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TransAI 2023, ai4i 2023, AIKE 2023

Joint Keynotes

(in alphabetical order)

Deborah Khider

ISI, University of Southern California, USA

AI in Paleoclimatology: Progress, Insights, and Future Directions.

Bio: Dr. Khider is a research lead in the artificial intelligence division at the University of Southern California Information Sciences Institute.  She obtained her PhD from USC in Ocean Sciences in 2011 and performed doctoral work at the University of Texas at Austin and the University of California Santa Barbara, where she has applied many data science techniques to paleoclimate datasets. Her current work lies at the intersection of the geoscience and AI, and in particular, how can we leverage AI to help scientists with their workflows. 


C.-C. Jay Kuo

University of Southern California, USA

On Sustainable Healthcare and Sustainable AI

Abstract: This talk addresses two sustainability challenges in our society. The first one is the sustainability of today’s healthcare services. As people’s life is prolonged, the need for healthcare increases, and the healthcare cost has grown exponentially. The deployment of artificial intelligence (AI) technology in the modern healthcare system is essential since it enhances the productivity of doctors and nurses. Besides, early diagnosis and treatment will cut the medical cost significantly. AI can provide an effective solution to it. On the other hand, today’s AI technology heavily relies on deep learning. Deep learning has a high carbon footprint, which has a great impact on global warming and climate change. Green AI technology is critical to the sustainability of the Earth. Thus, sustainable AI is needed to address the problem of sustainable healthcare. In the second half of the talk, I will present recent efforts to pursue green AI and future perspectives.

Bio: Dr. C.-C. Jay Kuo received his Ph.D. from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as William M. Hogue Professor, Distinguished Professor of Electrical and Computer Engineering and Computer Science, and Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is a Fellow of AAAS, ACM, IEEE, NAI, and SPIE and an Academician of Academia Sinica.


Dr. Kuo has received a few awards for his research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo was Editor-in-Chief for the IEEE Transactions on Information Forensics and Security (2012-2014) and the Journal of Visual Communication and Image Representation (1997-2011). He is currently the Editor-in-Chief for the APSIPA Trans. on Signal and Information Processing (2022-2023). He has guided 168 students to their Ph.D. degrees and supervised 31 postdoctoral research fellows

Eren Kurshan

Morgan Stanley, USA

Is System Design The Missing Piece for AGI?:  System-level Design Considerations for Next Generation AI

Abstract: Today’s AI solutions face a trifecta of challenges: The Great AGI Leap, The Energy Wall and The Alignment Problem. Recent AI solutions have been quite energy inefficient. They consume unprecedented amounts of energy during training and unsustainable peak power during run-time. Making things worse, the amount of compute used for training doubles every 3.5 months.  Current approach to AI lacks system design; even though system-level characteristics play a critical role in the human brain; from the way it processes information to how it makes decisions. For the AGI Leap, the required integration and balanced operation of multiple functional subsystems is impossible to achieve without system-design. Lastly, for the alignment problem, AI lacks the capacity to employ multiple subsystems (such as System 1 and 2, model-free and model-based learning) in a balanced way for moral decisioning.  In this talk, we investigate the importance of system design for next generation AI solutions and argue that system-design is the missing piece without which the three grand challenges may never be solved.

Bio: Dr. Eren Kurshan is an AI researcher and technology executive. She leads Research and Methodology efforts at M.Stanley towards building capabilities in emerging AI/ML techniques. Prior to this role, she was the Executive Head of AI for client protection at Bank of America, where she was responsible for leading the development of custom machine learning and deep learning solutions for fraud detection, prevention and operational improvement. Dr. Kurshan and her team built the first generation of in-house AI and Machine Learning models for Bank of America’s payment systems portfolio (credit, debit, ATM, mobile and other transactions). Dr. Kurshan has served as the technical lead for various AI and Data Science programs at Columbia University, J.P. Morgan Corporate and Investment Bank, and IBM. She was a Visiting Fellow at Princeton University Center for Information Technology Policy during 2015-2016 and served as an Adjunct Professor of Computer Science at Columbia University since 2014. Dr. Kurshan received her Ph.D. in Computer Science from the University of California. She has over 70 peer reviewed technical conference and journal publications and over 100 granted patents and close to 200 applications. She was the recipient of 2 Best Paper Awards from IEEE and ACM Conferences, Outstanding Research and Corporate Accomplishment Awards from IBM. 

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