-University Bochum, having been previously at the Computer Science Department III of the University of Bonn. I was a post-doctoral researcher at the Montreal Institute for Machine Learning (MILA). Between 2010 and end of 2014, Asja was employed both at the Institute for Neural Computation at the Ruhr-University Bochum and the Department of Computer Science at the University of Copenhagen working on her PhD in Machine Learning, which I defended in Copenhagen in 2014. I studied Biology, Bioinformatics, Mathematics and Cognitive Science at the Ruhr-University Bochum, the Universidade de Lisboa and the University of Osnabrück.
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I am a researcher at the Machine Intelligence and Perception group at Microsoft Research Cambridge. I am the research lead of Project Malmo, which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. My long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems.
Outside of Project Malmo, I work on online evaluation and interactive learning for information retrieval. This means that I try to understand how we can apply machine learning an artificial intelligence to develop more intelligent search and recommendation systems. Before joining Microsoft Research, I completed my PhD in Computer Science as part of the ILPSgroup at the University of Amsterdam. I worked with Maarten de Rijke and Shimon Whiteson on smart search engines that learn directly from their users. |
I am an assistant professor of computer science and data science at New York University. I was a postdoctoral fellow at University of Montreal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. I try my best to find a balance among machine learning, natural language processing, and life, but almost always fail to do so.
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I joined Google in 1999 and am currently a Google Senior Fellow, and I currently oversee Google's Research and Machine Intelligence Division, where I co-founded and also lead the Google Brain team. With my collaborators, we are working on machine learning systems for speech recognition, computer vision, language understanding, and various other tasks, applications of machine learning to healthcare, robotics, and other areas, as well as developing machine learning software systems like TensorFlow and collaborating with our hardware platforms team on developing new kinds of computational platforms for machine learning computations, such as Tensor Processing Units (TPUs). I have co-designed/implemented many generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. I am a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools.
I received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. I received a B.S. in computer science & economics from the University of Minnesota in 1990. I am a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing and the Mark Weiser Award. More info about me and a list of my publications are at http://research.google.com/people/jeff |
I am a scientist at Facebook Artificial Intelligence Research (FAIR). I am committed to building an axiological artificial intelligence to improve our society, particularly the lives of those who need it most. My current research efforts focus on essential prerequisites of such AI: Fairness, Transparency, and Reliability. The Newscientist and the MIT Technology Review have highlighted our recent work in this area. I was born and raised in the beautiful Senegal (you should visit!), where I studied maths and physics at Universite Gaston Berger. Later, I completed a Ph.D. in machine learning at Universite Pierre et Marie Curie in Paris (France).
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I am a senior scientist and lead the Computer Vision (CV) group. My research interests include fine-grained visual categorization and search, visual attention and image aesthetics analysis. Currently, my research focuses on image search and video action recognition.
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I am a principal scientist and lead the Machine Learning team at DeepMind. I was born in Zimbabwe and did my undergraduate studies and MSc at the University of the Witwatersrand, and a PhD at Trinity College, Cambridge. From 2001, he was a Professor at the University of British Columbia, before joining the Department of Computer Science at the University of Oxford in 2013. I was also a co-founder in 2014 of Dark Blue Labs.
For me research is like stone sculpture. You find a stone in the quarry after a long search. As you begin to chisel, you discover new patterns, you see the possibilities, you see things that were never seen before. Then you continue chiseling and, disaster! something breaks. So you have to be patient, and continue looking till something else comes to your mind. Always thinking, never losing focus. Eventually something beautiful emerges, and if it doesn't emerge, do not despair because enjoying the process is part of the deal. Machine learning is about discovery, it is about a search for AI, but also a personal search. It is also about building products to improve the lives of others, and by others I don't mean only people. |
I currently hold the ARETÉ Junior Research Chair in Applied Statistical Methods, Cosmology and Big Data based at AIMS South Africa. I did a BSc degree in Physics and Math at Addis Ababa University, Ethiopia, and completed my Honours and Master’s degree at UCT through the National Astrophysics and Space Science Program (NASSP). I did my PhD at the school for advanced studies (SISSA) in Trieste, Italy. Before taking the ARETÉ Chair position in June 2016, I was a postdoctoral researcher for one year at the University of Oslo, Norway, and for three years at the University of Rome Tor Vergata, Italy.
