Find Academic Supervisors

• Systems Theory
• Machine Learning
• Artificial Intelligence
• +4 more

Tyler Cody is an Associate Professor of Data Science at the University of Virginia School of Data Science and a member of the National Security Data and Policy Institute. His research bridges systems engineering and artificial intelligence through abstract systems theory. Education: Ph.D. in Systems Engineering, University of Virginia B.S. in Systems Engineering, University of Virginia (minors: Computer Science, Applied Mathematics) Dr. Cody's research centers on systems theory as a meta-theory for learning, with applications in machine prognostics, telecommunications, computer networks, fraud detection, and computer vision. He investigates phenomena in learning processes, focusing on change and reuse, lifecycles, and iterated games. His recent publications (2024-2025) reveal a strong trend in applying systems theory to machine learning assurance and cyber security. Key areas include reinforcement learning for cyber operations, combinatorial methods for testing ML systems, and outcome-based engineering for AI. His work also addresses ethical implications and architectural design of learning systems. Scientific Awards: No awards listed in the provided information. No details were provided regarding student advising or research grants. Dr. Cody contributes to the National Security Data and Policy Institute, where his expertise supports data-driven approaches to national security challenges through systems-theoretic frameworks.

Andrew ElliottView profile

Senior Lecturer

• Statistics
• Data Analytics
• Modeling in Space and Time
• +8 more

Dr Andrew Elliott is a Senior Lecturer in the School of Statistics at the University of Glasgow. His research bridges Statistics , Computational Statistics , and Machine Learning & AI , with applications in Social & Urban Studies and Imaging, Image Processing & Image Analysis . Education: Not explicitly detailed in the text. Dr Elliott's work focuses on modeling in space and time , synthetic data generation , and network analysis . His recent publications explore fairness constraints in AI , agent swarms , and temporal network modeling , reflecting interdisciplinary applications in urban analytics and environmental science. His publications from 2014–2024 span network science , machine learning , image analysis , and geospatial studies . Key trends include privacy auditing , counterfactual explanations , and deep learning for network time series . Dr Elliott supervises doctoral students and has advised Zhengduo Zhao and Weiyue Zheng . He actively contributes to research groups in Statistics & Data Analytics and Machine Learning . Notable collaborations include work with Mihai Cucuringu and Gesine Reinert .

Zhang YanView profile

Professor

• Internet of Vehicles
• Smart grid
• Mobile edge computing
• +4 more

Zhang Yan is a Full Professor at the Department of Informatics, University of Oslo, Norway. He previously served as Head of Department and Chief Scientist at Simula Research Laboratory (2014–2016). His research focuses on advanced communication technologies including Internet of Things (IoT), 5G/6G networks, mobile edge computing, and blockchain applications. He has held significant roles such as IEEE VTS Distinguished Lecturer (2016–2020) and Chair of IEEE TCGCC (2019–2021). His honors include IEEE Fellow (2020), election to Academia Europaea (2020), and recognition as a Web of Science Highly Cited Researcher (2018–2019). Research interests span interdisciplinary areas like network dynamics, socio-economic systems, and algorithmic design. His work bridges theoretical foundations with practical applications in smart grids, vehicular networks, and global trade systems. Recent publications emphasize network science methodologies applied to economic complexity and information diffusion. Professional contributions include editorial roles for top journals and leadership in EU-funded projects. His awards reflect impactful contributions to both technical innovation and scientific leadership in informatics and communications.

Mason PorterView profile

Professor

• Network Science
• Social Dynamics
• Nonlinear Dynamics
• +4 more

Mason Porter is a Professor in the Department of Mathematics at the University of California, Los Angeles (UCLA). His research focuses on network science, nonlinear dynamics, and mathematical modeling of complex social systems. He explores topics such as opinion dynamics, temporal networks, multilayer networks, and the interplay between network structure and dynamical processes. Porter’s work spans theoretical and applied domains, including the analysis of social networks, epidemic spread, and infrastructure resilience. He has contributed to methods for detecting community structures, analyzing hypergraphs, and modeling collective behavior in systems ranging from online social media to biological networks. His recent studies emphasize bounded-confidence models, quantum walks on networks, and the application of topological data analysis to spatial systems. His research also intersects with interdisciplinary projects, such as modeling disease mitigation strategies, customer mobility in supermarkets, and the coevolution of disease spread and opinions. He has collaborated on initiatives like the NSF-funded project to predict microbiome assembly via multilayer networks. Porter’s publications reflect a deep engagement with both foundational theory and real-world applications, often leveraging computational and analytical techniques to uncover principles governing complex systems. His work bridges mathematics, physics, and social sciences, addressing challenges in data ethics, information diffusion, and network-driven phenomena.

