Most of today's popular deep neural networks are handcrafted/designed following a generalist trend to solve as many tasks as possible. This design practice results in unnecessarily cumbersome and power-hungry models that are infeasible for embedded applications (e.g., autonomous driving perception). Targeting this problem, this talk will present two deep neural network compression approaches. First, we propose deep linear discriminant analysis (deep LDA) based pruning. During training, the approach proactively maximizes and separates useful deep discriminants and pushes them into a subset of latent neurons. Pruning then becomes dimensionality reduction in the deep feature space by discarding useless/interfering neurons over the layers. Also, the talk will discuss the influence of our pruning on model robustness. In addition to pruning, the second approach we take to deep model compression is adaptive knowledge distillation. WE propose a Multi-teacher Adaptive Instance Distillation (M-AID) framework, which adjusts the distillation weights in an instance, scale, and teacher adaptive manner. Our M-AID can help the student model select the best knowledge from each teacher w.r.t certain instances and scales. To the best of our knowledge, our M-AID is the first adaptive multi-teacher framework for object detection. Experimental results on a wide array of datasets illustrate our solutions' superior performance to state-of-the-art neural network compression methods. 

Dr. Qing Tian obtained his PhD degree from McGill University, Canada in 2021, with a specialization in deep learning and computer vision. Currently, he is an assistant professor of Computer Science at Bowling Green State University (BGSU). His primary research interests lie in deep neural network pruning, knowledge distillation, and adversarial AI. His current research project in fast and reliable self-driving perception is supported by the National Science Foundation (NSF) through a CRII award. Prior to joining BGSU, he conducted research projects on apparel detection at Amazon Visual Search. Before his PhD, he worked at Nakisa Inc. as a software developer.

Are there fundamental limitations on the safety, security, and timeliness of heterogenous cyber-physical systems? Why are there so few safety+security engineers? Why is it difficult for security engineers to design for safety and for safety engineers to design for security? Why are security and safety so challenging to teach and learn in the first place? This talk will address these topics to various degrees of depth, drawing from existing published work, preliminary ideas, and observations which have not yet been quantified, and suggest promising future directions for research.

Eugene Vasserman is an Associate Professor in the Department of Computer Science at Kansas State University, specializing in the security of distributed systems. He is also the director of the Kansas State University Center for Cybersecurity and Trustworthy Systems and runs the Cybersecurity degree program. He received a B.S. in Biochemistry and Neuroscience with a Computer Science minor from the University of Minnesota in 2003. His M.S. and Ph.D. in Computer Science are also from the University of Minnesota, in 2008 and 2010, respectively. His current research is chiefly in the area of privacy, anonymity, censorship resistance, and socio-technical aspects of security. His research has resulted in over 45 peer reviewed publications in computer science, psychology, and education, with work spanning the gamut from medical cyber-physical systems, authorization with integrated break-glass capabilities, security vulnerabilities emergent from the BGP infrastructure of the internet, blockchains, energy depletion attacks in low-power systems, secure hyper-local social networking, and privacy and censorship resistance on a global scale (systems capable of supporting up to a hundred billion users). He has collaborated with the U.S. Food and Drug Administration on medical device cybersecurity and contributed to FDA policies on building safety-focused cybersecurity into legacy and future medical devices and systems-of-systems.In 2013, he received the NSF CAREER award for work on secure next-generation medical systems. He contributed to the UL 2900 standardization process for cybersecurity of network-connectable devices, the AAMI interoperability working group, and the ANSI / AAMI / UL 2800 standards effort for medical device interoperability. He has served on numerous program committees including USENIX Security, ACSAC, PETS/PoPETs, USEC, ASIACCS, HotWiSec, WPES, and SecureComm.

A quantum walk is the quantum analogue of a random walk. A long-standing open question is whether quantum walks can be used to quadratically speed up the mixing time of random walks. We describe a new approach to this question that builds on the close connection between quantum walks and Chebyshev polynomials. Specifically, we use that the quantum walk dynamics can be understood by simply applying a Chebyshev polynomial to the random walk transition matrix. This decouples the problem from its quantum origin, and highlights connections to classical second-order methods and the use of Chebyshev polynomials in random walk theory as in the Varopoulos-Carne bound. We illustrate the approach on the lattice: we prove a weak limit on the quantum walk dynamics, giving a different proof of the quadratically improved spreading behavior of quantum walks on lattices.

Simon Apers (he/him) received a PhD from Ghent University, Belgium, on the topic of quantum walks and quantum algorithms. Afterwards, Simon completed postdocs at CWI (Amsterdam) with Ronald de Wolf, INRIA (Paris) with Anthony Leverrier and ULB (Brussels) with Jérémie Roland. Simon is currently CNRS researcher at IRIF in Paris.

It is predicted that with the advances made in the Internet-of-Things, Next Generation Networking, Cloud computing and Artificial Intelligence, hundreds of thousands of islands of Cyber Physical Systems (CPS) which are currently geographically distributed in different countries and regions worldwide, as well as in different locations in cities, will be federated to generate unprecedent datasets which, when submitted to emerging artificial intelligence models (e.g. machine learning), will provide solutions to some of the key problems that the world could not solve till today.  This is the case, for example, in the emerging field of virtual/soft sensing, where artificial intelligence techniques are used to complement Internet-of-Things (IoT) measurements to provide innovative and more practical and economical solutions to many of the issues the world is currently facing. However, the digital divide between developed and developing nations, leading to an imbalance in cloud processing resources, is a key issue that might jeopardize/hamper the development of such CPS federations. This talk focuses on the design and performance evaluation of Federated Cyber-Physical Systems (FCPS), with the objective of showcasing how a federated cloud computing (FCC) model can be used to build data-intensive ecosystems. Building on a use-case, the talk reveals that through federation and cooperative sharing of cloud resources,  a cloud computing system can be built and used to improve the quality of service delivered by data-intensive processing infrastructures across a continent. The details of a Federated Cloud Computing Infrastructure (FCCI) called “AFRICA 3.0” aiming at fostering the Fourth Industrial Revolution (4IR) on the African continent will be presented and discussed during the talk.

Bigomokero Antoine Bagula received a Ph.D. degree (Tech. Dr.) in Communication Systems from the Royal Institute of Technology (KTH), Stockholm, Sweden, and 2 MSc degrees (Computer Engineering – Université Catholique de Louvain (UCL), Belgium and Computer Science - University of Stellenbosch (SUN), South Africa).  He is currently a full professor and head of the Department of Computer Science at the University of the Western Cape (UWC) where he also leads the Intelligent Systems and Advanced Telecommunication (ISAT) laboratory. He is also an extraordinary Professor at ESIS-Salama where he is in charge of spearheading the institution research agenda. Prof. Bagula is well-published scientist in his research field. His current research interests include Data Engineering including Big Data Technologies, Cloud/Fog Computing and Network Softwarization (e.g., NFV and SDN); The Internet of Things (IoT) including the Internet-of-Things in Motion and the Tactile Internet; Data Science including Artificial Intelligence and Machine Learning with their applications in Big Data Analytics; and Next Generation Networks including 5G/6G.

Random walks serve as the basis of a number of algorithms, and this led to the idea that perhaps a quantum version of a random walk would be useful for finding quantum algorithms.  Constructing a quantum walk is not completely straightforward, and there are three ways of doing it, two of which will be discussed in this talk.  They have proved useful in search problems, where there is a distinguished vertex in a graph, and we would like to find it.  It is also possible to search for other objects, such as extra edges or more general structures that break the symmetry of a graph.  Most recently, it has been possible to use quantum walks to find paths between two special vertices, which is analogous to using them to find a path though a maze.

Mark Hillery is a professor of physics at Hunter College and the Graduate Center of the City University of New York.  He received his Ph.D. from the University of California at Berkeley and went on to a postdoctoral position with a quantum optics group that was based both at the University of New Mexico and the Max Planck Institute for Quantum Optics.  He then joined the City University of New York.  He has worked in quantum optics and, since 1996, in quantum information.  He had a long-time collaboration with a research group in Bratislava, Slovakia, which led to his being awarded the International Prize of the Slovak Academy of Sciences in 2019.  For 15 years he was an associate editor of the journal Physical Review A.  He is a fellow of both the American Physical Society and the Optical Society of America.

In the past decade, the revival of deep neural networks has led to dramatic success in numerous applications ranging from computer vision to natural language processing to scientific discovery and beyond. Nevertheless, the practice of deep networks has been shrouded with mystery as our theoretical understanding of the success of deep learning remains elusive.

In this talk, we will focus on the representations learned by deep neural network classifiers. In this setting, the recent work by Papyan et al. revealed an intriguing empirical phenomenon, called neural collapse, that persists across different neural network architectures and a variety of standard datasets. We will first provide a geometric analysis for understanding why neural collapse always happens on a simplified unconstrained feature model. We will then exploit these findings to understand the roles of different loss functions proposed in the literature for training deep neural networks. Among all the proposed loss functions, which one is the best to use is still a mystery because there seem to be multiple factors affecting the answer, such as the properties of the dataset, the choice of network architecture, and so on. Through the principles of neural collapse, we will show that all relevant losses produce equivalent features on training data and lead to largely identical performance on test data as well, provided that the network is sufficiently large and trained until convergence.

Zhihui Zhu is currently an Assistant Professor with the Department of Computer Science and Engineering at the Ohio State University. He was an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Denver from 2020-2022 and a Post-Doctoral Fellow with the Mathematical Institute for Data Science, Johns Hopkins University, from 2018 to 2019. He received his Ph.D. degree in electrical engineering in 2017 from the Colorado School of Mines, where his research was awarded a Graduate Research Award. He is or has been an Action Editor of the Transactions on Machine Learning Research and an Area Chair for NeurIPS.

The early suppression of fires on ro-ro vessels requires rapid fire identification as a fire of medium growth exponentially reaches 50kW after only 1 minute. Fire patrol members (e.g., able seamen) are asked to act as first responders in such fire incident cases. They do however lack the necessary digital technology for immediate localization, verification and coordination with the bridge and other first responders. Indoor localization requires dense referencing systems (such as Wi-Fi, UWB, Bluetooth antennas), but these technologies require expensive installations and maintenance. Also, Satellite-based indoor localization is obstructed by the bulky steel structures of vessels, so this doesn’t work either. In this work we develop a ground-breaking localization technology that requires zero infrastructure using computer vision on commodity smartphone devices attached to the gear of first responders. The developed solution comprises of three steps: (i) Training, where vessel owners supply video recordings that are processed on a deep learning data center to produce an accurate computer vision machine learning model; (ii) Logging, where a mobile app allows referencing non-movable objects to the (x,y,deck) coordinates of a vessel; and (iii) Localization, where first responders localize on a digital map. Additionally, in case a sparse communication network is available, first responders can share their location, emergency messages and heat scan images with nearby first responders and the bridge. Our proposed algorithm, coined Surface, is shown to be 80% and 90% accurate for localization and tracking scenarios, respectively, in both a remote study and an on-board study we carried out on a real ro-ro vessel. The overall developed Smart Alert System (SMAS), streamlines the lengthy fire verification, coordination, and reaction process in the early stages of a fire, improving fire safety. My talk will conclude with a summary of other relevant work in the scope of our open-source indoor localization system, named Anyplace.

More: https://anyplace.cs.ucy.ac.cy/

Demetris Zeinalipour is an Associate Professor of Computer Science at the University of Cyprus, where he leads the Data Management Systems Laboratory (DMSL). His primary research interests include Data Management in Computer Systems and Networks, particularly Mobile and Sensor Data Management; Big Data Management in Parallel and Distributed Architectures; Spatio-Temporal Data Management; Network and Telco Data Management; Crowd, Web 2.0 and Indoor Data Management; Data Privacy Management and Data Management for Sustainability. He holds a Ph.D. in Computer Science from University of California - Riverside (2005). Before his current appointment, he served the University of Cyprus as an Assistant Professor and Lecturer but also the Open University of Cyprus as a Lecturer. He has held visiting research appointments at Akamai Technologies, Cambridge, MA, USA, the University of Athens, Greece, the University of Pittsburgh, PA, USA and the Max Planck Institute for Informatics, Saarbrücken, Germany. He is a Humboldt Fellow, Marie-Curie Fellow, an ACM Distinguished Speaker (2017-2020), a Senior Member of ACM, a Senior Member of IEEE and a Member of USENIX. He serves on the editorial board of Distributed and Parallel Databases (Elsevier), Big Data Research (Springer), SI Editor for ACM Transactions on Spatial Algorithms and Systems (ACM TSAS), and is an independent evaluator for the European Commission (Marie Skłodowska-Curie and COST actions), the Hong Kong RGC and the Hellenic HFRI. His h-index is 30, holds over 3800 citations, has an Erdös number of 3, was awarded 12 international awards and honors (IEEE-MDM18, IEEE-MDM17, ACMD17, ACMS16, IEEES16, HUMBOLDT16, ACM-IEEE-IPSN14, EVARILOS14, APPCAMPUS13, IEEE-MDM12, MC07, CIC06) and delivered over 30 invited talks. He is/was General Co-Chair for 4 events (IEEE MDM22, EDBT21, VLDB's DMSN11, ACM MobiDE10), and is/was Program Co-Chair for 8 events (IEEE-MDM19, IEEE-MDM10, IEEE-ALIAS19, IEEE-MUSICAL16, IEEE-HuMoComP15, HDMS18, VLDB-DMSN10, ACM-MobiDE09). He has participated in over 20 projects funded by the US National Science Foundation, by the European Commission, the Cyprus Research Promotion Foundation, the Univ. of Cyprus, the Open University of Cyprus and the Alexander von Humboldt Foundation, Germany. Finally, he has also been involved in industrial Research and Development projects (e.g., Finland, Taiwan and Cyprus) and has technically lead several open source mobile data management services (e.g., Vgate, Anyplace, Rayzit and Smartlab) reaching thousands of users worldwide. More: https://www.cs.ucy.ac.cy/~dzeina/

