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    ICC Distinguished Lecture: Dr. Doina Caragea, Kansas State University

    The Institute of Computing and Cybersystems will present a Distinguished Lecture by Dr. Doina Caragea on Friday, October 29, 2021, at 3:00 p.m. Dr. Caragea is a professor and the Michelle Munson-Serban Simu Keystone Research Scholar in the Computer Science department at Kansas State University. Her talk is itiled, “Mining Social Media to Aid Disaster Response.”

    Join the Zoom lecture here:

    Dr. Caragea has expertise in machine learning and data mining, with applications to data intensive problems in recommender systems, text analytics, security informatics, and bioinformatics. In recent years, she has focused on semi-supervised and domain adaptation algorithms, under the assumption that labeled data for a domain of interest is limited, if available at all.

    Lecture Title: Mining Social Media to Aid Disaster Response

    Lecture Abstract: Disaster-affected communities are increasingly becoming the source of big (crisis) data during and following major disasters. At the same time, big data have the potential to become an integral source of information for response organizations, as they can help enhance the situational awareness and facilitate faster response where it is most needed. Despite such benefits, the challenges presented by big data preclude organizations from using them routinely. Manually sifting through voluminous streaming data to filter useful information in real time is inherently impossible. We study machine learning solutions to help emergency response organizations deal with the overload of relevant information, and improve situational awareness and crisis response. Our proposed machine learning solutions have the potential to transform the way in which crisis response organizations operate and, in turn, to provide better support to the victims of disasters in a timely fashion.

    Speaker Bio: Doina Caragea, Ph.D., is a Professor at Kansas State University. Her research and teaching interests are in the areas of machine learning and data science, with applications to crisis informatics, security informatics, and bioinformatics. Her projects build upon close collaborations with social scientists, security experts and life scientists, and aim to provide practical computational approaches to address real-world challenges. Dr. Caragea received her PhD in Computer Science from Iowa State University in August 2004, and was honored with the Iowa State University Research Excellence Award for her work. She has published more than 150 refereed conference and journal articles. She has a strong track record of extramural funding, with $12M+ total funding as PI, co-PI or senior personnel from NSF and industry.

    Please note that this lecture was originally scheduled for October 22, 2021.

    MTRAC Info Seminar Is Sept. 10

    by Office of Innovation and Commercialization

    For several years, Michigan Tech has partnered with the State of Michigan and other stakeholders to create an entrepreneurial and innovation ecosystem. Members of the community at large can participate in this process at an event on the Michigan Tech campus.

    Michigan Tech hosts one of five hubs that make up the Michigan Translational and Research Commercialization (MTRAC), funded by the state’s Michigan 21st Century Jobs fund through the Michigan Strategic Fund. MTRAC-supported projects have secured more than $315 million in follow-on funding.

    Join us at noon on September 10, 2021 in GLRC 202 to hear directly from the program directors of each hub to learn about program requirements and what makes for a competitive proposal. Directors will have a few appointments on a first come, first serve availability following the seminar for one-on-one meetings with prospective principal investigators.

    MTRAC provides matching funds for researchers to accelerate the transfer of new technologies from universities, hospital systems, and nonprofit research centers into the commercial market. Funding is available under any of the five statewide hub programs organized around the following technology areas:

    • Ag Bio Innovation Hub (managed by Michigan State University)
    • Life Sciences Innovation Hub (managed by the University of Michigan)
    • Advanced Transportation Innovation Hub (managed by University of Michigan)
    • Advanced Materials Innovation Hub (managed by Michigan Tech)
    • Advanced Computing Innovation Hub (managed by Wayne State University)

    Prospective entrepreneurs will learn about moving technology from lab to market. Program objectives, goals and scope will be discussed by representatives from the five MTRAC hubs and representatives from the Michigan Economic Development Corporation (MEDC).

    Please RSVP to the event.

    PinT 2021 – 10th Workshop on Parallel-in-Time Integration

    August 2-6, 2021. PinT 2021 will be offered in a virtual-format. 

    Register online on the Registration Page.

