| Mentor: Professor Ernst L. Leiss (Code: EL) |
| Project Title: |
Towers of Hanoi |
| Mentor: |
Professor Ernst Leiss |
| Description: |
The Towers of Hanoi is a problem that has been well studied and frequently generalized. We are interested in the generalization to arbitrary directed graphs and study how many moves in a given graph are necessary to move n disks from the starting peg to the destination peg. There are known upper and lower bounds on the minimal number of moves. The project involves designing algorithms and implementing them for solving the problem on a given graph and looking at improving the known upper and lower bounds. In particular, parallel moves are of interest. |
| Objective: |
Study recursion using the Towers of Hanoi problem and explore means of improving existing bounds. Students will learn implementation and analysis techniques. |
| Project Title: |
Inference Control in Statistical Databases |
| Mentor: |
Professor Ernst Leiss |
| Description: |
Inference control in statistical databases is intimately related to the preservation of privacy of data stored in such databases. It has proven to be quite difficult to prevent inferring information about individuals from responses to legitimate statistical queries. One of the few successful methods involves adding randomly selected elements to the query set. We want to study experimentally whether removing one or more elements from the query set achieves similar outcomes. This project extends prior work for averages to selector functions, in particular medians. The work will involve determining how to quantify inference control and how to simulate methods for measuring it. |
| Objective: |
Study the problems of securing statistical databases. Students will learn how to analyze security questions in statistical databases and how to carry out large-scale simulations. |
| Project Title: |
Digital Watermarks |
| Mentor: |
Professor Ernst Leiss |
| Description: |
Watermarks have attracted increased attention as concerns about establishing ownership of digital media have escalated. Robust invisible watermarks allow one to attach an indelible stamp of ownership; clearly the methods employed must be impervious to operations such as rescaling, filtering, or superimposing an additional watermark. Robustness is related to the redundancy of the watermark (e. g., if a certain small pattern is repeated many times in a watermark, the removal of the watermark through cropping an image is foiled). Similarly, the invisibility of a watermark is related to the extent of changes in the information that makes up the media. This imposes limits on the amount of information that can be encoded in the watermark. The primary emphasis of the research is on verification techniques. |
| Objective: |
Study digital watermarking algorithms and determine their properties. Students will learn about aspects of digital watermarks. |
| Project Title: |
Group Authorization |
| Mentor: |
Professor Ernst Leiss |
| Description: |
With the advent of grid and cloud computing, security and integrity in distributed computing environments has assumed greater importance. One interesting aspect is the notion of groups, and the concomitant extension of authorization of individuals to groups. |
| Objective: |
Explore distributed computing, authorization in such systems, and how to apply this to groups. Both granting and revoking of privileges are studied. Nesting of groups is of particular interest. Students will learn about authorization in distributed computing environments. |
| Mentor: Professor Ioannis Pavlidis (Code: IP) |
| Project Title: |
Put it on the Cloud |
| Mentor: |
Professor Ioannis Pavlidis |
| Description: |
In this project the student will work with a team of Research Assistant Professors and Ph.D. students to design and implement the migration of massive amounts of research data on the cloud. These data have been collected as part of an NSF project the last three years and need to be made available to the research community at large. Putting massive amounts of research data on the cloud for communal use is a clear trend and is projected to grow by leaps and bounds, becoming an R&D field and business all by itself. Migration of such data sets is a complex operation and involves issues of organizational design, data checking and integrity, user interfacing, as well as data mining and annotation. The student will learn and use XNAT and Azure among other cutting edge tools. |
| Project Title: |
Total Mobility |
| Mentor: |
Professor Ioannis Pavlidis |
| Description: |
