{"id":507,"date":"2018-06-13T10:38:12","date_gmt":"2018-06-13T10:38:12","guid":{"rendered":"http:\/\/madima.org\/?page_id=507"},"modified":"2019-12-16T10:33:23","modified_gmt":"2019-12-16T10:33:23","slug":"invited-speaker","status":"publish","type":"page","link":"https:\/\/madima.org\/madima2019\/invited-speaker\/","title":{"rendered":"Invited Speakers"},"content":{"rendered":"\n

<\/a><\/p>\n

Kiyoharu Aizawa<\/strong><\/h3>\n
Multimedia Processing Lab, The University of Tokyo, Japan<\/em><\/h6>\n

\"\"Kiyoharu Aizawa received the B.E., the M.E. and the Dr.Eng. degrees in Electrical Engineering all from the University of Tokyo, in 1983, 1985, 1988, respectively. He is currently a Professor at Department of Information and Communication Engineering of the University of Tokyo. He was a Visiting Assistant Professor at University of Illinois from 1990 to 1992. His research interest is in multimedia applications, image processing and computer vision. He has pioneered FoodLog which assists users to record their food intake by image recognition and retrieval techniques.<\/br> \nHe received the 1987 Young Engineer Award and the 1990, 1998 Best Paper Awards, the 1991 Achievement Award, 1999 Electronics Society Award from IEICE Japan and the 1998 Fujio Frontier Award, the 2002 and 2009 Best Paper Award and 2013 Achievement award from ITE Japan. He received the IBM Japan Science Prize in 2002.<\/br>\nHe is on Editorial Boards of IEEE MultiMedia, ACM TOMM, APSIPA Transactions on Signal and Information Processing and International Journal of Multimedia Information Retrieval. He served as the Editor in Chief of Journal of ITE Japan, an Associate Editor of IEEE Trans. Image Processing, IEEE Trans. CSVT and IEEE Trans. Multimedia. He is\/was a president of ITE and ISS society of IEICE, 2019 and 2018, respectively. He has served a number of international and domestic conferences; he was a General co-Chair of ACM Multimedia 2012 and ACM ICMR2018. He is a Fellow of IEEE, IEICE, ITE and a council member of Science Council of Japan.<\/p>\n

FoodLog: Multimedia Food Recording Platform and its Application for Athletes’ Nutrition Management<\/a><\/strong><\/p>\n

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<\/a><\/p>\n

Ramesh Jain<\/strong><\/h3>\n
Institute for Future Health, Department of Computer Science, University of California, USA and Peng Cheng Lab, Shen Zhen, China<\/em><\/h6>\n

\"\"Ramesh Jain is an entrepreneur, researcher, and educator.
He is a Donald Bren Professor in Information & Computer Sciences at University of California, Irvine. His research interests covered Control Systems, Computer Vision, Artificial Intelligence, and Multimedia Computing. His current research passion is in addressing health issues using cybernetic principles building on the progress in sensors, mobile, processing, artificial intelligence, computer vision, and storage technologies. He is founding director of the Institute for Future Health at UCI. He is a Fellow of AAAS, ACM, IEEE, AAAI, IAPR, and SPIE.
Ramesh co-founded several companies, managed them in initial stages, and then turned them over to professional management. He enjoys new challenges and likes to use technology to solve them. He is participating in addressing the biggest challenge for us all: how to live long in good health.<\/p>\n

<\/span>Eat, Drink and be Happy<\/strong><\/div>
\n

Food is essential for human life and it is fundamental to the human experience. Selecting right food at a right place in right situation is very important, but a challenging problem. Food recommendation must consider the personal food model, analyzing unique food characteristics, incorporating various social as well as personal contexts, and food related health and domain knowledge. Each of these is a challenge for multimedia computing. In this presentation, we will discuss technical challenges and opportunities for addressing food recommendation to make people happy and healthy. Recent progress in multimodal computing makes it possible to start addressing this important problem for all of us at personal level. We will discuss our ideas considering concrete emerging approaches for building a multimodal personal food recommendation system using FoodLogs and health context.<\/p>\n<\/div><\/div>\n

