Date
Thursday June 13, 2024 from 10:00 AM to 12:30 PMLocation
TU/e campus - Auditorium 5Price
freeDuring the TU/e Research Day on Thursday June 13th 2024, EAISI organizes an AI symposium in honor of its fifth anniversary.
At the symposium, chaired by Ymke de Jong, research results will be presented by researchers from the EAISI funding programs such as the Starting Grants, IMPULS and EMDAIR.
They will elaborate on their various projects such as Creative AI Machines, DNmAt, research on LLMs, robotics and much more.
The symposium will end at 12.30h, followed by a lunch. After lunch the Research Day program starts with an interactive keynote 'Shaping the Future with AI' by Carlo van de Weijer, general manager of EAISI. Separate registration is required for the afternoon program.
Program AI symposium
Chair: Ymke de Jong
TIME | SPEAKER | TITLE |
09.30 - 10.00 | Walk-in | |
10.00 - 10.15 | Carlo van de Weijer General Manager of EAISI | Opening together with Robert-Jan Smits | 5 years EAISI |
PRESENTATIONS | ||
10.15 - 10.35 | Terese Hellström Victoria Bruno | Duo presentation | Computer aided pancreatic cancer diagnosis with user-centered design |
10.35 - 10.55 | Marko Petkovic PhD candidate | Materials Simulation & Modelling | AP - TU/e | Zeolite Simulation using Deep Learning |
10.55 - 11.20 | Coffee / tea break | |
11.20 - 11.40 | Celine Budding PhD candidate | Philosophy & Ethics | IE&IS - TU/e | |
11.40 - 12.00 | 2 RESEARCH PITCHES
Tilman Nols Daan Bon |
More than just a cool tool: Humans, AI and the Teams of tomorrow
|
12.00 - 12.20 | Xinyi Wei Jeroen Overdevest |
Duo presentation | Mitigating Automotive Radar Interference using Model-based Deep Learning |
12.20 - 12.25 | Closing | |
12.25 - 13.30 | Lunch | |
13.30 Afternoon program |
Research Day - Afternoon program (separate registration required)
13:30-14:30 Interactive keynote Shaping the future with AI, Carlo van de Weijer, Blauwe Zaal Auditorium
14:30-16:00 TU/e Research Day Expo with EAISI, EIRES, ICMS and EHCI, Hal Auditorium
16:00-17:30 TU/e Research Day Ceremony with Honorary Doctorates and Science Awards, hosted by Max Birk, Blauwe Zaal Auditorium.
17:30-18:30 Drinks, Auditorium
Abstracts
TERESE HELLSTRÖM & VICTORIA BRUNO
Title | Computer aided pancreatic cancer diagnosis with user-centered design
In contrast to other AI applications, medical AI applications have not been as widely integrated in clinical settings. Medical devices have stricter regulations than devices that we use in our everyday lives. To tackle these challenges, we work in the multi-disciplinary team of Eindhoven MedTech Innovation Center (e/MTIC) where AI developers, UI/UX designers and clinicians work together to create a user friendly, reliable system which will enhance the experience of clinicians. In this presentation we will present our work on a system aimed at assisting in pancreatic cancer diagnosis and surgical planning using AI.
About Victoria
Victoria Bruno is pursuing her Engineering Doctorate (EngD) in the Human System Interaction program at the Department of Industrial Design, Eindhoven University of Technology.
She holds a bachelor’s degree in Mechanical Engineering and a master’s degree in Biomedical Engineering. She worked for over three years in the medical device sector in Product Management and Medical Writing roles.
Her curiosity for technology led her to pursue a career switch through the EngD towards software engineering, focusing on health technology. For her doctorate, she is currently collaborating with designers, AI engineers and clinicians of the e/MTIC consortium.
About Terese
Terese Hellström is a PhD candidate at the Eindhoven university of technology in the Netherlands. She has a MSc in Medical Physics from Stockholm University, Sweden, and a Medical Physicist license in Sweden.
Her research interests include medical technology and computer vision for medical diagnosis and treatment planning. She is currently working on projects related to development of robust and trustworthy AI for detection and categorization of various types of cancer in medical images, as part of the e/MTIC consortium.
MARKO PETKOVIĆ
Title | Zeolite Simulation using Deep Learning
The climate crisis demands urgent solutions, particularly in carbon capture and storage. Porous materials, such as zeolites and metal-organic frameworks (MOFs), are strong candidates for this task, due to their high surface area and tunable properties. However, conventional simulations to design and optimize these materials are often slow and computationally demanding. In this presentation, we will explore how Deep Learning and Generative AI can improve this process by accelerating simulations and enabling the discovery of novel porous structures with optimized properties.
Marko Petković is a doctoral candidate in the Materials Simulation & Modelling (MSM) group in the department of Applied Physics at TU/e as well as the Data Mining (DM) group in the department of Mathematics and Computer Science. Marko’s work focuses on creating AI solutions for modelling nanoporous materials, such as zeolites.
