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

  • The SemIoTic Ecosystem: A Semantic Bridge between IoT Devices and Smart Spaces 

    Yus, Roberto; Bouloukakis, Georgios; Mehrotra, Sharad; Venkatasubramanian, Nalini (ACM, 2022-03-28)
    Smart space administration and application development is challenging in part due to the semantic gap that exists between the high-level requirements of users and the low-level capabilities of IoT devices. The stakeholders ...
  • A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting 

    Mazumder, Arnab; Mohsenin, Tinoosh (2022-02-04)
    Keyword Spotting nowadays is an integral part of speech-oriented user interaction targeted for smart devices. To this extent, neural networks are extensively used for their flexibility and high accuracy. However, coming ...
  • CoughNet-V2: A Scalable Multimodal DNN Framework for Point-of-Care Edge Devices to Detect Symptomatic COVID-19 Cough 

    Rashid, Hasib-Al; Sajadi, Mohammad M.; Mohsenin, Tinoosh (IEEE, 2022-04-01)
    With the emergence of COVID-19 pandemic, new attention has been given to different acoustic bio-markers of the respiratory disorders. Deep Neural Network (DNN) has become very popular with the audio classification task due ...
  • Neural Variational Learning for Grounded Language Acquisition 

    Pillai, Nisha; Matuszek, Cynthia; Ferraro, Francis (IEEE, 2021-07-20)
    We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that ...
  • Adversarial Transfer Attacks with Unknown Data and Class Overlap 

    Richards, Luke E.; Nguyen, André; Capps, Ryan; Forsythe, Steven; Matuszek, Cynthia; Raff, Edward (ACM, 2021-09-24)
    The ability to transfer adversarial attacks from one model (the surrogate) to another model (the victim) has been an issue of concern within the machine learning (ML) community. The ability to successfully evade unseen ...
  • SmartSPEC: Customizable Smart Space Datasets via Event-driven Simulations 

    Chio, Andrew; Jiang, Daokun; Gupta, Peeyush; Bouloukakis, Georgios; Yus, Roberto; Mehrotra, Sharad; Venkatasubramanian, Nalini (2022-03-19)
    This paper presents SmartSPEC, an approach to generate customizable smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design, deploy and evaluate ...
  • A Survey of Multi-Tenant Deep Learning Inference on GPU 

    Yu, Fuxun; Wang, Di; Shangguan, Longfei; Zhang, Minjia; Liu, Chenchen; Chen, Xiang (2022-03-17)
    Deep Learning (DL) models have achieved superior performance. Meanwhile, computing hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x throughput and memory bandwidth for each generation. ...
  • Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO) 

    Ramsey, Jolene; McIntosh, Brenley; Renfro, Daniel; Aleksander, Suzanne A.; Erill, Ivan; et al (PLOS, 2021-10-28)
    Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an ...
  • Detecting Laser Fault Injection Attacks via Time-to-Digital Converter Sensors 

    Ebrahimabadi, Mohammad; Mehjabin, Suhee Sanjana; Viera, Raphael; Guilley, Sylvain; Danger, Jean-Luc; Dutertre, Jean-Max; Karimi, Naghmeh (2022)
    Fault Injection Attacks (FIA) have received a lot of attention in recent years. An adversary launches such an attack to abusively take control over the system or to leak sensitive data. Laser illumination has been ...
  • PriveTAB : Secure and Privacy-Preserving sharing of Tabular Data 

    Kotal, Anantaa; Piplai, Aritran; Chukkapalli, Sai Sree Laya; Joshi, Anupam (ACM, 2022-04-24)
    Machine Learning has increased our ability to model large quantities of data efficiently in a short time. Machine learning approaches in many application domains require collecting large volumes of data from distributed ...
  • The Continuous Spectrum of Periodically Stationary Pulses in a Stretched Pulse Laser 

    Shinglot, Vrushaly; Zweck, John; Menyuk, Curtis (Optica, 2022)
    A spectral method for determining the stability of periodically stationary pulses in fiber lasers is introduced. Pulse stability is characterized in terms of the spectrum (eigenvalues) of the monodromy operator, which is ...
  • Out of Distribution Data Detection Using Dropout Bayesian Neural Networks 

    Nguyen, Andre T.; Lu, Fred; Munoz, Gary Lopez; Raff, Edward; Nicholas, Charles; Holt, James (2022-02-18)
    We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how previous attempts to leverage the randomized ...
  • Does Aging Matter? The Curious Case of Fault Sensitivity Analysis 

    Ebrahimabadi, Mohammad; Fadaeinia, Bijan; Moradi, Amir; Karimi, Naghmeh
    An adversary with physical access to a cryptographic device may place the device under an external stress such as over clocking, and under-volting in order to generate erroneous outputs based on which the keys can be ...
  • Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography 

    Romero, Daniel; Kim, Seung-Jun (2022-02-01)
    Radio maps can be utilized to characterize a parameter of interest in a communication channel, such as the received signal strength, at every point of a certain geographical region. This article presents an introductory ...
  • Application of compressive sensing for gravitational microlensing events 

    Korde-Patel, Asmita; Barry, Richard K.; Mohsenin, Tinoosh (SPIE, 2022-02-18)
    Compressive sensing (CS) is a unique mathematical technique for simultaneous data acquisition and compression. This technique is particularly apt for time-series photometric measurements; we apply CS to time-series ...
  • Kernel-Based Lifelong Multitask Multiview Learning 

    Mowakeaa, Rami; Kim, Seung-Jun; Emge, Darren K.
    Lifelong learning capitalizes on the shared skill structure present in a stream of tasks that arrive over time to improve upon the performance of single-task learners. In contemporary lifelong learning applications, it ...
  • High-Performance Magnetic-core Coils for Targeted Rodent Brain Stimulations 

    Bagherzadeh, Hedyeh; Meng, Qinglei; Lu, Hanbing; Hong, Elliott; Yang, Yihong; Choa, Fow-Sen (Science Partner Journals, 2022-03-07)
    Objective and Impact Statement. There is a need to develop rodent coils capable of targeted brain stimulation for treating neuropsychiatric disorders and understanding brain mechanisms. We describe a novel rodent coil ...
  • TinyM²Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices 

    Rashid, Hasib-Al; Ovi, Pretom Roy; Gangopadhyay, Aryya; Mohsenin, Tinoosh (2022-02-09)
    With the emergence of Artificial Intelligence (AI), new attention has been given to implement AI algorithms on resource constrained tiny devices to expand the application domain of IoT. Multimodal Learning has recently ...
  • Amenable Sparse Network Investigator 

    Damadi, Saeed; Nouri, Erfan; Pirsiavash, Hamed (2022-02)
    As the optimization problem of pruning a neural network is nonconvex and the strategies are only guaranteed to find local solutions, a good initialization becomes paramount. To this end, we present the Amenable Sparse ...
  • An Attack Resilient PUF-based Authentication Mechanism for Distributed Systems 

    Ebrahimabadi, Mohammad; Younis, Mohamed; Lalouani, Wassila; Karimi, Naghmeh (2022-02)
    In most PUF-based authentication schemes, a central server is usually engaged to verify the response of the device's PUF to challenge bit-streams. However, the server availability may be intermittent in practice. To tackle ...

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