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

  • Semantically Rich Framework to Automate Cyber Insurance Services 

    Sane, Ketki; Joshi, Karuna; Mittal, Sudip (IEEE, 2021)
    With the rapid enhancements in technology and the adoption of web services, there has been a significant increase in cyber threats faced by organizations in cyberspace. Organizations want to purchase adequate cyber insurance ...
  • ISD: Self-Supervised Learning by Iterative Similarity Distillation 

    Tejankar, Ajinkya; Koohpayegani, Soroush Abbasi; S.P., Vipin; Favaro, Paolo; Pirsiavash, Hamed (2021-09-10)
    Recently, contrastive learning has achieved great results in self-supervised learning, where the main idea is to push two augmentations of an image (positive pairs) closer compared to other random images (negative pairs). ...
  • Backdoor Attacks on Self-Supervised Learning 

    Saha, Aniruddha; Tejankar, Ajinkya; Koohpayegani, Soroush Abbasi; Pirsiavash, Hamed (2021-05-21)
    Large-scale unlabeled data has allowed recent progress in self-supervised learning methods that learn rich visual representations. State-of-the-art self-supervised methods for learning representations from images (MoCo and ...
  • Cyclic Sparsely Connected Architectures for Compact Deep Convolutional Neural Networks 

    Hosseini, Morteza; Manjunath, Nitheesh; Prakash, Bharat; Mazumder, Arnab; Chandrareddy, Vandana; Homayoun, Houman; Mohsenin, Tinoosh (IEEE, 2021-09-15)
    In deep convolutional neural networks (DCNNs), model size and computation complexity are two important factors governing throughput and energy efficiency when deployed to hardware for inference. Recent works on compact ...
  • Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning 

    Wu, Yusen; Chen, Hao; Wang, Xin; Liu, Chao; Nguyen, Phuong; Yesha, Yelena (2021-09-05)
    Adversarial attacks attempt to disrupt the training, retraining and utilizing of artificial intelligence and machine learning models in large-scale distributed machine learning systems. This causes security risks on its ...
  • MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels 

    Joyce, Robert J.; Amlani, Dev; Nicholas, Charles K.; Raff, Edward (2021-08-20)
    Malware family classification is a significant issue with public safety and research implications that has been hindered by the high cost of expert labels. The vast majority of corpora use noisy labeling approaches that ...
  • Low Temperature Reactive Flux Growth of SiC and SiC-AlNusing Al-Si Flux 

    Singh, N. B.; Rai, Raghaw S. (Gexin Publications, 2021-07-24)
    Al - 12% Si alloy system was used as nutrient flux to grow silicon carbide at low temperature by reactive flux growth. Thick films were grown below 900oC on a SiC substrate having Al-Si melt rotated with a speed of 30rpm ...
  • Cyber-Physical System Security Surveillance using Knowledge Graph based Digital Twins - A Smart Farming Usecase 

    Chukkapalli, Sai Sree Laya; Pillai, Nisha; Mittal, Sudip; Joshi, Anupam (2021-08-31)
    Rapid advancements in Cyber-Physical System (CPS) capabilities have motivated farmers to deploy this ecosystem on their farms. However, there is a growing concern among users regarding the security risks associated with ...
  • GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Bus 

    Islam, Riadul; Devnath, Maloy K.; Samad, Manar D.; Kadry, Syed Md Jaffrey Al (2021-08-24)
    The national highway traffic safety administration (NHTSA) identified cybersecurity of the automobile systems are more critical than the security of other information systems. Researchers already demonstrated remote attacks ...
  • Leveraging Artificial Intelligence to Advance Problem-Solving with Quantum Annealers 

    Ayanzadeh, Ramin (2020-01-20)
    We show how to advance quantum information processing, specifically problem-solving with quantum annealers, in the realm of artificial intelligence. We introduce SAT++, as a novel quantum programming paradigm, that can ...
  • Security Issues in Unmanned Aerial Vehicle Routing Protocols. 

    Adegoke, Adekeye (2020-01-20)
    With the rapidly increasing demand for portable and flexible communications which has led to an evolution in networking between unmanned aerial vehicles, the potential attack surface for unmanned aerial vehicles has also ...
  • Detecting Offensive Social Media Text in Nepali Language 

    Timilsina, Sandesh (2020-01-20)
    Over the recent years, there has been an enormous increase in user-generated content on the internet. As a result of sentiments and opinions being freely shared on social media platforms, readers are at the increased risk ...
  • VISUAL COMPUTATIONAL CONTEXT: USING COMPOSITIONS AND NON-TARGET PIXELS FOR NOVEL CLASS DISCOVERY 

    Turner, JT (2019-01-01)
    During the deep learning revolution in computer science that has occoured since 2006, two factors have pushed our ability to successfully learn from large-scale data sources: exponential growth in computational power and ...
  • COMPARING JAVASCRIPT FRAMEWORKS TO IMPROVE PERFORMANCE OF WEB APPLICATIONS 

    Thummala, Aravind sai (2019-01-01)
    JavaScript has been in existence for many years already and is one of the most widely known and used front-end programming languages in web application development. Many web applications today are very interactive and the ...
  • Erasure Codes for IPFS 

    Shinde, Pratik (2019-01-01)
    IPFS (Interplanetary File System) is a peer to peer method of storing content addressable data in distributed file system. To provide Fault Tolerance to important files, IPFS copies them on at least 3 nodes in different ...
  • Modeling and Extracting Information about Cybersecurity Events from Text 

    Satyapanich, Taneeya (2020-01-20)
    People now rely on the Internet to carry out much of their daily activities such as banking, ordering food, and socializing with their family and friends. The technology facilitates our lives, but also comes with many ...
  • Energy Landscape Analysis of the Old vs Young Brain 

    Roopan, Roopan (2020-01-01)
    This study evaluates brain state activation differences in old vs the young individuals using energy-landscape analysis based on the resting-state fMRI data. The aim is to study brain Intra- and inter-network interaction ...
  • CSCMAC - Cyclic Sparsely Connected Neural Manycore Accelerator 

    Paneliya, Hirenkumar Sumanbhai (2020-01-01)
    In deep neural networks (DNNs), model size and computation complexity are two important factors that impact memory footprint and performance respectively, both of which can be minimized by compressing DNN with methods such ...
  • Semantically Rich Framework to Automate KnowledgeExtraction from Cloud Service Level Agreement 

    Natolana Ganapathy, Divya (2020-01-01)
    Consumers evaluate the performance of their cloud-based services by monitoring the Service Level Agreements (SLA) that list the service terms and metrics agreed with the service providers. Current Cloud SLAs are documents ...
  • Joint Models to Refine Knowledge Graphs 

    Padia, Ankur Sukhalal (2019-01-01)
    A knowledge graph can be viewed as a structural representation of beliefs with nodes and edges in which the nodes represent real-world entities or events and the edges are relations believed to hold between pairs of entities. ...

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