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Bang TranView profile

Assistant Professor

• Machine Learning
• Internet of Things (IoT) Security
• Cybersecurity
• +3 more

Bang Tran is an Assistant Professor of Computer Science at Assumption University’s D’Amour College of Liberal Arts and Sciences. Previously, he served as a Senior Lecturer at the University of Transport and Communications (UTC), Vietnam (2003–2018), receiving multiple UTC Excellent Teaching Awards (2013–2017). He earned his Ph.D. in Computer Science from the University of Massachusetts Boston (2023) under Prof. Xiaohui Liang, with prior degrees from Hanoi National University (BSc, 2001), Hochiminh National University (MSe, 2009), and additional research experience at Sonde Health Inc. His research focuses on machine learning applications for IoT privacy/security, voice assistant security, and cognitive health measurement. Notable achievements include the 2021 IEEE ICC Best Paper Award for work on mitigating voice assistant replay attacks, and the 2023 SOUPS poster award for VPASS, a voice privacy monitoring system. He teaches courses like Machine Learning, Data Structures, and Large Datasets. Recent grants include the 2025 Assumption Faculty Development Grant and a 2025 Summer Scholar Fellowship mentoring student Keegan Concannon on AI-driven network intrusion detection. He actively contributes to conferences like ICASSP, ICC, and SOUPS, and has supervised student projects in cybersecurity and AI. Education: Ph.D. UMass Boston (2023); MSe Hochiminh National University (2009); BSc Hanoi National University (2001) Awards: 3 UTC Teaching Awards, ICC2021 Best Paper, Erasmus Mundus Fellowship Labs/Teams: Assumption Cybersecurity Research Group, collaborations with Sonde Health Inc

Zachary KisselView profile

Associate Professor

• Cryptography
• Network Security
• Machine Learning
• +4 more

Zachary Kissel is an Associate Professor in the Computer Science Department at Merrimack College. His research focuses on the intersection of cryptography, complexity theory, and secure computing. He holds a Ph.D. in Computer Science from UMass Lowell, an M.S. from Northeastern University, and a B.S. from Merrimack College. His research interests include federated learning, machine learning with encrypted data, constrained pseudorandom functions, and cryptographically enforced access control. He has co-authored books such as Introduction to Network Security: Theory and Practice (2015) and published extensively in theoretical cryptography and network security domains. Recent work emphasizes privacy-preserving techniques like searchable encryption and policy-based redactable signatures. His publications span topics from verifiable privacy-preserving data structures to fault-tolerant parallel computation. He actively contributes to experiential learning frameworks for computer science education, focusing on practical skill development in technology disciplines. Professional contributions include work on cloud security, untrusted content distribution systems, and verifiable phrase search over encrypted data. His research often bridges theoretical foundations with real-world applications in secure computing environments.

Subhasish MazumdarView profile

Professor

• Data Privacy
• Mobile Databases
• Information Security
• +2 more

Subhasish Mazumdar is Professor and Department Chair of Computer Science and Engineering at New Mexico Institute of Mining and Technology. With degrees in Mathematics and Computer Science, his research bridges theoretical foundations and practical applications in data management. Research focus areas: Privacy-preserving techniques for mobile and spatial data Secure logging and tamper-resistant databases Document database architectures Bioinformatics data integration Heterogeneous system interoperability Recent work examines spatial transformation-based privacy models and security vulnerabilities in mobile environments. His publications span algorithms for Hilbert curve optimizations, ad library privacy threats, and model-based classification approaches. He teaches courses in algorithms, database systems, and data science, and serves as adjunct professor in Information Technology.

Lorie M. LiebrockView profile

Professor

• Cybersecurity
• Parallel Processing
• Graphics and Visualization
• +4 more

Lorie M. Liebrock is a Professor and Director of the Cybersecurity Centers at the Department of Computer Science and Engineering within the College of Engineering at New Mexico Institute of Mining and Technology (New Mexico Tech). She holds a Professor rank with no indication of part-time status. Education: B.S. and M.S. from Michigan Technological University, Ph.D. from Rice University Research Interests: Cybersecurity, Graphics and Visualization, Parallel Processing, and Well Posedness Analysis Her research focuses on parallel computing with emphasis on problem topology for automated data distribution in composite grid applications. She developed algorithms for irregular mesh problems in domains like aircraft aerodynamics and nuclear reactor simulations, creating program templates and style guidelines for High Performance Fortran transformations. Collaborative work with Pat Teller at UTEP explores adaptive parallelization techniques for evolving computational environments. In cybersecurity, her work spans browser security analysis , malware visualization , cloud permission management , and user authentication graph analysis . Google Scholar articles (2016–2025) reflect expertise in machine learning for security classification, digital forensics infrastructure, and hypervisor-assisted malware analysis. Her academic contributions include publications on SPMD hybrid parallel computation , RAID system forensics , and noise reduction in Linux environments . Though no personal scientific awards are explicitly mentioned, her leadership as Cybersecurity Centers Director and involvement in NSF REU programs underscores institutional recognition of her expertise.

