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Events

Computer Science Colloquium, October 30

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/92912867463?pwd=cHZVaHkwQmF4UTRoOWkzTG1vc0xkQT09
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.

AT&T Workshop for CS Juniors & Seniors

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.

Computer Science Colloquium: October 2

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/92912867463?pwd=cHZVaHkwQmF4UTRoOWkzTG1vc0xkQT09
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

Celebration of Student Achievement: May 6, 2020

This year’s Celebration of Student Achievement events will be live streamed on YouTube.

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

Call for Goldberg-Neff Scholarship Prize Applications – 2020

Charles H. Goldberg – Norman Neff Scholarship Prize in Computer Science

(Applications due Tuesday, April 21, 2020 by 5:00 PM)


The Charles H. Goldberg – Norman Neff Scholarship Prize is awarded annually by the Computer Science Department to a student(s) who has/have demonstrated academic excellence in Computer Science and who will be continuing into graduate study in Computer Science.

Eligible students are graduating Computer Science majors who have applied for admission for graduate study in Computer Science. The number of awards and the award amount are at the discretion of the Computer Science Department. The award check will be conveyed to the awardee(s) upon matriculation in a graduate program in Computer Science within one year of the announcement of the award.


How to Apply

Please complete the following form and submit your application (Word doc, PDF, or typed email response) to Ms. Zsilavetz via email (zsilave2@tcnj.edu) before the deadline.

 

1. Name: _____________________________________

 

2. How can we contact you after graduation:

 

Phone: _______________________________

 

E-mail: _______________________________

 

Postal address _________________________

 

3. List some of the graduate programs to which you are applying:

 

4. Please attach a short essay discussing your plans for graduate study.

Computer Science Colloquium: April 3

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-whub-gpj

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.

Computer Science Colloquium: March 6

On Friday, March 6,  the Computer Science Department will host its next colloquium for the Spring 2020 semester. Angela Huang (TCNJ Class of 2017) from the University of Pennsylvania will give a technical talk on bioinformatics entitled “A Subspace Clustering Algorithm for Identifying Cell Populations with scRNA-seq”.  An abstract of her talk can be found below.

Please join CS faculty and students in Science Complex P101 from 12:30 – 1:30 PM for this talk.
Refreshments will be provided.

Abstract:

Background: With the advent of single-cell RNA-sequencing (scRNA-seq), researchers now have the ability to define cell types from large amounts of transcriptome information. Over the years, various clustering algorithms have been designed. Motivation for such work can be seen in Cell Atlas projects, which aim to depict cell types present in an organism across its stages of development. Currently, most clustering algorithms measure cell-to-cell similarities using distance metrics on the full set of genes (after the optional step of dimension reduction). This requires clusters to be both compact and far enough apart that the algorithms can recover them. However, for developmental data, the clusters may not be compact; cells may be well spread out across developmental trajectories. The clusters may also not be very far apart; the developmental trajectories may intersect.

Methods: In our work, we propose to model each cell population with a single affine subspace, where all cells of the same type share a common set of constraints.

Results: We present an algorithm that leverages this subspace structure and learns a cell-to-cell affinity matrix based on notions of subspace similarity. We simulate scRNA-seq data according to the subspace model and benchmark the performance of our algorithm against pre-existing methods. We further test our algorithm on an in-house C. elegans dataset and other developmental datasets.

Significance: We show how our algorithm is able to recover information on both cell type and developmental time. Lastly, we demonstrate how the subspace model allows us to compactly recover the major genes involved in an organism’s development.

Bio:
Angela Huang is a Computer Science alumni of CS @ TCNJ. She is currently pursuing her PhD in Computer Science at The University of Pennsylvania. Her research interests are broadly in the areas of Computational Biology and Data Analysis. Prior to graduate school, her interest in Computational Biology grew through her research experiences in Dr. Dimitris Papamichail’s laboratory at TCNJ, Dr. Xin Li’s laboratory at Louisiana State University, and Dr. Michael Brent’s laboratory at Washington University, St. Louis. In her free time, Angela enjoys nature, art, choreography, and exploring new exhibits around Philadelphia.

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