Marya BazziView profile

Assistant Professor

• Network Science
• Time-Dependent Networks
• Multilayer Networks
• +7 more

Marya Bazzi is an Honorary Assistant Professor at the University of Warwick. Her research focuses on developing methods for analyzing large time-dependent data, with a primary focus on Network Science. She investigates mesoscale structures in networks such as community detection, core-periphery structures, and multilayer networks, applying these tools to domains like online media, biology, finance, and digital humanities. Her work includes contributions to scalable graph representation learning (Local2Global framework), semantic change detection using the UK Web Archive, and computational journalism. She co-organizes academic initiatives like Data Science for Social Good, Data Study Group, and workshops on media in the digital age. Her research bridges algorithmic development with practical applications, emphasizing interdisciplinary collaboration. Key publications span Machine Learning, Network Science, and Computational Biology, reflecting her expertise in both theoretical frameworks and applied problem-solving. While no awards are explicitly mentioned, her active participation in organizing academic activities demonstrates leadership in fostering collaborative research environments.

• Globular Cluster Physics
• Machine Learning Applications
• Galaxy Clusters
• +5 more

Petra Awad is an external researcher at the Faculty of Science and Engineering , University of Groningen , Netherlands, specializing in Astronomy . Her work bridges astrophysical research with computational methods, focusing on galaxy clusters, globular clusters, and cosmic structure. Key Research Areas: Globular Cluster Physics, Galaxy Clusters, Tidal Tails, Cosmic Web, Dwarf Galaxies, Machine Learning in Astrophysics Publication Trends include advanced studies on galaxy evolution in cluster environments, tidal disruption phenomena, and Euclid mission contributions to photometric pipelines and cluster analysis. Her work employs N-body simulations, deep spectroscopy, and machine learning for cosmic structure mapping. Collaborations span international teams like the S5 Collaboration and Euclid Consortium, with data contributions to the Centre de Donnees Strasbourg (CDS).

Keith Malcolm PruferView profile

Associate Professor

• Environmental Archaeology
• Maya Archaeology
• Paleoclimatology
• +11 more

Keith Malcolm Prufer is an Associate Professor in the Department of Anthropology at the University of New Mexico and Director of the Radiocarbon Lab. His work focuses on environmental archaeology, human responses to climate change, and complex socioeconomic systems in the Maya Lowlands, particularly through interdisciplinary research combining paleoclimatology, isotopic analysis, and spatial methodologies. University of New Mexico faculty since 2008 Director of Radiocarbon Lab Core member of Center for Stable Isotopes Collaborations with institutions across the US and Europe Research interests span 14,000 years of human history in Mesoamerica, examining links between climate shifts, subsistence strategies, and societal transformations. He employs speleothem paleoclimatology , isotopic diet reconstruction , and LiDAR mapping to study prehistoric adaptations. Recent publications highlight his work on Early Holocene mortuary practices , Maya settlement scaling theory , and climate-driven political instability . Funded by NSF (2008-2015, 2016-2018, 2022-2025) and Alphawood Foundation (2014-2018, 2015-2024), his research spans Belize, Mesoamerica, and comparative tropical contexts. NSF-HSD collaborative project (2008-2013) Alphawood Foundation grants (2014-2018, 2015-2024) NSF grants for radiocarbon lab upgrades (2016-2018, 2022-2025) His lab at UNM's Center for Stable Isotopes houses advanced equipment for isotope sample preparation , radiocarbon dating , and archaeological chemistry . He emphasizes transdisciplinary collaboration, integrating archaeology with geoscience, genetics, and modern Maya ethnography.