    Computer models and simulations play a central role in the study of complex systems in engineering, life sciences, medicine, chemistry, and physics. Utilizing modern supercomputers to run models and simulations allows for experimentation in virtual laboratories, thus saving both time and resources. Although the next generation of supercomputers will contain an unprecedented number of processors, this will not automatically increase the speed of running simulations. New mathematical algorithms are needed that can fully harness the processing potential of these new systems. Parallel-in-time methods, the subject of this workshop, are timely and necessary, as they extend existing computer models to these next generation machines by adding a new dimension of scalability. Thus, the use of parallel-in-time methods will provide dramatically faster simulations in many important areas, such as biomedical applications (e.g., heart modeling), computational fluid dynamics (e.g., aerodynamics and weather prediction), and machine learning. Computational and applied mathematics plays a foundational role in this projected advancement.

    The primary focus of the proposed parallel-in-time workshop is to disseminate cutting-edge research and facilitate scientific discussions on the field of parallel time integration methods. This workshop aligns with the National Strategic Computing Initiative (NSCI) objective: “increase coherence between technology for modeling/simulation and data analytics”. The need for parallel time integration is being driven by microprocessor trends, where future speedups for computational simulations will come through using increasing numbers of cores and not through faster clock speeds. Thus as spatial parallelism techniques saturate, parallelization in the time direction offers the best avenue for leveraging next generation supercomputers with billions of processors. Regarding the mathematical treatment of parallel time integrators, one must use advanced methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretization and integration, convergence analyses of iterative methods, and the development and implementation of new parallel algorithms. Thus, the workshop will bring together an interdisciplinary group of experts spanning these areas.

    Conference on Applied Cryptography: Call for Participation

    The 2021 EAI International Conference on Applied Cryptography in Computer and Communications (AC3 2021) takes place May 15-16, 2021.

    Register for the virtual conference here.

    Dr. Bo Chen, Computer Science, founding general chair of the new EAI conference, says the conference has brought together researchers, developers and practitioners from around the world who will focus on, discuss, and explore the area of applied cryptography in computer and communication systems.

    Conference Topics

    Conference topics include all aspects of applied cryptography, including symmetric cryptography, public-key cryptography, cryptographic protocols, cryptographic implementations, cryptographic standards and practices, as well as using cryptography to solve real-world problems.

    Technical Program

    The AC3 2021 technical program includes four main conference tracks at which 11 papers will be presented virtually in oral presentations.

    • Track 1 – Blockchain
    • Track 2 – Authentication
    • Track 3 – Secure Computation
    • 4 – Practical Crypto Application. Aside from the high-quality technical paper presentations, the technical program also features two keynote speeches, and one technical workshop.


    The two keynote speeches will be delivered by Prof. Kui Ren (ACM Fellow, IEEE Fellow), Zhejiang University, China; and IEEE Fellow Prof. Robert Deng, Singapore Management University.


    A workshop, the First International Workshop on Security for Internet of Things (IOTS 2021), includes four technical papers which aim to develop cryptographic techniques for ensuring the IoT security. The conference, originally planned to be held in Xiamen China, was moved it online for the health and safety of participants.

    Register to participate in the virtual conference here. Use the “Sign up for free access to the livestream” option.

    European Alliance for Innovation (EAI) is an international professional community and a nonprofit organization. The goal of EAI is to empower the global ICT research and innovation community, and to promote cooperation between European and International ICT communities.

    EAI Conferences span the globe with opportunities to meet, explore, and contribute to the world of ICT research. With 100+ annual events (including MobiQuitous, SecureComm, etc.), EAI is one of the world’s most prolific scientific communities.

    EAI Conferences are published via Springer’s LNICST and EAI’s EUDL, and they are indexed in all leading indexing services, including EI, ISI, Scopus, CrossRef, Google Scholar, dblp, MAS, EBSCO, Microsoft Academic Search, CiteSeerX, and more.

    Jidong Xiao, Boise State University, to Present Talk May 12

    Jidong Xiao, an assistant professor in the computer science department at Boise State University, will present a talk on Wednesday, May 12, at 3:00 p.m.

    Dr. Xiao’s research focuses on computer security, especially computer system security and cloud security.

    In his talk, “Identifying New Threats in Cloud Environments,” Dr. Xiao will present two research projects focusing on a concept called virtual machine extrospection and a new type of rootkit, which allows attackers to perform active or passive attacks in a nested virtualization environment.

    Join the virtual talk here.

