Abstract:
Generative AI refers to artificial intelligence models that generate new content, such as text, images, audio, or other data types. 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 produce new data similar in style or content to what they have been trained on.
This seminar will start with a tutorial on various approaches to generative AI and cover various projects at TU Delft on deep generative models. Then, there will be a brief presentation of novel kinds of deep generative models being developed by the team. Following that, the design of such models for rainfall nowcasting will be explained, highlighting how physical laws are integrated into the deep generative models. Finally, various AI-related initiatives that the team is involved in will be discussed.
Date:13 June, 2024
Time: 11:30 AM
Venue: CEIT 102, University of Dubai, Dubai 03:30 PM at Auditoriums Room: L02012, Khalifa University, Abu Dhabi
Dr. Justin Dauwels
Bio:
Dr. Justin Dauwels is an Associate Professor at the TU Delft (Signals and Systems, Department of Microelectronics). 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), the 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 human behaviour and physiology analysis. He obtained his PhD 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 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). 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 served as Associate Editor (2021-2023) and currently serves as 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 an 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 an Elected Member of the IEEE Machine Learning for Signal Processing Technical Committee and the IEEE Emerging Transportation Technology Testing (ET3) Technical Committee.
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 various industries, ranging from AI for healthcare to autonomous vehicles.
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