
REGISTRATION
Ingo Waldmann - UCL
Characterising extrasolar planets with deep learning
The discovery of extrasolar planets - i.e. planets orbiting other stars - has fundamentally transformed our understanding of planets, solar systems and our place in the galaxy. With over 3500 systems discovered to date, characterising these alien worlds is rapidly becoming a big data problem. Future surveys with ground and space based telescopes will simply provide too much data to be analysed in more classical ways. Here I will present how deep learning can be used to rapidly characterise the chemistry and prevalent weather patterns of these extrasolar planets and put our own solar system in the grander galactic context.
Dr. Ingo Waldmann is a senior research scientist at the University College London. He obtained his PhD in astrophysics in 2012 working on blind source-separation algorithms applied to observations of extrasolar planets with the Hubble and Spitzer space-telescopes. He has since specialised in the modelling of non-linear Bayesian inverse problems and deep learning applied to atmospheric physics of extrasolar planets and solar system objects. He is the data analysis lead of the European Space Agency ARIEL mission and the UK-led Twinkle space-mission.


MACHINE INTELLIGENCE: WHERE ARE WE HEADED?


Damian Borth - Director Deep Learning Competence Center - DFKI
Exploring the Latent Visual Space Between Adjectives With Generative Adversarial Networks
Damian Borth - DFKI
Exploring the Latent Visual Space Between Adjectives with Generative Adversarial Networks
Generative Adversarial Networks (GANs) have been applied for multiple cases, such as, generating images and image completion. One interesting feature of GANs is the exploration in the Latent Space, where new elements can appear caused by the interpolation between two seed elements. With this in mind, we are interested in exploring the latent space in terms of Adjective-Noun Pairs (ANP) able to capture subjectivity in visual content such as "cloudy sky" vs. "pretty sky". Although it is challenging for humans to find a smooth transition between two ANPs (similar to color gradient or color progression), the presented GANs are capable of generating such a gradient in the adjective domain and find new ANPs that lies in this (subjective) transition. As result, GANs offer a more quantified interpretation for this subjective progression and an explainability of the underlying latent space.
Dr. Damian Borth is the Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, the Principle Investigator of the NVIDIA AI Lab at the DFKI, and founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damians research focuses on large-scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams.




Aleksandr Chuklin - Software Engineer - Google Research Europe
Augmenting Search Quality Ratings with Logs Data
Aleksandr Chuklin - Google Research Europe
Augmenting Search Quality Ratings with Logs Data
When working on a search engine, we need to measure how our improvements contribute to user satisfaction. While human ratings are often used as a proxy for that, they are costly and do not always correlate well with the user-reported satisfaction. In this talk I present a machine learning model that allows us to leverage log data to build a more accurate rater-based quality metric. Combining log data—which is easily collectible at scale—with a limited-sized raters’ labels allows us to get an estimation of user satisfaction for previously unseen document layouts at no additional cost.
Aleksandr is based at Google Research Europe, Zurich Switzerland, where he is currently working on the problems related to search-powered digital assistants. Aleksandr has a PhD from University of Amsterdam, where his research was focused around modeling and understanding user behavior on complex search engine result pages. He has a number of publications in leading information retrieval conferences and journals, and co-authored a book and multiple tutorials on click models for web search. Prior to Google, Aleksandr worked on search-related problems in Applied Research group at Yandex.




