NYU is excited to announce the continuation and expansion of CURP in 2022! CURP is the Center for Data Science Undergraduate Research Program, which was launched in Spring 2021 by the Center for Data Science in partnership with the National Society of Black Physicists.
PROGRAM DATES: JUNE 6 – AUGUST 12, 2022
LOCATION: RESEARCH PROJECTS WILL TAKE PLACE REMOTELY
FELLOWSHIP AWARD: $6,000
APPLICATION DEADLINE: APRIL 8, 2022
A strength of New York University is the active engagement of our scholarly community in a global world. As such, NYU’s community is enriched by individuals reflecting diverse sociocultural identities, perspectives, and experiences. NYU recognizes the value of a diverse community in supporting an intellectually challenging and inclusive educational environment.
CURP is a research mentorship program designed for a diverse group of undergraduate students who have completed at least two years of university-level courses and would like to conduct research in data science. The objectives of CURP are to provide meaningful research opportunities to talented students as well as the opportunity to develop the necessary skills and knowledge to participate in successful research collaborations, and integrate a community of academic peers and world-renowned faculty mentors who can advise, encourage, and support them. This is currently an online program, hence students can participate from anywhere in the country.
The program will run for the Spring semester from June 6 – August 12, 2022. A CURP fellowship award of $6,000 will be offered.
Julia Kempe Director, Center for Data Science
“We are excited to launch this unique undergraduate research program here at NYU’s Center for Data Science in close collaboration with the National Society of Black Physicists (NSBP). We hope that talented undergraduate STEM students take full advantage of this extraordinary opportunity and enjoy the exposure to cutting-edge and exciting data science research projects and mentorship by outstanding faculty in an inclusive environment designed to especially support students of diverse backgrounds.”
Stephon Solomon Alexander, Former President of the National Society of Black Physicists
“The National Society of Black Physicists is excited to partner with NYU Center for Data Science (CDS) for the CDS Undergraduate Research Program. This opportunity will give students first hands experience and training in Big Data and Machine Learning. These skills will definitely open new opportunities in a broad range of scientific fields and technology.”
The Center for Data Science is dedicated to ensuring that its scholarly community and the field of data science is enriched by individuals who, through their various backgrounds and life experiences, contribute to an intellectually challenging and inclusive educational environment. Our priorities are to maximize opportunities for students to be connected to top-notch faculty, and to learn in an environment that embraces a diversity of perspectives and that recognizes the values and unique experiences that those from historically underrepresented/under-served communities bring to the table. To that end, CURP is open to students of all backgrounds and especially encourages applications from individuals who come from diverse backgrounds and whose academic and research experience contribute significantly to the diversity and academic excellence at NYU.
Under the direction of CDS joint and affiliated faculty, undergraduate students will complete a research project and give a presentation. Students will be part of a group of peers with common interests in data. They will have the opportunity to attend talks given by leading researchers in their fields, attend workshops aimed at developing skills needed for research careers in data science, and learn techniques that will prepare them for the admissions to graduate and doctoral programs as well as for fellowship applications.
After the research opportunity, each student will:
- be part of a network of mentors that will provide continuous advice in the long term as the student makes progress in their studies.
- have access to faculty advice and references for graduate applications.
- be part of a network of other students of diverse backgrounds with similar interests.
How to Apply
- Applicants must be enrolled in a postsecondary institution for the Summer 2022 semester and be a United States citizen or a permanent resident. We are also pursuing opportunities for undocumented and DACA students, and welcome applications from these students.
- Priority will be given to current juniors and seniors.
- Students should plan to commit approximately 20 hours per week to their research projects.
- Participation in our virtual Python Bootcamp is mandatory and students must make themselves available the week prior to the start of the program.
Our application is open until all available opportunities are filled. Applications received on or before April 8, 2022 will receive full consideration. Applications received after April 8 will be considered on a rolling basis.
Applications for CURP Summer 2022 must be submitted via the CURP application link and contain the required application materials.
A complete application consists of the following items:
Applicants must include a copy of their transcript showing courses and grades from all postsecondary institutions they have attended. Unofficial copies (as long as they are legible) are acceptable, though we may request official transcripts upon acceptance to the program. Transcripts should be current through Fall 2021. If possible, a list of your Spring 2022 courses should be included.
2. Statement of Interest
Applicants must write a personal statement addressing your research interest, particularly with respect to the faculty and topics listed below, and why you would like to participate in CURP. You may write in any style, but try to address the origins of your interest in science, experiences (school-related and other) that have particularly stimulated you, obstacles you have faced along the way, and future educational and career plans and aspirations. If you are currently attending a two-year institution, provide the name of the four-year institution to which you plan to transfer and the date when you plan to transfer in your statement.
Artificial intelligence has the potential to revolutionize how healthcare is delivered. It can improve patient outcomes overall through the enhancement of preventive care and quality of life, as well as providing accurate diagnoses and treatment strategies. One of the most popular healthcare datasets is based on Electronic Health Records (EHRs) which contains important information about patients’ health conditions and the care they receive. This research project will be focused on EHR data related to Autoimmune disease which is a condition that occurs when our immune system mistakenly attacks our own body. The student who works on this project will mine clinical data to predict which patients with a particular autoimmune disease will respond to a specific type of treatment. Moreover, the focus will be on building machine learning algorithms that will attempt to predict how a patient will respond to a given treatment. The model will first prove itself by independently arriving at conclusions that match the clinical outcomes in the EHR; once validated, the model will examine the most influential factors that predict a patient’s response to a given drug.
