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Author Archives: Ann Zsilavetz

Graduation 2020: CS Department & TCNJ Honors

Congratulations, TCNJ Class of 2020! Congratulations to all of our CS majors who graduated this year!

Below, we recognize our majors who graduated with TCNJ and CS Department honors.

Summa Cum Laude
Tomer Aberbach
Madeline R. Febinger
Ethan C. Kochis
Tomer Singal

Magna Cum Laude
Caroline G. Aboff
Daniel J. Beer
Brian L. Duke
Kyler R. Steele
Richard Wenxuen Wu

Cum Laude
Shm Garanganao Almeda
Kiernan T. Dempsey
Cole Nakamura Ding
Mark Raymond Meddleton
Vangelo Tasho

CS Department Honors
Tomer Aberbach

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 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 ( 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.

Fall 2020 Registration Wait-list

The registration period for Fall 2020 courses is April 7 – 17, 2020.  Some seats have been reserved for CS majors in all CSC courses.  Please review the registration newsletter for additional information on options courses offered this semester.

After your registration window opens, if the class you need is closed, put yourself on the wait-list using the Qualtrics form below.

Be sure to read all directions and enter all requested information.

Fall 2020 Wait-list:

If you make changes to your schedule after entering your submission to the wait-list and need to update the department on which course(s) must be dropped, email with this information or enter another wait-list submission.

We will not be signing students into courses until Monday, April 20, after the registration window closes.  Please do not email the department for updates before this time.  We will enroll students into any unfilled seats in order, based on their registration times and time they registered on the wait list.

Be sure that your intended course does not conflict with a course in your current schedule, and that you are willing to drop conflicting courses to make the change.  If you have a full course load or time conflict and do not indicate courses to drop on your wait-list submission, your submission will be disregarded.

As always, have a back-up plan in case you are not able to get into your preferred courses.

Please see the Advising Resources webpage for more information about submitting Mentored Research or Internship forms for Summer and Fall 2020.

Links to other School of Science Department Wait-lists can be found below:

Math/Stat: (link to form usually posted on bottom of website during registration)

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:

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:

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.

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.

Tips for Working & Studying Remotely

Students, faculty, and staff alike are learning how to work and study from home due to the coronavirus outbreak.

Here are some tips (in no particular order) for managing your time at home and taking care of yourself in this stressful, unprecedented time.

1.  Establish a routine, but break the day into segments.   Make sure that you attend classes and complete assignments that are time-sensitive, but outside of class-time, find a rhythm that works for you.  Try to wake up and get to bed at the same time when possible.  Study for a set amount of time and then take a break to do something else.  Use the “Schedule” template on Google Sheets or one of the many others available on Google Drive to plan your days out, or complete a daily journal if it’s helpful.

2.  Get familiar with TCNJ’s tools for working remotely.   Your instructors may use a variety of tools like Zoom, Google Meet, and Canvas modules, for example, and may share pre-recorded video lectures and/or hold live Q&A sessions.  You can also access Virtual Labs through the TCNJ Today page.   Faculty office hours and tutoring hours will be held online.  It’s more important than ever to check your email so that you don’t miss important reminders or updates.

3. Make sure you eat enough and stay hydrated.  Download a reminder app or set alarms on your phone to notify you that it’s time to eat or drink if you find it helpful to do so.

4. Sleep.  Getting enough sleep is crucial to your mental and physical wellness, especially when you’ll be spending more time working alone.

5. Move your body. Though we’re all doing our best to practice safe social distancing and/or self-isolation, make sure you get some exercise. Get out for a short walk if you’re able to do so safely or find one of the many online workout or yoga routines that fitness instructors have been sharing.  Youtube continues to be a great resource.

6. Find a quiet space (and time) where you can work.   If you are living with others and/or sharing technology resources, you may not have the luxury of creating a separate space to work.  Work out a routine with others living with you if you need to.  If you have headphones, use them to block out other noise or listen to a calming playlist.  If you do have your own laptop and room, create a work station for yourself that you can return to each day.

7. Make time to get in touch with friends and loved ones.  Whether it’s through text, email, phone call, or video call, check in with your friends and loved ones.  If you have the option to video call and see others’ faces.

8. Study with a group of peers (online).  Not only does studying together reinforce the concepts you’re learning, but it will counter the loneliness you may feel now that you are not attending class on campus.  Set up a study group once a week if you can to get in some much needed socializing.  It will help you organize your week and give you something to look forward to.

There are plenty more tips to be found online, but if you see something important that’s been missed, please email

Check out these articles for these and other ideas.  Most are geared towards employees who are working from home, but these tips are also applicable to students.


10 Tips for Working at Home Like a Pro:

How to Work From Home Now That Your Boss Doesn’t Want You Coming In:

Working at Home? Self-Isolation Doesn’t Have to Be Lonely (Opinion):

How to Stick to a Schedule When You Work From Home

Update Re: Department Tutoring for Spring 2020

The Computer Science Department offers drop-in tutoring hours during the academic semester.

Effective March 23, 2020, tutoring hours for Spring 2020 will be held online via Google Meet.  Hours will continue  as per normal Spring 2020 semester schedule.  Each day of the week’s session will have a separate Google Meet link, so please be sure to join the appropriate session.  Please make sure that you greet the tutor when you join the Google Meet call, as multiple students may be in the same session.

Mondays, 2:00 – 5:00 PM   (David)
Tuesdays, 2:00 – 4:00 PM   (Tom)
Wednesdays, 3:00 – 4:00 PM  (Tom)
Thursdays, 3:30 – 5:30 PM    (David)
Fridays, 2:00 – 4:00 PM   (Tom)

Links to the Google Meet sessions can be found in Ms. Zsilavetz’s email to all CS majors and students in CS courses for Spring 2020.  Please first check your email for the email. dated March 20.  Links were also shared with all CS faculty members.  You can also email for the links.

Hours may vary during reading days and final exam weeks, so check this page and watch your email for updates as we approach the end of the Spring 2020 semester.

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.


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.

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.