Physical Sciences and Mathematics Commons^™

Unmasking Shadows: Unraveling Crime Patterns In Nyc's Boroughs, Jack Hachicho, Muhammad Hassan Butt

Publications and Research

New York City's crime dynamics have been on the rise for decades. Brooklyn and The Bronx have been disproportionately affected. This research aims to understand the crime landscape in these boroughs to formulate effective policies. Using crime data from official sources, statistical analyses, and data visualizations, the study identifies patterns and trends. The data encompasses over 400,000 reported incidents collected over the past 10 years, meticulously categorized by borough, crime type, and demographic information. Brooklyn has the highest overall crime rate, followed by The Bronx. Most shooting victims are Black. This highlights the need for holistic community programs to address …

Syllabus For Computational Physics (Phys 39907), Mark D. Shattuck

Open Educational Resources

Syllabus for City College of New York Computational Physics course.

Natural Language Processing For Disaster Tweets, Akinyemi D. Apampa, Nan Li

Publications and Research

Our goal is to establish an automatic model that identifies which tweets are about natural disasters based on the content of the tweets. Our method is to construct a decision tree based on keyword searching. We will construct the model using 7,645 tweets and test our model on 3,465 tweets as an assessment of the performance.

Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii

Publications and Research

The spaces we live in go through many transformations over the course of a year, a month, or a day; My room has seen tremendous clutter and pristine order within the span of a few hours. My goal is to discover patterns within my space and formulate an understanding of the changes that occur. This insight will provide actionable direction for maintaining a cleaner environment, as well as provide some information about the optimal times for productivity and energy preservation.

Using a Raspberry Pi, I will set up automated image capture in a room in my home. These images will …

Leveraging The Popularity Of Virtual Conferencing Due To The Covid-19 Pandemic To Create New Opportunities For Stem Education, Andrew Singh, Nazrul I. Khandaker, Violeta Escandon Correa, Omadevi Singh, Ariel Skobelsky, Farhan Tanvir, Brian Sukhnandan, Matthew Khargie, Elton Selby, Masud Ahmed

Publications and Research

Due to the COVID-19 pandemic, virtual learning has become a necessity for K9-16 education. Virtual classwork has been administered through platforms such as Google Classroom, Clever, and iReady. During the summer of 2021, the City University of New York (C.U.N.Y) York College campus hosted its NASA MAA MUREP (Minority University Research and Education Project Aerospace Academy) program virtually using a combination of Zoom, Google Docs, and even Canva, which some students requested as a more intuitive alternative to Microsoft PowerPoint. Students were mentored to use the scientific method to explore their interests in the STEM field, with a geoscience or …

Addressing The Learning Loss During The Covid-19 Pandemic Through The Adaptation Of Virtual Platforms, Nazrul I. Khandaker, Anika Nawar Mayeesha, Violeta Escandon Correa, Toralv Munro, Andrew Singh, Matthew Khargie, Ality Aghedo, Jasmin Budhan, Krishna Mahabir, Belal A. Sayeed

Publications and Research

The York College-hosted NASA MAA (MUREP AEROSPACE ACADEMY) has always played a pivotal role in minimizing the learning loss during the summer months, which was heightened during the pandemic. Support from AT&T, Con Edison and NASA enabled the MAA program at York College to offer a virtual STEM education with an earth science concentration to 1000 plus underserved K1-12 students from the community last summer, including 160 high school students. Two factors made this endeavor fruitful: allowing additional time to engage in STEM lessons and increasing self-motivation to successfully accomplish assigned tasks. Students built partnerships and resolved technical issues with …

Teaching Machine Learning For The Physical Sciences: A Summary Of Lessons Learned And Challenges, Viviana Acquaviva

Publications and Research

This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units.

Content Analysis Of Two-Year And Four-Year Data Science Programs In The United States, Elizabeth Milonas, Duo Li, Qiping Zhang

Publications and Research

Data has grown exponentially in the last decade, and this growth has resulted in vast challenges for both business and IT domains (Hassan & Liu, 2019). This growth has given rise to the Data Science field, which has also grown exponentially in the last few years (Hassan & Liu, 2019; Song & Zhu, 2016). The Data Science field has its origins in the statistics and mathematics domain (Cao, 2017b), but is now considered a multidisciplinary field (Aasheim et al., 2015). Data Science warrants knowledge of data analytics, programming, systems, applications, informatics, computing, communication, management, and sociology (Aasheim et al., 2015; …

Using Data Science To Create An Impact On A City Life And To Encourage Students From Underserved Communities To Get Into Stem, Elena Filatova, Deborah Hecht

Publications and Research

In this paper, we introduce a novel methodology for teaching Data Science. Our methodology relies on the outlook of the student body in our college. Our college is an urban, commuter, HSI (Hispanic Serving Institution) school with 34% Hispanic and 29% Black students. 61% of our students come from households with an income of less than $30,000+. Thus, many students in our college come from the communities that are underrepresented in the STEM fields and in the decision-making positions in the government (on the city level, state level, country level). However, in our methodology, we want to flip the situation …

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An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja

Publications and Research

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …

Application Of Randomness In Finance, Jose Sanchez, Daanial Ahmad, Satyanand Singh

Publications and Research

Brownian Motion which is also considered to be a Wiener process and can be thought of as a random walk. In our project we had briefly discussed the fluctuations of financial indices and related it to Brownian Motion and the modeling of Stock prices.

