UMBC Computer Science and Electrical Engineering Department
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The Computer Science and Electrical Engineering Department aims to maintain a program of excellence in teaching, research, and service for all of its programs. At the undergraduate level, we will provide students with a firm foundation of both the theory and practice of Computer Science and Computer Engineering. Our curricula also give students the social, ethical, and liberal education needed to make significant contributions to society. Students receiving a bachelor’s degree are ready to enter the work force as productive computer scientists or computer engineers, or to continue their education at the graduate or professional level.
At the graduate level, we are committed to developing the research and professional capabilities of students in Computer Science, Computer Engineering, Electrical Engineering and Cybersecurity. Our programs provide a deeper mastery of the basics of these fields, as well as opportunities to collaborate on leading-edge research with our faculty. Our faculty are engaged in both practical and theoretical research, often in partnership with government agencies, private industry and non-governmental organizations. The aim of this research is to advance knowledge within our disciplines and also to contribute to solving problems faced by our society.
Recent Submissions
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Grounded Language Learning: Where Robotics and NLP Meet
Grounded language acquisition is concerned with learning the meaning of language as it applies to the physical world. As robots become more capable and ubiquitous, there is an increasing need for non-specialists to interact ... -
Learning to Understand Non-Categorical Physical Language for Human Robot Interactions
Learning the meaning of language with respect to the physical world in which a robot operates is a necessary step for shared autonomy systems in which natural language is part of a user-specific, customizable interface. ... -
A Manifold Alignment Approach to Grounded Language Learning
As robots become advanced and affordable enough to have in our daily lives, the next question is: How do we make using these machines as intuitive as possible? Language offers an approachable and relatively accessible ... -
Planning with Abstract Learned Models While Learning Transferable Subtasks
(AAAI, 2020-03-04)We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this ... -
Measuring deadtime and double-counts in a non-paralyzable scintillating neutron detector using arrival time statistics
(Elsevier, 2021-03-26)A 6LiF:ZnS(Ag) based cold neutron detector with wavelength shifting (WLS) fibers and SiPM photodetector was developed at the NIST Center for Neutron Research for the CANDoR instrument (Chromatic Analysis Neutron Diffractometer ... -
Convolutional LSTM for Planetary Boundary Layer Height (PBLH) Prediction
(AAAI, 2021-03-22)We describe new work that uses deep learning to learn temporal changes in Planetary Boundary Layer Height (PBLH). This work is performed in conjunction with a deep edge detection method that identifies edges in imagery ... -
LSTMs for Inferring Planetary Boundary Layer Height (PBLH)
(AAAI, 2021-03-22)In this paper, we describe new work which is part of a larger study to understand how machine learning could be used to augment existing methods for calculating and estimating the Planetary Boundary Layer Height (PBLH). ... -
On Finer Control of Information Flow in LSTMs
(Springer Nature, 2019-01-18)Since its inception in 1995, the Long Short-Term Memory (LSTM) architecture for recurrent neural networks has shown promising performance, sometimes state-of-art, for various tasks. Aiming at achieving constant error flow ... -
Origins Space Telescope: trades and decisions leading to the baseline mission concept
(SPIE, 2021-03-13)The Origins Space Telescope will trace the history of our origins from the time dust and heavy elements permanently altered the cosmic landscape to present-day life. How did galaxies evolve from the earliest galactic systems ... -
Event Representation with Sequential, Semi-Supervised Discrete Variables
(2020-10-16)Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential, ... -
Knowledge Graph Inference using Tensor Embedding
(2020-09-12)Axiom based inference provides a clear and consistent way of reasoning to add more information to a knowledge graph. However, constructing a set of axioms is expensive and requires domain expertise, time, and money. It is ... -
An introduction to physics-based animation
(Association for Computing Machinery, 2018-08)Physics-based animation has emerged as a core area of computer graphics finding widespread application in the film and video game industries as well as in areas such as virtual surgery, virtual reality, and training simulations. ... -
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
(NeurIPS Proceedings, 2020-11-01)Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In this paper, we propose a Cooperative ... -
Phrase-Verified Voting: Verifiable Low-Tech Remote Boardroom Voting
(CCSD, 2021-03-10)We present Phrase-Verified Voting, a voter-verifiable remote voting system assembled from commercial off-the-shelf software for small private elections. The system is transparent and enables each voter to verify that the ... -
Learning Assisted Side Channel Delay Test for Detection of Recycled ICs
(2020-10-23)With the outsourcing of design flow, ensuring the security and trustworthiness of integrated circuits has become more challenging. Among the security threats, IC counterfeiting and recycled ICs have received a lot of ... -
GBTL+Metall – Adding Persistence to GraphBLAS
It is well known that software-hardware co-design is required for attaining high-performance implementations. System software libraries help us in achieving this goal. Metall persistent memory allocator is one such library. ... -
A Hardware Accelerator for Language Guided Reinforcement Learning
(IEEE, 2021-03-02)Reinforcement learning (RL) has shown great performance in solving sequential decision-making problems. This paper proposes a framework to train RL agents conditioned on constraints that are in the form of structured ... -
Developing a chip-scale optical clock
(SPIE, 2021-02-27)We report our in-house R&D efforts of designing and developing key integrated photonic devices and technologies for a chip-scale optical oscillator and/or clock. This would provide precision sources to RF-photonic systems. ... -
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models
Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be generated ... -
Using Digital Sensors to Leverage Chips' Security
(IEEE, 2021-02-03)One way for an attacker to break a system is to perturb it. Expected effects are countermeasure deactivation or data corruption to disclose sensitive information. The prevention of such actions relies on detection of ...
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