Center for Data, Mathematical, and Computational Sciences

March 22, 2022DMC Advisory Board Member - Yoav Nathaniel

Ramapo College’s Center for Data, Mathematical, and Computational Sciences welcomes Yoav Nathaniel to our Advisory Board! Our Advisory Board’s mission is to assist the Center in aligning our curriculum with industry needs and expectations, helping us to promote our curriculum, and guide us in developing our sponsored fieldwork experiences to our students.

Yoav Nathaniel was born in Israel and grew up in New Jersey, where he attended Ramapo College to study computer science. In 2015, while in school, Yoav joined Avanan, a cloud security startup that was later acquired by CheckPoint, as it’s first employee. Initially focused on QA for Avanan’s product, but later shifted to development, sales, marketing and cloud security thought leadership. Yoav later joined Goldman Sachs and led its firm wide cloud security engineering team, governing the cloud security standards of over 10,000 engineers. In early 2022, Yoav co-founded Silk Security to help security teams accelerate their remediation processes and save hours of work per week.

Categories: Data Science, MSCS, MSDS

March 1, 2022Center for Data, Mathematical, and Computational Sciences Fair

Date/Time: Tuesday, 4/5/22, 5-7pm
Location: Ramapo College, Trustees Pavilion 1

Join us for the Data, Mathematical, and Computational Sciences Fair on April 5th, 2022! This event will feature keynote speaker Dr. Shawn Simpson, Principal Data Scientist at BlackRock AI Labs, who will share practical tips gleaned from her career as a Data Scientist in finance, media, and advertising. Following the keynote speech, attendees will have the chance to view posters showcasing Ramapo students’ research projects in Data, Mathematical, and Computational Sciences. Attendees will be able to network with Ramapo students and faculty, as well as prospective employers who may be looking to hire interns or full-time employees. Awards will be given for the best posters.

This event is open to current and prospective students, faculty, staff, and members of the public.

This page will be updated as more information about the event is announced

Schedule

5:00-5:05pm Guests arrive
5:05pm-5:10pm Welcome remarks (President Cindy Jebb)
5:10-5:20pm Welcome and overview of Ramapo’s bachelor and master degree programs in Data, Mathematical, and Computational Sciences (Scott Frees and Amanda Beecher)
5:20-6:15pm Keynote speech: “Field notes for future data scientists: tips from a career in industry” by
Dr. Shawn Simpson, Principal Data Scientist, BlackRock AI Labs. See additional information below.
6:15-7:00pm Student poster session and networking event

Keynote Speaker

Speaker: Dr. Shawn Simpson, Principal Data Scientist, BlackRock AI Labs

Title: Field notes for future data scientists: tips from a career in industry

Abstract: When training to be a data scientist there is an emphasis on data analysis and modeling
techniques — but what happens once you are out in industry? This talk will provide practical suggestions based on my career in data science, with examples drawn from applications in news and media, finance, and advertising technology. Topics will include going deep with data, understanding uncertainty, joining forces with engineers, taking a product mindset, knowing your end user, and architecting end-to-end systems.

Speaker bio: Shawn Simpson, Ph.D. is Principal Data Scientist in BlackRock AI Labs, where she leads an initiative that builds AI-powered decision tools for traders. Previously she was Senior Data Scientist at Tapad, a cross-device advertising technology firm. She built large-scale predictive models for telco applications using Scala, Spark/PySpark, Python, Hadoop MapReduce, and Google Cloud Platform technologies, and acted as lead data scientist on cross-functional product teams. Prior to that Shawn was Head of Data Science at Dow Jones. She was responsible for multivariate paywall testing on WSJ.com, predictive models for subscriptions and cancellations, company-wide data science training,
and internal consulting for newsroom, finance, and customer teams.

Before joining industry Shawn was an Assistant Professor of Statistics at Columbia University. Her research focused on analysis of recurrent events, Bayesian methods for large-scale data, and post-
marketing drug safety surveillance. She has a Ph.D. in Statistics from Columbia University and a B.S. in Electrical Engineering from the University of Illinois at Urbana-Champaign.

Categories: Uncategorized

February 15, 2022Multifractal Analysis of Daily US COVID-19 Cases

Wednesday, March 23rd at 6-7pm in ASB 323

In this talk we discuss how the multifractal detrended fluctuation analysis (MFDFA) technique can be used to explore the highly irregular behavior or volatility clustering of daily COVID-19 cases in the United States. By using the multifractal spectrum of the MFDFA we will characterize the path and predict the short or long memory behavior of the US COVID-19 Cases on different time scales.

Biography: Osei Tweneboah is Assistant Professor of Data Science at Ramapo College of New Jersey. His main research is Stochastic Analysis, Machine Learning and Scientific Computing with applications to Big Data and Complex Data sets arising in Finance, Public Health, Geophysics, and others. Dr. Tweneboah is co-author of the textbook Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets recently published by Wiley.

Categories: Data Science, Lecture Series, MSDS

February 7, 2022How to fail your tech interview successfully

Feb 22nd at 6-7pm in ASB 332

Computer Science is hard. Being a Software Engineer is hard. Translating your computer science skills into a successful Software Engineer gig after college shouldn’t be hard. After interviewing close to 100 candidates at all levels for Google, Facebook and Manticore, Dobri Yordanov has a few tips and tricks to share, as well as pitfalls to avoid. Join us if you’d like to hear about them, be it out of curiosity or practicality. Everyone is welcome!