My current work is mostly investigating the statistical properties of the Universe using the Cosmic Microwave Background (CMB) data from the Planck satellite. I also works on Big Data analysis and contribute to different software to the public. |
Speakers for Mathematics for Machine Learning
Learn the key concepts of probability and differential calculus necessary to gain the most from the rest of the week’s programme. This session will be split into 5 smaller classes run in parallel (each covering the same material).
I am Product Manager at Mwabu, where I have been working for the past 5 months now. I am from Zambia and got my BSc Hons in Computing at Zambia Centre for Accountancy Studies under an affiliation with Greenwich University in 2012. From October 2016 to December 2017 worked with Lima Links Zambia where I transitioned from a junior Python/Django Developer to the Project Manager until the time I left to join Mwabu.
I love learning new things, hence my constant search on what new things are trending in the Software Development Arena. In September 2017, I got interested in learning Machine Learning after we had a series of AI Masterclasses sponsored by Facebook via Facebook Developer Circles Lusaka where I am a Co-Lead. On the 3rd of March this year, I had an opportunity to do a presentation on Linear Algebra and Python basics at the Indaba in Lusaka Zambia. I am also an advocate for promoting girls getting into STEM careers in Zambia. Because of this passion, I have been volunteering as a Trainer and Mentor under Asikina Network. |
I am a doctoral student in the Torr Vision Group at the University of Oxford under the supervision of Professor Philip Torr and Dr Stephen Hicks. Prior to this, I completed a M.Sc Neuroscience at the University of Oxford, and before that a B.Sc Electrical and Computer Engineering at the University of Cape Town, South Africa.
Broadly speaking, I am interested in multi-modal representations of the world and how these can be used by AI systems interfacing humans with computers. In my PhD, I am primarily exploring the combinations of vision and language in the context of models for visual-based conversational dialogue. My motivation for grounding these dialogue models in real-world visual scenarios is toward building AI-based assistants to help blind/visually-impaired people. I have also dabbled in some other multi-modal combinations. Crossing vision with audio, I prototyped a VR device which created 3D soundscapes of virtual environments for audio-only spatial navigation! |
Kathleen is a data scientist who enjoys building and maintaining data infrastructure as well as discovering patterns that uncover insights from data. She works for Africa's Talking.
Passionate about the democratization of machine learning, she co-founded and manages 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 1000 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.Through the connections fostered and the work the community is doing, we have partnered with Google through the Together with Google Developers program in Sub Saharan Africa, Africa’s Talking Ltd, Moringa School, Intel, BRaVe Ventures and NVIDIA AI among others. As a writer, Kathleen blogs at “kathleensiminyu.com” and shares pieces around her journey as a self-taught data scientist, being a woman in technology as well as a millennial in the thick of her 20s. |
Dr. Kendi Muchungi received her PhD from the University of Surrey in 2015. Her research was in the field of Computational Neuroscience and focused on replicating retina functionalities on a computer for the purpose of informing the design of retinal implants. She is currently a Programme Leader at the Computer Science Department
in Africa Nazarene University. Her current proposed research is on a low-cost HoloEx device as she believes that education is vital for any sustainable economic development, and enriching the learning experience of students is at the heart of economic stability of a country. |
I was born and raised in Cape Town, South Africa. After completing my first degree in Computer Science at UCT, I went on to work for six years as a software engineer in the financial industry. After deciding that I was up for a new challenge, I pursued an opportunity to complete a masters in Computational Statistics and Machine Learning at University College London. Thereafter I joined DeepMind where I currently work as a Research Engineer. In this role, I focus primarily on the technical implementation of research projects. It provides me with the ideal balance between technical software engineering and machine-learning research. I am honoured to be part of the Deep Learning Indaba team and to contribute to the advancement of Machine Learning in Africa.
