Computer Science Research
Computer Science Department
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A dancer demonstrates the motion capture capabilities of UMD's Motion and Media Across Disciplines (MMAD) lab.
UMD Computer Science students frequently work side-by-side with faculty to conduct research in a wide variety of areas. Some of the recent joint student-faculty conference presentations, journal articles, and other publications are listed here.
Working with Students
The Undergraduate Research Opportunities Program (UROP) is a great way for students to get involved in undergraduate research.
Students can apply twice a year, at the beginning of October and March. Generally, more than 70% of proposals are approved and students are paid a stipend to carry out their research.
We have many facilities within our department for student, staff and faculty use.
Areas of Expertise
UMD Computer Science faculty are actively pursuing research to continue to be leaders in their respective fields. This translates into excellent learning opportunities for students because faculty skills remain current in a rapidly changing discipline.
Biomedical and Health Informatics: Combining computer science, engineering, and medicine – Arshia Khan
My research sits under the umbrella of biomedical and health informatics where wireless sensor based mobile assistive technology and robotics are used to enhance the delivery of care. In the recent months, my research interests have evolved into robotic assistive technology where we are using robotics to help patients recover after open heart surgery. In addition, we are also exploring the use of robots in identifying and predicting wandering behavior among individuals with dementia. I am fortunate to be able to contribute in the growth of the new and emerging field of medical and health informatics.
In the area of sensor based mobile technology my projects utilize sensors for tracking heart rate, blood pressure, body surface temperature, oxygen saturation, accelerometer, and pressure sensors to monitor and track various physiological conditions that play a role in prevention of pressure ulcers, tracking, monitoring and management of bipolar disorder, and detection of wandering in patients affected with dementia.
Computational Linguistics - Ted Pedersen
Computational Linguistics represents the intersection of Computer Science and Linguistics, and seeks to identify fundamental properties of human language by automatically processing large quantities of online text. Research at UMD focuses on lexical semantics, in particular discovering syntactic and lexical features that are necessary to automatically identify the meaning of words in text.
Computer Networking - Haiyang Wang
Computer networking underlies today's mobile devices, home networks and the Internet. Although networking is becoming a critical infrastructure of our information-based society, it still fails to achieve a reliability level of traditional phone systems. Research on computer networks at UMD concentrates on designing highly scalable and efficient networking infrastructures, by combining optimization, economics, and computer science. Besides network optimization, projects at UMD also address application specific issues in cloud computing, peer-to-peer (P2P) and multimedia systems. For example, our study on cloud computing aims to mitigate the performance as well as the energy issues in the existing visualization environments. Our students will be able to test and explore different real-world cloud systems (e.g., could gaming/synchronization apps) in our networking lab.
Data Mining and Machine Learning - Rich Maclin
Dr. Maclin pursues research in a number of machine learning and data mining related areas. Recent research has focused on a variety of topics such as the creation of effective classifiers for real world data sets (such as a program to recognize Venusian volcanos from Synethic Aperture Radar (SAR) data for NASA), the development and evaluation of ensemble learning methods (especially bagging and boosting), and effective techniques for priming reinforcement learners with prior knowledge. Dr. Maclin also pursues machine learning related research in the field of Bioinformatics.
Data Mining, Database Management and Parallel Algorithms for GPU’s – Eleazar Leal
Dr. Leal researches scalable GPU algorithms for spatial query processing and spatial data mining, applied to solving challenges in other scientific disciplines like biology, transportation, and urban planning. Examples of spatial queries that I have worked on are “find the ten bird species with the most similar migration trajectories to that of a given bird,” and “find the movement patterns of people in Bejing.”
Formal and Automated Reasoning - Hudson Turner
Logic-based artificial intelligence. Representing knowledge about the effects of actions. Automated reasoning about actions. Planning. Nonmonotonic logics, including declarative logic programming.
Analysis of Algorithms, Theory of Evolutionary Computation - Andrew Sutton
Search and optimization problems such as discovering new medicine, designing efficient jet engines and configuring spacecraft antennas require finding the "best" configuration over an enormous set of potential solutions. Randomized search heuristics are general purpose algorithms for optimization over discrete sets that can use as little (or as much) problem-specific knowledge as possible. These algorithms are often inspired by natural "optimization" processes (e.g., evolutionary algorithms, genetic algorithms, simulated annealing, swarm algorithms). Dr. Sutton is interested in applying methods from the analysis of randomized algorithms to understanding randomized search heuristics with a goal for improving their usability and performance. He is also interested in connections between the theory of evolutionary algorithms and population genetics in biology.
Information Retrieval - Steve Holtz
For the last several years, research in information retrieval has focused on very short queries and how to improve them. These are typically queries of one to three words in length. These queries are of particular interest because they mimic the type of query often input by users of the web. Untutored users (e.g. those without knowledge of how to structure search queries) often simply input a word or two in their area of interest and hope to retrieve relevant information. But as those who frequently search the web know, the results returned from such a search are often of questionable value.
Natural Language Processing - Ted Pedersen
Natural Language Processing seeks to make computers able to understand and use human language in written and spoken form. Research at UMD focuses on inventing techniques that allow a computer to organize and understand large quantites of online text though the use of statistical and machine learning techniques. We are currently developing methods that automatically identify the meaning of words in text, and that organize phrases or documents based on their conceptual similarity.
Operating Systems & Security - Peter Peterson
Peterson’s Laboratory for Advanced Research in Systems (LARS) investigates issues “behind the scenes” of typical computer operation. This includes investigating the efficiency and security of operating systems, libraries, frameworks, and hardware of those components that generally support computer operation. Research from LARS has included improving the security and efficiency of encrypted and compressed data to mitigate side-channel attacks as well as improving the efficiency of unencrypted communication channels using Adaptive Compression. LARS produces and maintains various computer security exercises and also publishes the weekly UMD Information Security News newsletter covering the biggest stories in information security each week.
LARS is currently the host of a vintage PDP-12 minicomputer from the 1970s that is available for student use and experimentation, and is also developing a testbed for security and education that will include the ability to measure the energy consumption of computers running various software loads.
Perception and Computer Graphics - Pete Willemsen
Dr. Willemsen conducts research focused on how humans perceive and act in immersive virtual environments. The goal of this research is to convey an accurate sense of space to users of virtual environments. This work is centered on understanding how people act in immersive virtual environments and comparing that behavior to performance in similarly constructed real conditions. We are acquiring information about how a person's behavior in either a real or virtual space transfers to their behavior in the opposite space, providing important insights about the mental processes underlying human computer interaction within head-mounted display virtual environments.
Simulation and Environment Representation for Virtual Environments - Pete Willemsen
Creating active virtual environments that resemble real-life situations is still a formidable task. In addition to difficulties with matching visual quality, populating virtual environments with autonomous activity, such as crowds of pedestrians or roadways filled with vehicles, requires substantial effort. It is necessary to provide a sufficient environment model that supports geometric, topological, socio-cultural, as well as relational environment information. These representations must place a strong emphasis on real-time, interactive solutions capable of supporting hundreds, if not thousands, of virtual entities. Designing virtual experiences in which this autonomous, ambient activity is meshed with replicable behavior that can be used by researchers for experiments is still an open issue. Infusing ambient activity with directable behavior requires tools and frameworks capable of merging requests for changes to otherwise autonomous behaviors, while still allowing actions to be reactive to the human subject.