Prof. Dr. Edouard Hannezo, Assistant Professor at the Institute of Science and Technology (IST) Austria, is interested in understanding how cells "know" how to make the right decisions at the right time and at the right place during development and normal tissue homeostasis, as well as how these decisions are dysregulated during cancer initiation.

The promise of oceanic discovery has intrigued scientists and explorers, whether to study underwater ecology and climate change, or to uncover natural resources and historic secrets buried deep at archaeological sites. To meet the challenge of accessing oceanic depths, Stanford University, working with KAUST's Red Sea Research Center and MEKA Robotics, developed Ocean One, a bimanual force-controlled humanoid robot that affords immediate and intuitive haptic interaction in oceanic environments.

The discussion focuses on the development of Ocean One, a bimanual humanoid robotic diver that brings intuitive haptic physical interaction to oceanic environments. The robot was deployed during an expedition in the Mediterranean to Louis XIV's flagship Lune, lying off the coast of Toulon at 91 meters. Ocean One's demonstrated ability to distance humans physically from dangerous and unreachable spaces, while connecting their skills, intuition, and experience to the task, promises to fundamentally alter remote work. Robotic avatars will search for and acquire materials, build infrastructure, and perform disaster-prevention and recovery operations - be it deep in oceans and mines, on mountain tops, or in space.

In this talk we will present the various optimization problems encountered in smart grids from the production, transmission and distribution of energy as well as the demand side management in smart homes and the pricing of energy. The optimization opportunities are highlighted for metaheuristics, multi-objective optimization, optimization under uncertainty, optimization-simulation, optimization-machine learning and multi-level optimization.

Conditions which affect our mental health often affect the way we use language; and treatment often involves linguistic interaction. This talk will present work on three related projects investigating the use of computational natural language processing (NLP) to help understand and improve diagnosis and treatment for such conditions. We will look at clinical dialogue between patient and doctor or therapist, in cases involving schizophrenia, depression and dementia; in each case, we find that diagnostic information and/or treatment outcomes are related to observable features of a patient's language and interaction with their conversational partner. We discuss the nature of these phenomena and the suitability and accuracy of NLP techniques for detecting them automatically.

Conditions which affect our mental health often affect the way we use language; and treatment often involves linguistic interaction. In his talk Dr Matthew Purver from Queen Mary University of London presents work on three related projects investigating the use of computational natural language processing (NLP) to help understand and improve diagnosis and treatment for such conditions.

RavenPack's prestigious annual event has experienced growing interest, with attendance exceeding 260 buy-side professionals. Word on the street is RavenPack's research symposium is a "must attend event" for quantitative investors and financial professionals that are serious about Big Data. An excellent set of senior finance professionals shared their latest research and experience with big data and machine learning.

The growth of the Web is a success story that has spurred much research in knowledge discovery and data mining. Data mining over Web domains that are unusual is an even harder problem. There are several factors that make a domain unusual. In particular, such domains have significant long tails and exhibit concept drift, and are characterized by high levels of heterogeneity. Notable examples of unusual Web domains include both illicit domains, such as human trafficking advertising, illegal weapons sales, counterfeit goods transactions, patent trolling and cyberattacks, and also non-illicit domains such as humanitarian and disaster relief.

Over the last decade, crowdsourcing has been used to harness the power of human computation to solve tasks that are notoriously difficult to solve with computers alone, such as determining whether or not an image contains a tree, rating the relevance of a website, or verifying the phone number of a business. The machine learning and natural language processing communities were early to embrace crowdsourcing as a tool for quickly and inexpensively obtaining the vast quantities of labeled data needed to train systems. Once this data is collected, it can be handed off to algorithms that learn to make autonomous predictions or actions. Usually this handoff is where interaction with the crowd ends. The crowd provides the data, but the ultimate goal is to eventually take humans out of the loop.

Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users' and items' attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user's attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering.