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Hongxin HuView profile

Professor

• Network Security
• Machine Learning for Security
• IoT Security
• +4 more

Hongxin Hu is a Professor and Associate Chair in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York (SUNY). His research spans security, networking, and machine learning, with publications across top conferences including security (S&P, CCS, USENIX Security, and NDSS), networking (SIGCOMM and NSDI), machine learning (NeurIPS, ICML, and EMNLP), and human-computer interaction (CHI and CSCW). His work has been funded by NSF (SaTC, CNS, IIS, OAC, SOC), USDOT, VMware, Amazon, Google, and Dell. Dr. Hu earned his PhD in Computer Science and Engineering from Arizona State University in 2012. His academic journey has led him to become a prominent researcher in cybersecurity with a strong publication record and significant research impact. Dr. Hu's research interests encompass a wide range of topics at the intersection of security, networking, and artificial intelligence. His work focuses on Emerging Network Technologies and Security (5G/Future-G, NFV, SDN, Edge computing), Machine Learning for Security and Privacy , Security and Privacy in IoT and Cyber-Physical Systems , and AI for Social Good (addressing online abuse, unsafe children's games, and cyberbullying). His interdisciplinary approach has enabled him to tackle complex security challenges through innovative solutions that combine networking expertise with machine learning techniques. His recent publications demonstrate a strong trend toward applying large language models and advanced machine learning techniques to security challenges, particularly in content moderation, vulnerability detection, and privacy protection. The research spans multiple domains including voice assistant security, IoT security, network security, and social media safety, showing a consistent pattern of addressing real-world security problems with cutting-edge technical approaches. IEEE Big Data Security Senior Research Award (2025) ACM SACMAT Test-of-Time Award (2024) NSF CAREER Award (2019) Multiple Best Paper Awards from ACM ASIACCS (2022), ACSAC (2020), IEEE ICC (2020), and ACM SIGCSE (2018) Amazon Faculty Research Award (2022) First Place Award in ACM SIGCOMM 2018 Student Research Competition Dr. Hu has successfully advised multiple PhD students, including Nishant Vishwamitra who joined UT San Antonio as a tenure-track Assistant Professor. His research has been generously funded by major agencies and industry partners. As an active member of the academic community, he serves as Associate Editor for IEEE Transactions on Dependable and Secure Computing and Computers & Security, and has held numerous leadership roles in major security conferences including TPC Co-Chair for ASONAM 2025 and IWSPA 2024/2025. Dr. Hu leads a vibrant research group that has produced significant contributions in network security function virtualization, intrusion detection systems, and privacy-preserving technologies. Current projects include developing LLM-assisted vulnerability detection systems, defenses against jailbreak attacks on large language models, and security mechanisms for emerging networking technologies. His team's work on IoT security, voice assistant applications, and online content moderation has received wide recognition and press coverage.

Olya MandelshtamView profile

Assistant Professor

• Algebraic Combinatorics
• Enumerative Combinatorics
• Symmetric Functions
• +3 more

Olya Mandelshtam is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo . Her research focuses on algebraic combinatorics, particularly symmetric and quasisymmetric functions, with connections to probability and interacting particle systems. Education: Ph.D. in Mathematics, University of California, Berkeley (2016), advised by Lauren Williams Presidential Postdoctoral Scholar at UCLA (2016–2017) Tamarkin Assistant Professor and NSF Postdoctoral Fellow, Brown University (2017–2021) Research Interests: Her work bridges algebraic structures (e.g., Macdonald polynomials, Koornwinder polynomials) and probabilistic models like the asymmetric simple exclusion process (ASEP) and zero-range processes (TAZRP). She explores combinatorial bijections, multiline queues, and integrable systems to study these connections. Recent Activities: She organizes the Combinatorics Seminar at Waterloo and participates in conferences such as ICERM workshops on Category Theory and Machine Learning, ICECA, and events on integrable systems in algebraic combinatorics. Advising: Current graduate students include Kartik Singh, Jerónimo Valencia Porras, Guilherme Zeus Dantas e Moura, and Harper Niergarth, with past advisee William Chan. Research collaborations include work on multiline queues, non-attacking fillings, and particle system dynamics.

