Research Areas

Artificial Intelligence (AI) has a long history in our department, and currently supports a very dynamic program of research and education. Our educational curriculum provides a broad range of courses including introductory AI, automated planning, cognitive modeling, commonsense reasoning, evolutionary computation, game theory, machine learning, multi-agent systems, natural language processing, and neural computation. The AI group has consistently ranked high in external national assessments: for example, in the US News ranking of best graduate schools, our AI program is ranked 9th among all universities and 6th among public universities.

Many of our former students have gone on to very high levels of achievement. Examples include Vipin Kumar (PhD 1982), Fellow of the AAAS, ACM, and IEEE; Qiang Yang (PhD 1989), Fellow of the IEEE; Naresh Gupta (PhD 1993), Senior Vice President at Adobe; Lee Spector (PhD 1992), Fellow of the ISGEC; Gary Flake (PhD 1993), Microsoft Distinguished Engineer and well known author of The Computational Beauty of Nature; Narendra Ahuja (PhD 1979), Fellow of the IEEE, AAAI , SPIE, and ACM; and Granger Sutton (PhD 1992), whose software was used in the first-ever assembly of the complete whole genome of a free-living organism, the Haemophilus influenzae genome.

Theoretical Computer Science (TCS) is concerned with understanding the very nature of computation: What problems can be solved by computers and how efficiently can such problems be solved? Can "hard" problems be used to our advantage in any way? TCS encompasses research in such diverse areas as complexity theory, algorithms, cryptography and coding theory, distributed and parallel computing, social networks, machine learning, game theory, and more. The common thread is a focus on precise models and rigorous mathematical analysis of particular problems within those models.

Computational Biology and Bioinformatics is a multidisciplinary area dedicated to answering questions arising from the genome revolution. It spans multiple areas in computer science, including algorithms for combinatorial optimization, machine learning, data management, and scientific computing. It brings together scientists and engineers from many fields, including computer science, molecular biology, genomics, genetics, mathematics, statistics, and physics. The Center for Bioinformatics and Computational Biology is organized as a center within the University of Maryland Institute for Advanced Computer Studies (UMIACS), an interdisciplinary research institute supporting high-performance computing research across the College Park campus. 

The University of Maryland has one of the oldest and largest research groups in computer vision in the US.  The Center for Automation Research (CFAR) was founded in 1964 by computer vision pioneer Azriel Rosenfeld, and CFAR still houses a large and thriving computer vision community, consisting of almost a dozen faculty and roughly fifty graduate students.  Research at UMD addresses every aspect of computer vision, featuring work in areas such as Face Recognition, Vision for Robotics, 3D Reconstruction, Image Segmentation, and Visual Classification.  Much of this work explores the use of Big Data and Deep Learning in Computer Vision.  The work at UMD is quite interdisciplinary, drawing students and faculty from CS, ECE and Applied Math, and forming close collaborations with other areas of computer science such as Human-Computer Interaction, Machine Learning, Computer Graphics, Augmented and Virtual Reality, Robotics, and Optimization.

Faculty work on multiple aspects of computer security, with particular strengths in cryptography, programming-language security, and network security. We also collaborate closely with faculty working in this field in other departments, through the Maryland Cybersecurity Center.

In recent years, we have seen a tremendous increase in the data available in the digital format, the World Wide Web being a prominent example, and this trend is expected to accelerate with the increasing proliferation of devices, ranging from genome sequencing machines to microscopic biomedical sensors, that are capable of capturing even the minutest details of our everyday world. The database group at the University of Maryland at College Park carries out a multi-faceted and diverse research agenda that focuses on exploring the data management challenges in a wide variety of environments. Some of the most important focus areas over the last few years include life sciences and biological databases, graph databases, sensor network data management, social network data management, mobile databases, P2P networks, and unstructured text databases. At the same time, the database group has continued innovating in the traditional data management topics such as managing and querying data warehouses, spatial databases, query processing and optimization, data streams, approximate query processing, and data mining.

The University of Maryland's Graphics and Visual Informatics Laboratory (GVIL) was established in 2000 by the Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies to promote research and education in computer graphics, scientific visualization, and virtual environments. Here, we work to improve the efficiency and usability of visual computing applications in science, engineering, and medicine. The scope of this laboratory's research covers design of algorithms and data structures for reconciling realism and interactivity for very large graphics datasets, leveraging principles of visual saliency for architecting visual attention management tools, building systems for rapid access to distributed graphics datasets across memory and network hierarchies, and study of the influence of heterogeneous display and rendering devices over the visual computing pipeline. The activities of the laboratory involve development of visual computing tools and technologies to support the following research-driving applications: protein folding and rational drug design, navigation and interaction with mechanical CAD datasets, and ubiquitous access to distributed three-dimensional graphics datasets.

