Speakers

Talk title: Advanced Robotics for Lab Automation: Challenges and Opportunities

Abstract: Laboratory work is highly manual due to the complexity of the tasks, which also require dedicated skill training. Laboratory automation through advanced robotics enables scaling up the volumes of tests and experiments to be conducted, especially tackling the repetitive and the hazardous. Nonetheless, lab automation requires a higher degree of intelligence to deal with the highly complex tasks. In this talk, I will be reflecting upon the challenges and opportunities for adopting advanced robotics in lab automation, from an industrial perspective.

Bio: Dejanira Araiza Illan is an Assistant Principal Engineer in Robotic Applications at the Enterprise Supply Chain Advanced Technology Centre at Johnson & Johnson. Her professional interests include industrial advanced robotic applications, software engineering for robotics, and verification and validation of autonomous systems. Previously she worked as a scientist and software developer at the ROS-Industrial Consortium Asia Pacific and the Advanced Remanufacturing and Technology Centre at A*STAR, in Singapore. She also contributed to the UK funded projects RIVERAS and ROBOSAFE, on the verification of control systems and trustworthy robotic assistants, as a postdoctoral researcher at the University of Bristol. She holds a PhD in Automatic Control and Systems Engineering by the University of Sheffield, UK.

Talk title: The role of interoperability standards in supporting the acceleration of Discovery in the Natural Sciences

Abstract: The proposal to connect laboratory automation and AI in the laboratory opens up huge opportunities to accelerate the scientific discovery process. However, there are many barriers to fully integrating these technologies, such as the diversity of needs, processes and tools. Managing such diversity through modularity and standardization has been a successful approach but the role of standards and ontologies is often poorly understood and explained. We will explain how interoperability standards such as SiLA (Standardization in Lab Automation) and AnIML (Analytical Information Markup Language) can help to lower these barriers. SiLA ensures seamless instrument control and workflow orchestration, while AnIML structures data for consistent analysis and exchange. We will present the impact of standards in closed loop systems to accelerate research will be presented, touching on progress in subfields such as automated hit finding, organ on chip, and material sciences.

Bio: Dr Patrick Courtney has 20 years’ industrial experience in the development of technology, notably in the areas of sensing and robotics with a special focus on technology transfer. He has worked as director for global firm PerkinElmer, as well as at Sartorius and Cap Gemini, with spinouts, SMEs and clients in the life science and healthcare sectors. He has a long involvement in EU and national programs, leads a European working group on analytical laboratory robotics, and is a member of the board of directors of SiLA (Standards in Laboratory Automation). He holds an MBA with a PhD in Robotic Engineering/Molecular Biology and has 100 publications and ten patents. Dr. Courtney can be contacted at: patrick.courtney@tec-connection.com.

Talk title: AI-Based Knowledge Discovery: The Quest for Science Automation

Abstract: In chemistry, pharmacy, medicine, biophysics and biology, the generation of experimental knowledge still requires mostly time-consuming manual experiments in the laboratory (e.g. molecule synthesis, drug discovery, microscopic cell analysis or individualized mRNA-based drugs in cancer therapy). Once the results are available, modern laboratory automation enables cost-effective, high-throughput production of exactly the same products (e.g. corona rapid test). The same applies to nano- and microrobotics, where, however, not only the search for suitable structures, but also the synthesis of individual components or their composition into functional miniature machines based on them, requires a great deal of manual effort and has not yet followed a constructivist approach. Thus, when nano- and microrobotic systems are designed for laboratory analysis, the manual design and execution of experiments hinders their rapid and efficient generation, optimization and individualization. This talk addresses the central scientific question of how cross-scale analysis and synthesis processes can be massively accelerated by intelligent robotic laboratory assistants that independently plan, modify and evaluate experiments. The goal is to ensure that these robotic systems develop maximum efficiency in conjunction with each other and in cooperation with humans. This approach will lead to a revolution in experimental knowledge generation and system synthesis in general.

