Bio: Professor Andrew Blake is Director of the Alan Turing Institute. Prior to joining the institute in 2015, Professor Blake held the position of Microsoft Distinguished Scientist and Laboratory Director of Microsoft Research Cambridge, England. He joined Microsoft in 1999 as a Senior Researcher to found the Computer Vision group. In 2008 he became a Deputy Managing Director at the lab, before assuming the directorship in 2010. Before joining Microsoft Andrew trained in mathematics and electrical engineering in Cambridge England, and studied for a doctorate in Artificial Intelligence in Edinburgh. He was an academic for 18 years, latterly on the faculty at Oxford University, where he was a pioneer in the development of the theory and algorithms that can make it possible for computers to behave as seeing machines.
Professor Blake has published several books including “Visual Reconstruction” with A.Zisserman (MIT press), “Active Vision” with A. Yuille (MIT Press) and “Active Contours” with M. Isard(Springer-Verlag). He has twice won the prize of the European Conference on Computer Vision, with R. Cipolla in 1992 and with M. Isard in 1996, and was awarded the IEEE David Marr Prize (jointly with K. Toyama) in 2001.
In 2006 the Royal Academy of Engineering awarded him its Silver Medal and in 2007 the Institution of Engineering and Technology presented him with the Mountbatten Medal(previously awarded to computer pioneers Maurice Wilkes and Tim Berners-Lee, amongst others.) He was elected Fellow of the Royal Academy of Engineering in 1998, Fellow of the IEEE in 2008, and Fellow of the Royal Society in 2005. In 2010, Andrew was elected to the council of the Royal Society. In 2011, he and colleagues at Microsoft Research received the Royal Academy of EngineeringMacRobert Award for their machine learning contribution to Microsoft Kinect human motion-capture. In 2012 Andrew was elected to the board of the EPSRC and also received an honorary degree of Doctor of Science from the University of Edinburgh. In 2013 Andrew was awarded an honorary degree of Doctor of Engineering from the University of Sheffield. In 2014, Andrew gave the prestigious Gibbs lecture at the Joint Mathematics Meetings (transcript available here). Professor Andrew Blake has been named as the recipient of the 2016 BCS Lovelace Medal, the top award in computing in the UK, awarded by BCS, The Chartered Institute for IT. The award is presented annually to individuals who, in the opinion of BCS, have made a significant contribution to the advancement of Information Systems.
Title: Harnessing the Power of Data Science through Research
Abstract: The Alan Turing Institute is the UK’s newly-created national centre for data science, headquartered at the British Library in the heart of London’s vibrant Knowledge Quarter. Our vision is to become a world leader in data science research and innovation. This lecture will take a whistle-stop tour through the Institute’s scientific and innovation programme. It tackles challenges of social and economic importance, from engineering to finance, from health and well-being to cloud computing and smart cities. The Institute aims to make unique contributions, focussing on work that is complementary to what can be done in universities. In particular, we aim to act as a national hub, attracting a diversity of talent, interest and influence. We emphasise engineering and the ability to build high quality prototypes. We encompass, in one connected physical space, a sweep of disciplines from pure mathematics, through statistics and machine learning, to social science.
Bio: Ralf Herbrich is Director of Machine Learning at Amazon and Managing Director of the Amazon Development Center Germany. He works on problems of demand forecasting, scalable machine learning, computer vision and linking structured content. In 2011, he worked at Facebook leading the Unified Ranking and Allocation team. From 2000 – 2011, he worked at Microsoft Research and was co-leading the Applied Games and Online Services and Advertising group which engaged in research at the intersection of machine learning and computer games and in the areas of online services, search and online advertising combining insights from machine learning, information retrieval, game theory, artificial intelligence and social network analysis. Ralf was Research Fellow of the Darwin College Cambridge from 2000 – 2003. He has a diploma degree in Computer Science (1997) and a Ph.D. in Statistics (2000). Ralf’s research interests include Bayesian inference and decision making, computer games, kernel methods and statistical learning theory. He is one of the inventors of the Drivatars system in the Forza Motorsport series as well as the TrueSkill ranking and matchmaking system in Xbox 360 Live. He also co-invented the adPredictor click-prediction technology launched in 2009 in Bing’s online advertising system.
