Dr. Justin Dauwels

Generative AI refers to a category of artificial intelligence models that are designed to generate new content, such as text, images, audio, or other types of data. Probably the best known example of generative AI is ChatGPT, the fastest consumer application to hit 100 million monthly active users. Generative AI models use machine learning algorithms to learn patterns and structures from existing data and then produce new data that is similar in style or content to what they have been trained on.

In this presentation, I will start with a tutorial on various approaches to generative AI. Next I will talk about projects in our group at the TU Delft on deep generative models. I will briefly present novel kinds of deep generative models that we are developing in our team. Next I will explain how we are designing such models for rainfall nowcasting, where we integrate physical laws into the deep generative models.  At last, I will talk various AI related initiatives that our team is involved in.

Biography:

Dr. Justin Dauwels is an Associate Professor at the TU Delft (Signals and Systems, Department of Microelectronics), and serves as co-Director of the Safety and Security Institute at the TU Delft. He was an Associate Professor of the School of Electrical and Electronic Engineering at the Nanyang Technological University (NTU) in Singapore till the end of 2020. At the TU Delft, he serves as scientific lead of the Model-Driven Decisions Lab (MoDDL), a first lab for the Knowledge Building program between the police and the TU Delft. He also serves as Chairperson of the EE Board of Studies at the TU Delft, and is a board member of the Netherlands Institute for Research on ICT.

His research interests are in data analytics with applications to intelligent transportation systems, autonomous systems, and analysis of human behaviour and physiology. He obtained his PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005. Moreover, he was a postdoctoral fellow at the RIKEN Brain Science Institute (2006-2007) and a research scientist at the Massachusetts Institute of Technology (2008-2010). 

He has been elected as IEEE SPS 2024 Distinguished Lecturer. He served as Chairman of the IEEE CIS Chapter in Singapore from 2018 to 2020, and served as Associate Editor of the IEEE Transactions on Signal Processing (2018 - 2023), and serves currently as Associate Editor (2021-2023) and Subject Editor (since 2023) of the Elsevier journal Signal Processing, Area Editor C&F for the IEEE Signal Processing Magazine (since 2023), member of the Editorial Advisory Board of the International Journal of Neural Systems (since 2021), and organizer of IEEE conferences and special sessions. He was also Elected Member of the IEEE Signal Processing Theory and Methods Technical Committee and IEEE Biomedical Signal Processing Technical Committee (both in 2018-2023), and is currently Elected Member of the
IEEE Machine Learning for Signal Processing Technical Committee and the IEEE Emerging Transportation Technology Testing (ET3) Technical Committee. He has been a JSPS postdoctoral fellow (2007), a BAEF fellow (2008), a Henri-Benedictus Fellow of the King Baudouin Foundation (2008), and a JSPS invited fellow (2010, 2011). His research team has won several best paper awards at international
conferences and journals.

His research on intelligent transportation systems has been featured by the BBC, Straits Times, Lianhe Zaobao, Channel 5, and numerous technology websites. Besides his academic efforts, the team of Dr. Justin Dauwels also collaborates intensely with local start-ups, SMEs, and agencies, in addition to MNCs, in the field of data-driven transportation, logistics, and medical data analytics. His academic lab has spawned four startups across a range of industries, ranging from AI for healthcare to autonomous vehicles.

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