In this project the student will work with a Research Assistant Professor to develop algorithmic software that differentiates various patterns of physical activity from iPhone accelerometer data. These anonymous data are collected by free experimental applications that the Computational Physiology Lab has released in the App Store the last couple of years and are used by thousands of people around the world. The ultimate goal is to automatically understand when someone walks versus when s/he runs versus when s/he climbs the stairs versus when s/he bikes. Such capability has tremendous value in total health management applications as well as in gathering detailed anthropological statistics of mobility at a global scale. The next Fittest Cities in America list may well be decided by the software that you will develop in this project! |
| Mentor: Professor Ioannis A. Kakadiaris and Shishir. K. Shah (Code: KS) |
| Project Title: |
Local and Global Relationship in Face Recognition |
| Mentor: |
Professor Ioannis Kakadiaris and Shishir Shah |
| Description: |
Automatic image analysis and computer vision techniques have been developed for face recognition. Nonetheless, the ability to replicate a human's ability to recognize a face has not yet been surpassed. To this extent, it is critical to understand the perceptual and reasoning power of humans. One of the questions that remain unanswered is that of the role of partial observations in recognizing a face. In this project, a student will learn to design an experiment to assess human recognition ability in whole and partially observed images. The student will develop a web-based platform for image tagging and combine it with a crowd-sourcing platform such as Amazon Mechanical Turk. |
| Specific Requirements: |
We are looking for a skillful and creative individual, familiar with web development technologies and database applications. |
| Project Title: |
3D Model of my Face |
| Mentor: |
Professor Ioannis Kakadiaris and Shishir Shah |
| Description: |
Computer Vision technologies have been starting to emerge in entertainment platforms and remotely controlled-interfaces using affordable imaging sensors and devices like Microsoft's Kinect. RGB-D cameras such as those used in Kinect capture image (RGB) and depth (D) data, using a range camera and IR light, and allow for 2D and 3D image data acquisition. Such data can be used for 3D scene reconstruction, target location and tracking. In this project, a student will use the Kinect sensor to capture faces and develop methods to reconstruct 3D models. The student will learn to use open-source drivers and software to facilitate data capture and analysis. |
| Web Link |
http://ils.intel-research.net/projects/rgbd
http://www.youtube.com/watch?v=7QrnwoO1-8A
http://www.youtube.com/watch?v=2ml8GiUPTao
|
| Specific Requirements: |
We are looking for a creative individual knowledgeable on one or more of the following fields: Open-source Software, Computer Vision, Computer Graphics, Computer Games & Animation, Data Visualization. |
| Project Title: |
Predict A Heart Attack |
| Mentor: |
Professor Ioannis Kakadiaris and Shishir Shah |
| Description: |
Every year 1.4 million Americans suffer a heart attack; in 2004, over 800,000 of these attacks were fatal. Large amounts of diverse data points are typically measured while screening individuals during routine health checkups and diagnosis. In collaboration with cardiologists and computational scientists, a student will learn fundamentals of machine learning and evaluate methods that can assess the value of collected data points for the design of a system to identify individuals at risk of having a heart attack. |
| Specific Requirements: |
We are looking for a driven and dynamic individual. |
| Mentor: Professor Stephen Huang and Larry Shi(Code: SH) |
| Project Title: |
Stepping-stone Intrusion Detection |
| Mentor: |
Professor Stephen Huang |
| Description: |
In order to avoid being detected, computer hackers typically go through a long chain of computers to break into a target machine. This can be achieved by using a chain of stepping-stones hosts or through Tor. We are interested in real-time algorithms that can detect such intruders effectively. The project involves the integration of algorithm design, network protocol, and computer security techniques into a system. |
| Web Link: |
For some selected papers in this area: http://www2.cs.uh.edu/shuang/IDRG/index.html |
| Specific Requirements: |
We are seeking students with some programming experience in C/C++ or Java, knowledge of OS or computer networks a plus. Students with experience using Tor (a network of virtual tunnels) are a plus. |
| Project Title: |
Mining and Visualization of Cytometry Data |
| Mentor: |
Professor Stephen Huang |
| Description: |
