ENTERPRISE AI SUMMIT PREVIOUS SPEAKERS

Deep learning for retail warehouse operations

Recent advances in deep learning have enabled research and industry to master many challenges in computer vision and natural language processing that were out of reach until just a few years ago. Yet these challenges represent only the tip of the iceberg of what is possible. In this talk, I will demonstrate how we have used deep neural networks in to solve a special case of the travelling salesman problem and in turn steer operations at Zalando’s fashion warehouses. Further, I will present a few tips and tricks on getting GPU enabled neural networks running with minimal technical overhead.

Calvin Seward is a Resarch Scientist for Zalando's Research Lab, working mainly on unsupervised and semi-supervised computer vision problems including classification, localization and semantic segmentation. The main line of attack for these problems is deep neural networks written in the Tensorflow framework and trained on GPU clusters. At the same time, he is involved in applying lessons from GPU-driven HPC computing and cutting edge Machine Learning to other fields of the Zalando universe. Past projects have included sizing solutions for online retail, warehouse management and recommendation systems.

What's the Future of Autonomous Driving?

Muzahid received his MSc from the University of Stuttgart in 2015 and since has worked a variety of roles including Technology and Product Manager, AI Product Innovation at Dassault Systemes & AI Project/Product Manager at AUDI AG. He is currently working on his MSc from the University of Cambridge.

Considerations When Building Your ML Teams

Because I believe in the untapped potential in each and every one of us, I develop AI products to automate intelligent processes, offering people more space to unfold their greatest potential. With every AI solution I deploy, someone is given the opportunity to step up to their true potential.

Deena is a Lead Data Scientist at Bayer. Having received her MsC in Life Science Informatics at the University of Bonn, Deena worked as a Teaching and Research Assistant before joining Recogizer as a Data Scientist.

Conversational AI Considerations

Dr. Carmen Martinez is a Conversation Analyst and Ethnographer of Communication working in Conversational Artificial Intelligence at FlixBus. As an expert in human-to-human conversation, she contributes to a cross-disciplinary team by automating customer service interactions, modelling both text- and voice-based human-to-machine conversations, and developing visual solutions for graphical and multimodal conversational agents. Carmen holds a PhD in Conversation Analysis and is the author of “Conversar en español: un enfoque desde el Análisis de la Conversación” published by Peter Lang Berlin.

Building Blocks for Scalable AI Platforms

Programming with data since 1996. Focused on solving data driven problems. Generating insights by squeezing data sources. Loves IT and tools in the field of data analytics and data science.

Supporting AI in Enterprise

Albin has 19 years+ experience in financial institutions, managing a diverse data-driven teams, enjoy creating best practices, implementing new tools and influencing cross-functional groups

Ethical, Legal & Cultural Considerations When Building ML Profiles

An accomplished Data & Cloud Architect and Product Leader with over a decade of hands-on experience with large-scale distributed systems, Big Data, and Cloud technologies. As a result-oriented thought leader, I have a track record of strategizing, designing multiple Advanced Analytics platforms, and building teams that drive value from data investment that matters.

AI Platform Development

Tobias is currently responsible for the product "Big Data Analytics", part of the domain "Data and Artificial Intelligence Platforms" at the BMW Group which includes platforms for processing large amounts of data and applications in the area of vehicle engineering and autonomous driving. Prior to BMW he led all global efforts related to Big Data, Personalization and Digital Analytics at PAYBACK which is part of the American Express group.

Assessing Real-World ML Performance

Profound experience in designing, negotiating and evaluating KPIs, CSFs, SLAs; strategic roadmap and product design/ development; project acquisition, tenders, market analysis & business development, strategic positioning & benchmarking, Telco/IT solutions architectural design & pilot realization, business case development and realization incl. CAPEX&OPEX optimization

Additionally Distinguished for: - International background (incl. full command of 7 languages, successful lead/completion of large multi-national projects), intercultural competence, and very strong communication/presentation skills - Community/consortium building and stakeholder management capabilities & having a powerful network of trusted partners and contacts throughout the IT/Telco ecosystem - High level of technical expertise in IT/Telco industry processes and systems and in solving complex problems

Build and Maintain Accurate Customer Profiles

Sarah is the Principle Data ML Lead for Artsy and a Lecturer at the Technische Universitat, Berlin. She is an experienced Data Scientist with a demonstrated history of working in art, marketing and other industries.

Biomedical Engineer with strong focus in Computer Science and Machine Learning after having successfully completed a bachelor in Telecommunication Engineering Bachelor. Extensive knowledge in:

Medical Imaging and Bio-Signal Processing: MRI, EEG, ECG, segmentation, tracking, registration, classification, preprocessing, image analysis.

Artificial intelligence: Pattern Recognition (PR), Deep Learning (DL), Computer Vision (CV), Machine Learning (ML), Convolutional Neural Networks (CNN), Transfer Learning, Classification, Regression, Supervised Learning Object Detection

Augmenting Data Analytics in Manufacturing with a Digital Assistant: Insights from the COALA project

The shortage of skilled workers is a barrier to applying data analytics. Augmented analytics is an approach to lower it by using machine learning to automate related activities and natural language applications to assist less-skilled employees. This talk will present a related case study from the white goods industry. It focuses on a quality test lab at the end of a production line where workers use a digital assistant prototype to interact with descriptive and predictive data analytics

Stefan works as a research associate at the institute for production and logistics in Bremen (BIBA), Germany. His background is in industrial and mechanical engineering. Over the last ten years, he acquired expertise in information management and computer science, while working in various international and inter-disciplinary research projects. Stefan currently coordinates the Horizon 2020 research project COALA. Its goal is to build and test trustworthy, voice-enabled digital assistants for manufacturing

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