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AI and Africa
In this session we wish to highlight the ongoing as well as new directions of AI/ML work happening in Africa. This session will have two sections, first we will have short presentations 5-10 minutes from senior representatives from AI/ML/Policy companies and organisation (both NGO and governmental). These presentations are meant to provide an overview of what each organisation offers, highlight their successes, learn from challenges encountered and understand opportunities discovered. Following the presentations, we will have a Q&A session to open up the conversation to the audience.
Sumir obtained his PhD in Bioinformatics from the University of the Western Cape as part of the Stanford South Africa Biomedical Informatics (SSABMI) programme where he developed computational and analyses pipelines to determine the intersection between bacterial virulence and positive selection in Professor Winston Hide's laboratory. He completed his postdoctoral studies in Professor Alan Christoffels' laboratory at the South African National Bioinformatics Institute (SANBI) where his focus was on genome assembly, annotation, data mining, large scale statistical analysis of genomics data and development of various computational pipelines and analyses workflows for a myriad of genomics' data types. Dr Panji's main interests are in creating and implementing computational and analyses workflows, statistical analysis of biomedical data, biological algorithms, high performance computing and the overall application of bioinformatics and genomics methods to better understand complex biological systems. Sumir is currently a bioinformatician within the H3ABioNet consortium who is interested in genome science, data analysis workflows, statistical analyses of large 'omics datasets, implementation and interpretation of bioinformatics solutions to diverse biological problems and providing bioinformatics support to the H3Africa projects.
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David is the Director-General for the Directorate of Science, Technology and Innovation, and Advisor to the President of Sierra Leone. Previously he was a Health research manager at IBM Research in Nairobi and the Johannesburg, leading the healthcare team that designs and implements AI-enabled systems for the prevention, diagnosis, treatment and management of disease in Africa. He studied at Harvard and MIT for his PhD where his thesis was about improving prosthetic comfort for amputees, a beneficial area of study as a citizen of a country where years of war left about 27,000 people disabled.
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In May 2016 I became the chief scientist of IBM Research Africa. I am based in Nairobi, Kenya. We have a great group of scientists and engineers working in the areas of healthcare, inclusive financial services, public sector, Blockchain, and water & agriculture (in the Kenya Lab), and healthcare, smarter urban ecosystems and astronomy (in the South Africa Lab).
One exciting project I am leading involves the management of water and rangeland resources in the arid north of Kenya, an area populated by semi-nomadic pastoralists who herd cattle, sheep, goats and camels. These pastoralists live on the razor's edge of existence and face extreme perils in drought years, such as the one we are currently experiencing. Prior to coming to Africa I was one of the principle investigators at the Cognitive Environments Lab, or CEL, at the IBM T.J. Watson Research Center in New York. At that lab we try to bring immersive computing technologies to social environments. A special interest of mine was (and still is) creating technology that helps us engage with each other and with the world in a more meaningful way. Most of our technology today seems to do the opposite. Prior to my work on cognitive environments I worked on a mobile robot for autonomously mapping, monitoring and managing the energy and thermal properties of a computer data center. I also worked on the strategy component of the IBM Joepardy-playing program known as Watson. My academic interests are in the areas of discrete, combinatorial, and computational geometry. I am particularly attracted to simple to state problems in discrete geometry. |
Generative Models and Healthcare
This sessions builds on the understanding of generative models, with a focus on applications in healthcare. For the first half, Konstantina will discuss the role of probabilistic thinking, uncertainty and causality, and then look at how these tools can be used to build personalised healthcare tools. In the second part, Shakir will recap the area of generative models, specifically the algorithms for LDA, VAEs and GANs, and then look at how these can be applied in healthcare settings ranging from analysis of electronic health records, medical notes, in drug discovery, and in medical imaging.
Konstantina is a Machine Learning Researcher in the Healthcare ML Division at Microsoft Research Cambridge. 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.
