Véronique Van Vlasselaer, PhD

Véronique is a Decision Scientist at SAS, and a true data science enthusiast. In her job, she passionately helps companies to envision and prepare for an AI-driven future, embrace the power of data science to support intelligent decisioning, and discover the real value in their data. Before she joined SAS, she graduated as Doctor in Business Economics at the KU Leuven (Belgium) with the department of Information Management and Decision Sciences under prof. dr. Bart Baesens. Her Ph.D. is oriented towards the development of fraud detection frameworks and solutions from a data science perspective. She is co-author of the book “Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection”.

First Aid Kit for Data Science: Keeping Machine Learning Alive (Demo Talk)

Matteo Mortari

Matteo Mortari is a Software Engineer at Red Hat, where he contributes in Drools development and support for the DMN standard. Matteo graduated from Engineering with focus on enterprise systems with a thesis involving rule engines which sparked his interests and influenced his professional career since. He believes there is a whole new range of unexplored applications for Expert Systems (AI) within the Corporate business; additionally, he believes defining the Business Rules on the BRMS system not only enables knowledge inference from raw data but, most importantly, helps to shorten the distance between experts and analysts, between developers and end-users, business stakeholders.

eXplainable Predictive Decisioning: Combine ML and Decision Management to Promote Trust on Automated Decision Making(Demo Talk)

Daniele Zonca

Matteo Mortari is a Software Engineer at Red Hat, where he contributes in Drools development and support for the DMN standard. Matteo graduated from Engineering with a focus on enterprise systems with a thesis involving rule engines which sparked his interests and influenced his professional career since. He believes there is a whole new range of unexplored applications for Expert Systems (AI) within the Corporate business; additionally, he believes defining the Business Rules on the BRMS system not only enables knowledge inference from raw data but, most importantly, helps to shorten the distance between experts and analysts, between developers and end-users, business stakeholders.

eXplainable Predictive Decisioning: Combine ML and Decision Management to Promote Trust on Automated Decision Making(Demo Talk)

Christopher Connelly

Christopher Connelly has a Masters in Exercise Science and Nutrition from Sacred Heart University. He has been working with NC State Strength and Conditioning for over two years where he has built a platform for athlete data monitoring in python. He has formerly worked with the NC Courage professional soccer team doing GPS and data monitoring. His graduate work was primarily in biomechanics and movement analysis where he did data analysis with 3D movement testing and electromyography. He has his CSCS certification with the NSCA as well as level 1 coach with USA weightlifting. Chris took part in the HPCC Systems summer internship program working on a project for cleaning and analysis of collegiate soccer GPS data in HPCC Systems.

Sports Analytics – Leveraging Open Source Technology to Improve Athlete Performance(Demo Talk)

Yamini Rao

Yamini Rao is a Developer Advocate for IBM, focusing on Data Science and AI. She compiles developer scenarios, workshops and training material based on IBM Cloud technologies. She works with local developer communities, presenting workshops and organising meetups and conferences.
She has a background in computer science and has worked extensively as an Implementation Engineer for various IBM Analytical tools.

Build and Deploy Custom AI Predictive Models(Demo Talk)

Nick Patience

Nick Patience is 451 Research’s lead analyst for AI and machine learning, an area he has been researching since 2001. He is part of the company’s Data, AI & Analytics research channel but also works across the entire research team to uncover and understand use cases for machine learning. He is a co-founder of 451 Research and rejoined the team in 2015 after almost three years running product marketing at machine learning-driven eDiscovery and information governance software company Recommind (now part of OpenText).

Is Infrastructure Holding Back Adoption of AI at Scale? (Demo Talk)

Soo Yang

Soo has been working with Computer Vision, Machine Learning Engineers, and Research Scientists, across industries to create training datasets for the last 4+ years. As a Solutions Architect at iMerit, she helps our clients by connecting the dots between the technical details of tooling, designing annotation workflows, and integrating a remote data labeling team for the execution. Previously, Soo served as the Data Operations Manager at a geospatial analytics startup where she built and scaled a Data Operations team from the ground up, leading a team 10 analysts.

Best Practices: Partnerships between ML/AI and Data Labeling Companies(Demo Talk)

The Evolution of Data Labeling(Talk)

Gavin Mendel-Gleason

Dr. Gavin Mendel-Gleason is the CTO of TerminusDB. He is a former research fellow at Trinity College Dublin in the School of Statistics and Computer Science. His research focuses on databases, logic, and verification in software engineering. His work includes contributing to the Seshat global historical databank, an ambitious project to record and analyze patterns in human history. He is the inventor of the Web Object Query Language and the primary architect of TerminusDB. He is interested in improving the best practices of the software development community and a strong believer in formal methods and the use of mathematics and logic as disciplines to increase the quality and robustness of the software.

Revision Control for Structured Data(Demo Talk)

Jan van der Vegt

During his career at KLM, Jan van der Vegt worked on several large machine learning projects. Technical challenges turned his focus more to the engineering side, with him initiating and leading an internal MLOps project. Afterward, he founded Cubonacci to create the Cubonacci MLOps platform.

Creating Efficiency and Trust with MLOps(Demo Talk)

Dr. Mo Haghighi

Dr Mo Haghighi is IBM’s head of Developer Ecosystems in Europe, former Research Scientist at Intel and former Java and Open Source developer at Sun Microsystems. Mo leads several teams of developer advocates in Benelux, DACH, UKI, SPGI, France, Italy, and CEE, as well as overseeing all advocacy and DevRel activities across Europe. Mo’s expertise primarily lies in the areas of Edge Processing, Distributed Computing, Cloud Native Development, Embedded Systems and AI, with several publications and patents in those areas. He has also been involved in various projects involving the UK government, European Commission and tech corporations around Edge, AI and Network Security. Mo has been a regular keynote speaker at various conferences including The Next Web, AiWorld, DevNexus, IEEE/ACM, JavaOne, DevRelCon, CloudConf, Open Data Science Conference, Pycon and O’Reilly.

Build Your Own Cloud Native Covid-19 Data Analytics with Kubernetes and OpenShift(Demo Talk)

Eric Landau

Prior to co-founding Cord, Eric spent 8 years in high-frequency trading where he was the lead quantitative researcher on an equity desk at DRW. Eric is now leading Cord’s deep learning research and technology. Eric holds an M.S. in Applied Physics from Harvard University and an M.S. in Electrical Engineering and B.S. in Physics from Stanford University.

Annotating Data with AI-assisted Labelling(Demo Talk)