Summer Research Initiative
Summer Research Initiative
The Center for Data Science (CDS), as a part of the Data Science and Software Services (DS3) initiative and with support from the Moore-Sloan Data Science Environment (MSDSE), have developed a summer research initiative to introduce MS in Data Science students to research projects around New York University. These research initiatives will help integrate the students into research and also help applied researchers around NYU to complete their projects. The DS3 has funds to fully support some summer research associates for approximately 250 hours of (free to the relevant research unit) time. Those funds will be allocated on a competitive basis, but the initiative itself is open to all.
To help students connect with researchers, CDS/DS3 holds a research project fair in the spring. During this fair, invited researchers will have the opportunity to meet and talk to students interested in working on open projects. We are especially keen to hear from colleagues in the natural, physical and medical sciences.
CDS is a world-class institute for Data Science research, and its students are diligent, highly skilled and keen to work on ‘real’ research projects wherever they exist in the university. They have been trained in graduate level probability, statistics, machine learning, big data, and inference. Their specific domain knowledge varies from case-to-case but covers natural science, social science, computer science, mathematics, and cognate disciplines.
2020 Summer Research Projects Selected for Funding
- Justin Pargeter, “Using 3D markerless pose estimation and deep neural networks to understand Paleolithic skill acquisition and the origins of social learning”
- Matt Maurano, “Analysis of allelic heterogeneity in high-dimensional genetic data”
- Narges Razavian, “Natural Language Processing for Clinical Notes”
- Anna Harvey, “Jail Data Initiative”
- Alexej Jerschow, “Image classification, recognition, and synthetic data in diagnostics of rechargeable batteries”
- Michael Rizzo, “Metrics of Racial Inequality”
- Krzysztof Geras, “Building deep neural networks to diagnose breast cancer with multiple imaging modalities”
- Anli Liu, “Assessment of Right Temporal Lobe Function through Drawing”
- Yvonne Lui, “A practical DL-based tool for medical image specifications identification”
- Yingkai Zhang, “Drug design with deep learning”
- Stephanie Cook, “Features of social media usage and physical health among emerging adults”
- Petter Kolm, “Textual and sentiment analysis for trading”
- Cem Deniz, “Predicting Knee Osteoarthritis”
2019 Summer Research Projects Selected for Funding
- Ying Lu, “Developing Data-driven Analytics for Precision Rehabilitation”
- Anastasios Noulas, “Deep Learning Pattern Recognition in Augmented Reality Environments”
- Kevin C Chan, “Quantitative evaluation of brain changes driven by visual impairment or by sight restoration interventions”
- Alexej Jerschow, “Image classification, recognition, and synthetic data in diagnostics of rechargeable batteries”
- Sonali Shukla McDermid, “Evaluating ecosystem-climate interactions from MsTMIP”
- Florian Knoll, “End-to-end medical image reconstruction and diagnostic classification”
- Linda Moy, “Predicting the development of breast cancer in the future using current image data “
- Todd Gureckis, “Using Internet advertising to recruit participants for behavioral science”
- Yvonne Lui, “Predicting Missed Appointments in An Academic Radiology Practice”
- Barton Beebe, “Is Europe Running Out of Trademarks? Trademark Registration Practices in the European Union”
- Petter Kolm, “Reinforcement learning for dynamic replication and hedging of option portfolios”
- Anna Harvey, “Jail Data Initiative”
- Aristotelis Tsirigos, “Predict response to immunotherapy in lung cancer using deep learning”
- Douglas Kondziolka & Kenneth Bernstein, “Comparative Analytics from a Large Prospective Neurosurgery Registry”
2018 Summer Research Projects Selected for Funding
- Cem Diaz, “Deep Learning-based Imaging Biomarkers for Knee Osteoarthritis”
- Krzysztof Geras, “Breast Cancer Screening with Deep Neural Networks Tracking Cancer Progression”
- Anna Harvey, “Predicting Risks to Officer Safety/Identifying Human Trafficking Victims”
- Carol Huang, “Towards a base-resolution, quantitative model of genetic and epigenetic effects on protein-DNA interaction”
- Anli Liu, “Optimization of Responsive Neurostimulation (RNS) Therapy Through Data Mining Approaches”
- Yvonne Lui, “Diffusion MRI and Working Memory”
- Narges Razavian, “Deep Learning for Segmentation of Brain MRI images”
- Heidi Schambra, “Quantifying Rehabilitation Training After Stroke”
Research Sponsors