Flow cytometry has been an important technique in hematology, and it can be used to detect and identify the minor cell population from bone marrow or blood. Most of the current studies are still based on manual gating by medical scientist and researchers. This process is not only labor intensive, but also may mislays some potential cells in other dimensions. We are working on a framework to visualize and analyze the flow cytometry readouts. The result can provide doctors and physicians useful information to diagnose blood or lymphatic diseases, such as Leukemia, Myeloma, and Lymphoma. We are seeking students with interest in data mining and/or machine learning. |
| Web Link : |
For an introduction of flow cytometry: http://probes.invitrogen.com/resources/education/tutorials/4Intro_Flow/player.html |
| Specific Requirements: |
We are seeking students with data mining and/or Matlab skills to help us analyze the data. |
| Project Title: |
Smartphone Security and Cross Platform System Application |
| Mentor: |
Professor Stephen Huang and Larry Shi |
| Description: |
In this project, we will work in a team on a research project supported by Department of Homeland Security. User identification and access control have become a high demand feature on mobile devices such as smartphones. Those devices are wildly used by employees in corporations and government agencies for business and store increasing amount of sensitive data. The research project is to enable reliable and user friendly identity control on commercial smartphones. The implementation will involve cross-platform smartphone programming. At the end, you will gain insights of the internals of smartphone systems. |
| Specific Requirements: |
We are looking for exceptional, self-motivated individuals who are passionate about research and programming. Specifically, candidates should:
- Have strong knowledge of either C#, .NET, Java, C++ or Objective C.
- Be familiar with TDD. Unit Testing and experience in mobile development will be a plus.
- Be interested in signal processing, AI and machine learning. Building interfaces on mobile platforms is appreciated.
|
| Mentor: Professor Rakesh Verma (Code: RV) |
| Project Title: |
Information Extraction and Text Mining |
| Mentor: |
Professor Rakesh Verma |
| Description: |
We are looking for enthusiastic, passionate and bright students for all our projects. This project will investigate how to extract the most relevant information from text documents and construct a summary and how to evaluate the quality of the information extracted. |
| Specific Requirements: |
None - interest in statistics/Perl is desirable |
| Objective: |
Student will learn text mining, information extraction techniques and meta-analysis. |
| Project Title: |
NLP and Machine Learning techniques for Computer Security and Counterterrorism |
| Mentor: |
Professor Rakesh Verma |
| Description: |
We are looking for enthusiastic, passionate and bright students for all our projects. This project involves the design and implementation of natural language processing and machine learning techniques for problems in counterterrorism and computer security. |
| Specific Requirements: |
Some knowledge of Perl or Weka is desirable. |
| Objective: |
Student will learn NLP and text mining techniques. |
| Project Title: |
NLP techniques for Lies and Fraud Detection |
| Mentor: |
Professor Rakesh Verma |
| Description: |
We are looking for enthusiastic, passionate and bright students for all our projects. This project involves the design and implementation of natural language processing techniques for detection of fraudulent reviews and financial documents. |
| Specific Requirements: |
Some knowledge of Perl is desirable but not required. |
| Objective: |
Student will learn NLP and text mining techniques. |
| Project Title: |
Computer Security |
| Mentor: |
Professor Rakesh Verma |
| Description: |
We are looking for enthusiastic, passionate and bright students for all our projects. This project involves the study and analysis of how man-in-the-middle attacks can be prevented in cryptographic protocols. |
| Specific Requirements: |
Interest in formal methods. |
| Objective: |
Explore assumptions of different computing paradigms and determine how their differences affect software.
Students will learn how to obtain good software from algorithms.
|
| Project Title: |
Wireless Sensor Networks |
| Mentor: |
Professor Rakesh Verma |
| Description: |
We are looking for enthusiastic, passionate and bright students for all our projects. This project involves the design and analysis of protocols for wireless sensor networks. |
| Specific Requirements: |
None |
| Objective: |
Wireless sensor networks in general and wireless sensor security in particular. |