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Shakir is a research scientist at DeepMind in London. Having been with DeepMind for over 5 years, he has been part of its growth from a small startup to the world's leading centre for AI and its applications. Shakir's research explores the intersection of probabilistic reasoning, deep learning and reinforcement learning, especially in the areas of generative models, variational inference and unsupervised learning. Shakir hold's a PhD in statistical machine learning from the University of Cambridge, that was followed by a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR) at the University of British Columbia. Shakir is proudly South African and is from Johannesburg, and where he completed his masters and undergraduate degrees in engineering at the University of the Witwatersrand, Johannesburg.
As part of the Deep Learning Indaba, Shakir helps drive it's mission of Strengthening African Machine Learning and artificial intelligence, through its programmes related to community-building, leadership, recognition and policy. The 2018 Indaba in South Africa will be the world's largest event focussed on teaching, debate and mentorship at that state of the art in artificial intelligence. |
Special Session on Reinforcement Learning
David Silver leads the reinforcement learning research group at DeepMind. David graduated from Cambridge University in 1997 with the Addison-Wesley award. Subsequently, David co-founded the videogames company Elixir Studios, where he was CTO and lead programmer, receiving several awards for technology and innovation. David returned to academia in 2004 to study for a PhD on reinforcement learning with Rich Sutton, where he co-introduced the algorithms used in the first master-level 9x9 Go programs. David was awarded a Royal Society University Research Fellowship in 2011, and subsequently became a lecturer at University College London. David consulted for DeepMind from its inception, joining full-time in 2013. His recent work has focused on combining reinforcement learning with deep learning, including a program that learns to play Atari games directly from pixels (Nature 2015). David led the AlphaGo project, culminating in the first program to defeat a top professional player in the full-size game of Go (Nature 2016), and the AlphaZero project, which learned by itself to defeat the world's strongest chess, shogi and Go programs (Nature 2017).
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Natural Language Processing
Learn about the recent history of NLP and discuss the biggest open problems in NLP with a panel of experts.
I am a lecturer in E&E engineering at Stellenbosch University, South Africa. Before moving back to South Africa, I did a postdoc at TTI-Chicago on multi-modal machine learning models combining speech and vision. I obtained my PhD from the University of Edinburgh, where I was supervised by Sharon Goldwater, Aren Jansen and Simon King; I worked on unsupervised speech processing, and played around with Bayesian and neural models.
My main interest is in the application of machine learning to problems in speech and language processing. I am particularly interested in methods that can learn from small amounts of labelled data, and in unsupervised methods that can learn directly from raw unlabelled data. Can an algorithm find meaningful units and structures in a corpus of speech audio, with only minimal guidance? How much supervision is required to build a useful speech system? These are central questions when building systems in low- and zero-resource settings. |
I am a PhD student Natural Language Processing (NLP) and Deep Learning at the Insight Research Centre for Data Analytics and a research scientist at Dublin-based text analytics startup AYLIEN. Prior to this, I've completed a B.A. in Computational Linguistics at the University of Heidelberg, Germany and visited Trinity College, Dublin and the University of Copenhagen, Denmark. I've also worked with Microsoft, IBM's Extreme Blue, Google Summer of Code, and SAP, among others.
I am mainly interested in developing models that can learn useful aspects of language from few examples and to create representations with prior knowledge that can help with this. In my research, I have focused on different aspects of transfer learning for NLP: domain adaptation, multi-task learning, semi-supervised learning, and cross-lingual learning. I am passionate about making ML more accessible. I enjoy writing and am blogging occasionally to share what I've learned. |
AI Ethics and Policy
This session will tackle the intersections of AI/ML, Ethics and Policy on the continent. Session will be a blend of a practical interactive Ethics session, a talk on fairness and robust discussion via an expert panel made up of researchers, practitioners, policy makers. At the end of the day, we would like to answer: How do we work to inject our own values into AI/ML development in Africa, allow a progressive environment for development and protect our communities?
Timnit Gebru is the technical co-lead of Ethical AI at Google and just finished her postdoc in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research, New York. Prior to joining Microsoft Research, she was a PhD student in the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. Her main research interest is in data mining large-scale, publicly available images to gain sociological insight, and working on computer vision problems that arise as a result, including fine-grained image recognition, scalable annotation of images, and domain adaptation. She is currently studying the ethical considerations underlying any data mining project, and methods of auditing and mitigating bias in sociotechnical systems. The New York Times, MIT Tech Review and others have recently covered her work. As a cofounder of the group Black in AI, she works to both increase diversity in the field and reduce the negative impacts of racial bias in training data used for human-centric machine learning models.
