Generative AI: Diffusion Models for Scientific Machine Learning

MIDAS mini-symposium

Friday, September 15, 2023
8:30 am – 5:00 pm

Weiser Hall, 10th floor
500 Church Street,
Ann Arbor, MI 48109


Recently, diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image generation, audio synthesis, inverse problem solving, and many scientific disciplines. However, despite their impressive results, they also encounter numerous challenges and constraints that inhibit their practical implementation in many scientific pursuits. Therefore, this mini-symposium, co-organized by MIDAS and the Department of Electrical and Computer Engineering, will serve as a timely platform where experts and researchers from both methodology and application research fields will explore the latest progress and developments in generative AI and diffusion models, and delve into the application of these models in scientific and medical fields, which will become a prime venue for idea exchange and fostering research partnerships in this emerging field.


Alexandros G. Dimakis, Professor in Chandra Department of Electrical and Computer Engineering, University of Texas, Austin; Co-Director of the National AI Institute for Foundations of Machine Learning

“Foundation Models and Inverse Problems”


Alex Dimakis is a UT Austin Professor and the co-director of the National AI Institute on the Foundations of Machine Learning. He received his Ph.D. from UC Berkeley and the Diploma degree from NTU in Athens, Greece. He has published more than 150 papers and received several awards including the James Massey Award, NSF Career, a Google research award, the UC Berkeley Eli Jury dissertation award, and several best paper awards. He served as an Associate Editor for several journals, as an Area Chair for major Machine Learning conferences (NeurIPS, ICML, AAAI) and as the chair of the Technical Committee for MLSys 2021. He is an IEEE Fellow for contributions to distributed coding and learning. His research interests include Information Theory and Machine Learning.


Jeffrey Fessler, William L Root Collegiate Professor of Electrical Engineering and Computer Science; Professor of Electrical Engineering and Computer Science; Professor of Biomedical Engineering, College of Engineering; Professor of Radiology, Medical School, University of Michigan

“A tutorial on score-based generative models with medical imaging applications

Generative models have recently been of increasing interest in machine learning. Score-based diffusion models seem to be especially powerful. This talk will give a tutorial about generative models, especially score-based modeling. Applications in imaging and medical imaging will be mentioned. The target audience is newcomers to generative modeling.


Jeff Fessler is the William L. Root Professor of Electrical Engineering and Computer Science at the University of Michigan. He received the BSEE degree from Purdue University in 1985, the MSEE degree from Stanford University in 1986, and the M.S. degree in Statistics from Stanford University in 1989. From 1985 to 1988 he was a National Science Foundation Graduate Fellow at Stanford, where he earned a Ph.D. in electrical engineering in 1990. He has worked at the University of Michigan since then. From 1991 to 1992 he was a Department of Energy Alexander Hollaender Post-Doctoral Fellow in the Division of Nuclear Medicine.

From 1993 to 1995 he was an Assistant Professor in Nuclear Medicine and the Bioengineering Program. He is now a Professor in the Departments of Electrical Engineering and Computer Science, Radiology, and Biomedical Engineering. He became a Fellow of the IEEE in 2006, for contributions to the theory and practice of image reconstruction. He received the Francois Erbsmann award for his IPMI93 presentation, the Edward Hoffman Medical Imaging Scientist Award in 2013, and an IEEE EMBS Technical Achievement Award in 2016. He has served as an associate editor for the IEEE Transactions on Medical Imaging, the IEEE Signal Processing Letters, the IEEE Transactions on Image Processing, the IEEE Transactions on Computational Imaging (T-CI), and is currently serving as an associate editor for SIAM J. on Imaging Science and a Senior AE for T-CI. He has chaired the IEEE T-MI Steering Committee and the ISBI Steering Committee. He was co-chair of the 1997 SPIE conference on Image Reconstruction and Restoration, technical program co-chair of the 2002 IEEE International Symposium on Biomedical Imaging (ISBI), and general chair of ISBI 2007. He received the 2023 Steven S. Attwood Award, the highest honor awarded to a faculty member by the College of Engineering. His research interests are in statistical aspects of imaging problems, and he has supervised doctoral research in PET, SPECT, X-ray CT, MRI, and optical imaging problems.

Ruiqi Gao

Ruiqi Gao, Research Scientist, Google DeepMind

“Understanding Diffusion Model Objectives from the Perspective of ELBO”

To achieve the highest perceptual quality, state-of-the-art diffusion models are optimized with weighted training objectives that look very different from the maximum likelihood and the Evidence Lower Bound (ELBO) objectives. In this talk, I’ll revisit the connection between diffusion model objectives and ELBOs, and show that they are actually closely related.