• Cybersecurity
• Machine Learning
• Privacy-Preserving Techniques
• +7 more

Xichen Zhang is an Assistant Professor at the Department of Finance, Information Systems and Management Science within the Sobey School of Business at Saint Mary's University. His research focuses on cybersecurity, privacy-preserving techniques, federated learning, fake news detection, and machine learning applications in edge-cloud collaboration and mobile crowdsensing. He holds a Ph.D. in a relevant field and has contributed to interdisciplinary projects involving blockchain-based voting systems and IoT security solutions. His work emphasizes practical solutions to modern challenges in data privacy, misinformation detection, and distributed computing. Notable contributions include the development of the TruthSeeker dataset for real/fake content analysis and frameworks like SCORE for scalable contact tracing. His articles explore topics from adversarial machine learning defenses to temporal user credibility models in social networks. Dr. Zhang's research integrates technical innovations with societal impact areas like healthcare informatics and election security. He collaborates across disciplines to address privacy challenges in both academic and industrial contexts. Current projects investigate federated reinforcement learning for IoT systems and multimodal fake news detection using image-text consistency analysis.

• Hardware Security
• Cybersecurity
• Reverse Engineering
• +3 more

Steffen Becker is a Junior Research Group Leader at the Research Center Trustworthy Data Science and Security, Ruhr University Bochum, Germany (since July 2024). Previously, he was a Postdoctoral researcher at the Max Planck Institute for Security and Privacy (MPI-SP) and the CASA Cluster of Excellence (2021–2024). His research focuses on Hardware Security, including Human Factors in Reverse Engineering, Hardware Obfuscation, and End-User Privacy. He holds a PhD from the Embedded Security Group at Ruhr University Bochum (2017–2021) and a Master’s (2014–2016) and Bachelor’s (2011–2014) in IT-Security from the same institution. He also completed a visiting research stint at the University of Michigan (2016) and an exchange program at Purdue University (2014–2015). Education: Bachelor of Science in IT-Security, Ruhr University Bochum (2011–2014) Master of Science in IT-Security, Ruhr University Bochum (2014–2016) PhD in IT-Security, Embedded Security Group, Horst Görtz Institute, Ruhr University Bochum (2017–2021) Research Interests: Hardware Trojans, Privacy Engineering, and Secure Embedded Systems. His work bridges human factors in hardware security and practical defenses against obfuscation and tampering. Affiliations: Research Center Trustworthy Data Science and Security, MPI-SP, CASA Cluster of Excellence.

Paarijaat AdityaView profile

Professor

• Algorithms
• Theory & Logic
• Programming Languages & Verification
• +4 more

Paarijaat Aditya is a faculty member at the Max Planck Institute for Software Systems (MPI-SWS) , focusing on interdisciplinary research at the intersection of theoretical computer science and applied systems. Their work spans algorithms, programming languages, security, and distributed systems , with a particular emphasis on serverless computing, federated learning, and privacy-preserving urban sensing. Research Areas : Machine Learning Security (e.g., model hijacking, federated learning) Serverless Computing (e.g., GPU sharing, high-performance frameworks) Privacy Engineering (e.g., urban sensing, image capture, mobile compliance) Distributed Systems (e.g., NFV, peer-assisted content delivery) Their publications reflect a trajectory of advancing scalable AI training, secure collaborative systems, and privacy-compliant infrastructures. Recent works address challenges in public cloud adaptability, while earlier contributions explore mobile privacy risks and secure resource management.

Tao HouView profile

Assistant Professor

• Cybersecurity
• Smart Grid Security
• IoT Device Security
• +4 more

Tao Hou is an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas. His research focuses on cybersecurity, with an emphasis on smart grid security, IoT device protection, post-quantum cryptography, and machine learning applications in defense mechanisms. He actively contributes to securing critical infrastructures and mitigating emerging threats in distributed systems. Dr. Hou's work spans theoretical advancements and practical implementations, including frameworks for data integrity via Intel SGX, adaptive feature engineering for attack detection, and dynamic encryption for medical IoT devices. His research also addresses risks posed by third-party JavaScript and adversarial attacks targeting deep learning systems. Key contributions include surveys on smart grid security challenges, keylogger evolution analysis, and post-quantum cryptographic strategies. He explores innovative solutions like IoTGAN for device anonymization and SecDINT for data-oriented attack prevention. Education and professional background details are not explicitly provided in available records. No awards or grants are listed in the current dataset.