Sven Behnke - Head of Autonomous Intelligent Systems Group - University of Bonn
Learning Semantic Environment Perception for Cognitive Robots
Sven Behnke - University of Bonn
Learning Semantic Environment Perception for Cognitive Robots
Robots need to perceive their environment to act in a goal-directed way. While mapping the environment geometry is a necessary prerequisite for many mobile robot applications, understanding the semantics of the environment will enable novel applications, which require more advanced cognitive abilities. In the talk, I will report on methods that we developed for learning tasks like the categorization of surfaces, the detection, recognition, and pose estimation of objects, and the transfer of manipulation skills to novel objects. By combining dense geometric modelling – which is based on registration of measurements and graph optimization – and semantic categorization – which is based on random forests, deep learning, and transfer learning – 3D semantic maps of the environment are built. Our team demonstrated the utility of semantic environment perception with cognitive robots in multiple challenging application domains, including domestic service, space exploration, search and rescue, and bin picking.
Prof. Dr. Sven Behnke is a full professor for Computer Science at University of Bonn, Germany, where he heads the Autonomous Intelligent Systems group. He has been investigating deep learning since 1997. In 1998, he proposed the Neural Abstraction Pyramid, hierarchical recurrent convolutional neural networks for image interpretation. He developed unsupervised methods for layer-by-layer learning of increasingly abstract image representations. The architecture was also trained in a supervised way to iteratively solve computer vision tasks, such as superresolution, image denoising, and face localization. In recent years, his deep learning research focused on learning object-class segmentation of images and semantic RGB-D perception.



COFFEE


Adam Grzywaczewski - Deep Learning Solution Architect - NVIDIA
Deep Learning Workload and its Impact on Hardware Requirements Throughout the AI Product Lifecycle
Adam Grzywaczewski - NVIDIA
Deep Learning Workload and its Impact on Hardware Requirements Throughout the AI Product Lifecycle
The goal of this talk is to review infrastructure challenges for supporting deep learning workloads, and the impact they have on the research and AI product development cycles. In particular, the talk will touch on three main areas: • The deep learning workflow and the hardware requirements for each stage of the product development process • Hardware requirements associated with deep learning model development • Current trends in deep learning and their impact on the future hardware requirements
Adam is an applied research scientist specializing in Machine Learning, with backgrounds in Deep Learning / System Architecture. He is currently a Deep Learning Solution Architect at NVIDIA where his primary responsibility is to support a wide range of customers in delivery of their deep learning solutions. In his previous role with Capgemini he was responsible for building up the U.K. government’s Machine Learning capabilities. He also worked in Jaguar Land Rover Research Centre and was responsible for a variety of internal and external projects, contributing to the ‘Self Learning Car’ portfolio, specifically.


RECOMMENDER SYSTEMS & NLP


Neal Lathia - Machine Learning Lead - Monzo
Bootstrapping a Destination Recommender System


Sotirios Fokeas - Data Scientist - Swisscom
Utilizing Sentence Embeddings to Extract High Coverage Key-Phrases
Sotirios Fokeas - Swisscom
Utilizing Sentence Embeddings to Extract High Coverage Key-Phrases
Extracting key-phrases is a Natural Language Processing task which has been studied for decades. What makes the task difficult is its intrinsic subjectivity and the fact that we cannot rely on labelled data. Traditionally, extracting key-phrases is tackled by graph based algorithms. This presentation aims to introduce an alternative technique, which leverages on recent research. Instead of using graph metrics, this novel technique relies on sentence embeddings to capture the main points of a document. A robust unsupervised technique, using compositional n-Gram features, is applied to form the embeddings. Finally, this method is incorporated into one of our core products with much success.
Sotirios Fokeas is a Machine Learning specialist working for Swisscom. He is located at the Innovation Park in Lausanne, where he remains at close contact with EPFL's research teams. Sotirios obtained his master's degree in Computer Science from EPFL with a focus on Machine Learning. Prior to Swisscom, he worked as a data scientist in the banking industry, where he developed a new approach for countering Money Laundering. Sotirios research is now focused on unsupervised techniques for analysing and extracting information from large volumes of textual data.