Chen PanView profile

Assistant Professor

• Energy-Harvesting Embedded Systems
• Internet of Things (IoT)
• Low-Power Computing
• +5 more

Chen Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio's Klesse College of Engineering and Integrated Design. His research focuses on energy-harvesting embedded systems, low-power computing, and IoT network optimization through machine learning techniques. Ph.D. from University of Pittsburgh Specializes in transient computing for batteryless devices Develops reinforcement learning solutions for UAV-assisted IoT systems Chen's recent publications emphasize energy-aware scheduling, non-volatile memory optimization, and sustainable communication protocols. His work intersects spatiotemporal modeling, fault tolerance, and resource-constrained AI execution across heterogeneous architectures.

Ninghui LiView profile

Professor

• Information Security
• Data Privacy
• Cybersecurity
• +4 more

Ninghui Li is the Samuel D. Conte Professor and Associate Department Head in the Department of Computer Science at Purdue University. He holds a B.S. from the University of Science and Technology of China and a Ph.D. from New York University. His research focuses on information security, privacy, and database systems, with notable contributions to differential privacy and secure data publishing. He has authored over 200 papers, including influential works like the 2007 t-Closeness paper, and has received multiple awards, including being named an ACM and IEEE Fellow. Li’s academic roles include Editor-in-Chief of ACM Transactions on Privacy and Security (TOPS) and leadership in organizations like ACM SIGSAC. He advises over 30 graduate students and has been instrumental in coaching Purdue’s ICPC teams to top global rankings. His current projects include NSF-funded initiatives like the Center for Distributed Confidential Computing (CDCC) and privacy-focused AI research. Key contributions span privacy-preserving data synthesis, federated learning security, and cybersecurity for IoT systems. He actively contributes to conferences as a program chair and through editorial roles, ensuring advancements in both theoretical and applied security domains.

Paul E. HandView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +3 more

Paul E. Hand is an Associate Professor of Mathematics and Computer Science at Northeastern University, affiliated with both the College of Science and the Khoury College of Computer Sciences. He holds a Bachelor of Science in Applied and Computational Mathematics from the California Institute of Technology (2004) and a PhD in Mathematics from the Courant Institute at New York University (2009), where he received the Kurt O. Friedrichs Prize for outstanding dissertation. PhD in Mathematics, Courant Institute, NYU (2009) BS in Applied and Computational Mathematics, Caltech (2004) His research focuses on developing theoretical frameworks and algorithms for machine learning and artificial intelligence, particularly in signal recovery, phase retrieval, and vision/imaging. He also explores intersections of deep learning with convex optimization and has contributed to bilinear recovery problems. Recent publications emphasize generative models, inverse problems, and robust optimization techniques. Key themes include deep learning with provable recovery guarantees , convex programming for signal inversion , and manifold-based optimization . Kurt O. Friedrichs Prize for Outstanding Dissertation (2009) NSF CAREER Grant DMS-1848087 He has taught courses on Deep Learning, Machine Learning, and Signal Processing at Northeastern University since 2016, previously holding academic roles at Rice University (2016-2018) and MIT (2009-2016). He directs educational outreach initiatives and developed the educational resource Leading Lesson for multivariable calculus problem-solving.

• Applied Analysis
• Numerical Analysis
• Data Science
• +6 more

Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.

