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Data science research at CCIS addresses challenges that are presented by our increasingly data-driven society. CCIS researchers develop new methods and tools for storage, retrieval, visualization, modeling, summarization, and interpretation of data. Our research directly impacts a broad range of disciplines from basic science and engineering, political and social science, to human health—all with the common goal to drive society forward.

Areas of investigation:

  • Business and predictive analytics
  • Computational epidemiology
  • Computational molecular biology and bioinformatics
  • Computational social science
  • Computer Vision
  • Data mining
  • Database systems
  • Digital humanities
  • Game analytics
  • Health informatics
  • Information retrieval
  • Information visualization
  • Knowledge representation
  • Machine learning
  • Natural language processing
  • Parallel and distributed data analysis
  • Statistics

Data science research is driven by the increasing speed of data generation, and by the exponential growth of size, scope and complexity of the acquired datasets. CCIS brings together expertise in database management, software engineering, algorithms, statistics, machine learning, and data visualization, to offer innovative solutions. The faculty’s cross-departmental appointments and multidisciplinary research interests ensure that the methods are closely connected to practical applications. PhD and Masters students will have the opportunity to build deep methodological and practical skills through research collaborations, and through hands-on industry internships—equipping students to recognize and solve the most critical and emerging challenges related to data science today and in the future.

Notable achievements:

  • Since 2010, members of a multidisciplinary research group lead by Prof. Ehsan Elhamifar have worked on learning low-dimensional models from complex high-dimensional data and reducing redundancy in massive datasets in the presence of corruptions and missing information. The research has resulted in a new class of efficient algorithms with provable guarantees, which have become the state of the art in several important problems in computer vision, including motion segmentation in videos, human activity modeling and data summarization, as well as other domains.
  • Riedewald’s group has a long and successful track record of collaborations with researchers from domain sciences such as ornithology, physics, mechanical and aerospace engineering, and astronomy. This work has been published in premier peer-reviewed venues, including a best poster award at ICDE for a novel approach for processing of imprecise queries. Together with a colleague from physics, Prof. Riedewald received a $1.8M NIH grant to develop techniques for automated reconstruction of structure and dynamics in neural circuits—a major step toward understanding the brain.
  • Members of Prof. Olga Vitek’s group develop statistical methods and software for modern biotechnologies, which study complex bio-molecular systems on a large scale. One such tool, Cardinal, developed for statistical analysis of mass spectrometry-based imaging experiments, received the 2015 John M. Chambers Statistical Software Award of the American Statistical Association.
  • Members of the interdisciplinary game research group, PLAIT lab, led by Prof. Magy Seif El-Nasr have developed and pioneered the field of Game Analytics. Research within this new area has resulted in the first book published specifically on Game Analytics called Game Analytics: Maximizing the Value of Player Data. The book includes multiple chapters focusing on data collection and cleaning given game contexts and states, knowledge discover using data collected through game play including data mining and visualization.
  • Since 2012, members of the PLAIT lab, in collaboration with several researchers at NYU and Texas A&M, have been working on multiple projects, including a project investigating the evolution of the game field using publication data, and visualization work to understand user behavior within games. These two works, funded by multiple grants, won best paper awards.
  • CCIS researchers won best paper award for their work on game data visualization.
  • In collaboration with researchers at Harvard Business School and London Business School and Harvard Medical School, Prof. Christoph Riedl conducted a study on biases in peer-review. The study used a field-experiment combined with using records of 20 million published research papers to measure “intellectual distance” between the knowledge embodied in research proposals and an evaluator’s own expertise. The work informs peer-review policies of large science funding organizations like NSF and NIH.
  • CCIS Data Science faculty Yizhou Sun and Olga Vitek are recipients of the prestigious National Science Foundation CAREER Awards.
  • CCIS members have held leadership positions on the committees of major conferences, including SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), the International World Wide Web Conference (WWW), the Association for Computing Machinery’s International Conference on Research and Development in Information Retrieval (SIGIR), Conference on Computer Vision and Pattern Recognition (CVPR), Neural Information Processing Systems (NIPS), and the International Conference on Data Engineering (ICDE).
  • CCIS faculty serves on the Editorial Board of major journals, such as Data Mining and Knowledge Discovery (DMKD) and Information Systems (IS).

Academic Program Links

Research Area - Data Science