LUNCH
COMPUTER VISION


Appu Shaji - Head, Research & Development - EyeEm
Recording The Visual Mind: Understanding Aesthetics with Deep Learning
Appu Shaji - EyeEm
Recording The Visual Mind: Understanding Aesthetics with Deep Learning
With the rise of mobile cameras, the process of capturing good photos has been democratized - and this overload of content has created a challenge in search. One of the important aspects of photography is that every image communicates with a different audience in different form. This talk will address how we use computer vision techniques at EyeEm measure visual aesthetics in photography -and beyond that- personalize the image search experience to find the photos you personally find beautiful.
Appu is the Head, Research & Development at EyeEm. His first company, sight.io, was acquired by EyeEm in 2014 and also held post-doctoral positions at EPFL working alongside Prof. Sabine Süsstrunk and Prof. Pascal Fua. In 2009, Appu obtained his Ph.D. from IIT Bombay, where he was awarded best thesis from Computer Science Dept. He was also selected as one of the most promising 20 entrepreneurs of Switzerland in 2013. His research has appeared in top computer vision journals and conferences such as TPAMI, CVPR, and ACM Multimedia etc.




Marcus Liwicki - Professor - University of Kaiserslautern
Deep Learning Through Space and Time for Document Image Analysis
Marcus Liwicki - University of Kaiserslautern
Deep Learning through Space and Time for Document Image Analysis
Document Analysis and Text processing are essential in our daily life for gather and access knowledge. Starting with the successful adaption of Long Short-Term Memories to Handwriting Recognition, very recent deep learning trends have revolutionized the Document Analysis field in all areas. This presentation gives an overview of the most successful methods in various areas and presents a framework for detection and segmentation of textual information from text-documents, natural, and born-digital (computer generated) images. Finally, the practicability is demonstrated by showing the application of such methods for business form processing in the health-care domain.
Marcus Liwicki, head of the MindGarage, is an apl.-professor in the University of Kaiserslautern and a senior assistant in the University of Fribourg. His research interests include machine learning, pattern recognition, artificial intelligence, human computer interaction, digital humanities, knowledge management, ubiquitous intuitive input devices, document analysis, and graph matching. In 2015, at the young age of 32, he received the ICDAR young investigator award, a bi-annual award acknowledging outstanding achievements of in pattern recognition for researchers up to the age of 40. He has more than 200 publications, including more than 20 journal papers (h-index 25).


NEURAL NETWORKS


Vahid Moosavi - Postdoctoral Researcher - ETH Zurich
Urban Modelling with Big Data and Machine Learning
Vahid Moosavi - ETH Zurich
Urban Modelling with Big Data and Machine Learning
Machine Learning and Big Data together offer a universal way of looking at the world phenomena, which is radically different than the classical expert based disciplinary research. This new approach of computational modelling has inverted the classical notion of expertise from “having the answers to the known questions” to “learning to ask good questions”, where the answers can always be found with an appropriate level of modelling skills. In this regard, I will show the results of some of our ongoing data driven projects such as urban morphology, real estate market, urban air pollution and urban water flow modelling.
Previously trained and practiced as systems engineer, currently Vahid Moosavi is a senior researcher at the chair for Computer Aided Architectural Design (CAAD), ETH Zurich. In his PhD he was focused on theories of computational urban modelling and issues of “representation” and “idealisation”. Parallel to research and teaching “Data Driven Modelling” to graduate architectural design students at ETH, he has been conducting several applied machine-learning projects such as urban traffic dynamics, urban design, air pollution modelling, networked economy and systemic risk, real estate analysis and recently on scalable emulations of urban water flow.




Ingo Waldmann - Senior Research Scientist - UCL
Characterising Extrasolar Planets with Deep Learning
Ingo Waldmann - UCL
Characterising extrasolar planets with deep learning
The discovery of extrasolar planets - i.e. planets orbiting other stars - has fundamentally transformed our understanding of planets, solar systems and our place in the galaxy. With over 3500 systems discovered to date, characterising these alien worlds is rapidly becoming a big data problem. Future surveys with ground and space based telescopes will simply provide too much data to be analysed in more classical ways. Here I will present how deep learning can be used to rapidly characterise the chemistry and prevalent weather patterns of these extrasolar planets and put our own solar system in the grander galactic context.
Dr. Ingo Waldmann is a senior research scientist at the University College London. He obtained his PhD in astrophysics in 2012 working on blind source-separation algorithms applied to observations of extrasolar planets with the Hubble and Spitzer space-telescopes. He has since specialised in the modelling of non-linear Bayesian inverse problems and deep learning applied to atmospheric physics of extrasolar planets and solar system objects. He is the data analysis lead of the European Space Agency ARIEL mission and the UK-led Twinkle space-mission.