CHANG Ee ChienView profile

Associate Professor

• Information Security
• Cloud Security
• Adversarial Machine Learning
• +6 more

CHANG Ee-Chien is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He serves as the Lead Principal Investigator of the National Cybersecurity R&D Laboratory (NCL) and previously served as Deputy Director of the Center for Technology, Robotics, Artificial Intelligence and the Law (TRAIL) from 2019 to 2022. He also chaired the Joint Academic Committee for the Bachelor of Computing in Information Security program from 2016 until April 2023. Dr. Chang received his Ph.D. in Computer Science from New York University in 1998, following his M.Sc. and B.Sc. in Mathematics from the National University of Singapore in 1993 and 1991 respectively. He completed postdoctoral work with DIMACS at Rutgers University and NEC Labs America. His research focuses on information security, multimedia security, and their intersection. His work spans from foundational research in cryptographic techniques for noisy data and image watermarking to contemporary challenges in data privacy, cloud security, and adversarial machine learning. Dr. Chang has published extensively in top-tier security and computer science venues including CCS, EUROCRYPT, USENIX Security, and ACM Multimedia. Analysis of his recent publications reveals a strong emphasis on practical security solutions using hardware features like Trusted Execution Environments, innovative approaches to watermarking using generative models, and addressing emerging threats in machine learning systems. His work bridges theoretical cryptography with real-world security challenges, particularly in cloud infrastructure and privacy-preserving technologies. Active leadership in National Cybersecurity R&D Laboratory Extensive service on program committees for major security conferences Consultancy roles with Huawei, i-Sprint, and Singapore Accreditation Council Media commentary on cybersecurity issues for major Singaporean outlets Dr. Chang has mentored numerous research fellows and students who have gone on to academic and industry positions worldwide. His educational contributions include developing cybersecurity curriculum and innovative teaching methods such as penetration testing using real-world university applications. He has taught core security courses including CS2107 Introduction to Information Security continuously since 2014.

Murat KantarciogluView profile

Professor

• Data Privacy
• Computer Security
• Machine Learning
• +5 more

Murat Kantarcioglu is an Ashbel Smith Professor of Computer Science at the University of Texas at Dallas within the Erik Jonsson School of Engineering and Computer Science. He holds visiting appointments at UC Berkeley and Harvard University, focusing on data privacy and security. With a Ph.D. in Computer Science from Purdue University (2005), he has made significant contributions to privacy-preserving data mining, blockchain analytics, and secure machine learning. Education: Ph.D. in Computer Science (Purdue, 2005) Current Roles: Ashbel Smith Professor (2021-present), Visiting Scholar at UC Berkeley (2020-present), Affiliate at Harvard (2013-present) Past Roles: Assistant (2005-2011), Associate (2011-2015), and Full Professor (2015-2021) at UTD His research focuses on data privacy , computer security , and machine learning , particularly addressing challenges in privacy-preserving distributed data mining , blockchain analytics , and adversarial machine learning . He has pioneered techniques for secure federated learning , topological analysis of blockchain networks , and privacy-utility tradeoffs in health data systems. Recent publications reveal a strong emphasis on IoT security , graph neural network vulnerabilities , and blockchain data structures . His work combines theoretical rigor with practical implementations using technologies like Intel SGX and homomorphic encryption. Notable Awards NSF CAREER Award (2009) IEEE Technical Achievement Award (2017) AMIA Homer Warner Best Paper Award (2014) Fellow of IEEE (2022), AAAS (2020), and ACM (2016) Key Projects Privacy-Preserving Genomics Data Sharing Adversarial Learning Frameworks Smart Contract Security Medical Data Protection Systems Labs Director of Data Security and Privacy Lab Collaborations with Vanderbilt, UC Berkeley (RISE Lab), and Harvard (Data Privacy Lab)

Qiongkai XuView profile

Lecturer

• Natural Language Processing
• Machine Learning
• Security
• +3 more

Dr. Qiongkai Xu is a Lecturer in Natural Language Processing (NLP) at Macquarie University's School of Computing, with an honorary fellowship at the University of Melbourne. He holds a PhD in NLP from the Australian National University (ANU). His research focuses on auditing machine learning models, particularly addressing privacy/security issues in NLP/ML models and developing evaluation frameworks. Key areas include text watermarking, adversarial attacks, data leakage mitigation, and model authentication. Recent publications emphasize robustness against backdoor attacks, generative model watermarking, and healthcare applications of LLMs. His work bridges theoretical security advancements with practical NLP challenges. Awards: DAAD Scholarship (2022), Research Pitching Session Winner (MQ, 2022 & 2024) Grants: Leading the Climate Litigation Risk project on AI-driven greenwashing detection. Labs/Teams: Data Horizons Research Centre, Future Communications Research Centre, and Frontier AI Research Centre.