The idea of Data Science has gathered many different perspectives throughout different domains and articles. In this talk, Data Science is discussed as the theories, techniques, and tools designed for data-centered analysis and the methodology to apply them to real world applications. We have revolutionized the process of gathering multimedia data from an abundance of sources. Things such as mobile devices, social networks, autonomous vehicles, smart household appliances, and the Internet have increasingly grown in prominence. Correspondingly, new forms of multimedia data such as text, numbers, tags, networking, signals, geo-tagged information, graphs, 3D/VR/AR, sensor data, and traditional multimedia data (image, video, audio) have become easily accessible. Therefore, older traditional methods of processing data have become increasingly outdated, emphasizing the demand for more advanced data-science techniques to process these heterogeneous, large data sets which fluctuate in quality and semantics. Additionally, the inconsistency of utilizing single modality severely hinders our ability to withstand multiple data sources simultaneously. Upon analyzing the capabilities of each model, it becomes pivotal for us to consider the multi-modal frameworks to leverage the multi-data sources to assist data analytics. With the implementation of data science and big data strategies, we are able to efficiently capture, store, clean, analyze, mine, and visualize exponential growth of multimedia data, which is responsible for the majority of daily Internet traffic. With the data science field growing with importance, the goal of big data revolution is to bridge the gap between data availability and its effective utilization which enables research, technological innovation and even decision making. In this talk, I will discuss the research opportunities and challenges in multimedia big data for data science. A set of core techniques and applications will be discussed. I will also discuss Data Science and Analytics Innovation Center (dSAIC) -  a University of Missouri System-wide Center.

Dr. Shu-Ching Chen is the inaugural Executive Director of Data Science and Analytics Innovation Center (dSAIC). dSAIC is a multi-university center and based at the University of Missouri-Kansas City (UMKC). His main research interests include data science, multimedia big data, disaster information management, content-based image/video retrieval, and multimedia systems. He has authored and coauthored more than 370 research papers and four books. He has been the PI/Co-PI of many research grants from NSF, NOAA, DHS, NIH (Co-I), Department of Energy, Army Research Office, Naval Research Laboratory (NRL), Environmental Protection Agency (EPA), Florida Office of Insurance Regulation, Florida Department of Transportation, IBM, and Microsoft. Dr. Chen received 2011 ACM Distinguished Scientist Award, best paper awards from 2006 IEEE International Symposium on Multimedia and 2016 IEEE International Conference on Information Reuse and Integration. He also received the best student paper award from 2022 IEEE International Conference on Multimedia Information Processing and Retrieval. He received the 2019 Service Award from IEEE Computer Society’s Technical Committee on Multimedia Computing. He was awarded the IEEE SMC Society’s Outstanding Contribution Award in 2005 and IEEE Most Active SMC Technical Committee Award in 2006. He has been a General Chair and Program Chair for more than 60 conferences, symposiums, and workshops. He is the Editor-in-Chief of IEEE Multimedia Magazine, founding Editor-in-Chief of International Journal of Multimedia Data Engineering and Management, and the Co-Chair of IEEE SMC’s Technical Committee on Knowledge Acquisition in Intelligent Systems. He was the Chair of IEEE Computer Society Technical Committee on Multimedia Computing and a steering committee member of IEEE Trans. on Multimedia. He is a fellow of IEEE, AAAS, SIRI, and AAIA.

This talk will discuss an Intelligent Crowd Engineering platform using Multimodal Internet of Things (IoT) and Machine Learning (ML) approaches to enhance the accuracy, scalability, and crowd safety management capacity in real-time. We design an ICE structure with three critical layers: IoT-based mobility characterization, ML-based video surveillance, and semantic information-based application layers. We built an IoT-based mobility characterization system by predicting and preventing potential disasters through real-time Radio Frequency (RF) data characterization and analytics. Among the many crowd mobility characteristics, we tackle object group identification, speed, direction detection, and density for the mobile group. Also, we tackled an ML-based video surveillance approach for effective dense crowd counting by characterizing scattered occlusions, named CSONet. CSONet recognizes the implications of event-induced, scene-embedded, and multitudinous obstacles such as umbrellas and picket signs to achieve an accurate crowd analysis result. Finally, we developed a couple of group semantics to track and prevent crowd-caused infectious diseases. We introduce a novel COVID-19 tracing application named Crowd-based Alert and Tracing Services (CATS) and a novel face masking and social distancing monitoring system for Modeling Safety Index in Crowd (MOSAIC). CATS and MOSAIC apply privacy-aware contact tracing and social distancing and calculate spatiotemporal Safety Index (SI) values for the individual community to provide higher privacy protection, efficient penetration of technology, greater accuracy, and effective practical policy assistance.

Dr. Sejun Song is an Associate Professor in the Department of Computer Science Electrical Engineering at the University of Missouri – Kansas City (UMKC). Song conducts research projects including Machine Learning, Internet-of-Things, Cybersecurity, Mobile and Wireless Communications, Networking, Embedded Systems, and Cloud Edge Computing. Song received his Ph.D. in Computer Science and Engineering from the University of Minnesota, Twin Cities, in 2001. He was working for Texas A&M University, College Station (TAMU) as an Assistant Professor in the Department of Engineering Technology and Industrial Distribution (ETID), and a director of the Cisco Test Engineering Center (Cisco-TEC). Before joining academia, Song worked in Cisco Systems and Honeywell Research Lab industries. He is a recipient of the Center for Teaching Excellence Scholar for excellence in undergraduate teaching, a Faculty Teaching Excellence Award, a NASA Summer Fellowship Award, Air Force Research Lab’s Visiting Faculty Research Fellowship Awards, a Cisco Summer Fellowship Award, and has received several best research video/paper awards including CCNC 2019, ISC2 2018, Mobisys 2014, ICCCN 2014, and CIEC 2013. Several agencies have funded his research, including NSF, AFOSR, MoDOT, Cisco Systems, AFRL, NASA, NIST, CDC, TAMU, KT Research, NIH, and ETRI.