Sakkopoulos EvangelosView profile

Associate Professor

• Personalized Software Technology
• Machine Learning
• Data Mining
• +9 more

Evangelos Sakkopoulos is an Associate Professor at the Department of Informatics, University of Piraeus. He holds a PhD from the Department of Mechanical Engineering and Informatics at the University of Patras. His research interests include personalized software technology, mobile programming, web systems, and their applications in education, healthcare, maritime safety, and legal informatics. He has over 60 international publications and actively participates in EU technical committees for internal affairs regulations. His teaching portfolio includes postgraduate courses on Software Technology and undergraduate programming subjects like Java. Education PhD in Mechanical Engineering and Informatics from the Patras University of Technology (University of Patras Polytechnic). Research Interests Evangelos focuses on interdisciplinary research in: AI-driven cultural e-learning (e.g., LYRICEL system for song lyrics analysis). Privacy-preserving data mining techniques, including frequent itemset hiding and database anonymization. Mobile and web-based systems for healthcare monitoring (Care@HOME), maritime surveillance (Mobile Data Fusion), and government services (Olympus architecture). Natural Language Processing for legislative documents and low-resource languages. Personalized identity management solutions and secure digital service frameworks. Articles Trends Recent publications emphasize AI in education, privacy-protecting data algorithms, and mobile/web systems for critical sectors like healthcare and maritime safety. His work addresses challenges in legal informatics, cultural preservation, and EU digital standardization. Awards No scientific awards explicitly mentioned in the provided texts. Advising & Grants Evangelos has collaborated in European and national research programs, including projects on: Ethnicity/naturalization process digitalization (Greek Citizenship Test). Healthcare personnel rostering solutions. Cultural artifact damage assessment systems (Data Engineering for Materials Identification). Labs & Teams He contributes to the MODIP Quality Assurance Unit and participates in research teams developing systems like HappyCruise for maritime safety and OLYMPUS for secure digital identity management.

Shuangqing WeiView profile

Professor

• Wireless Communications
• Signal Processing
• Network Security
• +4 more

Shuangqing Wei is the Michel B. Voorhies Distinguished Professor and Division Chairman in the Division of Electrical & Computer Engineering at Louisiana State University (LSU). He holds a Ph.D. (2003) from the University of Massachusetts and is affiliated with the School of Electrical Engineering and Computer Science. His research focuses on wireless communication systems, including OFDMA networks, multiple antenna systems, and privacy-enhanced communication protocols. Key research interests include: Multi-agent reinforcement learning and cooperative strategies Privacy-preserving techniques in federated learning and data analysis Covert communication over AWGN channels Optimization algorithms for distributed systems Cognitive radio networks and spectrum management Recent work emphasizes communication efficiency in distributed optimization (e.g., ADMM with Gaussian process regression) and trade-offs between privacy and utility in machine learning systems. His publications span over two decades, addressing topics from information theory fundamentals to applied security in vehicular networks and social media systems. He maintains an active research group at LSU, exploring interdisciplinary challenges at the intersection of signal processing, machine learning, and network security. Labs/Teams : Active involvement in LSU's wireless communication and machine learning research initiatives, including collaborative projects on federated learning frameworks and secure multi-agent systems.

Meisam MohammadyView profile

Assistant Professor

• Responsible Machine Learning
• Federated Learning
• Differential Privacy
• +3 more

Dr. Meisam Mohammady is an Assistant Professor in the Department of Computer Science at Iowa State University. He previously served as a Research Scientist at CSIRO Data61 in Australia, focusing on data-centric solutions. He holds a PhD from Concordia University, an M.Sc. from École Polytechnique Montréal, and a B.Sc. from Sharif University of Technology. His research emphasizes responsible machine learning, including federated learning, differential privacy, and learning theory. Key contributions include optimizing privacy mechanisms in federated settings and mitigating adversarial attacks. His work appears in top venues like IEEE S&P, ACM CCS, and IEEE TKDE. Recent publications address user-driven differential privacy (UD-LDP), location inference attacks in federated recommendation systems (VLIA), and backdoor attack mitigation in federated learning. These studies balance privacy and utility in distributed machine learning frameworks. Recipient of Distinguished PhD Dissertation Award Over 20+ publications in cybersecurity and privacy Research group actively seeking students and collaborators Labs/Teams: Leads research group focused on privacy-preserving machine learning and federated learning security.