Prerequisites: Machine Learning, Programming Skills (Python)
This project will investigate racial discrimination in police behavior. Existing research shows racial disparities in arrests, stop and search, and the use of force, with Black civilians subjected to these actions at considerably higher rates. Discrimination has a direct adverse impact on the people and communities subjected to higher levels of police interference and is likely to impact distrust in the police, which is widespread and consequential for public safety. This project will examine discrimination using a massive dataset on officer deployments, stop and searches, arrests, tickets, and the use of force in Chicago. The project will focus on documenting racial disparities in police-civilian interactions using a rigorous, data-driven approach. One particular emphasis of the project will be examining whether the extent of discrimination has changed in recent years.
Prerequisites: Proficient in R
This project will investigate and quantify the potential for audio signals to be reconstructed from lossy representations used in modern machine learning architectures for audio analysis. The eventual goal is to determine if we can find a representation that works well for a given downstream analysis task—e.g., sound event detection or music instrument recognition—without completely divulging the content of the original audio signal. The results will have implications for privacy preservation, copyright protection, and distribution of open-access audio datasets.
Prerequisites: Python and machine learning are a must; deep learning is preferred. Audio and/or signal processing experience is a plus.
Recent theoretical results suggest that gradient descent learning results in an implicit inductive bias in the weights of the network, such that the norms of the weights are minimized. Meanwhile, recent empirical findings suggest that hierarchical processing stages in deep networks geometrically transform feature representations such that “manifolds” corresponding to different categories become more separable. This project aims to connect these findings by exploring how the changes in the weight norms during learning contribute to representation untangling, if at all.
Prerequisites: Proficiency in python, linear algebra
In this project, you will help build a recommendation engine that provides personalized article recommendation. The content to be recommended come exclusively from academic articles (including archived academic papers or blog articles on towardsdatascience, as a few examples) and are geared towards people in the academic community. The project has several components, including crawling content from article sources, content understanding and interacting with users to adaptively improve on personalized recommendation.
Prerequisites: Proficient in python, knowledge of natural language processing is a plus
Stepped pressure equilibria have proved to be successful for the study of magnetic confinement fusion eWe have developed a tool that takes a text input and converts it into a dynamic decision tree. The candidate would use that tool to guide normal citizens to improve their health or to navigate the health bureaucracy. The main job of the student would be to assemble content in the proper form and to test the resulting dynamic decision tree with normal people. Some data preparation in python or some programming language would be helpful.
Prerequisites: Some programming
Reinforcement learning (RL) typically concerns how humans and machines learn through trial-and-error interaction with their environment. However, a lot of what humans learn is instead transmitted through language: we give and receive instructions, explanations, and hints that can enable us to perform a task well even on our first try. Standard RL models have no way of explaining how they perform RL tasks (i.e., summarizing their action policies in language), nor can they adjust their behavior based on others’ instructions. In this project, we plan to test how humans achieve these feats and develop new models with more human-like capacities.
Prerequisites: Experience in Python
3. Faculty Reference Letter of Recommendation
Applicants must designate one Faculty as a reference; this should be someone from whom you have taken a class or with whom you have done independent scientific work. Letters must be sent directly to firstname.lastname@example.org. It is the student’s responsibility to make sure the letter is received on time. You may include a second letter if you think it will strengthen your application significantly. The deadline for receiving letters of recommendation is April 15, 2022.
For additional information, please contact us at email@example.com. We look forward to seeing your application!
What CURP Scholars Have to Say…
Zoga Duka, 2022 CURP Scholar
As a CURP fellow, I am learning in-demand tech skills that will help me in my career. I also learned the importance of asking questions to further my knowledge in my research topics, which has given me an advantage compared to my peers. NYU has given me access to their resources.
Vishweshwar Ramanakumar, 2022 CURP Scholar
I believe part time, in-semester research programs like CURP should be more common… While I also enjoyed my time participating in an REU last summer, I feel like providing an entire semester for a project gives me the time to take a deep breath, soak in the opportunity, and really enjoy the process of research.
John Como, 2022 CURP Scholar
CURP has provided me with the unique opportunity to conduct research under the guidance of outstanding faculty. Not only have I learned about the research topic, but have also learned how to become a stronger researcher for the future.
Kennedy Sleet, 2021 CURP Scholar
I had the opportunity to work alongside NYU’s Center for Data Science through their amazing program CURP in 2021. Throughout this experience, I learned so much in regard to my career and future goals. My project focused on developing code used to identify and visualize different molecules, atoms, and elements. It was extremely interesting learning about datasets and statistics. This specific project and opportunity gave me a better understanding of how coding and machine learning can have such a huge impact on expanding subjects such as chemistry. Overall, I am extremely grateful for the experience and it helped me further my ideas for my career. I met some amazing hard-working individuals including my mentor. Furthermore, I highly recommend this opportunity to those wanting to expand their knowledge and futuristic ideas regarding computer science, physics, astronomy, data science, and more!
Isaac Robinson, 2021 CURP Scholar
CURP taught me how to design a research process and work with a team. It taught me to grapple with questions without clear answers, to work smarter and harder towards a goal I defined myself, and most of all, how to be a part of a scholarly community. A life-changing experience I would highly recommend!
Disclaimer: This webpage is still subject to some change, but it serves as a clear representation of the process as it stands now.
This program was made possible by generous support from our partner Capital One. We thank them for their support.