The sparse linear bandit problem is an interactive machine learning problem where a learning agent sequentially selects an action and receives reward feedback where the reward function depends linearly on a few coordinates of the covariates of the actions. This has applications in many real-world sequential decision making problems including product/movie recommendations. In this talk, we show a simple and computationally efficient sparse linear estimation method called PopArt that enjoys a tighter recovery guarantee compared to Lasso (Tibshirani, 1996) in many problems. Our bound naturally motivates an experimental design criterion that is convex and thus computationally efficient to solve. Based on our novel estimator and design criterion, we derive sparse linear bandit algorithms that enjoy improved regret upper bounds upon the state of the art (Hao et al., 2020), especially w.r.t. the geometry of the given action set. Finally, we prove a matching lower bound for sparse linear bandits in the data-poor regime, which closes the gap between upper and lower bounds in prior work.

Kwang-Sung Jun is an assistant professor at the CS department, University of Arizona. Before joining UA, he was a postdoc at Boston University with Dr. Francesco Orabona. Before then, he spent 9 years at University of Wisconsin-Madison for a PhD degree with Dr. Xiaojin (Jerry) Zhu and a postdoc with Drs. Robert Nowak, Rebecca Willett, and Stephen Wright. His research interest is bandits and online learning, but more broadly, interactive machine learning and adaptive data collection methods with various applications including scientific discovery.

In this talk, we will discuss how eyes can be used for liveness detection of mobile authentication and deepfake detection. As the need for contactless biometric authentication becomes more significant during COVID-19 and beyond, the popular biometric authentication method for mobile devices, iris detection, and facial recognition confronts various usability, security, and privacy concerns, including mask-wearing and various Presentation Attacks (PA). Specifically, liveness detection against spoofed artifacts is one of the most challenging tasks as many existing methods cannot conclusively assess the user's physical presence in unsupervised environments. Even though several methods have been proposed for tackling PA with motion challenges and 3D mapping, most require expensive depth sensors and fail to detect sophisticated 3D reconstruction attacks. We present a software-based face PA Detection (PAD) method named, which creates challenges and detects meaningful corneal specular reflection responses from human eyes. To detect human liveness, our system generates multiple screen image patterns as a challenge, then captures the response of corneal specular reflections using a frontal camera and analyzes the images using lightweight Machine Learning (ML) techniques. Liveness detection system components include challenge pattern generation, reflection image augmentation (e.g., super-resolution), and ML-based analyses. We have implemented the system as Android, iOS, and web apps. Our extensive experimental results show that our scheme achieves liveness detection with high accuracy at around 200 ms against various types of sophisticated PAs. The liveness detection can be applied for multiple contactless biometric authentications accurately and efficiently without any costly extra sensors nor involving users' active responses.

The second part is about deepfake detection through eyes and environment. Deepfake techniques presenting AI-generated fictitious facial images of people can negatively influence the authenticity of online information. Starting as benign mesmerizing memes, they can be used malignantly to originate deception, manipulation, persecution, and seduction, defying societal quality and human rights. However, due to the recent development of sophisticated deepfake generation technologies, it is getting harder to distinguish counterfeit images. Thus, instead of relying on a single aspect of the visual features, we detect various features from the specular reflection images such as colorcomponents, shapes, and textures to check the coordination with the surrounding environmental factors such as indoor/outdoor, bright/dark, backgrounds, and light strength. We have conducted extensive experiments to evaluate the performance of our method using various input parametersand advanced Deep Neural Network (DNN) architectures on multiple public DeepFake datasets. The empirical results show that our technique achieves high accuracy (99.0%) in detecting sophisticated deepfake images.

Dr. Baek-Young Choi is a Professor at the University of Missouri – Kansas City (UMKC). Prior to joining the University of Missouri – Kansas City, Dr. Choi held positions at Sprint Advanced Technology Labs, and the University of Minnesota, Duluth, as a postdoctoral researcher, and as a 3M McKnight distinguished visiting assistant professor, respectively. She published three books on network monitoring, storage systems, and cloud computing. She has been a faculty fellow of the National Aeronautics and Space Administration (NASA), U.S. Air Force Research Laboratory’s Visiting Faculty Research Program (AFRL-VFRP) and Korea Telecom’s - Advance Institute of Technology (KT-AIT). She is an associate editor for IEEE Consumer Electronics Magazine and was an associate editor for IEEE Internet-of-Things Journal, Springer Journal of Telecommunication Systems, Elsevier Journal Computer Networks. Her research interests generally lie in the broad area of networking and communications, with specific emphasis on Internet-of-Things, software-defined networking, cybersecurity, smart city technologies. She is a senior member of ACM and IEEE, and a chair of IEEE Women in Communications Engineering (WICE).

The Internet of Things (IoT) forms a critical communication infrastructure in this hyper-connected digital world. The number of connected IoT devices has globally reached 14.4 billion in 2022 alone, which is forecasted to reach 27 billion by 2025. With the proliferation of smart IoT devices, the associated security vulnerabilities also increase. Specifically, proper identification and authentication of the end-user in these devices are challenging. In this regard, biometrics-based authentication services provide a reliable solution. However, protecting biometric data from adversarial attacks is critical since it embeds discriminating information about individuals. Biometric template protection (BTP) schemes have emerged as a solution that can guarantee tight security bounds with acceptable model performance. However, the BTP schemes are limited by their computational and storage requirements. Since IoT devices are primarily resource-constrained, the existing BTP schemes are not directly applicable. 

Dr. Debanjan Sadhya is currently an Assistant Professor at ABV - Indian Institute of Information Technology & Management Gwalior, India. Before this, he was a Post-Doctoral Fellow at the Indian Institute of Technology Roorkee, India, for one and half years. He completed his Ph.D. from the Indian Institute of Technology (Banaras Hindu University), Varanasi, in biometric encryption in 2017. Before this, he completed his Master's (specialization - Wireless Communication and Computing) from the Indian Institute of Information Technology Allahabad, India, and his Bachelor's (specialization - Computer Science and Engineering) from the West Bengal University of Technology, India. His main areas of interest include biometrics, information security, and data privacy. He has been a member of the IEEE since 2015.

The talk will discuss how big data can help people with disabilities and will include a demonstration of several of Google's accessibility tools and features including a deeper dive into the Lookout - Assisted Vision app. Lookout uses computer vision and machine learning to assist people who are blind or have low vision with interpreting the visual information around them.

Dr. Tommy Szalapski got his undergraduate degrees in computer science and applied mathematics, followed by a PhD also in Computer Science from Missouri S&T in 2012. He was a GAANN Fellow in the department of computer science and published several papers on data compression in sensor networks with two IEEE best-paper awards. For the past 8 years, he has been working as a Software Engineer at Google and is currently on Google's Central Accessibility team.