Our cutting-edge displays, like the Augmentarium, allow for the effective visualization of large and complex data, and for higher-level products derived from data, which are essential to engage the creativity of the human brain to find patterns and relationships that would otherwise remain unobserved.

Further information is available at http://www.cs.umd.edu/gvil/

Augmented and virtual reality (AR and VR) are poised to change our world in ways we only could have imagined a few years ago. At the University of Maryland we are working on several driving applications for next-generation virtual and augmented reality, including augmented navigation, medical training, virtual manufacturing, and immersive education. We are developing technologies in five interconnected thrust areas: scene capture and generation; tracking and registration; multimodal rendering; displays; and interfaces and usability.

For scene capture and generation, we are working on both mobile and stationary multi-camera arrays that enable us to capture the light fields of real-world immersive environments with resolution matching human visual acuity. Using one of these unique arrays, we have recorded live footage of actual surgeries at UMB’s Shock Trauma Center, and we are building towards high-fidelity telepresence using arrays of over 1000 cameras.

We are designing, developing, and validating both multimodal rendering algorithms and low-latency embedded systems that are extremely efficient, consume very little power, use information about the salient components of the scene and just-in-time tracking to scale up to very high resolution displays at high frame rates needed to maintain the illusion of immersion, vital to VR experiences.

To address interface and usability issues in VR and AR, we must understand the cause of psychophysical problems that arise from extended exposure to immersive environments. We are currently developing real-time algorithms for multi-stream visualization and data mining from EEG data on modern parallel environments to classify and quantify the onset of cybersickness.

More information is available at http://augmentarium.umd.edu.

High Performance Computing involves using large computers to solve major scientific and engineering problems.  HPC is used from weather prediction to designing consumer products.  The members of the HPC group at Maryland investigate many aspects of High Performance Computing from innovations in core numerical algorithms, to system software and tools to enabling productive use of large-scale computation.

Numerical analysis and scientific computing have a long history in our department, dating from the 1970s. Our research today includes work on numerical linear algebra, ill-posed problems, fast summation methods, and the use of such ideas in diverse applications such as imaging, fluid dynamics and acoustics. The group has always had close ties with numerical analysts in the Mathematics Department, and our students are drawn from the Department of Computer Science as well as the Applied Mathematics and Scientific Computing Program.

Founded in 1983, UMD’s Human-Computer Interaction lab is one of the world's oldest centers for the study of human centered computing. With a long history of creating innovative interaction designs and understanding human performance, we have contributed to the development of applications that serve the community. The HCIL is an interdisciplinary lab jointly operated by UMIACS and the College of Information Studies (iSchool), as well as comprised of faculty and students from Computer Science, Information Studies, Psychology, and other campus units. Our current work includes new approaches to information visualization, education, social computing, mobile devices, medical informatics and explores technology design methods with and for children.

Information Retrieval is an interdisciplinary area which corresponds to the process of finding relevant information units that satisfy an information need from a collection of information sources.  The data is usually text although the increasing use of the web as a data repository has led to the inclusion of multimedia data such as images, videos, and sound where exact matches are replaced by similarity searches. The main component of the retrieval is a search which is facilitated by imposing an index on the underlying data.  The growth of the web and the increasing popularity of smartphones and the embedding of GPS in these devices has led to the increasing importance of location as a component of all types of data and when coupled with this data is known as spatial data.  Database systems that deal with spatial data and its retrieval are known as Geographic Information Systems (GIS), and the department has been in the forefront of dealing with this type of data for over 50 years.

Data science is an emerging field encapsulating interdisciplinary activities used to create data-centric products, applications or programs, that address specific scientific, socio-political, or business questions. It is making deep inroads in industry, government, health, and journalism. Data Science incorporates practices from a variety of fields in computer science: Machine Learning, Statistics, Databases, Visualization, Natural Language Processing, Systems, Algorithms, and others. The University of Maryland Computer Science Department, and other partner departments on campus, have world-class expertise in these areas.

Natural Language Processing research aims to design algorithms and methods that enable computers to perform human language-related tasks, as well as to use computational methods to improve the scientific understanding of the human capacity for language. Areas of interest at UMD include deep learning, human-in-the-loop machine learning, multilingual text processing and machine translation, summarization, computational psycholinguistics, computational social science, and more. Natural Language Processing research at UMD is highly interdisciplinary, and builds on the intellectual diversity of the Computational Linguistics and Information Processing (CLIP) lab and the UMD Language Science Center.