Sai Mounika ErrapotuView profile

Assistant Professor

• Cybersecurity
• Privacy Preservation
• Smart Grid Security
• +6 more

Sai Mounika Errapotu is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). She leads the Cyber Physical Systems Privacy and Security Enhanced Computing (CyPSEC) Lab, focusing on developing practical security and privacy solutions for hardware and software systems in distributed networks, IoT, and smart grids. Her research integrates cryptography, differential privacy, and optimization to address emerging challenges in cyber-physical systems. Education: Ph.D. in Electrical and Computer Engineering (University of Houston, 2018), B.Tech. in Electronics and Communication Engineering (Jawaharlal Nehru Technological University, 2013). Postdoctoral work: AI, Networking Technologies, and Security Lab, University of Houston (2018–2019). Her research interests span cybersecurity, privacy-preserving protocols, trust modeling, and optimization in smart grids, IoT, and wireless networks. She emphasizes application-specific solutions that balance security, privacy, and scalability. Recent articles highlight advancements in intrusion detection via GANs and neural networks, smart home IoT security protocols, and privacy-preserving techniques in healthcare and transportation systems. Major Grants: $782,500 ARL grant for GAN-based intrusion detection, $1.1M DOE NNSA consortium for power systems, and $140K PNNL grant for healthcare synthetic data. Awards: Miguel Izquierdo Teaching Excellence Award (2021), Rising Stars Award, and Best Dissertation Award (2018). Advising highlights include mentoring students like Humaira (CPS Rising Star), Nicholas Lopez (M.S. grad in IoT security), and Ismael Holguin (LLNL intern). The CyPSEC Lab actively publishes in IEEE and NAPS conferences, addressing vulnerabilities in protocols like DNP3 and smart home IoT systems. Labs/Teams: CyPSEC Lab focuses on bridging theory and practice in CPS security, collaborating with industry partners and national labs.

• AI Security
• Cybersecurity
• Artificial Intelligence
• +12 more

Petar Radanliev is a part-time academic and project supervisor at the Department of Computer Science, University of Oxford. He is actively involved in teaching and research, focusing on AI security, cybersecurity, and emerging technologies such as quantum computing and blockchain. His work bridges academic rigor with practical industry applications, and he supervises professional masters students. His research interests center on the security and ethical implications of artificial intelligence. Key areas include AI attack surfaces, agentic AI, quantum AI, malware analysis, and responsible AI practices. He explores vulnerabilities in machine learning systems and develops frameworks for secure and resilient AI deployment. His recent publications and courses reflect a strong trend toward interdisciplinary integration of AI with blockchain and quantum computing, emphasizing real-world threats, ethical considerations, and future-proofing digital systems. These works highlight a consistent focus on proactive defense, red teaming, and the societal impact of emerging technologies. Fulbright Fellowship Prince of Wales Innovation Scholarship Petar Radanliev has supervised numerous professional masters students and contributes to academic discourse through extensive publications, books, and public lectures. He is affiliated with major research platforms including ORCID, Google Scholar, and the Alan Turing Institute. His teaching includes advanced courses on AI security, quantum AI, and red teaming, often delivered through recorded events and online platforms. He is associated with research themes in Security, Artificial Intelligence, and Machine Learning at Oxford, and has contributed to projects such as SOCIAM. His work emphasizes practical, actionable insights for professionals and academics navigating the evolving landscape of AI and cybersecurity.