In a disaster or battlefield zone, rescue workers, soldiers, and other survivors (referred to as nodes) may need to survey damages and send images to the command and control center (the server) in a hop-by-hop fashion in the absence of any communication infrastructure. The server considers some area/landmark as the point of interest (POI) and distributes a request to the nodes to collect more information about them. Nodes take photos of POIs and share them using the store and forward paradigm, also called Delay-tolerant Networks (DTNs), to send the photos to the server. Due to the highly intermittent contact characteristics of nodes in a DTN network and bandwidth and storage limitations, redundant photos need to be removed in this forwarding technique, whereas photos that cover different angles and views of the POIs need to be shared. Another challenge is that, over time, some server-listed POIs may not be of importance anymore, whereas some new POIs might be of more interest. In this work, we propose a scheme that can dynamically update the list of POIs based on the current photo metadata, with reduced consumption of the bandwidth, energy, and storage at DTN nodes by sending only important photos of POIs. In the performance evaluation, we show the scalability of our approach, and also show that it provides the same level of photo coverage but consumes much less energy and bandwidth than other related schemes.

Dr. Shudip Datta received his Doctor of Philosophy in Computer Science from the Missouri University of Science and Technology in 2022 under the supervision of Dr. Sanjay Madria, a curators’ distinguished professor in the department of computer science. He received his bachelor's in computer science and engineering from the Bangladesh University of Engineering and Technology (BUET), Bangladesh. His research interest includes the application of Delay Tolerant Network (DTN) in the field of disaster and battlefield environment, analyzing and designing efficient ways of collecting and forwarding information among the various nodes associated with the network, and application of machine learning for predicting node movements and optimizing routes in DTN. Besides research experience, he has worked for two years as a software engineer at System Solutions and Development Technology in Bangladesh. He has published research papers in top rank journals and conferences such as IEEE SRDS and IEEE Transaction. His research at S & T was sponsored by AFRL and NSF.

In this talk, we will design and analyze "cool" algorithms (that are elegant and efficient) for simple practical problems yet of recreational nature. Emphasis will be given on fundamental concepts discussed in an interactive manner. Afterall the goal is to have fun!!

“... pleasure has probably been the main goal all along. But I hesitate to admit it, because computer scientists want to maintain their image as hard-working individuals who deserve high salaries. Sooner or later society will realize that certain kinds of hard work are in fact admirable even though they are more fun than just about anything else.” 

– Donald E. Knuth (Computer Scientist, Turing Award Winner, 1974

• Sajal K. Das, whose academic genealogy includes Thomas Alva Edison, is a professor of Computer Science and the Daniel St. Clair Endowed Chair at the Missouri University of Science and Technology, where he was the Chair of Computer Science Department during 2013-2017. He served the National Science Foundation as a Program Director in the Computer Networks and Systems division during 2008-2011. Prior to 2013, he was a University Distinguished Scholar Professor of Computer Science and Engineering and founding director of the Center for Research in Wireless Mobility and Networking (CReWMaN) at the University of Texas at Arlington. His research interests include wireless and sensor networks, mobile and pervasive computing, UAVs, mobile crowdsensing, cyber-physical systems and IoT, smart environments (e.g., smart city, smart grid, smart transportation, smart agriculture, and smart health), distributed and cloud computing, cyber security, biological and social networks, and applied graph theory and game theory. He has made fundamental contributions to these areas and published extensively in high quality journals and refereed conference proceedings. He holds 5 US patents and coauthored 4 books– Smart Environments: Technology, Protocols, and Applications (John Wiley, 2005), Handbook on Securing Cyber-Physical Critical Infrastructure: Foundations and Challenges (Morgan Kauffman, 2012), Mobile Agents in Distributed Computing and Networking (Wiley, 2012), and Principles of Cyber-Physical Systems: An Interdisciplinary Approach (Cambridge University Press, 2020). His h-index is 94 with more than 36,500+ citations according to Google Scholar. He is a recipient of 11 Best Paper Awards at prestigious conferences including ACM MobiCom and IEEE PerCom, and numerous awards for teaching, mentoring and research including the IEEE Computer Society’s Technical Achievement Award for pioneering contributions to sensor networks, and University of Missouri System President’s Award for Sustained Career Excellence. Dr. Das serves as the founding Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing Journal, and as Associate Editor of several journals including the IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Mobile Computing, and ACM Transactions on Sensor Networks. He has graduated 11 postdocs, 48 Ph.D. students, 31 MS theses, and numerous undergraduate research students. Dr. Das is an IEEE Fellow (class of 2015) and a recipient of 2021 Distinguished Alumnus Award of the Indian Institute of Science, Bangalore.

In his classic book Aircraft in Warfare, F. W. Lanchester discussed different types of warfare and presented equations, called the Lanchester equations, that can be used to model the results of battles between two forces of different sizes or capabilities. This paper introduces the Lanchester equations and provides a theoretical discussion leading to an analysis of the relative value of increasing the effectiveness of military assets vs. increasing the quantity of those assets. In particular, we show that increasing the effectiveness contributes only linearly to the power of a combatant, but increasing the quantity contributes quadratically. This talk also presents a Python script that models the Lanchester equations, together with a visualization demonstrating the effect of force concentration. The talk concludes with a discussion of the relevance of Lanchester’s equations to cyberwarfare, specifically to drone warfare and DDoS attacks. More generally, we provide a foundation for analyzing the tradeoff between quality and quantity in warfare.

Dr. George Markowsky is currently Professor of Computer Science at Missouri S&T. He holds an M.A. and Ph.D. in Mathematics from Harvard University and a B. A. in Mathematics from Columbia University. He served as Chair of Computer Science at S&T, Chair and Professor of Computer Science and Chair of Mathematics & Statistics at the University of Maine, a Visiting Scholar at the Rochester Institute of Technology, a Visiting Professor at the Lally School of Management and Technology at RPI, and Manager of Special Projects in the Computer Science Department at the IBM Watson Research Center in NY. 

Unmanned Aerial Vehicles (or drones) can be used for a myriad of applications, such as search and rescue, precision agriculture, last-mile package delivery, etc. Interestingly, the cooperation between drones and ground vehicles (trucks) can even enhance the quality of service. In this talk, we will investigate the symbiosis between a truck and drones in the last-mile package delivery, introducing the Multiple Drone-Delivery Scheduling Problem (MDSP). From the main depot, a truck transports a team of drones for delivery of packages to the customers. Each delivery is associated with a drone's energy cost, a reward that characterizes the priority of the delivery, and a time interval representing the launch and rendezvous times from and to the truck. The objective of MDSP is to find an optimal scheduling for the drones that maximizes the overall reward subject to the drone's battery capacity while ensuring that the same drone performs deliveries whose time intervals do not intersect. After proving that MDSP is an NP-hard problem, we will devise an optimal solution based on Integer Linear Programming (ILP), a heuristic algorithm for the single drone case and two heuristics for the multiple drone case. We will compare the performance of our algorithms.