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<\/a><\/p>\n

Frederic Ronga, PhD<\/strong><\/h3>\n
Ne<\/em>stl\u00e9<\/i> Research<\/em> Center, Lausanne, Switzerland<\/i><\/h6>\n

\"\" Frederic is leading the Digital Nutrition & Health <\/i>group in the department of Nutrition & Dietary Recommendations<\/i> at the Nestl\u00e9 Research Center in Lausanne, Switzerland. The mission of his group is to develop impactful digital solutions for personalized nutrition. Frederic\u2019s research interest spans innovative technologies for data capture and data processing in the field of nutrition and health. At Nestl\u00e9 Research, Frederic has worked on developing new algorithms for dietary assessment and recommendations, meal planning and dietary constraint management, feeding into holistic digital platforms that contribute to Nestl\u00e9\u2019s purpose of enhancing quality of life and contributing to a healthier future. Frederic holds a PhD in Physics. Before joining Nestl\u00e9, he worked on extracting new scientific insights from large datasets, at CERN (Switzerland) and KEK (Japan).<\/p>\n

<\/span>Personalized Nutrition in the connected era: challenges and opportunities<\/i><\/strong><\/div>
\n

Today\u2019s consumer lives in a connected world, with trends in self-quantification and personalization driving preferences and habits. This talk will discuss the mission of Nestl\u00e9 Research\u2019s Digital Nutrition & Health<\/i> team, as well as its challenges and approaches on trying to feed into holistic digital platforms that contribute to Nestl\u00e9 Purpose of enhancing quality of life and contributing to a healthier future, with a special focus on dietary intake capture, assessment, and recommendations.<\/p>\n<\/div><\/div>\n

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<\/a><\/p>\n

Karan Sikka<\/span>, PhD<\/strong><\/h3>\n
Center for Vision Technologies, SRI International, Princeton, USA<\/em><\/h6>\n

\"\"<\/p>\n

Karan Sikka is an Advanced Computer Scientist at Center for Vision Technologies, SRI International in Princeton, USA. He graduated with a PhD degree in 2016 from Machine Perception Lab <\/a>at UCSD and was advised by Dr. Marian Bartlett<\/a>. Before joining UCSD, he completed his bachelor’s in ECE at Indian Institute of Technology Guwahati<\/a> in 2010. At SRI he is a co-PI for several Govt. funded programs (ONR CEROSS, DARPA M3I, and AFRL Mesa) related to understanding and analysing social media structures with multimodal content. His research is focused on solving several fundamental problems in Computer Vision such as multimodal learning, weakly supervised learning, few\/zero-shot learning, action recognition etc. He has won a best paper honorable mention award at IEEE Face and Gesture 2013, and a best paper award at the Emotion Recognition in the Wild Workshop at ICMI 2013. He serves as a reviewer\/program-committee for venues such as CVPR, ACCV, IJCV, IEEE TAC, IEEE TM, ICMI, IEEE AFGR etc. <\/span><\/p>\n

<\/span>Learning User Preferences from Social Multimedia Analysis and Overview of the iFood2019 Challenge<\/i><\/strong><\/div>
\n

There has been an explosion of multimodal content generated on social media networks in the last few years, which has necessitated a deeper understanding of social media content and user behavior. In the first part of the task, I will present a novel method that provides a unified framework for understanding content as well as modeling user preferences from noisy social media posts. I will discuss some applications in understanding food preferences and trends using this algorithm.<\/br>I will then give an overview of the second large-scale food classification challenge in images (iFood challenge) held as part of the sixth Fine Grained Visual Classification Workshop at CVPR19. We introduce a new dataset of 251 fine-grained (prepared) food categories with 118K training images collected from the web, and human verified labels for both validation set (11K images) and the test set (12K images). 40 teams from academia and industry competed in this challenge with the top team obtaining a 5.6% top-3 error percentage, which is almost 2-points better than previous year challenge.<\/p>\n<\/div><\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Kiyoharu Aizawa Multimedia Processing Lab, The University of Tokyo, Japan Kiyoharu Aizawa received the B.E., the M.E. and the Dr.Eng. degrees in Electrical Engineering all from the University of Tokyo, in 1983, 1985, 1988, respectively. He is currently a Professor at Department of Information and Communication Engineering of the University of Tokyo. He was a … <\/p>\n

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