    Talk Title

    Identifying New Threats in Cloud Environments

    Talk Abstract

    Cloud computing has become prevalent over the past decade. While individuals and organizations rely on cloud computing more and more, various security problems in cloud platforms are discovered. In this talk, I will present two research projects. In the first project, I will present a concept called virtual machine extrospection, in which attackers or cloud customers collect sensitive information about the physical machine from within a virtual machine. In the second project, I will present a new type of rootkit, which allows attackers to perform active or passive attacks in a nested virtualization environment, and then I will describe our detection approach. At the end of the talk, I will briefly discuss my future research projects and plans.


    Dr. Jidong Xiao is an assistant professor in the computer science department at Boise State University. His research focuses on computer security, especially computer system security and cloud security. He received his PhD degree in computer science from the College of William and Mary. Prior to joining Boise State University, he spent approximately 5 years in industry working at Intel and Symantec.

    Dr. Xiao’s research was recognized in different venues, including publications that won the best paper award at the USENIX Large Installation System Administration Conference (LISA) 2015, won the distinguished poster award at the Network and Distributed System Security Symposium (NDSS) 2016, and won the best paper award nomination at the International Conference on Dependable Systems and Networks (DSN) 2020. Dr. Xiao has been awarded several grants by the NSF, NSA, and the Army Research Office (ARO).

    Jinging Yao, NJ Inst. of Technology, to Present Talk May 11

    Jingjing Yao, a PhD candidate at New Jersey Institute of Technology, will present a talk on Tuesday, May 11, at 3:00 p.m,.

    In her talk, “Intelligent and Secure Fog-Aided Internet of Drones,” Yao discusses the utilization of energy harvesting technology to charge drone batteries and investigate wireless power control to adjust the drone wireless transmission power to reduce drone energy consumption.

    Yao’s research interests include Internet of Things (IoT), Internet of Drones (IoD), Deep Reinforcement Learning, Federated Learning, Cybersecurity, Mobile Edge Computing/Caching, and Energy Harvesting.

    Join the virtual talk here.

    Talk Title

    Intelligent and Secure Fog-Aided Internet of Drones

    Talk Abstract

    Internet of drones (IoD), which deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing, can be applied in traffic surveillance and disaster rescue. Fog-aided IoD provisions future events prediction and image classification by machine learning technologies, where massive training data are collected by drones and analyzed in the fog node. However, the performance of IoD is greatly affected by drones’ battery capacities. Also, aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage. The speaker will share her vision and research to address these two challenges.

    In this talk, the speaker utilizes energy harvesting technology to charge drone batteries and investigate wireless power control to adjust the drone wireless transmission power to reduce drone energy consumption. The joint optimization of power control and energy harvesting scheduling is investigated in time-varying IoD networks to minimize the long-term average system energy cost constrained by the drone battery capacities and quality of service (QoS) requirements. A modified actor-critic deep reinforcement learning algorithm is designed to address the joint optimization problem in time-varying IoD networks.

    To prevent the privacy leakage of IoD, the speaker utilizes federated learning (FL) by performing local training in drones and sharing all training model parameters in the fog node without uploading drone raw data. However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. The power control problem is hence investigated to maximize the FL system security rate constrained by drone battery capacities and the FL training time requirement. An algorithm with low computational complexity is then designed to tackle the security rate maximization problem and its performance is demonstrated by extensive simulations.


    Jingjing Yao is a Ph.D. candidate in Computer Engineering with the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). She will receive her Ph.D. degree from NJIT in May 2021.

    She received the M.E. degree in Information and Communication Engineering from the University of Science and Technology of China (USTC), and the B.E. degree in Information and Communication Engineering from the Dalian University of Technology (DUT).

    She has published 13 first-author journal articles and 7 first-author conference papers. Her research interests include Internet of Things (IoT), Internet of Drones (IoD), Deep Reinforcement Learning, Federated Learning, Cybersecurity, Mobile Edge Computing/Caching, and Energy Harvesting.

    Tara Salman, Washington Univ., to Present Talk April 27

    Tara Salman, a final-year PhD candidate at Washington University in St. Louis, will present a talk on Tuesday, April 27, 2021, at 3:00 p.m.

    In her talk, “A Collaborative Knowledge-Based Security Solution using Blockchains,” she will present her work on redesigning the blockchains and building a collaborative, distributed, intelligent, and hostile solution that can be used for security purposes.