Federico Coro received his M.Sc. degree in computer science from the University of Perugia, Italy, in 2016, and the Ph.D. degree in computer science from the Gran Sasso Science Institute, L’Aquila, Italy, in 2019. From 2019 to 2020, he was a Postdoctoral Researcher in the Department of Computer Science at Sapienza University Rome, Italy. He is currently a Postdoctoral Researcher in Computer Science at the Missouri University of Science and Technology University. His research interests include several aspects of theoretical computer science, including combinatorial optimization, network analysis, and the design and efficient implementation of drone algorithms.

As the generation of data becomes more prolific, the amount of time and computational resources necessary to perform analyses on these data increases. What is less well studied, however, is the data pre-processing steps that must be applied before any meaningful analysis can begin. This problem of taking data in some initial form and transforming it into a desired one is known as data integration, and it can be a substantial part of the overall computational load associated with data analytics.  Our group is exploring the execution of data integration via a range of compute engines, including Field-Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs), Near-Memory Processing (NMP) architectures, and mixed-mode analog/digital systems. Applications are drawn from computational biology, scientific experiments (astrophysics and nuclear science), enterprise systems, image processing, graph processing, and the Internet of Things (IoT).

Dr. Roger Chamberlain is Professor of Computer Science and Engineering at Washington University in St. Louis.  He completed his doctoral degree in computer science in 1989 from the same institution. He has served for a time as Associate Chair of the CSE Department and currently is serving as the director of the interdepartmental computer engineering program. Prof. Chamberlain teaches in the areas of digital systems, parallel processing, computer architecture, embedded systems, and reconfigurable logic. His research interests include specialized computer architectures for a variety of applications (e.g., astrophysics and biology), high-performance parallel and distributed application development, energy-efficient computation, and high-capacity I/O systems. Prof. Chamberlain has helped found a number of companies, he has over seventy patents issued both in the US and internationally, and he is a senior member of the National Academy of Inventors.

Scalability is one of the key issues that hinder the widespread use of cryptocurrencies, like Bitcoin, due to the underlying consensus algorithms for ensuring the security in a decentralized system. Recently, payment channel network (PCN) has been proposed as a promising off-chain solution. It allows instant and inexpensive payments by not requiring expensive and slow blockchain operations. However, to enable high-throughput transactions in PCNs, there are still many barriers to overcome, including transaction fee, node reliability, always-online requirement, balance depletion, and cryptocurrency utilization. In this talk, I will present some of our recent work on addressing these challenges. More specifically, I will first introduce two distributed algorithms for minimizing the transaction fee and providing robustness despite unreliable nodes, respectively. Next, I will demonstrate how to design smart contracts to deter the potential collusion in PCNs based on a game theoretic approach. Finally, I will share our thoughts on addressing other challenges.

Dr. Dejun (DJ) Yang is an Associate Professor in the Computer Science Department at Colorado School of Mines. He received the Ph.D. degree in Computer Science from Arizona State University in 2013 and the B.S. degree in Computer Science from Peking University in 2007. His research interests include networking, blockchain, Internet of Things, and mobile sensing, with a focus on the application of game theory, optimization, algorithm design, and machine learning to resource allocation, security and privacy problems. He received the 2019 IEEE Communications Society William R. Bennett Prize (only one of all the papers published in the IEEE/ACM Transactions on Networking and the IEEE Transactions on Network and Service Management in the previous three years) and Best Paper Awards at GLOBECOM, ICC (x2), and MASS, as well as a Best Paper Runner-Up at ICNP. He is currently an associate editor for the IEEE Internet of Things Journal and the IEEE Transactions on Network Science and Engineering.

Physical layer security approaches have often used the hardness of blind deconvolution to achieve privacy when transmitting signals over unknown wireless channels.  Herein, we exploit the communication channel in a new way to provide a layer of privacy. In particular, we take advantage of the fact that it has been shown that exact recovery of block-sparse signals via linear measurements is achievable under conditions where classical compressed sensing would provably fail. We exploit this result to propose a novel private communication framework where secrecy is enhanced by transmitting instances of an unidentifiable compressed sensing problem over a public channel. The legitimate receiver can attempt to overcome this ill-posedness by leveraging secret knowledge of a block structure that is used to encode the transmitter's message. We study the privacy guarantees of this communication protocol in a variety of cases with the goal of understanding how often we need to refresh the shared secret between the transmitter and intended receiver.  Additionally, we propose an algorithm for an eavesdropper to learn the block structure via the method of moments and highlight the privacy benefits of this framework through numerical experiments.

[This is joint work with postdoctoral researcher, Dr. Maxime Ferreira Da Costa]

Dr. Urbashi Mitra received the B.S. and the M.S. degrees from the University of California at Berkeley and her Ph.D. from Princeton University.  Dr. Mitra is currently the Gordon S. Marshall Professor in Engineering at the University of Southern California with appointments in Electrical & Computer Engineering and Computer Science. She was the inaugural Editor-in-Chief for the IEEE Transactions on Molecular, Biological and Multi-scale Communications. She has been a member of the IEEE Information Theory Society's Board of Governors (2002-2007, 2012-2017), the IEEE Signal Processing Society’s Technical Committee on Signal Processing for Communications and Networks (2012-2016), the IEEE Signal Processing Society’s Awards Board (2017-2018), and the Chair/Vice-Chair of the IEEE Communication Theory Technical Committee (2017-2020).  Dr. Mitra is a Fellow of the IEEE.  She is the recipient of: the 2021 USC Viterbi School of Engineering Senior Research Award, the 2017 IEEE Women in Communications Engineering Technical Achievement Award, a 2015 UK Royal Academy of Engineering Distinguished Visiting Professorship, a 2015 US Fulbright Scholar Award, a 2015-2016 UK Leverhulme Trust Visiting Professorship, IEEE Communications Society Distinguished Lecturer, 2012 Globecom Signal Processing for Communications Symposium Best Paper Award, 2012 US National Academy of Engineering Lillian Gilbreth Lectureship, the 2009 DCOSS Applications & Systems Best Paper Award, 2001 Okawa Foundation Award, 2000 Ohio State University’s College of Engineering Lumley Award for Research, and a 1996 National Science Foundation CAREER Award.  She has been an Associate Editor for the following IEEE publications: Transactions on Signal Processing, Transactions on Information Theory, Journal of Oceanic Engineering, and Transactions on Communications.  Dr. Mitra has held visiting appointments at: King’s College, London, Imperial College, the Delft University of Technology, Stanford University, Rice University, and the Eurecom Institute. Her research interests are in wireless communications, structured statistical methods, communication and sensor networks, biological communication systems, detection and estimation and the interface of communication, sensing and control.   