Kok-Leong OngView profile

Professor

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +5 more

Kok-Leong Ong is a Professor of Business Analytics at RMIT University's College of Business & Law, where he serves as Director of the CoBL Technology Initiative, Director of the Enterprise AI and Data Analytics Hub, and Head of the Department of Information Systems and Business Analytics. With over $1.4 million in research grants, he specializes in translating analytics and machine learning into practical business applications across multiple verticals including e-Commerce, public health, sports, urban studies, marketing, and learning. His research has consistently ranked in the top 25% and 5% of works in their domains according to Altmetric. Professor Ong's research spans Business Analytics, Artificial Intelligence, Machine Learning, and Information Systems, with a focus on making data actionable through analytics-2-business translation, automation, and applications. His work addresses critical challenges in cybersecurity for wearable health devices, ECG-based authentication systems, federated learning privacy, carbon accounting for maritime transport, and AI-driven solutions for vehicle damage detection. He has developed frameworks for operationalizing analytics in business contexts and has made significant contributions to mHealth applications for infant care and breastfeeding support. Among his notable scientific achievements, Professor Ong has received two VC's Teaching Awards and was named one of Australia's Leading Data Academics by CDO Magazine in 2021. He has secured over $1.4 million in research funding and serves on prestigious conferences including KDD and PAKDD. His research has been recognized for its high impact, with many works ranking in the top percentiles of their respective fields. Professor Ong actively supervises numerous research students across diverse topics including human-aligned AI, securing LLMs for financial applications, cyber risks in enterprise AI systems, ECG authentication security, and AI transformation for SMEs. He previously played a key role in establishing Australia's first Business Analytics degree and led La Trobe Business School's analytics program from 2015 to 2019 before joining RMIT in September 2021. Through the Enterprise AI and Data Analytics Hub and his role with RMIT University's Digital3 board, Professor Ong leads initiatives focused on bridging the gap between advanced analytics capabilities and business value creation. His work emphasizes practical implementation of AI and analytics solutions that address real-world challenges across multiple industry sectors.

Martim BrandãoView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Responsible AI
• +12 more

Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems

Sayanton DibboView profile

Assistant Professor

• Deep Learning
• Computer Vision
• Secure & Trustworthy AI/ML
• +5 more

Dr. Sayanton Dibbo is an Assistant Professor in the Department of Computer Science at the University of Alabama, where he leads the Trustworthy AI Lab. He is also a faculty affiliate of the Alabama Center for the Advancement of AI (ALA-AI) and Alabama Cyber Institution & HPC. His academic journey includes a PhD in Computer Science from Dartmouth College (2020-2025) and an MS in Computer Science from the University of California, Riverside (2017-2019). Dr. Dibbo's research spans several critical areas in modern computing: Deep Learning and Computer Vision Secure & Trustworthy AI/ML modeling Analysis of foundation models (LLMs and Multimodal systems) Security and privacy aspects of machine learning systems Biometric-based user authentication for IoT devices His recent work focuses on developing defenses against model inversion attacks using sparse coding architectures, improving robustness in audio classification, and creating secure authentication systems for wearable devices. Dr. Dibbo's research spans multiple data modalities including images, tabular data, audio, and text, addressing critical security and privacy challenges in AI systems. Dr. Dibbo has received recognition for his work including the Dartmouth Graduate Student Council Travel Grant for ICASSP 2024 and the Cybersecurity Cluster Research Fellowship. His service to the academic community includes reviewing for Women in Computer Vision (WiCV) at ECCV 2024, Computers and Security Journal, and Neural Networks Journal. As an educator and mentor, Dr. Dibbo is actively seeking highly motivated undergraduate and graduate students to join his research team at the University of Alabama. His lab focuses on cutting-edge problems at the intersection of AI security, privacy, and robustness, offering students opportunities to work on impactful research with real-world applications.

• Microgrids
• Power Electronics Systems
• Renewable Energy Systems
• +3 more

Dr. Ali Davoudi is a Professor of Electrical Engineering at The University of Texas at Arlington (UTA), where he also serves as Co-Director of Research Programs in the College of Engineering. He holds a B.Sc. from Sharif University of Technology (2003), M.A.Sc. from The University of British Columbia (2005), and Ph.D. from The University of Illinois at Urbana-Champaign (2010). His research focuses on microgrids, power electronics systems, renewable energy systems, and electrified transportation, with particular expertise in control systems and energy conversion. Dr. Davoudi has authored/co-authored over 136 journal articles, 87 conference papers, and holds two U.S. patents. His work has been recognized with prestigious awards, including the IEEE Fellow (2023), Fellow of the Asia-Pacific AI Association (2022), and the 2017 IEEE Richard M. Bass Outstanding Young Engineer Award. He has led federal-funded projects totaling over $6.2 million, focusing on resilient DC power systems, naval microgrids, and energy storage optimization. His research emphasizes distributed control, formal verification, and resilience in power systems, with applications to naval, smart grid, and electrified transportation technologies. Key contributions include adaptive dynamic programming for multi-agent systems, bipartite output containment, and robust control of DC microgrids under adversarial attacks.