Bio: Sami Haddadin is the founding and Executive Director of TUM’s Munich Institute of Robotics and Machine Intelligence, holds the Chair of Robotics and Systems Intelligence and is an IEEE Fellow. His research interests include human-centered robotics, embodied AI, collective intelligence and human-robot symbiosis. He is best known for his contributions to tactile mechatronics, contact-aware robots, safety methods in human-robot interaction, and autonomous manipulation learning.
Before joining TUM, he was Chair of the Institute of Automatic Control at Gottfried Wilhelm Leibniz University Hannover from 2014 to 2018 and received faculty offers from MIT and Stanford. Prior to that, he held various positions as a researcher at the German Aerospace Center DLR. He holds degrees in electrical engineering, computer science and technology management from the Technical University of Munich and the Ludwig-Maximilians University of Munich. He received his PhD with summa cum laude from RWTH Aachen University. He has published more than 200 scientific articles in international journals and conferences, many of them award-winning. He has received numerous awards for his scientific work, including the George Giralt PhD Award (2012), the RSS Early Career Spotlight (2015), the IEEE/RAS Early Career Award (2015), the Alfried Krupp Award for Young Professors (2015), the German President’s Award for Innovation in Science and Technology (2017) and the Leibniz Prize (2019).
Sami Haddadin is the founder of the revolutionizing industrial robotics company Franka Emika GmbH (Munich, 2016), producing not only the world’s first commercial tactile robot but also the worldwide most used robotics research platform. During his time at DLR, he played a major role in the development of the lightweight robot technology, which became the KUKA LBR iiwa. 26 years after mp3 his patent on “Tactile Robots” was listed as the most recent addition to the “Milestone made in Germany” (DPMA). He is a member of the German National Academy of Sciences Leopoldina and the national academy of science and engineering acatech. He was a member of the High-Level Expert Group on AI of the European Commission and is the chairman of the Bavarian AI Council.

Talk title: Autonomous Synthetic Chemistry using Dynamic Adaptation and Experimentation 

Abstract: Automation and real-time reaction monitoring have revolutionized synthetic chemistry by enabling data-rich experimentation, leading to a comprehensive understanding of chemical processes. This talk emphasizes the significance of data-rich experimentation (DRE) in reaction process optimization and discovery and explores the potential of artificial intelligence (AI) and machine learning (ML) tools in enhancing automated real-time reaction monitoring.
DRE focuses on extracting real-time reaction progress data to gain insights into reaction kinetics, intermediates, rate constants, and by-product reaction pathways. Automation plays a crucial role in enabling DRE by accurately capturing and analyzing reaction aliquots, processing complex analytical data, and executing precise reaction manipulations. This approach enhances decision-making capabilities, reduces optimization time and resource requirements, and facilitates the exploration of reaction mechanisms and dynamics.
This presentation highlights the current paradigm of data-driven reaction investigation, which often relies on human interpretation. However, the integration of real-time monitoring data with AI and ML tools presents an opportunity to accelerate process optimization and reaction discovery. Real-time monitoring telemetry allows automated systems to receive critical feedback and adapt to variable circumstances, enabling error-free autonomous synthesis. ML-based predictive models and autonomous optimization platforms reduce the number of experiments needed and consider a broader range of reaction parameters beyond simple yield measurements.
Real-time reaction monitoring provides comprehensive kinetic data that addresses issues of data integrity, bias, and oversimplification. It captures variations in reaction performance, identifies intermediates and by-products, and facilitates the classification of underlying reaction mechanisms. By combining automated data-gathering methods with AI and ML tools, synthetic chemistry can predict optimal conditions and discover new synthetic routes more efficiently.

Bio: Jason Hein is an Associate Professor of Chemistry at the University of British Columbia and an Adjunct Professor at the University of Bergen, Norway. His career began with a B.Sc. in Biochemistry (2000) and a Ph.D. in asymmetric reaction methodology (2005) from the University of Manitoba. Prof. Hein's postdoctoral work at the Scripps Research Institute focused on in-situ kinetic reaction analysis, leading to his role as a senior research associate and subsequent independent career at the University of California, Merced in 2011. In 2015, he joined the University of British Columbia, where he was promoted to Associate Professor in 2019. He has been instrumental in the development of autonomous discovery platforms, notably as co-lead of Project ADA for thin film materials, and is the CEO of Telescope Innovations, specializing in AI-enabled automation for chemical process development. Prof. Hein's research, resulting in over 100 publications and an H-index of 34, has garnered him several awards, including the ACS Young Investigators Prize (2015) and an NSERC Discovery Accelerator Supplement (2021). A prolific speaker, he has delivered over 150 invited talks, including keynotes at major international symposiums.

Talk title: Challenges and Solutions to Lab Automation: an ABB’s View of Practice

Talk abstract: Devices and instruments in typical healthcare and chemical labs already have high automation level. But lab scientists/technicians are still necessary to form a complete operation cycle. Replacing human by robots in lab environment, robots need to deal with material transferring, sample handing, interacting with lab testing equipments, lab task scheduling, and more challenges. In order to meet such challenges, ABB Research closely collaborating with ABB Robotics, developed a few technologies, such as mobile manipulation, object perception and grasping, teach by demonstration, etc. This presentation will share an ABB’s view of challenges to lab automation and introduce a few key technologies from ABB in responding such challenges.

