On Friday, October 30,  the Computer Science Department will host its second colloquium of the Fall 2020 semester. Dr. Uddipan Das of the Computer Science Department at TCNJ will give a technical talk entitled “Quality-aware Data Management in Smart Grid Communication Networks”.  An abstract of his talk can be found below.

Abstract:
The inclusion of various intelligent electronic devices such as smart meters for Advanced Metering Infrastructure (AMI) in Smart Grids is expected to result in intermittent or frequent communications network congestion if additional network infrastructure investments are not made. One approach to deal with such a data volume challenge in smart grids without additional investments to increase network capacity is to aggregate data streams within the underlying communication network whenever network congestion happens. However, this needs to be done carefully so as not to significantly impact the smart grid applications that need the data. In this talk, I will present my research on data management in capacity-constrained communication networks of AMI in Smart Grids considering the requirements of Quality-of-Service (QoS). I will discuss my research contributions in this area to deal with the challenge of managing data traffic while keeping a balance between data granularity and network latencies.

Bio:
Uddipan Das is currently working as an Assistant Professor in the Department of Computer Science at The College of New Jersey. He graduated with a Ph.D. degree in Electrical Engineering and Computer Science from Wichita State University in 2020. Previously, he earned his M.E. degree in Software Engineering from Jadavpur University and B.Tech degree in Computer Science and Engineering from West Bengal University of Technology. His primary research interest centers around the design and development of data-driven algorithms and systems in smart cities; specifically, data management in smart electric power grid communication networks and smart environments for navigation and wayfinding for people with disabilities. He has published peer-reviewed research publications in the top-tier journal and conference venues, and he serves as a reviewer for the IEEE Systems Journal, IEEE ICDCS, IEEE NAPS, and IEEE IGSC, etc. He is a member of IEEE, IEEE Computer Society, and IEEE Power & Energy Society.

On Friday, October 2,  the Computer Science Department will host its first colloquium of the Fall 2020 semester. Niluthpol Chowdhury Mithun of SRI International will give a technical talk entitled “Learning Multimodal Retrieval Models with Limited Labeled Data”.  An abstract of his talk can be found below.

Abstract:
In recent years, tremendous success has been achieved in many computer vision and multimedia tasks using deep neural network models trained on large hand-labeled datasets. In many applications, this may be impractical or infeasible, either because of the non-availability of large datasets or the amount of time and resource needed for such labeling. In this respect, an increasingly important problem is in the light of data-hungry deep neural network models is how to learn useful models with limited labeled data. Developing robust models with a limited degree of supervision could be extremely useful for multi-modal retrieval and analysis tasks as collecting training data for these tasks is extremely labor-intensive and prone to significant errors. In this talk, I will go over several multi-modal retrieval tasks (i.e., video-text retrieval, RGB-LiDAR Localization, and text-based video moment retrieval) focusing on developing efficient solutions leveraging available incidental signals or weak labels.

Bio:
Niluthpol Chowdhury Mithun is currently an Advanced Computer Scientist at the Center for Vision Technologies, SRI International in Princeton, NJ, USA. He graduated with a Ph.D. degree in 2019 from Video Computing Group at the University of California, Riverside (UCR). Before joining UCR, he was a Sr. Software Engineer at Samsung R&D Institute Bangladesh. Previously, he received his Bachelors and Masters degree from Bangladesh University of Engineering and Technology. His current research is focused on solving fundamental problems in Computer Vision, and Machine Learning with more focus on representation learning with multiple modalities (e.g., vision, language, LiDAR), learning under limited/weak supervision and multi-modal embedding. He has successfully applied these methods to several real-world problems such as image-text retrieval, video moment localization, video summarization, object detection, visual localization. His work has been published at several high-quality venues such as CVPR, MM, TIP, T-ITS etc. He has won the ACM International Conference on Multimedia retrieval 2018 best paper award and the SRI CVT SharkTank 2019. He serves as a program-committee member/reviewer for venues such as CVPR, ICCV, ECCV, AAAI, MM, ICIP, T-PAMI, T-MM, T-CSVT, T-ITS, PR, PRL