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  • 08:00

    REGISTRATION & LIGHT BREAKFAST

  • 09:00

    WELCOME NOTE & OPENING REMARKS

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  • LATEST ADVANCEMENTS IN DL

  • 09:15
    Srayanta Mukherjee

    What Have We Learnt About Deep Learning in 2022?

    Srayanta Mukherjee - Director - Data Science & AI - Novartis

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    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

  • 09:40
    Walid Yassine

    Self-Supervised Learning for Unstructured Data

    Walid Yassine - Info & Comm Sys. Development - Airbus

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    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®)

  • 10:05
    Sheraz Ahmed

    Research into DL

    Sheraz Ahmed - Senior Researcher - DFKI

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    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

  • 10:30

    COFFEE BREAK

  • DEEP LEARNING LANDSCAPES

  • 11:00
    Dzhuliana Nikolova

    Deep Learning Models for Building Trusted Relationships

    Dzhuliana Nikolova - Co-Founder and CTO - OneUpOneDown

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    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

  • 11:25
    Ameya Divekar-1

    What to Visualize for Training Reinforcement Learning Agents

    Ameya Divekar - Principle Data Scientist - Michelin

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    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

  • 11:50

    Improving Data Quality with Automated AI

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  • 12:15
    Aleksandra Kovachev

    Latest Research in Deep Learning

    Aleksandra Kovachev - Data Science Manager - Delivery Hero

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    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.

  • 12:40

    LUNCH

  • MODEL ARCHITECTURE

  • 13:40
    Arindam Ghosh

    Getting the Most out of Vision Transformers

    Arindam Ghosh - Data Science Team Lead - Oviva

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    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.

     

  • 14:05

    Implementing Real Time Anomaly Detection

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  • 14:30
    Christoph Spohr

    Preparing your Data for DL

    Christoph Spohr - Lead Architect - Volkswagen AG

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    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.

  • 14:55

    Case Study: ING

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  • 15:20

    COFFEE BREAK

  • DL CONSIDERATIONS

  • 15:50
    Özlem Gürses

    Ethical, Legal & Cultural Considerations in Deep Learning

    Özlem Gürses - Professor - Kings College London

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    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

  • 16:15

    Panel: What are the Deep Learning Trends you Should Be Aware of?

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  • Prokopsi

    PANELLIST

    Prokopis Gryllos - Senior Data Scientist - Shopify

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    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 Eldred

    PANELLIST

    Rosona Eldred - Machine Learning Engineer - BASF

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    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 Seipel-1

    PANELLIST

    Fabian Seipel - Deep Learning for Audio Event Detection - Technische Universitat Berlin

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

  • 17:00

    NETWORKING RECEPTION

  • 18:00

    END OF DAY ONE