Ruiqi is a research scientist at Google Deepmind team. Her research interests are in statistical modeling and learning, with a focus on deep generative models and representation learning. She received her Ph.D. degree from the University of California, Los Angeles (UCLA), advised by Song-Chun Zhu and Ying Nian Wu. Her recent research themes include scalable training and inference algorithms of deep generative models, such as diffusion models and energy-based models, to capture the structure of the complex visual world. .


Ulugbek Kamilov, Associate Professor of Electrical & Systems Engineering and Computer Science & Engineering, Washington University in St. Louis

“Plug-and-Play Methods for Inverse Problems: Self-Calibration, Conditional Generation, and Continuous Representation”


Ulugbek S. Kamilov is the Director of Computational Imaging Group and an Associate Professor of Electrical & Systems Engineering and Computer Science & Engineering at Washington University in St. Louis. He also a Research Faculty at Google Research. His primary research interests include computational imaging, machine learning, and optimization. He obtained the BSc/MSc degree in Communication Systems and the PhD degree in Electrical Engineering from EPFL, Switzerland, in 2011 and 2015, respectively. From 2015 to 2017, he was a Research Scientist at Mitsubishi Electric Research Laboratories, Cambridge, MA, USA. He was also an exchange student at Carnegie Mellon University from 2007 to 2008, a visiting student at MIT in from 2010 to 2011, and a visiting student researcher at Stanford University in 2013. He is a recipient of the NSF CAREER Award and the IEEE Signal Processing Society’s 2017 Best Paper Award. He was among 55 early-career researchers in the USA selected as a Fellow for the Scialog initiative on “Advancing Bioimaging” in 2021. His PhD thesis was selected as a finalist for the EPFL Doctorate Award in 2016. He has served as a Senior Member of the Editorial Board of IEEE Signal Processing Magazine and as an Associate Editor of IEEE Transactions on Computational Imaging. He was awarded Outstanding Teaching Award in 2023 from the Department of Electrical & Systems Engineering at Washington University in St. Louis.

Jon Tamir

Jon Tamir, Assistant Professor in the Chandra Family Department of Electrical and Computer Engineering, University of Texas, Austin

“Deep Generative Physical Modeling for Medical Imaging and Wireless Communications”


Jon Tamir is an Assistant Professor in the Chandra Family Department of Electrical and Computer Engineering at UT Austin. He received his PhD in EECS from UC Berkeley. His research focus spans computational medical imaging, signal processing, and machine learning, with specific emphasis on magnetic resonance imaging. He is a Fellow of the Jack Kilby/Texas Instruments Endowed Faculty Fellowship in Computer Engineering and a recipient of the inaugural Oracle for Research Fellowship. He received the NSF CAREER Award in 2023.

John Wright

John Wright, Associate Professor of Electrical Engineering, Columbia University

“Deep Networks and the Multiple Manifold Problem”


John Wright is an Associate Professor in the Electrical Engineering Department at Columbia University. He received his PhD in Electrical Engineering from the University of Illinois at Urbana-Champaign in October 2009, and was with Microsoft Research from 2009-2011. His research is in the area of high-dimensional data analysis. In particular, his recent research has focused on developing algorithms for robustly recovering structured signal representations from incomplete and corrupted observations, and applying them to practical problems in imaging and vision. His work has received an number of awards and honors, including the 2009 Lemelson-Illinois Prize for Innovation for his work on face recognition, the 2009 UIUC Martin Award for Excellence in Graduate Research, a 2008-2010 Microsoft Research Fellowship, and the 2012 COLT Best Paper Award (with Wang and Spielman).

Jong Chul Ye, Professor of the Kim Jaechul Graduate School of Artificial Intelligence; Adjunct Professor at the Department of Mathematical Sciences and the Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)

Diffusion Models for Inverse Problems in Medical Imaging”


Jong Chul Ye is a Professor of the Kim Jaechul Graduate School of Artificial Intelligence (AI) of Korea Advanced Institute of Science and Technology (KAIST), Korea. He received the B.Sc. and M.Sc. degrees from Seoul National University, Korea, and the Ph.D. from Purdue University, West Lafayette. Before joining KAIST, he worked at Philips Research and GE Global Research in New York. He has served as an associate editor of IEEE Trans. on Image Processing, and an editorial board member for Magnetic Resonance in Medicine. He is currently an associate editor for IEEE Trans. on Medical Imaging, and  a Senior Editor of IEEE Signal Processing Magazine. He is  an IEEE Fellow, was the  Chair of IEEE SPS Computational Imaging TC,  and IEEE EMBS Distinguished Lecturer. He was a General Cochair (with Mathews Jacob) for IEEE Symp. On Biomedical Imaging (ISBI) 2020. His research interest is in machine learning for biomedical imaging and computer vision.


Liyue Shen, Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, University of Michigan

Qing QuAssistant Professor of Electrical Engineering and Computer Science, College of Engineering, University of Michigan


Questions? Message the MIDAS team: