REMINDER: All CS Majors are required to attend one internship information session before they can apply for internship (CSC 399) for credit.
Be sure to check Dr. Papamichail’s website and come prepared with any additional questions you might have.
Wednesday, March 3 (6:00 – 6:45 PM)
Tuesday, March 16 (7:00 – 7:45 PM)
Zoom session link: https://tcnj.zoom.us/j/98358115019?pwd=eFFqVzB3Smw3TzlWN3E4V1BtMkI0Zz09
Hold the date! Dr. Michael Kearns, of UPENN and co-author of The Ethical Algorithm: The Science of Socially Aware Algorithm Design, will give a virtual colloquium talk on Friday, March 12 from 12:30 – 1:30 PM. Dr. Kearns is know for his work in the fields of machine learning, algorithmic game theory, and computational social science. A full biography and abstract will be available in the coming weeks.
Streaming links have been shared with students via mailing lists.
Please contact cs@tcnj.edu if you have any questions about the schedule.
Students who are taking CSC 099 and CSC 199 were assigned a poster to review. Please use the Qualtrics Survey link provided by your instructor to submit your response. Please note that you must identify yourself in the survey (in the appropriate survey field) in order for your review to be counted toward your course requirements.
REMINDER: All CS Majors are required to attend one internship information session before they can apply for internship (CSC 399) for credit.
Be sure to check Dr. Papamichail’s website and come prepared with any additional questions you might have.
Wednesday, November 11
3:00 – 3:45 PM
Zoom session link: https://tcnj.zoom.us/j/95197657763
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.
Please join CS faculty and students via Zoom from 12:30 – 1:30 PM for this talk.
Zoom Meeting: (ID: 936 1764 8901 / Password: 783429)
https://tcnj.zoom.us/j/
You must sign in with your TCNJ credentials. Waiting room will be enabled.
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.
Join a panel of TCNJ alumni and representatives from AT&T for the “My Identity: Personal Narrative Workshop” for CS juniors and seniors. The workshop focus on building interviewing skills for all students, and discussion of internship opportunities for juniors and full-time positions for seniors.
Students must RSVP for the event. Email cs@tcnj.edu or see Dr. Pulimood’s email from September 25 for more information about the event and the link to RSVP.
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.
Please join CS faculty and students via Zoom from 12:30 – 1:30 PM for this talk.
Zoom Meeting: (ID: 929 1286 7463 / Password: 020525)
https://tcnj.zoom.us/j/
You must sign in with your TCNJ credentials. Waiting room will be enabled.
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
Streaming links have been shared with students via mailing lists.
Please contact cs@tcnj.edu if you have any questions about the schedule.
| Presentation Sessions | 12:00 – 2:20 PM |
| Department Awards Ceremony | 2:30 – 3:15 PM |
| UPE Induction Ceremony | 3:30 – 4:15 PM |
Congratulations to all of our students who participated in HackTCNJ 2020 and a special thank you to the HackTCNJ e-board for all of their hard work!
For more information on the event, please see the School of Science’s news posting linked below:
On Friday, April 3, the Computer Science Department will host its final colloquium of the Spring 2020 semester. Ryan Levering (TCNJ Class of 2002) of Google will give a technical talk on recent trends in data management in the industry entitled “Knowledge in Search Engines”. An abstract of his talk can be found below.
Please join CS faculty and students via Google Hangout Meet from 12:30 – 1:30 PM for this talk.
Google Hangout Meet:
https://meet.google.com/omi-
If you’d like to participate in the talk and ask questions, please email Dr. Yoon to be added as a guest to the room. CSC 299 students will automatically be added as guests.
If you only would like to listen to the talk, use the live stream link below at the time of the talk. You will need to log in with your TCNJ credentials: https://stream.meet.google.com/stream/9188dcfd-f173-454c-8561-13eac32b5c95
Abstract:
Search engines have come a long way in the past twenty years as user needs and technology have changed. More and more, users are expecting search engines to know what they want rather than just be an index of web pages. In order to solve this very hard problem, they continue to incorporate techniques and patterns from many different computer science disciplines. From natural language understanding to databases, these disciplines help to build a semantic graph of knowledge. In this talk, we’ll go over some of those exciting problems and how Google is approaching them.
Bio:
After graduating in 2002 from The College of New Jersey Computer Science Department, Ryan Levering attended graduate school at SUNY Binghamton. There he made it almost all the way through a PhD dissertation in applied machine learning before deciding that he’d rather write code than papers. He spent some time in a flight search company working on machine learning systems before the company was acquired by Google, where he’d always wanted to work. Now he works on APIs and tools for Google to ingest structured data from the people who own it. He lives near Boston with his wife and two children.