    Talk Title

    A Collaborative Knowledge-Based Security Solution using Blockchains

    Talk Abstract

    Artificial intelligence and machine learning have recently gained wide adaptation in building intelligent yet simple and proactive security solutions such as intrusion identification, malware detection, and threat intelligence. With the increased risk and severity of cyber-attacks and the distributed nature of modern threats and vulnerabilities, it becomes critical to pose a distributed intelligent solution that evaluates the systems’ and networks’ security collaboratively. Blockchain, as a decade-old successful distributed ledger technology, has the potential to build such collaborative solutions. However, to be used for such solutions, the technology needs to be extended so that it can intelligently process the stored information and achieve a collective decision about security risks or threats that might target a system.

    In this talk, I will present our work on redesigning the blockchains and build a collaborative, distributed, intelligent, and hostile solution that can be used for security purposes. In particular, we will discuss our work on (1) extending blockchains for general collaborative decision-making applications, where knowledge should be made out of decisions, risks, or any information stored on the blockchain; (2) applying the proposed extensions to security applications such as malware detection and threat intelligence.


    Tara Salman is a final year Ph.D. candidate at Washington University in St. Louis, where she is advised by Raj Jain. She previously received her MS and BSc degrees from Qatar University in 2015 and 2012, respectively. Her research aims to integrate state-of-the-art technologies to provide scalable, collaborative, and intelligent cybersecurity solutions.

    Her recent work focuses on the intersection of artificial intelligence, blockchains, and security applications. The work spans several fields, including blockchain technology, security, machine learning, and deep learning applications, cloud computing, and the Internet of Things. She has been selected for the EECS Rising Star in UC Berkeley 2020. Her research has been published in more than twenty internationally recognized conferences and journals and supported by national and international funds.

    Xinyu Lei, Michigan State, to Present Talk April 29

    Xinyu Lei, a Ph.D. candidate in the Department of Computer Science and Engineering at Michigan State University, will present a talk on Thursday, April 29, 2021, at 3:00 p.m.

    In his talk, “Secure and Efficient Queries Processing in Cloud Computing,” Lei will discuss his work developing new techniques to support secure and efficient queries processing in cloud storage.


    Secure and Efficient Queries Processing in Cloud Computing


    With the advent of cloud computing, data owners are motivated to outsource their databases to the commercial public cloud for storage. The public cloud with database-as-a-service (DBaaS) model has many benefits (including lower cost, better performance, and higher flexibility). However, hosting the datasets on the commercial public cloud deprives the data owners’ direct control over their databases, which brings in security concerns. For example, the corrupted cloud employees may spy the data owner’s commercial valuable databases and sell them for money. To protect data privacy, the sensitive database must be encrypted before outsourcing to the cloud. However, it becomes hard to perform efficient queries (e.g., keyword query) processing over the encrypted database.

    In this talk, I focus on developing new techniques to support secure and efficient queries processing in cloud storage. An index-aid approach is proposed to address the problem. In my approach, the data items are formally encrypted, and a secure index is generated for efficient queries processing. The cloud can perform queries directly over the secure index rather than the encrypted data items. The secure index is constructed based on a new data structure named random Bloom filter. Then, multiple random Bloom filters are organized into a binary tree structure to support fast query processing. The proposed approach can achieve data privacy, index privacy, search token privacy, and fast query processing simultaneously.


    Xinyu Lei is a Ph.D. candidate in the Department of Computer science and Engineering at Michigan State University. He once worked as a research assistant in Texas A&M University at Qatar and Ford Motor Company. He has research interests in cybersecurity problems in different computer systems (including IoT, blockchain, cloud computing). He has published 20+ papers with 600+ citations. His work has been published on top-tier conferences and journals such as ACM MobiSys, ACM CodaSpy, IEEE ICDE, IEEE ICDCS, etc.

    Spend 1010 with Dr. Brian Yuan, April 29

    You are invited to spend one-zero-one-zero—that is, ten—minutes with Dr. Brian Yuan on Thursday, April 29, at 4:30 p.m. EST.

    Dr. Yuan is an assistant professor in both the Applied Computing and Computer Science departments. His areas of expertise include machine learning, security and privacy, and cloud computing.

    Yuan will discuss his research, the Applied Computing and Computer Science departments, and answer questions.

    Dr. Yuan earned his PhD in Computer Science at University of Florida.

    We look forward to spending 1010 minutes with you!

    Visit the 1010 with … webpage here.