Undergraduate Research Program in Mathematics and Computer Science
Preparing for Undergraduate Research
If you are interested in getting involved with a research project related to Mathematics, Computer Science, or Data Science, you can choose from the following projects:
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Faculty Mentor
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Project Title
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Project Area
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Description
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Secondary Statistical Research
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Statistical research using large data sets
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Secondary statistical research using large, publicly available, real-world data from sources such as the longitudinal studies Add Health https://addhealth.cpc.unc.edu/ or National Survey on Drug Use and Health(NSDUH). https://nsduhweb.rti.org/respweb/homepage.cfm
The student a) becomes familiar with the goals of the study and the codebook b) formulates a research question, a hypothesis, and explores the available data for evidence in favor or against the hypothesis c) does not collect data but will download relevant data from the official website. Instruction will be given on a) the research process b) statistical methods appropriate to the choice of variables and needs of analyses, e.g., regression, t-test(s), chi-square, ANOVA, c) use of statistical software such as SPSS or R. In a two-semester project, we complete the sections of an APA-style manuscript: introduction, methods, results, discussion, references, abstract, and title; prepare a poster, and identify potential venues for publication or presentation. In a one-semester project we focus on methods, results, discussion, and a summary, and address the remaining parts as time permits. |
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Predictive Modeling and Statistical Approaches for Health Research in the All of Us Program
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Machine Learning
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The All of Us Research Program is an NIH initiative aimed at advancing medical research. By collecting genetic and health-related data from participants, it provides researchers with a valuable resource to analyze and address critical health questions. Students will engage in two interconnected projects: conducting statistical analysis to identify links between pre-cancerous conditions and cancer, and utilizing machine learning methods to create predictive models for disease forecasting.
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Segmentation and Classification of White Blood Cell Images
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Image processing, machine learning, biomedical imaging
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Students will apply machine learning algorithms to classify white blood cell images on a labeled dataset and automated segmentation of slides to create a test dataset to be labeled by a pathologist.
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Stock Price Prediction Using Machine Learning Algorithms
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Machine Learning
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This project involves developing a machine learning model to predict stock prices based on historical data. By analyzing patterns and trends, the model aims to provide accurate forecasts that can assist in making informed investment decisions.
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Historical Research of the Awards Received from NSF Aimed to Increase Women’s Participation in STEM Education and Careers.
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Mathematics Education
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This research aims to investigate the historical allocation of government grants and initiatives, particularly those by the NSF, that have sought to increase women’s participation in STEM fields, focusing on mathematics. This study will involve a systematic review of federal grants, policy documents, and other relevant records. The goal is to understand the impact of these initiatives on gender diversity in mathematics education and careers.
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Mathematica modeling of epidemics
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Applied Mathematics
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Mathematical modeling plays a crucial role in enhancing our understanding of the world around us. This project focuses on applying dynamical systems to epidemic modeling, particularly through the use of compartmental models. We will primarily explore systems of ordinary differential equations (ODEs) that describe the dynamics of epidemics, such as the Susceptible-Infected-Susceptible (SIS) and Susceptible-Infected-Recovered (SIR) models. Our work will include parameter estimation by fitting these models to data, as well as conducting simulations to study their behavior.
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Cultural Competency in Computing (or in STEM)
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Math and Computer Science but content can be adjusted to include more disciplines depending on the students who sign up for it.
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The United States is already a racially, ethnically, and culturally diverse society. In addition, technology has made social and professional interactions across countries and cultures very common. As a result, employers and professional organizations are emphasizing the importance of cultural competence and related skills in the workplace. In this course, students (1) will improve their cultural competence and their understanding of ethical and justice issues in computing and its impact on other STEM fields, Education, Criminal Justice, etc. as well as on everyday life; (2) will learn about the role that identity and intersectionality play in the tech sector, both in terms of the working environment and the technologies developed (e.g. facial recognition, voice recognition, recidivism, and predicting policing algorithms); (3) will learn about the policies designed to address racism, bias, and discrimination. The scope of the course and topics covered can be adapted to meet the interest of students from varied programs of study.
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| Mercedes Franco | DataJam | Data Analysis/Science | A team of 2-8 students will be formed and together we will propose a problem we want to tackle using data analysis/science. As a team, we will get support from peer mentors assigned to us through the DataJam organization. This will be a semester-long project and it will be presented at the DataJam Competition at the end of the semester. |
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| Venessa N. Singhroy | Enhancing Math Assessments: The Case for Ordinal Response Models (ORMs) | Data Analytics | Current math assessments mainly use multiple-choice questions, which are scored using Nominal Response Models (NRMs). NRMs are simple and cost-effective because they treat all answers as unrelated categories, without considering any order or depth in the choices. However, this method doesn't capture the reasoning behind students' responses. I propose using an Ordinal Response Model (ORM) instead. ORMs take into account the order or ranking of answers, which can give a clearer picture of how well students understand the material. In an initial study using Mokken Scaling, a technique similar to ORMs, I found that ORMs provide insights into students' mathematical thinking that NRMs miss. The goal of the proposed UR project is to investigate the effectiveness of ORMs and promote their use through academic publication. |
Overview of the Student Research Program in Mathematics & Computer Science
If you are interested in researching an advanced topic in Mathematics or Computer Science, then consider taking on an undergraduate research project. You can work one-on-one with a faculty mentor with expertise in that field.
Involvement with research projects will be recognized on your transcript via one or both of the 2 credit honors sections courses MA905 and MA906: Undergraduate Research in Mathematics and/or Computer Science I and II.
Prerequisites:
- Record of good academic performance
- MA440 or permission by the department
- Recommendation of Faculty members
Keep in mind that a research project requires a lot of time and hard work. You should be confident that you will have the time to devote to such a project while maintaining good grades in your other courses.
To learn more about the program, contact the Research and Independent Studies Committee:
- Dr. Yusuf Danisman, Office: S-349, ydanisman@qcc.cuny.edu
- Dr. Esma Yildirim, Office: S-235B, EYildirim@qcc.cuny.edu
The CUNY Research Scholars Program
Consider applying for the CUNY Research Scholars Program (CRSP). CRSP is a one academic year long commitment to research beginning in the Fall semester. Accepted student applicants will receive $5000 for their participation in the program.
See the QCC CRSP webpage for more information.