Title: Machine Learning at Amazon
Abstract: In this talk I will give an introduction into the eld of machine learning and discuss why it is a crucial technology for Amazon. Machine learning is the science of automatically extracting patterns from data in order to make automated predictions of future data. One way to differentiate machine learning tasks is by the following two factors: (1) How much noise is contained in the data? and (2) How far into the future is the prediction task? The former presents a limit to the learnability of task—regardless which learning algorithm is used— whereas the latter has a crucial implication on the representation of the predictions: while most tasks in search and advertising typically only forecast minutes into the future, tasks in e-commerce can require predictions up to a year into the future. The further the forecast horizon, the more important it is to take account of uncertainty in both the learning algorithm and the representation of the predictions. I will discuss which learning frameworks are best suited for the various scenarios, that is, short-term predictions with little noise vs. long-term predictions with lots of noise, and present some ideas to combine representation learning with probabilistic methods.
In the second half of the talk, I will give an overview of the applications of machine learning at Amazon ranging from demand forecasting, machine translation to automation of computer vision tasks and robotics. I will also discuss the importance of tools for data scientist and share learnings on bringing machine learning algorithms into products
Bio: Anjali Joshi is Vice President of Product Management at Google, where she currently leads Google’s efforts in health search and is working on defining new initiatives in the health space. Previously she led the product management team for search and image search. With the growth in the number of people using smartphones, the focus of her teams was to build new search experiences optimized for mobile devices across all geographies and languages. Prior to that she led product management teams for Google Maps, Infrastructure, Research, Translate, News, Finance and Google.org. She also led the early Google efforts for Cloud services and Fiber networks.
Before joining Google, Anjali was Executive Vice-President of Engineering at Covad Communications, the first DSL Competitive Carrier in the US. She was also Principal Member of Technical Staff at AT&T Bell Labs where she led projects to build the first large scale high speed data networks in the US. She received her Master’s degree in Management Science and Engineering from Stanford University, a Master’s degree in Computer Engineering from the State University of New York, and a Bachelor’s degree in Electrical Engineering from IIT, Kanpur. She was selected as one of the top 50 alumni who have graduated from the Indian Institute of Technology, Kanpur in the last 50 years. She serves in advisory roles to several start-ups and was on the board of TIE, Silicon Valley.
Title: Primum non nocere: Healthcare in the digital age
Abstract: Internet search has become the first stop in many users’ health journeys. Today, about 1 in 20 Google searches are related to healthcare. These queries span a broad range of information needs as people are looking for possible conditions related to their symptoms and are seeking to understand their diagnoses and prescribed treatments, decipher their test results, find pathways of self-care as well as connect to people with similar experiences.
This talk will cover the approaches that we at Google have used to meet these diverse user needs. We will also discuss how we constructed and curated the Health Knowledge Graph, a large scale resource of highly accurate medical knowledge that powers many of our health applications. In the second part of the talk, we will show how the confluence of advances in technology enables us to revolutionize health data collection and perform it at unprecedented scale and granularity. Combined with contextual signals, anonymous aggregated user activity can be used to quantify public health phenomena and provide concerned authorities with actionable information about seasonal or situational health issues.
We will conclude the talk with an outline of research directions that could enable people and organizations in personal and public health settings obtain actionable information in a timely manner.
The work presented here was the product of collaboration of multiple teams at Google.
Bio: Nick is a Principal Applied Science Manager in Microsoft Bing, working on core relevance. He leads a team of applied researchers in the UK, US and Australia. He joined Microsoft in 2004 and joined the Bing team in 2006, while remaining active in research publication. He received his PhD from the Australian National University and worked at Australia’s CSIRO before joining Microsoft. His research interests include web search ranking, log mining and evaluation. He has seven WSDM papers including one best paper award.
Title: Neural Models for Full Text Search
Abstract: A fundamental concern in search engines is to determine which documents have the best content for satisfying the user, based on analysis of the user’s query and the text of documents. For this query-content match, many learning to rank systems make use of IR features developed in the 1990s in the TREC framework. Such features are still important in a variety of search tasks, and particularly in the long tail where clicks, links and social media signals become sparse. I will present our current progress, in particular three different neural models, with the goal of surpassing the 1990s models in full text search. This will include evidence, using proprietary Bing datasets, that large-scale training data can be useful. I will also argue that for the field to make progress on query-content relevance modeling, it may be valuable to set up a shared blind evaluation similar to 1990s TREC, possibly with large-scale training data.