Bio: Liwei Qi is the manager for the robotics and mechatronics research team in ABB Research in Västerås, Sweden since March, 2019. This research team has 20+ top research scientists come from 8 different countries, mainly focusing on robot fundamental research, new robot concepts, and autonomous robotics. Liwei Qi got a doctoral degree of Mechanical Engineering from Shanghai JiaoTong University from China in 2002. From 2003 he joined ABB, worked in ABB’s Robotics R&D for 20 years with different technical and managerial roles. Liwei’s major technical interests are robot design, robot programming and robot applications. Most recently, One major focus of Liwei and his team in Västerås is autonomous robot solution for emerging application areas such as lab automation, that engages mobile manipulation, AI and vision, easy robot programming, etc. Liwei Qi published 10+ journal and conference papers, applied 8 patents when working in ABB Robotics

Talk title: Mobile Robots in Life Science Laboratories – Strategies and Concepts

Talk abstract: Advancing technology has revolutionized our ability to perform complex scientific investigations. One of the most exciting advances in this area is the integration of mobile robots into the laboratory environment. These autonomous robots are no longer just science fiction figures, but are increasingly becoming indispensable tools for researchers in a variety of disciplines. Mobile robots in the laboratory are autonomous or remotely controlled robots designed to work in scientific laboratory environments. They are able to move freely, perform tasks, and collect data without requiring human intervention. These robots are capable of performing a wide range of tasks, including sampling, performing experiments, collecting data, and even interacting with other laboratory equipment. The integration of mobile robots into the laboratory environment offers numerous advantages and is essential to achieving the fully automated laboratory. Depending on the field of application, applications, spatial and other resources, different concepts and strategies are conceivable both for the type of mobile robots used and for their integration into complex laboratory processes. In the context of the lecture, these will be presented and explained in an exemplary application.

Bio: Prof. Dr.-Ing. habil. Kerstin Thurow studied chemistry and received her PhD in organometallic chemistry from the Ludwig Maximilian University of Munich in 1995. In 1999 she habilitated in measurement and control engineering. In the same year, she was appointed to the worldwide first professorship for "Laboratory Automation" at the Faculty of Engineering at the University of Rostock. Since 2004 she has held the chair of "Automation Technology / Life Science Automation" at the University of Rostock and is the Director of the Center for Life Science Automation (University of Rostock). Prof. Thurow has authored more than 300 scientific publications including 3 monographies. She is a founding member of the Academy of Sciences Hamburg and member of the technical academy Germany (acatech). Her research topics include the automation of life science processes, robotics, mobile robotics as well as system integration and systems engineering.

Talk title: How can AI and automation in lab plants contribute to an increased reproducibility and product quality?

Abstract: To explore new products and, process recipes lab plants are operated in unknown settings in academia and industry.  Additionally new technologies are explored using lab plants with low degree of automation and instrumentation. The talk will address both cases and showcase how data analytics and machine learning can even with very small data sets provide beneficial insights. The use cases with real lab machinery from metal forming and biochemical separation techniques benefit from enhanced automation and ML and report most critical challenges, methods used and results gained. 

Bio: The research interests of Birgit Vogel-Heuser are in the area of systems and software engineering as well as in the modeling of distributed and intelligent embedded automation systems for machines and plants. She is graduated in electrical engineering and did her PhD in mechanical engineering at RWTH Aachen, Germany. After ten years in industry and different professorship positions, she was appointed to the Chair of Automation and Information Systems at the Technical University Munich in 2009. She is a fellow of the IEEE, IEEE RAS Distinguished Lecturer and IEEE RAS Automation Coordinator.

Talk title: Autonomous Laboratory for Inorganic Materials Synthesis and Discovery

Talk abstract: Autonomous laboratories are paving the future for chemical and materials research. By integrating computational design, automated instrumentation, robotics, and AI/ML, autonomous laboratories drive more rapid execution of experimentation, data interpretation, and decision making, which will eventually accelerate chemical and materials discovery. This presentation will give an overview of the challenges, progress, and perspectives we have learnt from establishing an autonomous lab for inorganic powder materials. I will talk about 1) instrument integration for solid-state synthesis and characterization, 2) making tools and devices to tackle the challenges in powder handling, 3) using machine learning for the acquisition and interpretation of data from XRD and SEM/EDS, and 4) developing decision making algorithms to tackle synthesis problems.

Bio: Yan Zeng, Assistant Professor in the Department of Chemistry and Biochemistry at Florida State University. Yan was a Staff Scientist, and earlier a postdoctoral researcher, at Lawrence Berkeley National Laboratory between 2020 and 2023, where she built an autonomous inorganic solid-state synthesis laboratory (the A-Lab) with a team at LBNL and UC Berkeley. She is also interested in finding new materials and exploring synthesis methods to make them. Yan obtained her Ph.D. degree (2020) in Materials Engineering from McGill University, developing Li-ion battery cathode materials using hydrothermal synthesis. Her current research interests lie at the intersection of lab automation, energy storage materials, synthesis, and battery recycling.