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Osonde Osoba is an engineer at the RAND Corporation and a professor at the Pardee RAND Graduate School. He has a background in the design and optimization of machine learning algorithms. He has applied his machine learning expertise to diverse policy areas such as health, defense, and technology policy. His more recent focus has been on data privacy and accountability in algorithmic systems and artificial intelligence.
Prior to joining RAND, he was a researcher at the University of Southern California (USC). His research there focused on improving the speed and robustness of popular statistical algorithms like the expectation-maximization (EM) and backpropagation algorithms used in applications like automatic speech recognition. He also made contributions on the robustness and accuracy of approximate Bayesian inference schemes. Osoba received his Ph.D. in electrical engineering from USC and his B.S. in electrical and computer engineering from University of Rochester. |
Dr Mmaki Jantjies holds a PHD in Computer Science that looked at the development of multilingual mobile learning applications in developing country contexts. She is a senior lecturer in the Information Systems department at the University of the Western Cape and lectures IT security and data risk management. She has a vast interest on the role of data ethics, privacy and governance in developing countries. She holds a Google grant to develop curricula and teach teachers how to impart digital skills to support computer science learning in schools. Dr Jantjies is part of G20 Women 20 Dialogue research group focusing on the effects of digitization on women in the G20, She is also part of the UN Computing and Society Equals partner research group.
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Linet Kwamboka is the Founder and CEO of DataScience LTD – a software engineering company that is focused on building information systems that generate and use data to discover intelligent insights about people, products and services.
She was a Mozilla fellow (2017/2018) focusing on data protection and Privacy policies in the East African Region. She has been the Kenya Open Data Initiative Project Coordinator for the Government of Kenya at the Kenya ICT Authority where she also led the Open Government Partnership for the Government of Kenya and has also helped the Government of Somalia build their open data project from concept to portal and support under the Open Government Initiative. She has worked at the World Bank (World Bank Spot Award recipient) as a GIS and Technology Consultant and the UNDP in the Strengthening Electoral Processes in Kenya. She was a Software Engineering Fellow at Carnegie Mellon University, Pittsburgh and her background is in computer science, data analysis and Geographical Information Systems. She was recently recognised as one of the World’s 100 Most Influential People in Digital Government, a recognized unsung hero by the American Embassy in Kenya in her efforts to encourage more women into technology and computing, has been a finalist in the Bloomberg award of global open data champions and is a member of the Open Data Institute Global Open Data Leaders’ Network. |
Vukosi Marivate holds a PhD in Computer Science (Rutgers University) and MSc & BSc in Electrical Engineering (Wits University). He has recently started at the University of Pretoria as the ABSA Chair of Data Science. Vukosi 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(due to the abundance of text data and need to extract insights). As part of his vision for the ABSA Data Science chair, Vukosi is interested in Data Science for Social Impact, using local challenges as a springboard for research. In this area Vukosi has worked on projects in science, energy, public safety and utilities. He is passionate about developing young talent, supervising MSc and PhD students and mentoring budding Data Scientists.
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Machine Learning in Production
Learn the tricks of the trade for deploying and scaling ML models in a production environment from experienced practitioners.
Omoju is a Senior Machine Learning Data Scientist with Github. She has over a decade of experience in computational intelligence. In the past, she has co-led the non-profit investment in Computer Science Education for Google and served as a volunteer advisor to the Obama administration’s White House Presidential Innovation Fellows.
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Stuart Reid is the Chief Scientist and a partner at NMRQL Research, a South African based asset manager and consultancy which uses state of the art machine learning algorithms to inform investment decisions. Their specialty is developing machine learning algorithms that are able to learn from non-stationary time-series data generated by complex dynamical systems such as financial markets (prices, rates, etc.), economies & logistics (supply & demand), weather systems (extreme conditions), and more.
