Grants Awarded
NSF-CNS-MRI
- Acquisition of mobile robots to support indoor navigation and online 3D object detection.
- $100,450 + $43,050 Hunter’s co-share.
- October 2016 – September 2019.
Description
The objective of this proposal is to enhance mobile robotics with our extensive prior work on cognitively based navigation, 3D range scanning processing and real-time classification of objects. This proposal requests funding to purchase two robots, along with two laser scanners, on which to support projects on autonomous navigation and real-time classification from dense 3D range data. This project will provide novel solutions in the areas of automatic robot navigation and real-time classification from dense range 3D data in indoor scenes.
People
- PI: Ioannis Stamos.
- Co-PIs: Susan Epstein.
Google Research Award
- Classification of vehicles in points clouds of urban scenes.
- $45,500.
- September 2015 – December 2016.
Description
The photorealistic modeling of large-scale scenes, such as urban structures, has received signi cant attention in recent years. This is a challenging problem as urban environments are a mixture of buildings, people, vehicles, street level structures, roadways, curbs etc. The complexity of urban environments has to do with the variability of objects, partial visibility and occlusions, and varying object resolution. A major goal is the photorealistic rendering of such scenes for inclusion in products such as Google Map or Google Earth. A signi cant obstacle in that direction is that many objects are only partially sensed. Vehicles represent a major class of objects in this category. Locating the vehicles in the point cloud, identifying their pose and type is an important step towards their complete photorealistic representation.
People
- Co-PI: Ioannis Stamos.
- Graduate Student: Allan Zelener, Bradley Custer.
Mathematical Research Council
- Summer school in Mathematical Finance.
- $80,000 - $100,000.
- June 2015.
Description
Financial Mathematics is a branch of applied mathematics based on stochastic analysis and optimization that has gone through a period of extensive growth over the last years. Originally concentrated in portfolio management and derivatives pricing, the use of sophisticated mathematical methods has grown to a wide array of different applications in finance. This workshop will focus on three topics of current interest in the area of financial mathematics: high frequency trading, optimal investment under transaction costs, and systemic risk. All of the above areas have received great attention in recent years, and a significant number of open problems emerged in each of these topics. The objective of our workshop is to familiarize students with these topics and present to them some of the open problems as well as hands-on guidance on possible solutions. The professional development component of the workshop will shed new lights on the various possibilities of a career in the area of financial mathematics, both in academia and in industry.
People
- Co-PIs: Maxim Bichuch, Michael Carlisle, Birgit Rudloff, Stephan Sturm.
NSF - DMS - ATD
- Sequential detection and identification of multiple co-dependent epidemic outbreaks.
- CUNY portion: $278,154.
- 2012–2017.
Description
This project is key to the development of next generation quantitative algorithms for detection of epidemic outbreaks. The investigators address two focus problems that arise in epidemic surveillance, namely that of quickest detection of (a) spatially and (b) pathogen heterogeneous outbreaks. An early and accurate response is achieved by taking advantage of the co-dependent nature of the corresponding syndromic observations and by appropriate modeling of this dependency. To this end, the investigators develop innovative online quickest detection and sequential classification techniques to analyze multiple correlated data streams undergoing distinct changes. These techniques are assessed through their ability to optimally issue timely outbreak alerts with minimal false alarm rates. Moreover, the investigators address the problem of early detection and identification of an epidemic outbreak by designing a simultaneous min-max change-point detection and classification algorithm of a single data stream with unknown post-disorder characteristics. In this way, the investigators are able to also address the problem of model uncertainty and build robust algorithms. Finally, the investigators combine their expertise by carrying out a multi-faceted comparison of alternative formulations (especially Bayesian versus min-max) for the focus problems, thus creating a model-free state-of-the-art toolkit targeting highly complex bio-surveillance data.
People
- Co-PI: Michael Ludkovski.
- Graduate students: Heng Yang, Chris Knaplund.
NSF CCF MSC
- Sequential Classification and Detection via Markov Models in Point Clouds of Urban Scenes.
- $380,000.
- 2009–2013.
Description
One of the most important problems in 3D computer vision and graphics is the automatic scene reconstruction from 2D and 3D images. Recently, the reconstruction of complex urban scenes has attracted significant interest. This is because accurate 3D city models are paramount in the further development of a variety of fields such as urban planning, architecture, and archeology. They are also very important for applications commonly used in everyday life such as street map visualization and navigation, as well as in the film and construction industries. Automatic 3D image reconstruction and classification of urban scenes, though, is a problem whose complexity still challenges today's research community. 3D reconstruction of city models is achieved through data acquisition using a variety of devices such as laser scanners and regular cameras. While laser scanners provide dense, detailed and accurate 3D points, they suffer from slow speed which dramatically increases the cost of acquisition. For more information please go to link.
People
- Co-PI: Ioannis Stamos.
- Graduate Students: Hongzhong Zhang.
- Undergraduate Students: Mansen Lin, Anh Dinh.
NSF DMS IGMS
- Sequential Detection and Classification in 3D Computer Vision.
- $100,000.
- 2009–2011.
Description
The problem of quickest detection and classification in the statistical behavior of sequential observations is a classical one, with numerous applications in engineering, economics and epidemiology. In today's fast-growing technologies new areas of applications constantly emerge. In particular, the automatic 3D image reconstruction and classification of urban scenes is a problem whose complexity still challenges computer scientists. It has traditionally been treated through the acquisition of data using laser-scanners, which produce high-resolution images, but can be very slow. It is thus essential to concentrate laser scanning only to the areas of interest, which leads to fast decision-making about areas of interest. This can save significant time and cost, while still producing high-resolution 3D images. The goal of this project is to develop and implement real-time algorithms for processing and analyzing 3D laser range data. The high-dimensional nature of the data is reduced by a clever innovative selection of a measurement model. Interdependent streams of observations are then processed by on-line parametric and non-parametric classification and detection techniques. And finally, new statistical models are used to capture obstacles in urban scenes. This provides a systematic treatment of the problems of fast and efficient 3D image classification using high-resolution laser data. For more information please goto link.
People
- Undergraduate Students: Artur Sahakyan (Brooklyn College alumnus, currently employed at IBM’s dispatching division).
NSA MSP Probability
- Young Investigator’s award.
- Quickest Detection in correlated multi-sensor systems.
- $30,000.
- 2009–2012.
Description
This project is central to the detection and identification of abrupt changes in sequential observations in complex multi-source systems. The detection and identification of abrupt changes arises in many different areas. Examples of these areas are the detection of enemy activity, quality control, the detection of intrusions in computer networks and signal detection from multiple sources such as wireless communications. Although the classical problem of quickest detection has been treated in many forms in the literature dating back to the 1930s, the challenges presented by today's fast-growing technologies cannot be properly addressed by the traditional techniques. We intend to address these challenges using a combination of mathematical tools drawn from probability, modeling, stochastic processes and partial differential equations. This is a comprehensive project whose solution will involve the synergy of a variety of mathematical tools. The research proposed will not only present novel methods of incorporating dependencies across channels, but could also potentially transform the systems used in defense, target detection, wireless communications, portfolio management and intrusion prevention of attacks in networks.