Discovering Kepler’S Third Law From Planetary Data, Boyan Kostadinov, Satyanand Singh

Publications and Research

In this data-inspired project, we illustrate how Kepler’s Third Law of Planetary Motion can be discovered from fitting a power model to real planetary data obtained from NASA, using regression modeling. The power model can be linearized, thus we can use linear regression to fit the model parameters to the data, but we also show how a non-linear regression can be implemented, using the R programming language. Our work also illustrates how the linear least squares used for fitting the power model can be implemented in Desmos, which could serve as the computational foundation for this project at a lower …

Public Interest Technology – Exploring Covid-19 Health Data, Sarah Zelikovitz

Open Educational Resources

This module is part of a Introduction to Data Science course that covers the different parts of the data science process: data acquisition, cleaning, exploratory data analysis, and modeling. The COVID-19 pandemic has created much interest in public health data, as well as interest in visualization of all types of data. Public health data has a set of challenges that is unique to health data, with HIPAA laws, and real time collection of data. With COVID-19, the challenges are particularly amplified, as data collection and statistics collected are constantly changing in response to feedback from labs, hospitals, drug companies, and …

Principal Component Analysis For Predicting The Party Of The Legislators, Afsana Mimi

Publications and Research

In Spring 2020, I did a project, "Decision Tree Predicting the Party of Legislators," and construct a decision tree model to predict legislators' parties' based on their votes. We also use this model to identify legislators who frequently voted against their parties. We used the legislators' roll call votes, Office of Clerk U.S. House of Representatives Data Sets (Categorical values) collected in 2018 and 2019. In this new project, We study the 2018 and 2019 vote data using Principal Component Analysis (PCA). The goal is to find a (compressed) model using unsupervised learning to distinguish the legislators' parties, and PCA …

Open Data, Collaborative Working Platforms, And Interdisciplinary Collaboration: Building An Early Career Scientist Community Of Practice To Leverage Ocean Observatories Initiative Data To Address Critical Questions In Marine Science, Robert M. Levine, Kristen E. Fogaren, Johna E. Rudzin, Christopher J. Russoniello, Dax C. Soule, Justine M. Whitaker

Publications and Research

Ocean observing systems are well-recognized as platforms for long-term monitoring of near-shore and remote locations in the global ocean. High-quality observatory data is freely available and accessible to all members of the global oceanographic community—a democratization of data that is particularly useful for early career scientists (ECS), enabling ECS to conduct research independent of traditional funding models or access to laboratory and field equipment. The concurrent collection of distinct data types with relevance for oceanographic disciplines including physics, chemistry, biology, and geology yields a unique incubator for cutting-edge, timely, interdisciplinary research. These data are both an opportunity and an incentive …

Sensor Data Analysis In Smart Buildings, Manuel A. Mane Penton

Publications and Research

Data analysis and Machine Learning are destined to evolve the current technology infrastructure by solving technology and economy demands present mainly in developed cities like New York. This research proposes a machine learning (ML) based solution to alleviate one of the main issues that big buildings such as CUNY campuses have, that is the waste of energy resources. The analysis of data coming from the readings of different deployed sensors such as CO2, humidity and temperature can be used to estimate occupancy in a specific room and building in general. The outcome of this research established a relationship between the …

Using Data Mining To Identify The Most Influential Factors In Training Results, Xiaoqing Wu, Daanial Ahmad

Publications and Research

Data Science is used as a tool to find hidden facts in the data. We want to find out what factors such as ‘AGE’, ‘TAX’, ‘PUPIL-TEACHER RATIO’, ‘PER-CAPITA INCOME’ contribute the most to housing prices. To answer this question, we studied the dataset of “Boston Houses Prices”. By applying the Lasso Regression (a Data Mining Technique) on the data set of “Boston Houses Prices” we identified the influential factors in the linear model. As a conclusion we found that there were six inputs which contributed the most to the prices of houses and those inputs are as follow: (i) CRIM-per …

Philosophical Perspectives, Jochen Albrecht

Publications and Research

This entry follows in the footsteps of Anselin’s famous 1989 NCGIA working paper entitled “What is special about spatial?” (a report that is very timely again in an age when non-spatial data scientists are ignorant of the special characteristics of spatial data), where he outlines three unrelated but fundamental characteristics of spatial data. In a similar vein, I am going to discuss some philosophical perspectives that are internally unrelated to each other and could warrant individual entries in this Body of Knowledge. The first one is the notions of space and time and how they have evolved in …

On Properties Of Distance-Based Entropies On Fullerene Graphs, Modjtaba Ghorbani, Matthias Dehmer, Mina Rajabi-Parsa, Abbe Mowshowitz, Frank Emmert-Streib

Publications and Research

In this paper, we study several distance-based entropy measures on fullerene graphs. These include the topological information content of a graph I_a(G), a degree-based entropy measure, the eccentric-entropy If_s(G), the Hosoya entropy H(G) and, finally, the radial centric information entropy H_ecc. We compare these measures on two infinite classes of fullerene graphs denoted by A_12n+4 and B_12n+6. We have chosen these measures as they are easily computable and capture meaningful graph properties. To demonstrate the utility of these measures, we investigate the Pearson correlation between them on the fullerene graphs.

Content Analysis Of Data Science Graduate Programs In The U.S., Duo Li, Elizabeth Milonas, Qiping Zhang

Publications and Research

Data science is an emerging academic field (Paul & Aithal, 2018), which has its origins in “Big Data/Cloud Computing” and complexity science domains. Data Science is about managing large and complex data (Big Data management) and analytics technologies (Paul & Aithal, 2018). Data, technology, and people are the three pillars of data science. In addition, Data Science is composed of three key areas: analytics, infrastructure, and data curation (Tang & Sae-Lim, 2016). Stanton (2012) defined data science as “an emerging area of work concerned with the collection, preparation, analysis, visualization, management, and preservation of large collections of information (Song & …

Time Series Analysis For Psychological Research: Examining And Forecasting Change, Andrew T. Jebb, Louis Tay, Wei Wang, Qiming Huang

Publications and Research

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that …