Prof. Jennifer Coates
Friedman School of Nutrition Science and PolicyBoston, MA, USA

Jennifer Coates, PhD is an Assosiate Professor of Food Policy and Applied Nutrition at the Tufts Friedman School of Nutrition Science and Policy and a Senior Researcher at the Tufts Feinstein International Center.

Dr. Coates’s research focuses on the development of methods for improving the design, implementation, and evaluation of international nutrition and food security programs in both development and humanitarian emergency contexts. Methods-related initiatives include the development and validation of: methods for scaling up global dietary data collection and use (INDDEX); indicators of the affordability of quality diets in Africa (IMMANA-IANDA); a standardized approach to malnutrition causal analysis and response assessment (ACF); methods for evaluating the micronutrient impact and functional health outcomes of national fortification programs (GAIN); dietary diversity indicators in emergency-prone contexts (WFP); and global experiential food security measures (FANTA). She also conducts research to understand the implementation and impact of integrated food security programs, including identifying promising models for sustainable exit from Title II-funded food aid programs (FANTA III), and examining the implementation challenges of integrating agriculture and nutrition programming (Ethiopia/ENGINE). Dr. Coates serves on the Technical Working Group to improve the measurement of food consumption in household consumption and expenditure surveys, through the United Nations Inter-Agency Working Group On Agricultural And Rural Statistics; on the UN Expert Advisory Group on Food and Nutrition Security Measurement; and the Editorial Board of the Global Food Security Journal.

In addition to her research and policy engagement, Dr. Coates teaches a range of graduate courses at the Friedman School.

Innovative Technology and Dietary Assessment in Low-Income Countries

Prof. Efstratios Gavves
University of Amsterdam, The Nethelands

Dr. Efstratios Gavves is an Assistant Professor with the University of Amsterdam in the Netherlands and Scientific Manager of the QUVA Deep Vision Lab. After Efstratios completed his PhD with Prof. Arnold Smeulders and Dr. Cees Snoek in 2014 at the University of Amsterdam, he worked as a Post-doctoral Researcher at the KU Leuven with Prof. Tinne Tuytelaars. He has authored several papers in major Computer Vision and Multimedia conferences and journals, including CVPR, ICCV, ECCV, PAMI, IJCV, CVIU. His research interests include, but are not limited to, statistical and deep learning with applications on computer vision tasks, like object recognition, image captioning, action recognition, memory networks and recurrent networks, tracking.

A Novel Perspective of Image Search for Tracking and Actions

Dr. Thomas Mensink
University of Amsterdam, The Nethelands

Dr. Thomas Mensink received the M.Sc. degree, with honors, in artificial intelligence, from the University of Amsterdam (UvA) in 2007. He worked towards a Ph.D. jointly with the LEAR team of INRIA Grenoble and the Computer Vision team of Xerox Research Centre Europe. His PhD has received the AFRIF Thesis award 2012 for the best Ph.D. thesis in pattern recognition in France. Currently, he is post-doctoral scholar at the University of Amsterdam and received the prestigious young talent VENI award from the Netherlands Organisation for Scientific Research (NWO) in 2015. He focuses on machine learning for image and video classification. More specifically, his current research addresses learning semantic representations for visual data, which is essential when learning from few, or even zero, examples. He is lecturer of a MSc course on Visual Search Engines and have taught several classes on zero-shot learning for computer vision for different audiences. Some milestones include: over 1200 citations for the ECCV’10 paper about the Fisher Vector for image classification, ACM Multimedia 2014 best paper award, and ACM ICMR 2016 best paper award.

Learning to Reuse Visual Knowledge