Commonly used machine learning models are often trained on large datasets. As a result, a natural stumbling block in applying them more widely is the human effort required to label or annotate the data. Active learning is a framework that can reduce the number of labelled data needed to achieve a desired performance. In this talk, I will explain the benefit of active learning in two problem settings: active testing and active regression. In active testing, the sequential design of experiments developed by Chernoff in 1959 is widely used and known to be asymptotically optimal. We obtained a novel non-asymptotic bound on the number of labelled data needed for Chernoff’s algorithm. We will then look at an extension of the algorithm for active regression. In addition to obtaining a theoretical performance guarantee, we find that our extension requires fewer labelled data compared to existing methods in both simulated and real-world datasets.

Dr. Ardhendu Tripathy is an assistant professor in the Computer Science department at Missouri S&T. He leads the Statistical Machine Learning Lab which provides a supportive and creative environment for undergraduate and graduate students to engage in current machine learning research. He received a research excellence award from Iowa State University for his PhD thesis on network coding for function computation.

While deep learning models have achieved great success, they are also vulnerable to potential manipulations, ranging from model inversion attacks that attempt to infer sensitive training data from trained models, to adversarial example attacks that create manipulated data instances to deceive a model. In this talk, I will present our recent work on achieving 1) differential privacy (DP) to ensure privacy of the training data and 2) certified robustness against adversarial examples for deep learning models. First, I will present a practical DP training framework with better empirical and theoretical utility (IJCAI’21) and an extended DP notion for more quantifiable protecting against model inversion attacks (BigData’20). Second, I will present a certified robustness approach via randomized smoothing for quantized neural networks (ICCV ’21). Finally, I will present a framework that kills two birds with one stone and achieves DP and certified robustness via randomized smoothing simultaneously.

Li Xiong is a Professor of Computer Science and Biomedical Informatics at Emory University. She held a Winship Distinguished Research Professorship from 2015-2018. She has a Ph.D. from Georgia Institute of Technology, an MS from Johns Hopkins University, and a BS from the University of Science and Technology of China. She and her research lab, Assured Information Management and Sharing (AIMS), conduct research on the intersection of data management, machine learning, and data privacy and security. She has published over 170 papers and received six best paper (runner up) awards. She has served and serves as associate editor for IEEE TKDE, VLDBJ, IEEE TDSC, general or program co-chairs for ACM CIKM 2022, IEEE BigData 2020, and ACM SIGSPATIAL 2018, 2020. Her research has been supported by National Science Foundation (NSF), AFOSR (Air Force Office of Scientific Research), National Institute of Health (NIH), and Patient-Centered Outcomes Research Institute (PCORI). She is also a recipient of Google Research Award, IBM Smarter Healthcare Faculty Innovation Award, Cisco Research Awards, AT&T Research Gift, and Woodrow Wilson Career Enhancement Fellowship. She is an IEEE fellow and ACM distinguished member. More details at http://www. cs.emory.edu/~lxiong.

Federated Learning (FL) has received extensive attention from the research community because of its capability for privacy-preserving, collaborative learning from heterogeneous data sources. As the server cannot govern the clients’ actions, a nefarious client may attack the global model by sending malicious local gradients. Moreover, some clients, despite benign, may have unreliable data due to their heterogeneous hardware configurations and environments. Such unreliable clients can misguide the training process, resulting in a poorly fit model. Further, the performance disparities across clients in the presence of attacks have largely been unexplored.

This talk will first address the challenge of malicious client detection in federated learning under targeted and untargeted attacks. Additionally, we will introduce unreliable clients leading to another realistic yet challenging problem. As a solution, an innovative sequential approach will be proposed that will identify and mitigate the influence of both attackers and unreliable clients. Finally, we will introduce the problem of performance fairness and a solution to improve it across the clients.

Dr. Ashish Gupta is a Post-Doctoral Fellow working under the guidance of Prof. Sajal K. Das in the Computer Science department at Missouri S&T. He received his Ph.D. degree in Computer Science and Engineering from the Indian Institute of Technology (BHU), Varanasi in 2020. His research interests are in applied machine learning, sensor data analytics, and federated learning. He has published in reputed journals and conferences, such as IEEE TMC, IEEE TII, and IEEE TAI, IFIP Networking, IEEE INFOCOM, and ACM ICDCN. Currently, he is investigating federated learning through the adversarial lens and performance fairness perspective.

Feature selection is an important topic in machine learning, pattern recognition and data mining. Despite being an extensively studied problem, most existing approaches assume that the feature selection is conducted offline, and all the features of instances are provided a priori during testing. Such assumptions may not always hold for real-world applications in which features arrive sequentially or it is expensive to access all features at the same time. For instance, in an online social network, millions of messages are exchanged between users, making it difficult to employ a standard feature selection technique to identify harassment messages in a timely, efficient, and scalable manner. In this talk, I will introduce a novel mathematical framework that sequentially selects the number of features and the order by which they are reviewed for each data instance, before stopping and proceeding with a decision about the data instance. The proposed framework is guaranteed to reach an accurate decision by reviewing the optimum least number of informative features. I will demonstrate the performance of the proposed framework on a variety of benchmark datasets and discuss extensions in regression settings and structured environments.

Daphney-Stavroula Zois is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany, State University of New York. She received her B.S. degree in Computer Engineering and Informatics from the University of Patras, Greece, in 2007, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California in 2010 and 2014, respectively. During 2014-2016, she was a Postdoctoral Research Associate with the Coordinated Science Laboratory at the University of Illinois, Urbana-Champaign. She is the recipient of various fellowships and awards including the Viterbi Dean's graduate fellowship, the NSF CAREER award, and a Google AI for Social Good "Impact Scholars" award. She has served and is serving as Co–Chair, TPC member or reviewer in international conferences and journals, such as AAAI, ICLR, ICASSP, GlobalSIP, and IEEE Transactions on Signal Processing. Her research interests include decision making under uncertainty, machine learning, detection & estimation theory, intelligent systems design, and signal processing.

This talk is motivated by the problem of learning to design dynamic prices/incentives for demand management in safety-critical infrastructure systems such as the power grid. Faced with uncertainty regarding how customers will respond to posted prices, we highlight the need for safety-aware bandit optimization algorithms for designing prices that control the probability of violation of system constraints in spite of uncertainty. Then, in the first part of the talk, we discuss how we can manage safety constraints in admittedly simpler stochastic bandit problems with affine constraints and provide formal regret guarantees. In the second part of the talk, we discuss how our theoretical insights can be generalized for safe and efficient price design in our motivating example of dynamic pricing in power systems.