CELINE BUDDING
Title | Explaining what large language models know
Given the impressive performance of large language models (LLMs), there is an increased interest in not only studying their behavior, but also their underlying processing. For example, it has been proposed that LLMs do not just perform next-word prediction based on surface statistics, but that they in fact acquire something like knowledge (Meng et al., 2022). Yet, it remains unclear what kind of knowledge LLMs might acquire and when this can and should be attributed. Taking inspiration from earlier debates between symbolic and connectionist AI in the 1980s and 90s, I propose that tacit knowledge (Davies, 1990) provides a suitable way to conceptualize potential knowledge in LLMs. Tacit knowledge, in this context, refers to implicitly represented rules or structures that causally affect the system’s behavior. In particular, Davies’ account of tacit knowledge provides clear constraints that should be met in order to attribute this knowledge, and could thus help better evaluate what LLMs learn and how they work.
About Celine Budding. I am a PhD candidate at the Philosophy & Ethics group at TU/e. In my PhD project, I investigate what large language models like ChatGPT "know" about language. Specifically, I take inspiration from philosophical debates in AI in the 80s and 90s and argue that large language models can be considered to have tacit knowledge of language. More generally, I am interested in what AI systems learn from the data, how we can evaluate and explain their performance, and how we can translate insights from various academic fields–machine learning, cognitive science, and philosophy–to effective regulation and policy for AI. To this end, I am also involved in standardization of AI through the NEN, the Dutch standardization organization. Before joining TU/e, I completed an MSc degree in computational neuroscience at the BCCN Berlin. → Back to program
TILMAN NOLS
Title | More than just a cool tool: Humans, AI and the Teams of tomorrow
While many companies are drawn to AI and robots to support their decision-making, many theorists believe that human-technology collaboration is further developing in the future of Industry 4.0. In light of advanced autonomous capabilities, AI will be able to transcend from a mere tool to a full-fledged teammate, that occupies formal roles and responsibilities. In these team scenarios, humans and AI will become increasingly interdependent, complement each other strengths and weaknesses, and rely on each other to achieve organizational goals.
However, past research shows intricate difficulties that stand in the way of implementing the workforce of tomorrow: cognitive, affective, and behavioral barriers limit human-AI teams' ability to realize their synergy, including communication difficulties, opacity, restricted adaptability and trust issues. Accordingly, the present pitch will shortly review the theoretical underpinnings and challenges of human-AI teams and provide a conceptual framework to optimize collaboration opportunities across varying, complex environments.
Tilman is a work and organizational psychologist with work experience in the semiconductor industry. He obtained his research masters from Maastricht University, Leuphana University Lüneburg and University of Valencia and is particularly interested in issues of innovation, change management and team science. In his PhD at the Human Performance Management Group, Tilman studies the future of tomorrow's workforce by exploring the collaboration opportunities between artificial-intelligence-powered technology and humans. In that regard, he specifically focuses on the mechanisms that enable humans and AI or robots to team up and coordinate their actions in complex environments. Doing so, he aims to facilitate a dynamic, holistic and multi-disciplinary approach to tackle his line of research. → Back to program
DAAN BON
Title | Using machine learning to solve PDEs
AI is used more and more to solve complex physical models, usually described by Partial Differential Equations (PDEs). For example, they allow measured data to be combined with classical methods of solving PDEs, or fully learning a solution operator from data. We investigate one such method and derive bounds on the capabilities of Neural Networks (NNs) to approximate solutions of different PDEs. This finds applications in further related problems such as inverse parameter estimation for PDEs.
Daan is a PhD candidate in the data driven computational science group at the Mathematics and Computer Science department at the TU/e. His research focusses on the use of data driven methods to solve (inverse) problems involving parametric PDEs, specifically the use of neural networks to approximate the solutions of PDEs. → Back to program
JEROEN OVERDEVEST & XINJI WEI
Title | Mitigating Automotive Radar Interference using Model-based Deep Learning
In advanced driver assistance systems, radar sensors are often regarded as the most reliable due to their robustness against adverse light and weather conditions. However, the proliferation of automotive radars in semi-autonomous vehicles has led to an increasing problem of radar-to-radar interference. Within the RAISE/RADAR project, we collaborate with NXP Semiconductors, and one of the challenges we aim to address is interference mitigation. Our objective is to improve interference removal by replacing classical signal processing algorithms with model-based deep learning techniques. Using the known underlying physical models, we leverage model-based, data-driven models to enhance the performance and reliability of radar systems in diverse driving scenarios.
Jeroen Overdevest received the B.Sc. and M.Sc. degrees in electrical engineering from the Delft University of Technology in 2015 and 2018, respectively. After pursuing the M.Sc. thesis at NXP Semiconductors, he joined the Algorithms & Software Innovation Group in NXP Semiconductors, Eindhoven, The Netherlands, in 2018. His research interests include signal processing, deep learning, and waveform design for mm-wave automotive radar. In October 2021, Jeroen started his PhD with SPS within the RAISE (Robust AI for SafE radar signal processing) program, where he seeks model-based deep learning approaches for mitigating real-world radar artifacts.
Xinyi Wei received the B.S. degree from Shanghai Maritime University, Shanghai, China, and the M.S. degree in electrical engineering from the Eindhoven University of Technology (TU/e), Eindhoven, The Netherlands, where she is currently working toward the Ph.D. degree with TU/e. Her research interests include automotive radar interference mitigation and deep learning. → Back to program