Speakers and TOPICs

Shakir Mohamed (DeepMind):

Variational Inference

Shakir is senior staff scientist at DeepMind in London, having joined DeepMind in 2013. Shakir's research is in the areas of probabilistic machine learning and artificial intelligence; and organises his research and thinking around three pillars, on foundations of reasoning and intelligence, towards addressing global challenges, and Transformation. Shakir has worked in generative models and statistical inference, particularly in variational inference which will be the topic of his MLSS talk, but also in applied topics in healthcare, weather and climate, and in areas of social transformation and diversity.

At DeepMind, Shakir is a member of its diversity steering committee, and leads its LGBT+ employee resource group. Shakir leads the Deep Learning Indaba, an independent grassroots organisation whose aim is to build pan-African capacity and ownership in AI. Shakir also sits on the diversity advisory board for the NeurIPS conference, and was recently elected to the ICML board of directors. He was a programme chair for ICLR 2019 and DALI2019, and will be the senior programme chair for ICLR 2020.

Prior to joining DeepMind, Shakir held a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR) as part of the programme on Neural Computation and Adaptive Perception, working with Nando de Freitas. Shakir holds a PhD in Statistical Machine Learning from the University of Cambridge having worked under the supervision of Zoubin Ghahramani. He is from Johannesburg, South Africa, where he completed his undergraduate and masters degrees in electrical and information engineering.

Kevin Webster (FeedForward, Imperial College London)

Deep Learning

Kevin obtained his PhD in 2003 from the Department of Mathematics at Imperial College, in the area of dynamical systems. He has also held postdoctorate positions at Imperial College, and was awarded a Marie Curie Individual Fellowship, which he spent at the Potsdam Institute for Climate Impact Modelling in Germany. During these positions his research interests became more focused on machine learning, and specifically adapting ML technologies for numerical analysis problems in dynamical systems. He was the Head of Research at the London music AI startup Jukedeck, where he oversaw the development of the deep learning framework for automatic music composition. In 2018 he set up his own machine learning consultancy, FeedForward, with a focus on the music & the creative industries. His particular interest in the field of deep learning is generative modelling. @kn_webster / kevin.webster@imperial.ac.uk

Kai Arulkumaran (Imperial College London)

Deep Learning

Kai is currently researching his PhD in deep learning at the Department of Bioengineering at Imperial College. During his PhD he has been a research intern at Microsoft Research, Twitter Magic Pony, Facebook AI Research and DeepMind. He also founded the Deep Learning Network at Imperial College to organise guest lectures and a reading group on the topic of deep learning. He is an advocate for open-source software and a well-known contributor to the Torch/PyTorch ecosystems. Before his PhD he studied computer science at the University of Cambridge and worked as a web developer. @KaiLashArul / kailash.arulkumaran13@imperial.ac.uk

Sanmi Koyejo (University of Illinois at Urbana-Champaign; Google AI Accra):

Interpretability

Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and a research scientist at Google AI in Accra. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to neuroscience and biomedical imaging. Koyejo has been the recipient of several awards including a best student paper award from the conference on uncertainty in artificial intelligence (UAI), a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves on the board of the Black in AI organization, which he co-founded.

Moustapha Cissé (AIMS Rwanda and Google AI, Accra):

AI for Good

Lorenzo Rosasco (IIT, University of Genova, MIT):

Kernels

Lorenzo Rosasco is team leader at the Istituto Italiano di Tecnologia (IIT) and research scientist at the Massachusetts Institute of Technology (MIT). He holds an assistant professor position at the Computer Science Department of the University of Genova, Italy, from which is on leave of absence. He is currently involved in establishing the Computational and Statistical Learning Laboratory, a joint laboratory based on a collaborative agreement between IIT and MIT. He received his PhD from the University of Genova in 2006. Between 2006 and 2009 he was a postdoctoral fellow at Center for Biological and Computational Learning at the Massachusetts Institute of Technology. His research focuses on studying theory and algorithms for computational learning. Dr. Rosasco has developed and analyzed methods to learn from small as well as large samples of high dimensional data, using analytical and probabilistic tools, within a multidisciplinary approach drawing concepts and techniques primarily from computer science and but also from physics, engineering and applied mathematics.

Sarah Filippi (Imperial College London):

Approximate Bayesian Computation

Sarah Filippi holds a joint Lectureship position between School of Public Health and the Department of Mathematics at Imperial College London. She previously was a Lecturer in the Statistic Department of Oxford University (2014-2016) and a faculty fellow at the Alan Turing Institute. Prior to this, she held a Medical Research Council Fellowship (2011-2104) in the Theoretical Systems Biology group at Imperial College London.

The core of her research lies in biostatistics and computational statistics methodology development motivated by applications in and around computational biology, biomedical genetics and clinical studies. Her work focuses in addressing how novel statistical and computational approaches can aid in the analysis of large-scale real-world health problems. She develops novel Bayesian statistical procedures and applies them to investigate complex biological systems in health and disease. Over the past eight years, she has conducted her research in inter-disciplinary teams and have become increasingly immersed in biomedical research problems through collaboration with experimentalists and clinicians.

Karen Livescu (Toyota Technological Institute at Chicago):

Speech Processing

Karen Livescu is an Associate Professor at TTI-Chicago. She completed her PhD and post-doc in Electrical Engineering and Computer Science at MIT and her Bachelor's degree in Physics at Princeton University. Karen's main research interests are at the intersection of speech and language processing and machine learning. Her recent work includes multi-view representation learning, acoustic word embeddings, cross-modal training of speech and language models, and automatic sign language recognition. At TTIC she teaches courses on unsupervised learning and speech technologies. She regularly serves as area chair for speech, natural language processing, and machine learning conferences, and was a program chair for ICLR 2019 and technical chair for ASRU 2015/2017/2019.

Samory Kpotufe (Columbia University):

Learning Theory

Samory Kpotufe is Associate Professor in the Statistics department at Columbia University. Prior to this he was Assistant Professor at ORFE, Princeton University, 2014 to 2018. Samory’s research is in the area of Statistical Machine Learning, with a focus on high-dimensional problems and nonparametric methods. His current interests revolve around statistical tradeoffs in the practice of ML under constraints, e.g., time or space constraints, and constraints in sampling.