COFFEE
BIG DATA & THE HUMAN FACTOR


Tijmen Blankevoort - Co-founder & CTO - Scyfer
Human in the Loop / Active Learning AI
Tijmen Blankevoort - Scyfer
The Future of AI: Active Learning
The real potential of Artificial Intelligence, is making everyone’s home and work environments smart. Software for work, home and leisure will know greater intelligence, changing itself to your specific person and purpose. Think of medical software learning from doctors, steel inspection software learning from operators and home robots learning from you. The only way to democratize AI is to enable anyone to teach an AI to perform a task. In this presentation, Tijmen Blankevoort, CTO of Scyfer, explains this philosophy, how we should change focus in AI to reach this goal, and ramifications for our future.
Tijmen Blankevoort is co-founder and CTO of Scyfer, a spin-off company of the University of Amsterdam specialized in bringing Artificial Intelligence to business. He advises multinationals and other companies in the Netherlands on Machine Learning, Artificial Intelligence and its practical applications Tijmen can often be found on stage presenting about the newest developments in AI in an enthusiastic and engaging fashion.




Mihai Rotaru - Head of Research and Development - Textkernel
Machine Intelligence for Better Matching People & Jobs
Mihai Rotaru - Textkernel
Machine Intelligence for Matching People and Jobs
For many people, their job is no longer just a source of income: it is an avenue for personal growth and accomplishments. This puts additional pressure both on the job seeker and on hiring managers or HR professionals. In this talk I will briefly discuss how Machine Learning and Deep Learning are revolutionizing the tooling for matching people and jobs. On the document understanding side, I will show how LSTM neural networks are improving the quality and robustness of Textkernel’s resumes and job ads parsing product. In addition, I will present our recent efforts in building a language-independent resume parsing system akin to Google’s universal machine translation system. I will also discuss how Siamese architectures can be used to learn similarities between job titles (e.g. “java developer” vs “java programmer”). On the searching and matching side, I will describe how relevance can be improved and customized via Learning to Rank.
Mihai Rotaru is the Head of R&D at Textkernel where he is responsible for the research agenda and for coordinating the joint research efforts with the parent company, CareerBuilder. In this role he is guiding 3 teams: the Document Understanding team which builds the resume and job parsing product, the Search R&D team which is improving the Search & Match product and the Ontology team which builds the HR domain knowledge graph. Originally from Romania, he joined Textkernel in 2008 after obtaining a PhD degree in Computer Science at University of Pittsburgh, USA. Among others, he is interested in Machine Learning, NLP, Deep Learning and how these new technologies can be applied in industry.



PANEL: What are the Main Challenges and Opportunities of Investing in AI?
Julius Rüssmann - Earlybird Venture Capital
Julius Ruessmann works as an Investment Analyst with Earlybird Venture Capital in Munich. At Earlybird, Julius focuses on the Mobility Technology Sector, Industrial Technology, Big data and Analytics. Julius has gained experience in the Investment Banking (Mergers & Acquisition) industry as well as in the Private Equity Industry. Through his studies at EBS University and FGV São Paulo he focused on Finance, Strategy and Management and graduated with Bachelor degree.