Programming languages are our means of expressing computations. Thus, programming languages are a powerful locus of research toward building high-quality software, i.e., software that is flexible, secure, reliable, available, efficient, reusable, and more. We can design new languages, or we can build tools that analyze programs in existing languages, toward maximizing quality.

The Lab for Programming Languages and the University of Maryland (PLUM) is engaged in exciting research that aims to improve software quality through new languages and software tools. Our work involves formalism and proof (e.g., to show that a particular analysis establishes a certain property of the programs it considers) as well as implementation and evaluation (e.g., to show that our ideas work on real software at reasonable cost). Current interests focus on cloud computing, mobile computing, high-availability systems, static analysis, functional programming, debugging, and privacy-preseving computation.

Quantum computing aims to exploit a quantum mechanical representation of information to enable new computers and new communication devices capable of performing tasks that would otherwise be infeasible. In particular, it studies the implications of quantum mechanics for computational complexity, cryptographic security, data transmission, and other aspects of information processing.

Quantum computers promise to address computational challenges with significant applications. For example, quantum simulation can efficiently determine properties of chemical systems and models of condensed matter physics, with potentially revolutionary impact on problems such as drug design and the development of new materials. Quantum computers also enable attacks on classically-secure cryptosystems, motivating the design of novel cryptographic primitives that are secure against quantum attacks. Furthermore, quantum information provides tools to study diverse topics including condensed matter physics, quantum gravity, and the foundations of quantum mechanics through the lens of information and computation.

Ongoing work also applies the principles of classical computer science to the design of quantum computers. Topics under investigation include the development of quantum algorithms, programming languages, compilers, and hardware architectures that offer robust, scalable advantages over classical devices.

Computer Systems provides the foundation upon which all other software applications rely. The goal of systems research is to develop the key abstractions and services that enable software to be efficiently and portably run on hardware. Areas of interest to the systems group include operating systems, computer networks, parallel and distributed computation, and computer security. The systems group tackles problems from both theoretical and experimental approaches. To support our experimental work, the group maintains several laboratories in the Computer Science department and in UMIACS.

The Laboratory for Parallel and Distributed systems includes a collection of parallel computers and clusters to support systems research. Current equipment includes a 24 processor SPARC SMP, 8 processor IBM Power 4 system, and a 128 processor Myrinet-connected Linux cluster. The Distributed Systems Software Laboratory contains flexible networking environment to allow students to configure networking switches to allow for experimental research. In addition, the laboratory includes about 20 machines to support experiments.

The history of the systems group at Maryland dates back more than 30 years. One early member of the group, Yaohan Chu, wrote the book Computer Organization and Microprogramming which was the first major book on the subject. This book was used extensively at many universities and colleges. David Mills was an early researcher in computer networks. He set up an ARPANet IMP (predecessor of the current Internet) in his basement using a PDP 11/45 (an early mini-computer). At the time, this was the only full ARPANet node not located at a University or a Government facility. In the late 1970's, Chuck Rieger and Mark Weiser built ZMOB, an early parallel computer based on commodity microprocessors. The system consisted of 128 Z-80 processors.

Students and PostDocs from the systems group have gone on to faculty and industry positions around the world. Former graduate students in faculty positions include: Gagan Agrawal (Ohio State University), Suman Banerjee (University of Wisconsin), Ugur Cetintemel (Brown University), Ibrahim Matta (Boston University), Bongki Moon (University of Arizona), Ron Larsen (Dean of College of Information Science, University of Pittsburgh), Sang Son (University of Virginia), Dave Levin (University of Maryland), and Aaron Schulman (University of California San Diego).

Many of our former students have gone on to careers at major research labs including AT&T Labs (Vijay Gopalakrishnan, Seungjoon Lee), Google (Ruggero Morselli), and IBM T.J.Watson (Henrique Andrade, I-Hsin Chung, Andrzej Kochut, Kyung Ryu). The group also has a rich history of PostDoc researchers who have gone on to successful careers. For example, Anurag Acharya and Guy Edjlali are now at Google. The Systems group receives support from the Department of Defense, Department of Energy, NASA, and the National Science Foundation. Additional support is provided by industrial partners including DoCoMo, Fujitsu, IBM, Microsoft, Samsung, and Sun Microsystems.