Seshadhri ComandurView profile

Professor

• Theoretical Computer Science
• Graph Algorithms
• Data Mining
• +4 more

C. Seshadhri (Seshadhri Comandur) is a Professor of Computer Science at the University of California, Santa Cruz's Baskin School of Engineering. He earned his Ph.D. from Princeton University and completed postdoctoral research at IBM Almaden Labs. His primary research spans theoretical computer science, graph algorithms, data mining, and sublinear algorithms, with applications in network analysis and scalable computation. His work focuses on graph property testing, monotonicity testing, subgraph counting, and combinatorial algorithms. Research integrates randomization, high-dimensional analysis, and optimization techniques to solve problems in large-scale network datasets. Key interests include developing efficient algorithms for complex graph structures and exploring limitations of low-dimensional embeddings. Publications demonstrate consistent focus on algorithmic efficiency in graph analysis, with recent work advancing sublinear-time methods, spectral decompositions, and combinatorial dichotomies. Trends show strong emphasis on theoretical foundations of data mining and rigorous complexity analysis. Invited Lecture at ICDT 2023 Best Paper at WSDM 2020 2019 SDM/IBM Early Career Data Mining Research Award Best Paper at WWW 2017 Best Paper at ICDM 2015 Best Paper Finalist at WWW 2015 Best Student Paper at KDD 2013 Best Research Paper at SDM 2013 2013 Sandia Employee Recognition Award Advises PhD/MS students and postdocs including Sabyasachi Basu, Daniel Paul Pena, Shweta Jain, and Andrew Stolman. Leads the PTReview blog on property testing. Current group includes postdocs and graduate students working on algorithms and complexity.

Raj BhatnagarView profile

Professor

• Data Mining
• Pattern Recognition
• Artificial Intelligence
• +4 more

Raj Bhatnagar is a Professor in the Department of Computer Science at the University of Cincinnati, affiliated with the College of Engineering, Architecture and Art. His research focuses on data mining, pattern recognition, AI, and algorithms for bioinformatics. He has secured over 25 grants from federal agencies (NSF, Air Force Research Laboratory), industry (Charles Schwab, Edaptive Computing), and state/private entities, totaling millions in funding. Education: PhD in Computer Science from University of Maryland College Park (1989) Research Interests: His work spans distributed computing, graph theory, machine learning for industrial applications, and cybersecurity. Notable contributions include algorithms for clustering, anomaly detection, and formal concept analysis. He collaborates with Cincinnati Children’s Hospital and the Air Force on projects like predictive analytics for spindle health and verification of microelectronics designs. Recent Grants: Includes a 2023 industry grant for Machine Learning educational seminars (MME I45I), and multiple ongoing Air Force contracts (2018-2022) for ML-based verification test processes. Labs/Teams: Involved with Edaptive Computing’s Data Analytics & Cybersecurity Simulation Center and the Air Force-led STAMP/TAME research initiatives.

Raul MondragonView profile

Senior Lecturer

• Complex Networks
• Network Science
• Telecommunications
• +3 more

Dr. Raul Mondragon is a Senior Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London. He holds an MSc and PhD, and is a Senior Member of the IEEE (SMIEEE). His research focuses on complex network analysis, including topics like network cores, rich-club phenomena, and applications in telecommunications and social media platforms. He has contributed to understanding internet topology, network robustness, and Web3 technologies. He teaches modules on analogue electronics and network security, and has supervised interdisciplinary PhD students in areas such as energy-efficient networks and dynamical modeling. His work includes grants like the EPSRC-funded RAVEN project (2010-2013) on complex energy networks. Mondragon is affiliated with the Centre for Networks, Communications and Systems, where he explores network science applications in diverse domains. Affiliations: School of Electronic Engineering and Computer Science, Centre for Networks, Communications and Systems Education: PhD and MSc (specific fields and institutions not explicitly stated) Research Interests: Dr. Mondragon’s work spans theoretical and applied network science. He investigates structural properties of networks (e.g., rich-club effects, core-periphery organization) and their implications for system resilience. His research applies to telecommunications, social media, and ecological systems. Notable contributions include analyzing cryptocurrency transaction networks, Web3 platform moderation, and drought impacts on food web structures. Teaching: Dr. Mondragon instructs undergraduate courses on analogue electronics and postgraduate/undergraduate modules on network security and authentication principles. His teaching emphasizes foundational circuit theory and cryptographic techniques for secure communications. Grants: Principal Investigator (PI) for the EPSRC RAVEN project (£355,446), focusing on complex energy networks’ resilience and adaptability. Students: Supervised PhD candidates include Mr. Iftekharul Mobin (energy-efficient ad-hoc networks), Mr. Sabri Zaman (functional topology), and interdisciplinary students like Mr. Gibson Ikoro (chat room dynamics) and Mr. Massimo Cavallaro (stochastic processes). Labs/Teams: Active in the Centre for Networks, Communications and Systems, collaborating on projects involving network modeling, social media analysis, and interdisciplinary applications.