Carlos Eduardo Espinal - Seedcamp
Carlos Eduardo Espinal is a Partner at Seedcamp, Europe’s first seed fund, identifying and investing early in world-class founders building valuable, global businesses using disruptive technology. Seedcamp has invested in over 240 companies since launch in 2007 and Carlos is an expert in helping startups set fundraising milestones and find product-market-fit.
Prior to Seedcamp, Carlos was an Associate at Doughty Hanson Technology Ventures and an engineer for the Advanced Communications Technologies group of The New York Stock Exchange (SIAC). Carlos is an active podcaster and hosts the applauded Seedcamp podcast 'This Much I Know' as well as a published author, with his debut book 'The Fundraising Fieldguide' published in 2015.


Daniel Gebler - Picnic
From 1-click buying to 0-click fulfillment
First, we will demonstrate how our fleet of real-time connected vehicles, smart planning algorithms, precise monitoring tools and predictive distribution models create an unprecedented distribution logistic system for groceries. Then we provide a look behind the scenes of our smart planning algorithm that broke world records of the famous traveling salesman problem (TSP). Finally, we deliver insights into the architecture and design of our distribution eco-system (planning, monitoring, execution, control components) and explain how we made them ready for autonomous distribution and self-driving vehicles.
Daniel Gebler is CTO of Picnic, the world’s fastest growing online supermarket that makes grocery shopping simple, fun, and affordable for everyone. Previously, he was Director R&D of Fredhopper, responsible for the product and technology roadmap, and led engineering teams located in Amsterdam and Sofia. Daniel holds a PhD in Computer Science and an MBA. He is also a dad of two young kids, used to be a passionate skater and nowadays an enthusiastic climber!


Matthew Bradley - Forward Partners
Matthew worked on trading floors at Lloyds and BarCap before seeing the light. Following an MBA at SDA Bocconi he founded a sunglasses business and was an angel-operator in a couple of science-led startups. At Forward Partners he invests in eCommerce, marketplace and Applied AI companies as well as adding value across the later stage portfolio.



CONVERSATION & DRINKS

REGISTRATION
STARTUP SESSION
Daniel Gebler - Picnic
ML for Online Supermarkets - From Convenient to Smart Shopping
Picnic is the world’s fastest growing online supermarket that makes grocery shopping simple, fun, and affordable for everyone. We will show you how we transformed an already convenient shopping experience into a delightful ultra-fast shopping blitz-stop. In this talk we provide a view behind the scenes of our deep-learning based behavioral analytics and prediction engine. We will talk you through our ups-and-downs of product, category and promotional recommendations of FMCGs and do not shy away from demoing also the failures. Now we are able to predict with >95% likelihood the top 12 articles of the next order of each of our customers.
Daniel Gebler is CTO of Picnic, the world’s fastest growing online supermarket that makes grocery shopping simple, fun, and affordable for everyone. Previously, he was Director R&D of Fredhopper, responsible for the product and technology roadmap, and led engineering teams located in Amsterdam and Sofia. Daniel holds a PhD in Computer Science and an MBA. He is also a dad of two young kids, used be a passionate skater and nowadays an enthusiastic climber!




Christopher Bonnett - Senior Machine Learning Researcher - alpha-i
Bayesian Deep Learning for Accurate Characterisation of Uncertainties in Time Series Analysis
Christopher Bonnett - alpha-i
Bayesian deep learning for accurate characterisation of uncertainties in time series analysis
At alpha-i we are developing deep learning models for accurate characterisation of uncertainties in time series analysis. We achieve this by combining deep learning methodologies with powerful Bayesian formalism. The alpha-i deep learning network is able not only to make forecasts from time series but also to associate each prediction with a confidence level, which is derived from the information about the model and the data available. One of the key aspect of this Bayesian deep learning methodology is its aversion to over-fitting obtained thanks to the robust probabilistic inference framework. We are also developing novel Bayesian inference methodologies to significantly boost the online performance of our machinery.
Christopher Bonnett has a Masters in Astronomy from the University of Leiden and a PhD in Cosmology from the University of Pierre et Marie Curie. He has 6 years of post-doctoral experience as a key member of several large international collaborations measuring the accelerated expansion of the universe. He has extensive experience in applying deep learning to inverse problems in astronomy. He attended the Insight data science fellowship program in NYC.