Biography: Dobri is a Ramapo College ’15 graduate, originally from Bulgaria. Since graduating, he’s built a career as a Software Engineer and a self-described Prototype Wizard in large tech working for Google and Facebook, and most recently landing in the game industry as a principal engineer at Manticore Games. As of ’21, you can occasionally catch him in Ramapo teaching some of our Computer Science classes. He likes long walks on the beach in VR and philosophical ponderings about the nature of our existence and ethics in technology. He will also absolutely listen to you about your favorite algorithm or design paradigm – he is 100% that kind of dork.

Categories: Lecture Series, Uncategorized

November 16, 2021Data is the new OIL - Slides and Thank you

Thank Arnab Mukhopadhyay for his insightful overview of how corporations view data, and how that view has evolved. In case you missed it, take a Data is the new OIL from the presentation.

Categories: Data Science, MSDS

November 3, 2021Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets

Congratulations to Ramapo’s Data Science Professor Osei Tweneboah. Dr. Tweneboah has co-authored the textbook Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets recently published by Wiley. This book is perfect for data practitioners, undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs. It provides a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services.

Categories: Uncategorized

November 1, 2021Lecture Series: Data is the New Oil- Why Now?

Wednesday, November 10, 2021 at 5:00 PM until 6:00 PM – Eastern Standard Time

Ramapo College is proud to host a Graduate Lecture Series about a variety of interesting topics throughout the year.

During the RCNJ Lecture Series: Data is the New Oil- Why Now, you’ll have the opportunity to hear from Arnab Mukhopadhyay, Head of Enterprise Architecture at Valley Bank more about exploring a systemic or architecture-based viewpoint on exploring various aspects of data – ranging from an overview on data technologies, data integration, data analytics, data governance, and industry usage of data.

For more information about this particular topic, or questions about the event, please email Professor Scott Frees at sfrees@ramapo.edu and we will be happy to assist you.

You won’t want to miss this exciting and informative Graduate Lecture Series Event.

Categories: Lecture Series, Uncategorized

October 11, 2021Using Machine Learning to Analyze and Predict the Movement of Horseshoe Crabs in Long Island Sound - Recording

We want to thank Dr. Samah Senbel for the wonderful talk on using Machine Learning to analyze the movement of Horseshoe Craps in the Long Island Sounds. In case you missed it, her talk can be viewed here.

Categories: Uncategorized

August 27, 2021Using Machine Learning to Analyze and Predict the Movement of Horseshoe Crabs in Long Island Sound

Dr. Samah Senbel, Assistant Professor of Computer Science at Sacred Heart University will be describing her work on developing machine learning models to predict animal movement patterns. Please join us (virtually) on Monday, September 27th at 1pm to learn more about this important area of study in ecology, conservation and wildlife management.

Connection details will be provided after registration.

Abstract: Developing models to predict animal movement patterns is an important area of study in ecology, conservation and wildlife management. Models can be used to decipher patterns in mark-recapture data and machine learning can help to make predictions about future animal movement patterns. Project Limulus (PL), a community research program, has been tracking the movement of tagged individuals in the population of American horseshoe crabs (Limulus polyphemus) in Long Island Sound since 1997. During the spring spawning season, horseshoe crabs are captured by hand in spawning areas along the Connecticut (CT) shoreline, tagged and then released. Recaptured horseshoe crabs give valuable information about their behavior, if they exhibit site fidelity and movement patterns around the Sound. In this paper, we tested various models to find the best predictor for the movement of spawning horseshoe crabs to shorelines in the Sound based on the observed movement activity in previous years. The dataset consists of all the previous horseshoe crab movements: initial longitude and latitude, sex, initial date, and recapture longitude and latitude and recapture date. This dataset has 19,219 recapture records covering twenty years of activity. We experimented with three different models: Linear Regression, Decision Tree, and Random Forest Regression models. We used the data for 2018 as our test set and the data of all previous years as our training set. The Random Forest Regression model proved to be the best predictive model for animal movement and resulted in the smallest RMSE and MAE, as well as the smallest maximum error in prediction. The predicted horseshoe crab locations can be targeted in the next season for recapturing previously tagged horseshoe crabs, which provides valuable information about their movement patterns. It also concentrates the scientists’ effort and time to find the maximum number of horseshoe crabs.

This talk is supported by a grant from the Ramapo College Foundation.

Categories: Data Science, Lecture Series, MSDS

April 21, 2021MSDS Students at Scholars Week - College Football Recruiting Using Machine Learning Techniques

Colette Barca, Keith Osani, Nisha Srishan, and William “Brady” Wulster are inaugural members of the Master’s in Data Science program. During their MATH 570 Applied Statistics course with Professor Osei Tweneboah, they designed and completed a research project to predict high school football recruits’ college commitments using several Machine Learning techniques.

The four designed their project with the understanding that college football is big business. To create a winning team that will continue to generate revenue, Division I Football Bowl Subdivision (FBS) Power Five schools need to choose their recruits wisely. Recruits are selected
based on certain player attributes. Power Five schools are the most frequented schools by NFL scouts, making these schools highly desired by recruits. In this regard, Barca, Osani, Srishan, and Wulster built a model to predict whether a high school recruit will commit to a school in one of the Power Five Conferences. Such a model could allow a school to optimize its recruiting process, maximizing its return on investment. They used their dataset to fit several Machine Learning models. After completing the project, it was determined the Random Forest model produced the most accurate results. This model also revealed which particular attributes are indisputably the most important predictors of commitment to a Power Five school. This final model has the potential to be successfully used to improve the recruiting process.

Abstract

Poster Presentation

Categories: Data Science, MSDS