AI Lab for Bioscience Webinars II
The AI Lab for Bioscience (AI4b.io) invites you to join a selection or all AI4b.io webinars taking place on April 12 & 13. The lab is organizing this meeting for those interested in and active in the fields of Artificial Intelligence and Bioscience. You can find the full webinar program below. Topics range from large-scale manufacturing to microbiome-based precision nutrition, going from large to small scale. Experts active in these topics will present their in-depth insights.
For each of the 5 webinars, a separate registration form is available. Once you register through one of those forms, an agenda item will be sent to you by e-mail so you can book time in your agenda. Please use the links below provided for each webinar. Please join and participate in the Q&A session.
If you have questions about these webinars, please send an e-mail to Renger.Jellema@DSM.COM Renger Jellema is the AI4b.io Program Manager.
Program Day 2
Day 2, April 13th
Webinar 4 – registration form webinar 4
09:30 – 10:10 CET | Robots Learning (Through) Interactions
Jens Kober (TU Delft)
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. I will discuss various learning techniques we developed that enable robots to have complex interactions with their environment and humans. Complexity arises from dealing with high-dimensional input data, non-linear dynamics in general and contacts in particular, multiple reference frames, and variability in objects, environments, and tasks. A human teacher is always involved in the learning process, either directly (providing data) or indirectly (designing the optimization criterion), which raises the question: How to best make use of the interactions with the human teacher to render the learning process efficient and effective?
10:10 – 10:40 CET | End-to-end experimental and machine learning workflows for predictive genetic design
Pierre-Aurelien Gilliot, Matthew J. Tarnowski and Thomas E. Gorochowski (School of Biological Sciences, University of Bristol, UK)
High-throughput experiments combined with emerging machine learning (ML) technologies are enabling data-centric biological design workflows for synthetic biology. In this talk, I will introduce some of the ways my group are contributing to this area, both from an experimental perspective, where nanopore sequencing is being used to characterise diverse libraries of genetic parts, to improved data processing pipelines and the rigorous optimisation of machine learning models for predicting the function of genetic parts from sequence alone. I aim to show the value of considering these workflows from end-to-end and how this can help improve quality and reproducibility of results.
Day 2, April 13th
Webinar 5 – registration form webinar 5
14:45 – 15:25 CET | Atinary SDLabs: Revolutionize R&D with Machine Learning
Loïc Roch (Atinary, Lausanne, Switzerland)
Atinary Technologies is a Swiss-American deeptech startup that develops market-leading ML algorithms and technology tools to revolutionize R&D and innovation of advanced materials. In his talk, Dr. Roch will describe how Atinary SDLabs accelerates R&D by orders of magnitude by putting Machine Learning (ML) at the heart of the innovation process. Atinary’s cloud-based platform SDLabs can integrate with off-the-shelf robotics and lab equipment to enable the Self-Driving Labs. Companies and users can deploy our ML solutions seamlessly in the cloud, starting with simulations or straight in wet labs.
AI4b.io is the Artificial Intelligence Lab for #Bioscience which was founded in 2021 by TU Delft and is funded by DSM. This laboratory is the first of its kind in Europe to apply artificial intelligence to full-scale #biomanufacturing, from microbial strain development to process optimization and factory scheduling. The lab is led by Professor Marcel Reinders, Director TU Delft Bioengineering Institute. AI4b.io is part of ICAI.