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Mentored Research Topics

Research projects are listed in alphabetical order by faculty member’s last name.  As new proposals arrive, this list will be updated.  If you have a great idea for a project that you don’t see listed, please meet with the faculty member most closely interested in that area and propose your idea.

Dr. Michael Bloodgood:

  1. Stopping Machine Learning. We are researching principled methods for detecting when to stop the collection of additional training data for building machine learning systems. 
  2. Using global constraints, reranking, and fast approximations of the Hungarian algorithm to improve cognates detection. We are researching how to use global constraints, reranking, and fast approximations of the Hungarian algorithm to improve cognates detection.
  3. Active Learning to Improve Cost-Effectiveness of Annotation. We are researching how to selectively sample data for annotation with the aim of maximizing the benefits received from annotation effort. 
  4. Computer-Assisted Translation Technologies. We are researching how to improve CAT (computer-assisted translation) technologies so that professional translation can be performed faster and with higher quality. For related publications and news articles covering my inventions in this area, please see

For related publications and more information about research projects, please see

Dr. Deborah Knox:

  1. Mobile computing for iOS, Android, or non-native programming – No previous mobile application development experience is required. Join a successful mobile app development team!
    Possible projects include:

    • TCNJ Campus Tour app needs a redesign. Project may integrate of points-of-interest technology.
    • Indoor navigation using technology such as beacons or WiFi (indoor positioning systems) to augment navigation in the TCNJ Library
    • Music repertoire personal assistant. Explore optical character recognition (OCR) technology and integrate data management and personalization strategies to create a new approach to tracking works performed or under study
    • Explore platform independent application development tools (producing non-native apps for wider distribution on any platform)
    • Recent products from the TCNJ LakesideApps Development Team, led by Dr. Knox include:
      iOS Platform

      • TCNJ Connect (Version 2 released October 6, 2014)
      • TCNJ Library  (Version 2 expected Fall 2014)
      • TCNJ Campus Tour

      Android Platform

      • TCNJ Connect (expected Fall 2014)
      • TCNJ BookNav (release date pending)

      Campus stakeholders test all apps branded with the TCNJ name and campus administrative approval was received prior to public release.

  2. Big Data and Sensor Based Monitoring  Sensors collect volumes of data, some of which is sensitive. Explore methods of data collection from sensors, handle storage, and perform extraction. Conduct research in current technologies for data masking, which maintains data’s utility, as well as redaction approaches for unstructured data for maintaining privacy. Other projects may be proposed. Other research areas may be explored.  Please request an appointment to discuss your interests.

Dr. Jikai Li:

  1. Secure cloud file storage – This project will try to develop a framework, including encryption process and key management on client side, to encrypt/decrypt the files to/from cloud. We expect the framework will work on a broad range of electronic devices. The software is simple enough to be able reinvented if it is lost. The key management is solid and convenient to users.
  2. Spoofing caller detection – It is becoming common practice that a hacker/telemarketer to spoof caller ID. This ongoing project will explore the possibility of using tools like Asterisk to detect the spoofing caller ID. Additional info used for detection can be online registration info etc.

Dr. Ying Mao:

My research focuses on resource virtualization and management for data-intensive systems. Given the limited computing resources in the cluster, figuring out how to appropriately allocate them to serve the data processing jobs, especially a large volume of jobs, is a critical and challenging issue. The existing platforms and prior work often relies on fixed user-specific configurations, or simple heuristic strategies, which do not perform well with various workloads in practice. In my research work, projects explore new approaches from different perspectives to improve the resource utilization and shorten the job execution time. The specific systems that are involved in my projects are Hadoop Yarn, Spark, Docker Containers, Google Kubernetes, AWS, Microsoft Azure.

  1. Efficient Container Distribution for Large-scale Virtualized Computing Systems: The project will systematically study architecture and design issues of the container distribution for virtualized computing platforms, like Yarn, Spark, and Docker. The system aims to enable advanced container preparation, identify the tasks occupation pattern of the containers, and exploit the diversity in various containers. 
  2. Diversify Resource Management Plans for Multitenancy Data-intensive Systems: This project investigates the current system designs in the field of large-scale data processing and develops new resource management schemes that can dynamically reserve resource for specific types of jobs and diversify users with various job execution patterns to improve system performance and scalability.
  3. Distributed and Parallelized Learning Systems: With respect to the nature of distributed data collection, processing and storage in today’s big data application scenarios, the distributed and parallelized learning systems are frequently required to train various models over distributed data centers. This project proposes to accelerate the overall performance of heterogeneous multi-task learning processes from system aspects with a novel virtualization technique, namely, containerization.

