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

  • Blockchain-Enabled and Data-Driven Smart Healthcare Solution for Secure and Privacy-Preserving Data Access 

    Younis, Mohamed; Lalouani, Wassila; Lasla, Noureddine; Emokpae, Lloyd; Abdallah, Mohamed (IEEE, 2021-07-12)
    The major advances in body-mounted sensors and wireless technologies have been revolutionizing the healthcare industry, where patient’s conditions can be remotely monitored by medical staff. Such a model is gaining broad ...
  • Multi-Qubit Correction for Quantum Annealers 

    Ayanzadeh, Ramin; Dorband, John; Halem, Milton; Finin, Tim (2021-07-10)
    We present \emph{multi-qubit correction} (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic ...
  • Real-Time IC Aging Prediction via On-Chip Sensors 

    Huang, Ke; Anik, Md Toufiq Hasan; Zhang, Xinqiao; Karimi, Naghmeh (2021)
    Real-time aging prediction for nanoscale integrated circuits (ICs) is a crucial step for developing prevention and mitigation actions to avoid unexpected circuit failures in the field of operation. Current practices for ...
  • A Fast Method to Fine-Tune Neural Networks for the Least Energy Consumption on FPGAs 

    Hosseini, Morteza; Ebrahimabadi, Mohammad; Mazumder, Arnab Neelim; Homayoun, Houman; Mohsenin, Tinoosh (UMBC Energy Efficient High Performance Computing Lab, 2021)
    Because of their simple hardware requirements, low bitwidth neural networks (NNs) have gained significant attention over the recent years, and have been extensively employed in electronic devices that seek efficiency and ...
  • Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches 

    Lu, Fred S.; Nguyen, Andre T.; Link, Nicholas B.; Molina, Mathieu; Davis, Jessica T.; Chinazzi, Matteo; Xiong, Xinyue; Vespignani, Alessandro; Lipsitch, Marc; Santillana, Mauricio (PLOS, 2021-06-17)
    Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing ...
  • Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels 

    Ordun, Catherine; Raff, Edward; Purushotham, Sanjay (2021-06-15)
    Thermal images reveal medically important physiological information about human stress, signs of inflammation, and emotional mood that cannot be seen on visible images. Providing a method to generate thermal faces from ...
  • Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery 

    Boutsikas, John; Eren, Maksim E.; Varga, Charles; Raff, Edward; Matuszek, Cynthia; Nicholas, Charles (2021-06-15)
    The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful ...
  • Countering PUF Modeling Attacks through Adversarial Machine Learning 

    Ebrahimabadi, Mohammad; Lalouani, Wassila; Younis, Mohamed; Karimi, Naghmeh (IEEE, 2021-07)
    A Physically Unclonable Function (PUF) is an effective option for device authentication, especially for IoT frameworks with resource-constrained devices. However, PUFs are vulnerable to modeling attacks which build a PUF ...
  • Practical Cross-modal Manifold Alignment for Robotic Grounded Language Learning 

    Nguyen, Andre T.; Richards, Luke E.; Raff, Edward; Kebe, Gaoussou Youssouf; Darvish, Kasra; Ferraro, Frank; Matuszek, Cynthia (IEEE, 2021)
    We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings by ...
  • A Spoken Language Dataset of Descriptions for Speech-Based Grounded Language Learning 

    Kebe, Gaoussou Youssouf; Higgins, Padraig; Jenkins, Patrick; Darvish, Kasra; Sachdeva, Rishabh; Barron, Ryan; Winder, John; Engel, Donald; Raff, Edward; Ferraro, Francis; Matuszek, Cynthia (2021-06-08)
    Grounded language acquisition is a major area of research combining aspects of natural language processing, computer vision, and signal processing, compounded by domain issues requiring sample efficiency and other deployment ...
  • Hardware Security in Emerging Technologies: Vulnerabilities, Attacks, and Solutions 

    Karimi, Naghmeh; Basu, Kanad; Chang, Chip-Hong; Fung, Jason M. (IEEE, 2021-06-11)
    This Special Issue of the IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS (JETCAS) is dedicated to demonstrating the latest research progress in the area of hardware security in emerging technologies.
  • Person Re-Identification with a Locally Aware Transformer 

    Sharma, Charu; Kapil, Siddhant R.; Chapman, David (2021-06-08)
    Person Re-Identification is an important problem in computer vision-based surveillance applications, in which the same person is attempted to be identified from surveillance photographs in a variety of nearby zones. At ...
  • Platform Incubator with Movable XY Stage: A New Platform for Implementing In-Cell Fast Photochemical Oxidation of Proteins 

    Johnson, Danté; Punshon-Smith, Benjamin; Espino, Jessica A.; Gershenson, Anne; Jones, Lisa M. (JOVE, 2021-05-17)
    Fast Photochemical Oxidation of proteins (FPOP) coupled with mass spectrometry (MS) has become an invaluable tool in structural proteomics to interrogate protein interactions, structure, and protein conformational dynamics ...
  • Hyperspectral Band Selection based on Improved Affinity Propagation 

    Zhu, Qingyu; Wang, Yulei; Wang, Fengchao; Song, Meiping; Chang, Chein-I (IEEE, 2021)
    Dimensionality reduction is a common method to reduce the computational complexity of hyperspectral images and improve the classification performance. Band selection is one of the most commonly used methods for ...
  • AOT: Anonymization by Oblivious Transfer 

    Javani, Farid; Sherman, Alan T. (2021-05-22)
    We introduce AOT, an anonymous communication system based on mix network architecture that uses oblivious transfer (OT) to deliver messages. Using OT to deliver messages helps AOT resist blending (n−1) attacks and helps ...
  • On the Impact of Aging on Power Analysis Attacks Targeting Power-Equalized Cryptographic Circuits 

    Anik, Md Toufiq Hasan; Fadaeinia, Bijan; Moradi, Amir; Karimi, Naghmeh (Asia and South Pacific Design Automation Conference, 2021-01-20)
  • Benchmarking Machine Learning: How Fast Can Your Algorithms Go? 

    Ning, Zeyu; Iradukunda, Hugues Nelson; Zhang, Qingquan; Zhu, Ting (2021-01-08)
    This paper is focused on evaluating the effect of some different techniques in machine learning speed-up, including vector caches, parallel execution, and so on. The following content will include some review of the previous ...
  • ICA with Orthogonality Constraint: Identifiability And A New Efficient Algorithm 

    Gabrielson, Ben; Akhonda, M. A. B. S.; Boukouvalas, Zois; Kim, Seung-Jun; Adali, Tülay (IEEE, 2021-05-13)
    Given the prevalence of independent component analysis (ICA) for signal processing, many methods for improving the convergence properties of ICA have been introduced. The most utilized methods operate by iterative rotations ...
  • Tree tensor network classifiers for machine learning: from quantum-inspired to quantum-assisted 

    Wall, Michael L.; D'Aguanno, Giuseppe (2021-04-06)
    We describe a quantum-assisted machine learning (QAML) method in which multivariate data is encoded into quantum states in a Hilbert space whose dimension is exponentially large in the length of the data vector. Learning ...
  • 2021 Roadmap on Neuromorphic Computing and Engineering 

    Mazumder, Arnab Neelim; Hosseini, Morteza; Mohsenin, Tinoosh; et al. (IOP Publishing, 2021-05-12)
    Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In this architecture, processing and memory units are implemented as separate blocks interchanging data intensively and ...

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