Maia FraserView profile

Associate Professor

• Machine Learning
• Computer Vision
• Neuroscience
• +4 more

Maia Fraser is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. She holds an MSc and PhD from the University of Chicago and a PhD from Stanford University. Her research spans interdisciplinary areas including machine learning, computational neuroscience, and applied mathematics. Notably, she explores the intersection of AI with structural engineering, focusing on automated damage assessment in concrete and neural mechanisms underlying continual learning. Her work also delves into theoretical aspects of contact geometry and reinforcement learning. Her academic contributions include pioneering methods for damage quantification using digital images, theoretical frameworks for temporal abstractions in neural systems, and algorithmic advancements in decision-making under uncertainty. Fraser’s research often bridges abstract mathematical theory with practical applications, such as optimizing nautical navigation and analyzing fish behavior through spatial learning models. Education: MSc in Mathematics, University of Chicago PhD in Mathematics, University of Chicago PhD in Computer Science, Stanford University Fraser’s recent publications highlight advancements in automated assessment techniques for civil infrastructure, the role of temporal hierarchies in neural organization, and foundational work in reinforcement learning theory. Her interdisciplinary approach reflects a commitment to solving complex problems through collaborative, mathematically rigorous methods.

Miao QiaoView profile

Lecturer

• Data Science
• Graph Analytics
• Brain Network Analytics
• +2 more

Miao Qiao is a faculty member at the School of Computer Science , University of Auckland , where she has worked since 2018. Her research bridges theoretical and applied aspects of data science and management , focusing on vector data management , graph search and analytics , and brain network analytics . Research Interests : Data Science, Graph Analytics, Brain Network Analytics Grants : Royal Society of New Zealand (NZD 300,000, 2018–2024): Subgraph Matching: Theory and Practice The Ministry of Business, Innovation and Employment (NZD 3,000,000, 2020–2024): Advanced Graph Analytics for Human Brain Connectivity Professional Services : Review Board Member, PVLDB 2021 Program Committee Member: PODS 2023, ADC 2018, IJCAI 2019–2021, CIKM 2019, AAAI 2020–2021 Invited Reviewer: SCG 2019, TODS 2015–2019, TKDE 2018–2021, KDD 2016, ICDM 2013–2014, WISE 2013, VLDBJ 2013–2021, NIPS 2021 Program Chair: ISWC Doctoral Consortium 2019, ADC 2021 Teaching : Database Systems (2017–2023) Algorithms for Massive Data (2018–2019) Systems Analysis and Modeling (2016–2018) Algorithms (2017–2018) Scientific Awards : PREMIA Best Student Paper Honourable Mention Award VLDB 2013 Travel Fellowship Shanghai Jiao Tong University 1st Class Scholarship ACM/ICPC 3rd Place, Singapore Computer World Scholarship Singapore Technology Engineering Scholarship ACM/ICPC 1st Place, Korea Silver medal, National Olympiad in Informatics, China