Kaj-Mikael BjörkView profile

Senior Lecturer

• Supply Chain Management
• Artificial Intelligence
• Machine Learning
• +3 more

Kaj-Mikael Björk serves as Principal Lecturer in Analytics and Logistics at Arcada University of Applied Sciences in Helsinki, Finland, focusing on the practical integration of data-driven methodologies in supply chain systems and computational logistics. His research spans Artificial Intelligence , Machine Learning , and Optimization with specialized applications in acid sulfate soil mapping , signature verification systems , and federated learning architectures . Björk's work consistently leverages Extreme Learning Machines (ELM) to address challenges in imbalanced datasets, privacy-preserving computation, and resource-constrained edge devices, demonstrating significant interdisciplinary impact across environmental science and security domains. Recent publications reveal a pronounced trend toward applied machine learning in critical infrastructure , particularly in developing ELM-based solutions for Finnish environmental challenges and secure distributed AI systems. His 2023-2025 output shows increasing emphasis on 6G-enabled computing continuums and federated learning security , with over 40% of recent work addressing environmental monitoring applications.

• Computer Security
• Mobile Security
• Network Security
• +6 more

Georg Merzdovnik is a Lecturer at Vienna University of Technology (TU Wien) with a focus on computer security and privacy . His work spans binary rewriting, web privacy, IoT security, and Android application analysis, reflected in publications in top venues like ACM Computing Surveys and IEEE Euro S&P. He actively supervises student research projects in security-related domains. Primary Affiliation: Vienna University of Technology (TU Wien) Research Interests include: Binary rewriting and obfuscation techniques Web privacy mechanisms post-GDPR IoT device security in IPv6 networks Mobile application threat analysis Malware detection in Windows shellcodes Evaluation of tracker-blocking tools Thesis Supervisions (2012–2024) cover topics like IoT testbeds, Android intent firewalls, and secure messaging services.

August SeeView profile

Researcher

• Bot Detection
• Protocol Analysis
• Automated Binary Analysis
• +3 more

Dr. August See is a Researcher/Postdoc at the University of Hamburg's Department of Informatics, part of the Faculty of Computer Science. His primary role is within the Computer Networks research group, focusing on bot detection, protocol analysis, and automated binary analysis. He is based in Office F-616 and can be reached via email at see@informatik.uni-hamburg.de or by phone at +49 40 42883 2329. His research interests include detecting and mitigating illegitimate bot activities through methods like keystroke dynamics analysis, mouse behavior monitoring, and homomorphic encryption. He has contributed to developing polymorphic protocols to counter bot influence and improving secure memory handling in web applications. Recent work emphasizes accelerating reverse engineering processes through novel 'point-of-interest beacon' techniques. Dr. See has published nine peer-reviewed articles between 2021-2024, with a focus on cybersecurity, network security, and data privacy. His collaborations include Prof. Mathias Fischer and researchers like Tatjana Wingarz and Kevin Röbert. While no specific awards are listed, his work demonstrates impactful contributions to automated threat detection and protocol security. He advises students on thesis topics involving cryptography, reverse engineering, and machine learning applications in security. His research integrates both theoretical protocol design and practical implementations in networked systems.

Stefan LindskogView profile

Professor

• Cybersecurity
• Network Security
• Intrusion Detection Systems
• +7 more

Stefan Lindskog is a full Professor at the Department of Computer Science, Karlstad University, Sweden. His research focuses on tunable security services, network security protocols, intrusion detection systems, and encryption methodologies. He has held academic positions at Karlstad University since 1990 and holds a PhD in Computer Engineering from Chalmers University (2005) and a Docent degree in Computer Science from Karlstad University (2008). His work emphasizes practical cybersecurity solutions, including firewall usability, multipath TCP security, and privacy-preserving technologies. Lindskog has authored over 60 peer-reviewed publications, including studies on encrypted network traffic analysis, Tor anonymity vulnerabilities, and firewall rule optimization. He collaborates extensively with industry partners and academic networks, contributing to projects like the EU-funded NEWCOM++ initiative. Teaching: Real-Time Systems (7.5 ECTS) Key Research Themes: Security service tunability, adaptive intrusion detection, and secure communication protocols Collaborations: HITS project (High Quality Networked Services in a Mobile World) Publications span topics from malware detection in encrypted traffic to systematic reviews of firewall usability. Lindskog’s work bridges theoretical security models and operational system implementation, with a focus on real-world applicability in industries like offshore energy and mobile communications.