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Amine is leading the AI product development team at InstaDeep. He joined the company two years ago after a career in software development where he lead engineering teams in various industries (semiconductor, consumer electronics, investment banking). Initially, Amine started deep learning and AI as a hobby, but ended up dropping everything to pursue his goal of deploying artificial intelligence to solve real life problems. He is driven by the ambition of replacing classic operational research techniques with AI. He has a Master of Engineering in software and a Master in Business Administration.
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Frontiers of Computer Vision
Learned the basics of Convolutional Neural Networks? Want to know go beyond? Join to extend your understanding of CNNs and how they extract image features for higher-level computer vision tasks like object detection, localisation and semantic segmentation. Following this, we invite a panel of computer vision experts to give their personal insights, advice and expert views on the frontiers of the field: what are the biggest unsolved problems in computer vision, how are they relevant to Africa, and where should African researchers be directing their energy to solve these problems. Also join to hear short spotlight talks given by fellow Indaba attendees - a great opportunity to learn more about current state-of-the-art methods being used in computer vision!
Sara Hooker is an AI Resident at Google Brain doing deep learning research on reliable explanations of model predictions for black-box models. Her main research interests gravitate towards interpretability, model compression and security. In 2014, she founded Delta Analytics, a non-profit dedicated to bringing technical capacity to help non-profits across the world use machine learning for good. She grew up in Africa, in Mozambique, Lesotho, Swaziland, South Africa, and Kenya. Her family now lives in Monrovia, Liberia.
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Life of a Machine Learning Startup
I am Co-Managing Partner of a Cape Town based Venture Capital firm, Knife Capital. My closest connection to AI is that I have invested in an AI company.
Having grown up in Germany, I have a Masters Degree with focus on Marketing and Management of Technology and Innovation from the Technical University in Aachen, Germany and a MBA from Henley Management College in the UK. After years in industry, I have spend the last 14 years as an investor, working with technology companies in South Africa, assisting them to scale internationally. As an ecosystem activist, I believe that technology entrepreneurs have a significant influence on the future of Africa, impacting the generation of Intellectual Property, skills transfer, employment creation, creating role models and building significant sustainable businesses. I believe that entrepreneurs need access to knowledge - subject matter expertise and business knowledge -, networks - advisors, customers, skills - and funding and I am passionate about providing all of the above. |
Karim helps companies get a grip on the latest AI breakthroughs and deploy them. A graduate of France’s Ecole Polytechnique and former Program Fellow at the Courant Institute in New York, Karim has a passion for teaching and using applied mathematics.
This led him to launch InstaDeep, a fast-growing African AI startup that focuses on decision making for the Enterprise. Nominated at the MWC17 in the Top 20 most intriguing startups by PCMAG, InstaDeep now has offices in Tunis, London, Paris and Nairobi. Karim is also the founder of the TensorFlow Tunis Meetup and a Google Developer Expert in ML. He regularly organises educational events and workshops to share his experience with the community, including mentoring in ML at Google Launchpad Accelerator Africa. Karim is on a mission to democratise AI and make it accessible to a wide audience. |
Reinforcement Learning II
Benjamin Rosman is a Principal Researcher in the Mobile Intelligent Autonomous Systems group at the Council for Scientific and Industrial Research (CSIR) in South Africa, and is also a Senior Lecturer in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand, where he runs the Robotics, Autonomous Intelligence and Learning Laboratory. He received his Ph.D. in Informatics from the University of Edinburgh in 2014, and previously obtained his M.Sc. in Artificial Intelligence from the University of Edinburgh. He also has a B.Sc. (Hons) in Computer Science and a B.Sc. (Hons) in Applied Mathematics, both from the University of the Witwatersrand. His research interests focus primarily on reinforcement learning and decision making in autonomous systems, in particular how learning can be accelerated through abstracting and generalising knowledge gained from solving related problems. He additionally works in the area of skill and behaviour learning and transfer for robots. He currently serves as the Chair of the IEEE South African joint chapter of Control Systems, and Robotics and Automation, is a founder and organiser of the Deep Learning Indaba. He was a 2017 recipient of a Google Faculty Research Award in machine learning.
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