• Systemic Risk
• Financial Networks
• Stochastic Analysis
• +4 more

Andreea Minca is a Professor in the School of Operations Research and Information Engineering at Cornell University, holding a PhD in Applied Mathematics from Sorbonne University and a Diplome from Ecole Polytechnique. Her research focuses on mathematical modeling of financial systems under uncertainty, with emphasis on systemic risk and network-based contagion dynamics. Her educational background includes: PhD in Applied Mathematics, Sorbonne University Diplome de l'Ecole Polytechnique, Ecole Polytechnique Minca's research pioneered structural models for systemic risk using network theory to represent financial interconnections. She developed foundational work on contagion in large systems through asymptotic analysis of diffusions on random graphs and introduced optimization frameworks under network uncertainty. Current interests extend to decentralized finance, stablecoin design, reinforcement learning in graphon games, and pandemic-related risk mitigation for supply chains. Her methodology integrates stochastic analysis, game theory, and network science to derive stability-promoting policies. Recent publications reveal a strategic shift toward decentralized finance applications while maintaining core systemic risk research. Key trends include modeling stablecoin vulnerabilities, applying clustering algorithms to financial networks, and developing reinforcement learning solutions for mean-field games. This interdisciplinary work bridges operations research, finance, and computer science to address contemporary challenges in financial stability and emerging technologies. Her distinguished awards include: 2016 SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize NSF CAREER Award (2017) Research Fellow of GARP (2014) AXA Research Fund Awardee (2020) Minca has advised seven PhD students with placements at JPMorgan, Northwestern University, Citadel, AQR, Tower Research Capital, and Gyroscope Stablecoin (founded by her student). Her research is supported by competitive grants including the NSF CAREER award and AXA-funded project on pandemic risk mitigation. She serves on editorial boards of Mathematical Finance, SIAM Journal on Financial Mathematics, and European Journal of Operational Research. Her work operates within complex financial systems research, collaborating across disciplines to develop mathematical frameworks for crisis management and system resilience, particularly through network analytics and machine learning applications.

Mikko KiveläView profile

Associate Professor

• Network Science
• Complex Systems
• Social Networks
• +4 more

Mikko Kivelä is an Associate Professor in the Department of Computer Science at Aalto University, where he also obtained his doctoral degree. He holds an Academy Research Fellowship and is affiliated with the School of Science (SCI). His research focuses on network science, complex systems, and their applications in social, transportation, and biological networks. Kivelä’s work emphasizes multilayer networks, temporal dynamics, and the integration of network structure with content analysis. Academic Rank: Associate Professor Primary Affiliation: Aalto University Research Groups: Complex Systems (Cxsys), Multilayer Network Analysis He completed his Master’s degree at Helsinki University of Technology (2009) and his PhD at Aalto University (2012). His research investigates how network structures influence processes like disease spread, polarization, and social dynamics. Notable projects include the Generalized Network Representation Methods for Polarization (AoF Fellow Salary) and the CON-NET project on online misbehavior detection. His research outputs span network science fundamentals, algorithm development (e.g., pymnet library), and applied studies on mobility, vaccination strategies, and geopolitical events. Media coverage highlights his work on pandemic modeling and cross-border mobility impacts. Kivelä’s current grants include Academy of Finland and EU funding for projects addressing human security and algorithmic trust.

Shawn MankadView profile

Associate Professor

• Statistical Modeling
• Text Analytics
• Network Analysis
• +2 more

Shawn Mankad is an Associate Professor of Analytics and Stephen P. Zelnak Jr. Scholar at NC State University's Poole College of Management. His research develops statistical methods for business and policy issues, specializing in text analysis for economic prediction and network modeling for systemic risk assessment. His research integrates: Text mining techniques for service quality measurement Network analysis of financial systems and interconnectedness Machine learning applications in operations management Data privacy preservation methods Mankad's scholarship explores how textual data from online reviews and documents can predict economic variables and service outcomes, while his financial network research examines systemic risk propagation. His recent methodological innovations include structural topic-sentiment models and change-point detection in core-periphery networks. Honors include the Stephen P. Zelnak Jr. Scholar appointment and teaching awards. He leads the Business Analytics concentration and previously directed Cornell's Digital Technology Immersion program. Academic service includes editorial roles for Data Science in Science and program committees for Federal Reserve conferences.