05 - 06 October 2022

Deep Learning Summit Deep Learning Summit schedule

Berlin AI Summit



Download PDF
  • 08:00

    REGISTRATION & LIGHT BREAKFAST

  • 09:00

    WELCOME NOTE & OPENING REMARKS

  • LATEST ADVANCEMENTS IN DL

  • 09:15

    What Have We Learnt About Deep Learning in 2022?

  • 09:35
    Walid Yassine

    Self-Supervised Learning for Unstructured Data

    Walid Yassine - Info & Comm Sys. Development - Airbus

    Down arrow blue

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

    Teamwork and organisation are something I excel in; demonstrated in active team sports and university assignments. I have good communication skills that I am capable of applying in any environment, and the ability to manage a busy workload with a high level of enthusiasm.

    Creative, highly organised candidate, an expert in design, development, and high-performance technology solutions. Demonstrated success in problem-solving and a proven track record with strong attention to detail.

  • 09:55

    Meeting the Expected Computational Requirements for Edge Intelligence

  • 10:15

    COFFEE BREAK

  • DEEP LEARNING LANDSCAPES

  • 10:50

    Human & Multi-Agent Collaboration

  • 11:10
    Ameya Divekar

    What to Visualize for Training Reinforcement Learning Agents

    Ameya Divekar - Principle Data Scientist - Michelin

    Down arrow blue

    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

    Linkedin
  • 11:30

    Improving Data Quality with Automated AI

  • 11:50
    Aleksandra Kovachev

    Latest Research in Deep Learning

    Aleksandra Kovachev - Data Science Manager - Delivery Hero

    Linkedin
  • 12:30

    LUNCH

  • MODEL ARCHITECTURE

  • 13:30
    Arindam Ghosh

    Getting the Most out of Vision Transformers

    Arindam Ghosh - Data Science Team Lead Oviva -

  • 13:50
    Sergei Bobrovskyi

    Implementing Real Time Anomaly Detection

    Sergei Bobrovskyi - Data Scientist - Airbus

    Down arrow blue

    Industrial Time Series Anomaly Detection

    Time series are ubiquitous in aerospace engineering and their processing can enable the generation of large business value. Manufacturing tools and more importantly, aerospace assets themselves produce large amounts of sensor signals, which cannot be analyzed and even captured in its totality by humans. In this talk we focus on automatic anomaly detection tasks for aircraft sensors. We assess the industrial viability of various semi-supervised anomaly detection systems based on Deep Learning for automatic discovery of point, contextual and collective anomalies on large datasets with little prior knowledge. Moreover, we present the results of a challenge with the same goals hosted by Airbus on its AIGym co-innovation platform, engaging over 150 academic and industrial teams worldwide.

    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.

    Twitter Linkedin
  • 14:10
    Christoph Spohr

    Preparing your Data for DL

    Christoph Spohr - Lead Architect - Volkswagen AG

  • 14:30

    Finding the What and the Why with Neuro-Symbolic Learning Algorithms.

  • 14:50

    COFFEE BREAK

  • DL CONSIDERATIONS

  • 15:15

    ExplainabIlity Considerations for AI

  • 15:35
    Özlem Gürses

    Ethical, Legal & Cultural Considerations in Deep Learning

    Özlem Gürses - Professor - Kings College London

    Down arrow blue

    Insurtech and the Principles of Insurance Contract Law

    Insurance law and practice have developed in parallel with the ways by which the insurance business has been conducted. Moreover, the ever-changing circumstances in which the subject matter insured may be in, inevitably shape how insurance contracts operate and how the principles of law are developed and adapted to respond to such variations. For instance, the duty of good faith applied since late seventeenth century because of the information asymmetry between the assured and insurer with regards to the risk and the subject matter insured. This asymmetry now appears to have been reversed that insurers have more information than they may need about the assured. Moreover, through telematics especially, they have more power than before to observe the risk throughout the currency of the policy. This talk will discuss if the general legal principles of insurance law have to change in parallel with the advances that the insurance industry has been experiencing in conducting their business?

    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

  • 15:55

    Harness the Power of Unstructured Data for Deep Learning

  • 16:15

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

  • 17:00

    NETWORKING RECEPTION

  • 18:00

    END OF DAY ONE

  • 08:00

    REGISTRATION & LIGHT BREAKFAST

  • 09:00

    WELCOME NOTE & OPENING REMARKS

  • APPLICATIONS IN DEEP LEARNING

  • 09:10

    Finding the What and the Why with Neuro-symbolic Learning Algorithms

  • 09:30

    Creating World Class Applications with Convolutional Neural Networks

  • 09:45

    Smart Living

  • 10:05

    GANs for Accuracy

  • 10:25

    COFFEE BREAK

  • 11:00

    NLP Case Study

  • 11:20
    Nima Siboni

    Reinforcement Learning

    Nima Siboni - Team Lead Machine Learning - Max Planck Institute

    Down arrow blue

    AI-practitioner and experienced Simulation Scientist with focus on Complex Systems

    Linkedin
  • NATURAL LANGUAGE PROCESSING

  • 11:40

    Post Pandemic NLP for a Touchless Society

  • 11:55
    Bernhard Pflugfelder

    NLP for Context Awareness

    Bernhard Pflugfelder - Head of Product AI - BMW

    Down arrow blue

    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.

  • 12:15

    LUNCH

  • TOOLS FOR DEEP LEARNING

  • 13:20
    Alisson Machado

    Accelerating Distributed Model Training

    Alisson Machado - Senior Big Data DevOps Engineer - Schaeffler

    Down arrow blue

    IT Specialist with over 9 years of experience in Linux Environments and Development.

    Working with DevOps since 2014, where I deployed my first Docker Server in the Cloud to provide a Linux Shell for the students of the company I worked for and from there we created a system called BeavOps, written in Python, storing data in MongoDB, responsible for enroll all students and provide them with a Bash, an Apache Server, a Gitlab repository, and a Jenkins login to create their own DevOps Pipelines.

    After that, I started doing consulting teaching how some Brazilian companies can create DevOps Pipelines and automate their processes using bash, python, jenkins, ansible, docker, openshift, kubernetes, puppets and public clouds like AWS.

    In the midst of all these projects, I also worked with virtualization using RHEV (RedHat Enterprise Virtualization Manager) and oVirt (OpenVirtualization), attaching it to an ISCSI storage server like FreeNAS and OpenFiler. Implemented GlusterFS for replicating phone call recordings for a telemarketing company and many other projects.

    I also managed the entire AWS infrastructure of a Startup, where we had Kubernetes clusters provisioned using Kops, initially monitored with DataDog, which was replaced by prometheus with cAdvisor, Grafana, ElasticSearch and Kibana to view the Logs and the APM that was installed together with the applications, in the same startup we created a report generation system using Kubernetes, Python, S3 and RedShift that optimized the generation of reports that took 22 hours to generate to 2 hours.

    I also have a blog in Portuguese with over 2,500 views per month, where I write about some of the technologies I work on.

    Linkedin
  • 13:40

    New Research into DL

  • 14:00

    User Centric AI-Development

  • 14:20

    Panel: The ROI of DL

  • 15:00

    END OF SUMMIT

Berlin AI Summit

Berlin AI Summit

05 - 06 October 2022

Get your ticket
This website uses cookies to ensure you get the best experience. Learn more