Nazmul Siddique
Nazmul Siddique

School of Computing, Engineering and Intelligent Systems,
Ulster University.

Talk Title: Reinforcement Learning for bio-inspired robotics.

Animals and insects are extremely effective at moving in their natural dynamic noisy environments. This draws inspiration to implement robust robotic movement and navigation mimicking insect taxis. This talk will present the application of Reinforcement Learning for the control of target-seeking Braitenberg vehicles in highly stochastic and noisy scenarios for source-seeking robots.

Nazmul Siddique (M) is with the School of Computing, Engineering and Intelligent Systems, Ulster University. He obtained Dipl.-Ing. degree in Cybernetics from Dresden University of Technology, Germany, and PhD in Intelligent Control from the Department of Automatic Control and Systems Engineering, University of Sheffield, England. His research interests include robotics, cybernetics, computational intelligence, nature-inspired computing, and stochastic systems. He has published over 200 research papers in the broad area of computational intelligence, vehicular communication, robotics and cybernetics. He authored and co-authored fours books and six edited books published by John Wiley, Springer and Taylor & Francis. He is a Fellow of the Higher Education Academy, and a senior member of IEEE. He is on the editorial board many journals including Nature Scientific Research (2018-2021), Journal of Behavioural Robotics, Engineering Letters, International Journal of Machine Learning and Cybernetics, International Journal of Applied Pattern Recognition, International Journal of Advances in Robotics Research and also on the editorial advisory board of the International Journal of Neural Systems.

Enamul Hoque Prince
Enamul Hoque Prince

Director, School of Information Technology,
Associate Professor,
3052 Victor Phillip Dahdaleh Building (DB),
York University,
Toronto, Ontario, Canada, M3J 1P3

Talk Title: Towards inclusive and accessible data visualizations and analytics

Analyzing data is at the heart of many decision-making tasks. However, most people are unable to make sense of data mainly because of the high threshold of required analytical skills for interpreting, visualizing, and making sense of large datasets. Moreover, the lack of understanding of the important data aggravates inequalities in access to information among different user populations ranging from vulnerable and marginalized communities (e.g., refugees, indigenous communities) to people who face various physical and cognitive challenges (e.g., blindness, dementia). In this talk, I will present how to tightly integrate state-of-the-art natural language processing (NLP) and visualization (Vis) techniques to support a diverse range of users with different levels of skills and backgrounds in performing data analysis tasks. We will introduce several NLP tasks for Vis, including automatic natural language interactions with visualizations, chart question answering, and chart summarization. We will then demonstrate several applications that leverage such NLP tasks to make data visualizations and analytics more accessible and inclusive. 

Enamul Hoque Prince is an Associate Professor and the Director of the School of Information Technology at York University. Previously, he was a postdoctoral fellow in Computer Science at Stanford University. His research addresses the challenges of the information overload problem using an interdisciplinary lens, combining information visualization and human-computer interaction with natural language processing. Since his research is uniquely positioned at the intersection of information visualization, NLP, and HCI, he regularly publishes in top venues in each of these areas including IEEE Vis, ACL, EMNLP, CHI, IUI, and UIST. He serves as an Area Chair for the ACL Rolling Review (2021-) and as a program committee member (2018-) for the IEEE Vis. His research has been funded by NSERC Canada, Canada Foundation for Innovation, and National Research Council Canada, among others.