Dr. Mahnoosh Alizadeh is an assistant professor of Electrical and Computer Engineering at the University of California Santa Barbara (since January 2017). She received the B.Sc. degree in Electrical Engineering from Sharif University of Technology in 2009 and the M.Sc. and Ph.D. degrees from the University of California Davis in 2013 and 2014 respectively, both in Electrical and Computer Engineering. From 2014 to 2016, she was a postdoctoral scholar at Stanford University. Her research is focused on the design of network control and optimization algorithms for cyber-physical systems, with a particular focus on renewable energy integration in the power grid and electric transportation systems. She is a recipient of the NSF CAREER award.

Process sensors have no capability for passwords, multi-factor authentication, encryption, keys, signed certificates, etc.  Despite the lack of any cyber security, these devices are the 100% trusted input to OT networks and manual operation. Moreover, process sensors have no cyber forensics. We have a serious problem because the people responsible for the design and operation of equipment do not consider cyber security of interest, and the network security people who consider cyber security important do not consider process sensors or other engineering equipment important. Ironically on March 16, 2022 NIST issued NIST Special Publication 1800-10 Protecting Information and System Integrity in Industrial Control System Environments: Cybersecurity for the Manufacturing Sector. The NIST report states “It is acknowledged that many of the device cybersecurity capabilities may not be available in modern sensors and actuators.” Network cyber threats such as vulnerabilities in Log4j, the Treck TCP/IP Stack, and ransomware make off-line monitoring of process sensors more important than ever. This talk will present an overview of the current state of affairs in this important area.

Joseph Weiss is an industry expert on control systems and electronic security of control systems, with more than 40 years of experience in the energy industry. Mr. Weiss spent more than 14 years at the Electric Power Research Institute (EPRI), the first 5 years managing the Nuclear Instrumentation and Diagnostics Program. He was responsible for developing many utility industry security primers and implementation guidelines. Mr. Weiss serves as a member of numerous organizations related to control system security. He is also an invited speaker at many industry and vendor user group security conferences, has chaired numerous panel sessions on control system security, and is often quoted throughout the industry. He has published over 80 papers on instrumentation, controls, and diagnostics including chapters on cyber security for Electric Power Substations Engineering and Securing Water and Wastewater Systems. He coauthored Cyber Security Policy Guidebook and authored Protecting Industrial Control Systems from Electronic Threats. In February 2016, Mr. Weiss gave the keynote to the National Academy of Science, Engineering, and Medicine on control system cyber security. Mr. Weiss has conducted SCADA, substation, nuclear and fossil plant control system, and water systems vulnerability and risk assessments and conducted short courses on control system security. The risk assessments include utility-scale solar farms and wind turbines. He has amassed a database of almost 12 million actual control system cyber incidents. He was a member of Transportation Safety Board Committee on Cyber Security for Mass Transit. He was a subject matter expert to the International Atomic Energy Agency on nuclear plant control system cyber security. Mr. Weiss has received numerous industry awards, including the EPRI Presidents Award (2002) and is an ISA Fellow, Managing Director of ISA Fossil Plant Standards, ISA Nuclear Plant Standards, ISA Industrial Automation and Control System Security (ISA99), a Ponemon Institute Fellow, and an IEEE Senior Member. He has been identified as a Smart Grid Pioneer by Smart Grid Today. He is a Voting Member of the TC65 TAG and a US Expert to TC65 WG10, Security for industrial process measurement and control – network and system security and IEC TC45A Nuclear Plant Cyber Security. Mr. Weiss was featured in Richard Clarke and RP Eddy’s book- Warning – Finding Cassandras to Stop Catastrophes. He has patents on instrumentation, control systems, and OT networks. He is a registered professional engineer in the State of California, a Certified Information Security Manager (CISM) and Certified in Risk and Information Systems Control (CRISC).

Website: www.controlglobal.com/unfettered

Non-Abelianess can be attributed to the hardness of many computational problems.  It also shows up when modeling certain quantum systems.  In some cases, it serves as an obstruction, creating barriers for efficient computational simulations. And yet, for others, non-Abelianess can be utilized, possibly developing more robust quantum computers. In this talk I will highlight three instances: 1) the non-Abelian hidden subgroup and the hidden shift problem and limitations of Fourier sampling, 2) propagation of quantum walks on non-abelian groups and their applications – some of our recent results, and 3) quest for non-Abelian anyons for building topological quantum computers. These topics span several areas including quantum physics, complexity theory, group theory etc. In the talk I will present the key insights at a high level and only a minimal knowledge in the above areas is assumed.

Avah Banerjee is an assistant professor in Computer Science at Missouri S&T. She received her PhD in Computer Science from George Mason University. Before joining Missouri S&T, she held a postdoctoral position at the Center for Computation and Technology at Louisiana State University. Broadly speaking, she works in the area of Theory and Algorithms. Her current interest includes quantum circuit compression and noise reduction and quantum algorithms for graph problems. She has publications in the following areas: random orders, graph reconfigurations, comparisons tree complexity, and evolutionary algorithms. She was a recipient of the Dean’s fellowship from George Mason University.

Cybersecurity and cyber forensic readiness are vital parts of intelligent integrated and interconnected energy systems coupled with demand-oriented city infrastructures, governance models and services that foster energy sustainability. This talk will provide an overview of the RESPONSE project, which is funded by the European Union, and which aims to create a strategic vision for climate-neutral smart cities by 2050. He will also speak about the European Union-funded ELECTRON project, which aims to deliver a new generation of electrical power systems that can respond to cyber, privacy, and data attacks. This project makes use of decentralized, federated learning. Dr. Kotsiuba will also discuss the CIBOK framework that helps defenders deal with cybercrime.

Dr. Igor Kotsiuba has served for more than a decade in industry and academia and has completed numerous transnational projects with leading players in Cybersecurity and Industrial IoT. He is a counsellor in government and industry and the founder of the cybersecurity company iSolutions, which has established a digital forensics lab in Ukraine to build capacity and restore the rule of law in cyberspace. Dr. Kotsiuba is an evangelist for an inclusive approach to delivering full-stack cybersecurity managed services ranging from initial compromise assessments to digital forensics evidence verifications. He has been advising Ukrainian Businesses and Governmental Institutions on cybersecurity issues. In 2018 he was elected Head of the Cybersecurity Work Group of the American Chamber of Commerce, Ukraine. He received his Ph.D. from the Pukhov Institute for Modeling Energy Engineering (PIMEE) of the National Academy of Sciences of Ukraine. The title of his dissertation is Information Technology for Cybersecurity of Energy Grids.