Laurens Hogeweg - Deep Learning Engineer - COSMONiO
The Challenges of Developing an Active Deep-Learning Platform
Laurens Hogeweg - COSMONiO
The Challenges of Developing an Active Deep-Learning Platform
Deep learning is usually linked to Big Data. However, there are several scientific, engineering and medical imaging problems with limited data available (e.g. rare medical conditions). Can Deep Learning prove a useful tool in such cases? COSMONiO is designing NOUS, an active deep learning platform that aims to make high-accuracy predictions using significantly smaller training datasets. NOUS aims to allow experts from any field to train neural networks without any prior experience. We will discuss the main challenges and how we address them.
Dr. Laurens Hogeweg completed his PhD on medical image processing using machine learning in 2013 at the Radboud University Nijmegen in the Netherlands after having acquired MSc degrees in both medicine and biomedical technology from the University of Groningen. After his PhD he went to industry and developed cloud-based solutions for processing of large image datasets. In 2016 he joined COSMONiO as a research scientist on the topic of deep learning for image processing. His research interest is in the area of learning from small data.




Ben Scott-Robinson - Founder - The Small Robot Company
Three Small Robots and Their Dream to Feed the World
Ben Scott-Robinson - The Small Robot Company
Three Small Robots and their dream to feed the world.
Food production needs to increase by 70% in the next 30 years, but current systems have no chance of achieving that without catastrophic environmental damage. Meet three small farming robots called Tom, Dick and Harry, and the company building the world’s first Farming as a Service ecosystem.
Ben is the founder of The Small Robot Company, a startup specialising in robotics and AI. Previously he ran Ordnance Survey's internal agency creating beautiful products and campaigns with the world's best maps. This includes the award winning Resilience Direct product for the Cabinet Office, the Momo Commended OS Locate mobile app, and now OS Maps - the integrated leisure mapping application for exploring the Great British outdoors.



COFFEE
APPLICATIONS OF MACHINE INTELLIGENCE: COSMETICS & FINANCE


Panagiotis-Alexandros Bokaris - Augmented Reality Engineer - L'Oréal
Machine Learning and Augmented Reality for Beauty Products
Panagiotis-Alexandros Bokaris - L'Oréal
Beauty Personalization in Augmented Reality and Connected Devices
At the core of L’Oréal’s vision is the personalization of beauty in order to address the demands of the extremely diverse populations. This talk will focus on the usage of machine learning in augmented reality applications and connected devices that allow us not only to personalize our final beauty products but also the user experience. Machine learning has a dual role in our tasks through the exploitation of the huge amount of available data and the recent advances in computer vision that helped the emergence of augmented reality.
Panagiotis-Alexandros is an augmented reality engineer at L’Oréal working on AR solutions for beauty personalization in the group of Applied Optics and Algorithms. In 2016, he obtained his PhD in computer science from the University of Paris-Saclay, where he focused on video-projected augmented reality. He followed the EIT Digital doctoral programme on ICT Innovation during which he performed a part of his PhD research at the University College London. He holds the Erasmus Mundus Master Degree CIMET and a Diploma in Engineering from the University of Patras