Dr. Dimitris Papamichail:

My research focuses on applied algorithms and related software. Projects I undertake usually involve the design and/or implementation of algorithms. Several projects involve the creation of a client-server application and a web based interface. I’m looking for students interested to work on the following topics:

  1. Synthetic gene design – Design algorithms and software to construct novel genes. I have several different projects in this area, ranging from very theoretical to very practical. Part of this project is funded by the NSF and involved students ideally should have taken a course in algorithms and/or have significant programming experience with web development. It would be useful to have some introductory knowledge of biology, but not a requirement.
  2. Phylogenetic stemmatics – This project aims to develop a set of computational tools that can be used in computational textual criticism of Latin and other texts. The aim is to create methods for the accurate representation of the relationships of various manuscripts from different time periods, which have been copied from an original seminal work. This project involves design of algorithms on trees/graphs, and implementation of efficient software tools.
  3. Automated transcriptions for music instruments – I am interested to pursue algorithmic techniques for automatically transcribing midi files into music scores (or tabs) for a variety of instruments, starting with guitar. Interested students should have some background in algorithms, programming experience, and playing knowledge of at least one music instrument (preferably polyphonic).
  4. Other algorithmic projects – I have a variety of other smaller scale projects, involving algorithms, tools and software, mostly related to optimization problems in computational biology, but several other areas as well, such as GPU programming and gamification. Feel free to come and ask about other available projects, or bring your own ideas involving algorithms, heuristics, and anything related!

Dr. Monisha Pulimood:

My research centers around the confluence of human and artificial intelligence, and am looking at computational problems that cannot be completely solved by computers alone, but need some intervention by humans. It builds on work I have been doing (with students and other faculty) on how concepts from human computation and crowdsourcing can help build more vibrant communities around collaborative repositories and make them less time-consuming to maintain. I am particularly interested in investigating the challenges of integrating data, available in disparate formats from a variety of sources, into data-dependent applications, and the feasibility of leveraging the collective intelligence of large numbers of humans to improve the reliability and integrity of data, and the sustainability of such applications.

Students working with me will test out their ideas on one or both of the following projects.

  1. CABECTPortal – Continue research, design and development of the collaborative infrastructure to support the NSF-funded TUES grant described on her Research ( page. This project will entail integrating social computational concepts into the development of the web and mobile applications.
  2. SOAP (Students Organizing Against Pollution) – Continue development of the web application that manages data on brownfields, and legislation related to pollution and the environment. See ( for more details on this project.

Dr. Andrea Salgian:

  1. Programming the Microsoft Kinect – expand the conducting tutor application, or develop gesture recognition interfaces for other applications (games, sign language, etc.)
  2. Programming the NAO robot – NAO is a humanoid robot with 25 degrees of freedom (joints), and a variety of sensors, and it comes with its own graphical programming language as well as a Python and a C++ SDK. Learn how to program it in these environments and get it to do new tricks.
  3. Virtual conductor – refine our existing algorithm for a virtual conductor conducting an orchestra; investigate the MusicXML format for digitized sheet music, add feedback capabilities, create an animated conductor
  4. Computer vision applications on mobile platforms – investigate how to write applications to process images from the camera or the photo album on iOS and Android devices, as well as the use of the OpenCV library on on mobile devices. Possible applications include image processing effects, object, face and gesture recognition algorithms, steganography. Investigate the use of the cloud for high computational needs.

Dr. Sejong Yoon:

Dr. Yoon’s research interests span around artificial intelligence, machine learning, computer vision, and multimedia. In general, his research goal is to fill in the gap between individual human decision processing and the group-level behavior, which typically based on distributed or decentralized individual decisions, and eventually devise a generalized computational model to reproduce them. More details on his research can be found in his webpage ( Students who are interested in the problems listed therein are encouraged to contact him for specific project topics they can work on. Recent example mentored research projects are listed below:

  1. Predicting media interestingness (active) – Given a video recording, we want to build a system that can automatically predict whether a video clip would be interesting to general viewers than the others.
  2. Determining plagiarism in modern art paintings and industrial designs – Given two modern art drawings or industrial designs, we want to build a system that can automatically assess how similar they are, and what is the likelihood of one plagiarizing the other.
  3. Improve predictive policing using large scale data – In this project, the student applied time series analysis models to predict the probability of crime incident in the future given large amount of publicly available call-for-response data.