Deep Learning Summit schedule

Representation Learning on Graphs: Applications and Innovations across the Pharma value chain.

Low dimensional representation learning of data that are naturally graph structured have gained popularity over recent years with varied applications. Graph neural networks, by design, are capable of integrating node informations, topological structure and relationship between data elements leading to increased accuracy of representing data from non-euclidian domains. Traditional deep learning representation typically struggles with tasks which require representing complex interdependence between data objects due to their inherent design of projecting embeddings in Euclidian space leading to the aggregation of relationships. GNNs however come in various flavors, graph convolution, temporal graphs, auto-encoders, transformers, spatio-temporal and others, which make GNNs quite versatile to tackle a diversity of use-cases frequently encountered in a large industrial setting. Here, we showcase our experiments using GNNs for low dimensional representation learning and using them to tackle various use cases across the pharma value chain. We compare and contrast these methods with more traditional ones and also highlight how such applications lead to generation of insights, and predictions with direct application towards various use cases.

Srayanta is a Data Scientist and computational biologist with 10 years research experience, having worked a diverse spectrum of problems including predictive modeling and operations research.

He has extensive experience in machine learning methods and is a specialist in stochastic simulations, deep learning and decision trees.

His roles have included leading his team towards end-to-end data science solutions, achieved strategic milestones and drove adoption

Self Supervised Learning For Unstructured Data

Walid is a Data Scientist at Airbus, and a techie turned AI/ML Engineer based in Germany. He brings a unique combination of technical expertise, active communication and natural critical thinking. He is currently working in three of the most critical areas of AI - Conversational, Computer Vision and Time Series Forecasting. He is also a Certified ScrumMaster® (CSM®)

Sheraz Ahmed is a Senior Researcher at Deutsches Forschungszentrum fur Kunstliche Intelligenz.

He has worked for a variety of different research institutes including the University of Western Australia, Osaka Prefecture University and Fraunhofer ITWM

Deep Learning Models for Building Trusted Relationships

Dzhuliana's primary focus and strengths are education and self-development which is how she ended up being a Co-founder and CTO at OneUpOneDown - a highly scalable AI mentor matching platform and framework that connects women worldwide with their perfect match

What to Visualize for Training Reinforcement Learning Agents

Leveraging reinforcement learning over continuous action spaces for autonomous system control. Leading Moonshots program for innovation bringing step change in value created at enterprise level using cutting edge AI technologies like NLP, Computer Vision and Deep Learning.

Expertise areas: Reinforcement Learning , GANs, Computer Vision, Natural Language Processing, Deploying ML Models, Amazon Web Services - S3, Lambda, EC2, Sagemaker, Azure ML

Patented technologies(applied) include : Staggered Pattern(CATIA), Semantic Painter (CATIA), Automatic Mate of Components using Machine Learning(Solidworks - filed), AI Driven Drawing Checker

Latest Research in Deep Learning

Aleksandra did her PhD in the area of complex networks with the goal of knowledge extraction by combining multiple data sources and diverse algorithms. She has passion in bioinformatics and improving health trough food and nutrition data. Currently she works as ML Engineer for the global food delivery service, Delivery Hero.

Getting to Most out of Vision Transformers

Arindam Ghosh is a Data Science Lead at Oviva, a healthcare company who combine personalised care from a healthcare professional with unique digital tools to manage longterm health plans. He previously was a post-doctoral researcher at the University of Trento.

Preparing your Data for DL

Christoph Spohr is the Lead Architect of Big Data Platforms at Volkswagen following roles at both EPAM Systems and DATEV eG.

Ethical, Legal & Cultural Considerations in Deep Learning

Özlem Gürses is Professor of Commercial Law at King’s College London. She specialises in insurance and reinsurance law. Özlem is the author of Reinsuring Clauses (Informa), Marine Insurance Law (Routledge), Insurance of Commercial Risks (Sweet and Maxwell), and The Compulsory Motor Vehicle Insurance (Informa) as well as numerous articles published on insurance and reinsurance related topics. Özlem sits in the British Insurance Law Association Committee and the Presidential Council of the International Insurance Law Association (AIDA). She is Vice-Chair of the Reinsurance Working Party of AIDA. Özlem teaches insurance and reinsurance law at King’s College London and abroad, including National University of Singapore, University of Hamburg and World Maritime University, Malmö

Prokopis is a product-minded Data Scientist who enjoys Economics, Finance, and Algorithms

Skills: data science, programming, product development, distributed systems Academic: statistics, machine learning, economics, game theory, social network analysis

Rosona is a Data Professional with 5 years of industry experience following an academic career in Mathematics culminating in a Max Planck research fellowship. Excels in collaborative teams with proactive independent contributors. Having worked with all parts of the ML life-cycle from requirements engineering to productionization, she is especially motivated by structural solutions to problems, by translating business potential to business value, getting promising prototypes effectively into production.

Fabian is interested in audio related research fields such as virtual acoustics, spatial audio, music information retrieval, digital signal processing and machine learning.

Deep Learning for Smart Living

Hendrik is a Professor of Information Technology at the University of Applied Sciences and Art, and an expert in the application of artificial intelligence methods in embedded systems.

Reinforcement Learning

Nima in the Team Lead for Machine Learning at Max Planck Institute and AI-practitioner and experienced Simulation Scientist with focus on Complex Systems

NLP Case Study: Munich Re

Roshan Amasa is the Lead Solution Architect of Data & AI at Munich Re, following previously working as a Data Science & Big Data Advisor for BMW Group.

NLP for Context Awareness

Bernhard Pflugfelder has 10+ experience in the in the fields of information retrieval, natural language processing (NLP), Big Data and AI. He worked across various businesses such as Information Services, Media and Automotive in very different setups and roles with startups, IT consultancy and industry companies.

After entering the Automotive with the Volkswagen Data:Lab, he is now already working over 5 years in Automotive. Currently, he is leading a NLP group in the BMW Group IT.

Bernhard's skills are quite diverse and focusing both technological and methodological solutions. He collected experience with Big Data, Advanced Analytics and Data Science as well as NLP and AI. He enjoys new challenges and is eager to learn more.

The most favorite area of Bernhard is NLP. Current development and dynamics in research and industry on NLP like for example Conversational AI or Neural Language Models are amazing and inspiring. Bringing those new technological and methodological opportunities into businesses is an important task of Bernhard in BMW Group.

Accelerated Distributed Model Training

Alisson is the Senior Big Data DevOps Engineer at Schaeffler. An IT Specialist with over 9 years of experience in Linux Environments and Development.

Real Time Supervised Anomaly Detection

Dr. Sergei Bobrovskyi is a Data Scientist within the Analytics Accelerator team of the Airbus Digital Transformation Office. His work focuses on applications of AI for anomaly detection in time series, spanning various use-cases across Airbus. Prior to Airbus he worked on automated fraud detection for one of the largest e-commerce companies in Germany. Before that he was engaged in various research related positions in the space industry.

Sergei holds a PhD in theoretical physics as well as a physics Diploma from the University of Hamburg. Besides physics he also studied philosophy with an emphasis on the philosophy of mind.