Dor Kedem - Data Scientist - ING Nederland
Meeting Its Potential: A Cost-Sensitive Approach for Resource Allocation in Virtual Machines
Dor Kedem - ING Nederland
Meeting Its Potential: A Cost-Sensitive Approach for Resource Allocation in Virtual Machines
Throughout the recent years, ING has made a shift from physical servers to virtual machines cluster warehouses for its IT units and use cases. While this transition has contributed to ING’s development teams in providing agility in requirements and elasticity in resource allocations, the potential for cost reduction on infrastructure spending has not fully been realized. Many virtual machines have not been shifting their resource allocation actively with their utilization pattern, which resulted that a large portion of over 60M EUR spent yearly for ING-DBNL’s infrastructure goes to idle computing infrastructure. Dor will discuss the factors teams evaluate when faced with a cost reduction request and discuss his analysis and modelling that enables teams make better decisions on their infrastructure setup.
Dor has over a decade of experience developing big data products for security industries, financial markets and banking industries. He has earned his masters’ degree as a McDonnell leadership scholarship recipient, working in the machine learning research group in Washington University. His work on metric learning and cost-sensitive learning has earned him publications in NIPS, AISTATS and a monetary prize in Cha-Learn competitions. As a data scientist at ING domestic banking, he is involved with multiple projects modelling consumer and market behavior and optimizing virtual environments. He is currently completing an MBA on a Big Data Track from Amsterdam University.


APPLICATIONS OF MACHINE INTELLIGENCE: RETAIL


Roland Vollgraf - Research Lead - Zalando Research
Fashion DNA - a Structural Feature Mapping of Fashion Articles
Roland Vollgraf - Zalando Research
Fashion DNA - a Structural Feature Mapping of Fashion Articles
The universe of fashion articles is a heterogenous set of items with most different individual properties. For this set, a meaningful structure needs to be defined. It seems natural to define it in terms of similarity of items: every item then has it's well defined location in an abstract space, similar items being close by. Fashion DNA derives this structure from a priori available information about fashion products. This comprises product images, textual descriptions, vendor product attributes or combinations thereof. Main building blocks are Deep Neural Networks which process all available information and create for every item a unique coordinate vector with the above mentioned property of encoding similarity. With the help of Fashion DNA articles can be identified (even hypothetical ones that don't yet exist), styles can be described formally, and order can be brought into the chaotic universe of fashion articles.
Roland is the Research Lead at Zalando Research and obtained his Ph.D. at the Technical University of Berlin in Machine Learning and Statistical Signal Processing. Roland was integral to the establishment of Zalando Research and has been with Zalando since 2013. He previously worked as Head of Research for GA Financial Solutions GmbH and conducted the development of asset risk models and quantitative trading strategies.



LUNCH


Peter Tegelaar - Chief Data Scientist - Catawiki
Augmenting Human Experts Using Deep Learning
Peter Tegelaar - Catawiki
Augmenting Human Experts Using Deep Learning
Deep learning is overtaking traditional machine learning techniques for several use cases in e-commerce. At the same time, it is opening up others that were not possible before. This talk will explore a handful of both types of use cases, such as the pricing of items with deep neural nets, classification of product images with transfer learning, modelling users as images to leverage convolutional neural nets (CNNs), generative adversarial networks (GANs) to generate pictures of watches, and more.
Peter Tegelaar is Chief Data Scientist at Catawiki, the fastest-growing tech company in the world in 2011-2015. There he built up the data science & engineering teams and oversees all machine learning activities. He has more than a decade of experience in Internet companies and financial institutions, including Chief Data Scientist at mobile advertising company LiquidM, quantitative analyst at Deutsche Bank, and being the first Dutch Y Combinator founder. He holds 4 degrees from the University of Amsterdam, in Mathematics,Law and two in Economics.


APPLICATIONS OF MACHINE INTELLIGENCE: ROBOTICS & SPORTS


Theo Gevers - Professor of Computer Vision - University of Amsterdam
Deep Learning for Automatic Object Recognition
Theo Gevers - University of Amsterdam
Deep Learning for Automatic Object Recognition
In this talk, I explain how deep learning is used to automatically recognize objects. Object recognition is important for different applications including robots (the ability of robots to naturally interact with the environment), autonomous driving (obstacle detection), and image search (finding images on the Internet). I will show how deep learning can be used for facial expression analysis including automatic emotion recognition, and age/gender estimation. From a live video stream (webcam), automatic emotion recognition is demonstrated in real-time. Finally, 3D face reconstruction is explained and a live demo is given.
Theo Gevers is a Full Professor of Computer Vision at the University of Amsterdam, The Netherlands and a part-time Full Professor at the Computer Vision Center (UAB), Barcelona, Spain. He was granted a VICI-award (for excellent researchers) from the Dutch Organisation for Scientific Research. He is the founder of Sightcorp and 3DUniversum (spin-off's of the University of Amsterdam) and Visual Tagging Services (spin-off of the Computer Vision Center, Barcelona). His main research interests are in the fundamentals of content-based image retrieval, colour image processing and computer vision specifically in the theoretical foundation of geometric and photometric invariants.




Laith Alkurdi - Senior Machine Learning Engineer - Freeletics
A Bayesian Approach Towards Coaching A.I.
Laith Alkurdi - Freeletics
A Bayesian approach towards Coaching A.I.
The Freeletics coaching A.I., is envisioned to be an empathetic, understanding coach that is capable of measuring each user’s capability, motivation and preferences. Achieving this dictates describing the available workouts and exercises in a space that users can also be mapped into. Furthermore, this requires building a probabilistic user model that describes every athlete as a unit and in relation to the complete population. In this talk, a high-level description on how the Coaching A.I. makes use of the multiple interaction points within the Freeletics ecosystems and how it leverages itself to be each athlete's personal coach.
Laith is the Senior Machine Learning Engineer at Freeletics, he is responsible for the research, development and implementation of the coaching A.I. of the Freeletics coach. Working towards his Dr-Ing degree in Cognitive Robotics from the Technical University of Munich, Laith’s research focus has been towards modeling cognitive intention recognition capabilities in robots. He previously worked at the Bristol Robotics Laboratory as a robotics research associate focusing on the field of Intention, plan and action understanding.


CYBER SECURITY

PANEL: How to Respond to Growing Threats with Cyber Security
Colin Williams - University of Warwick
Colin has twenty years of experience in enterprise IT, IA and cyber security. As a director of SBL he leads the business development strategy of a market leading provider of vendor independent cyber security solutions to public sector. In his academic practice he is developing work around the human, intellectual, cultural, societal and historical context of computing and Cybernetics. He is currently delivering a lecture series on the human story of Cybernetics. He is editor in chief of “CyberTalk” and new journal for the promotion and development of fresh and interdisciplinary thinking about cyber and the human relationships with computers.


Tanya Harris - Harrman Cyber
Tanya Harris is the CEO of Harrman Cyber, a UK company that provides a comprehensive protection against the insider threat of Cyber Security. Insider Threat is especially important for the finance and health sector as they are at the greatest risk of being hacked, as well as Government departments and organisations that have valuable IP. She is the founder of ICOM4 a comprehensive workforce development platform that identifies leadership and team development gaps, that when filled heighten the engagement levels of the workforce. Tanya holds a Degree in Human Resource Performance Psychology and successfully completed courses in International Cyber Conflicts and Cyber Security and Mobility.


Ian Bryant - Trustworthy Software Foundation
Professor Ian Bryant is a Professional Engineer by background, and serves in a number of capacity including acting in the overlapping roles as Standards Development Advisor for TSFdn, and as the Chair of the aligned British Standards Institution (BSI) Expert Committee of Trustworthy Systems (ICT/00-/09). He is also Adjunct Faculty at the Cyber Security Centre or the University of Warwick, a Visiting Lecturer at a number of other Universities, and a frequently requested at governmental, national and international conferences.

Eelco Stofbergen - CGI
Cyber Security and IT Risk leader with 15+ years of industry experience in cyber security and information risk management. I am Director Consulting for Cyber Security at CGI, leading the development and delivery of innovative cyber security services and solutions. I support clients in strategic security challenges. My topics of interest include security strategy and governance, risk management, security intelligence, threat analysis, security operations, incident response and crisis management.



END OF SUMMIT