Find Academic Supervisors

Michelle (Shelby) BensiView profile

Associate Professor

• Probabilistic Risk Assessment
• Natural Hazards Engineering
• Bayesian Networks
• +4 more

Miriam Bensi holds the Deborah J. Goodings Professorship in Engineering for Global Sustainability and is an Associate Professor at the University of Maryland's A. James Clark School of Engineering, Department of Civil and Environmental Engineering. She leads the RISE Lab and is affiliated with the Center for Risk and Reliability. Her education includes a Ph.D. from UC Berkeley, M.A.Sc. from University of Delaware, and B.A. from University of Toledo. Dr. Bensi's research focuses on probabilistic assessment of natural hazards, infrastructure risk modeling, and machine learning applications in civil engineering. She develops Bayesian network frameworks for multi-hazard analysis, particularly in coastal flood resilience and nuclear facility safety. Her work addresses compound flooding mechanisms, seismic hazard interactions, and uncertainty quantification in extreme events. Recent research trends emphasize integrating social vulnerability analysis with technical risk assessments, leveraging machine learning for improved hurricane prediction, and creating standardized frameworks for multi-hazard probabilistic assessment. Her team collaborates across disciplines to bridge gaps between researchers, educators, and practitioners. Scientific Awards: NSF CAREER Award recipient Grants: DOE-funded nuclear facility risk assessment, NASA lunar seismic hazard projects Labs/Teams: RISE Lab (Resilience Innovation in Sustainable Engineering) Her teaching includes courses on probabilistic methods in civil engineering and natural hazard assessment, emphasizing data-driven decision making under uncertainty.

• Urban Building Energy Modelling
• Decarbonisation Pathways
• Uncertainty and Sensitivity Analysis
• +6 more

Dr. Pamela Fennell is a Lecturer in Urban Energy Modelling at the Bartlett School of Environment, Energy & Resources, University College London (UCL). With a PhD in Building Energy Modelling from UCL (2018) and expertise spanning civil engineering, architecture, and policy, she leads high-impact research projects like UBASE, CITES, and ConceptIM while co-investigating the GEMDev initiative. As Deputy Director of UCL’s MSc in Smart Energy and the Built Environment, she teaches advanced modules on urban energy systems. Education: PhD (Building Energy Modelling), UCL (2018) MRes (Energy Demand Studies), UCL (2014) MBA, Imperial College (2007) MA and BA in Architecture, University of Cambridge (2000, 1997) Her research focuses on integrating real-world urban data with high-resolution dynamic simulations to address energy poverty in the Global South. She specializes in uncertainty analysis for large-scale models and develops open-source tools like her QGIS plugin to democratize access to urban energy modeling. Her work has been cited in policy frameworks such as Da Nang City’s Climate Action Plan. Recent publications analyze thermal comfort in Peruvian informal settlements, retrofit optimization methodologies, and non-domestic building stock modeling. Her projects combine technical rigor with social equity concerns, particularly in South-North research collaborations. She chairs the education committee of IBPSA and leads UCL’s Energy and Development Group.

Mitra BaratchiView profile

Associate Professor

• Spatio-Temporal Data Analysis
• Machine Learning
• Mobility Data Modeling
• +5 more

Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.

Siegfried NijssenView profile

Assistant Professor

• Data Mining
• Pattern Mining
• Exploratory Data Analysis
• +5 more

Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.

Tillman WeydeView profile

Academic

• Machine Learning
• Signal Processing
• Data Analysis
• +5 more

Dr. Tillman Weyde is a Reader in the Department of Computer Science at City, University of London , where he has been employed since 2021. He leads the Machine Intelligence and Media Informatics Research Group and is a member of the Machine Learning Group . Prior to this, he served as Senior Lecturer (2009–2021) and Lecturer (2005–2009) at City, and worked as a researcher at the University of Osnabrück (2001–2005), coordinating the MUSITECH project. His academic background includes PhD in Music Technology (2002), MSc in Computer Science (1999), and MSc in Mathematics, Music, Philosophy & Pedagogy (1994), all from the University of Osnabrück. Research Focus: Machine learning and signal processing methods for data analysis with applications in finance, audio, NLP, music, health, security, and education. His recent work emphasizes inductive biases in neural networks for rule-learning, extrapolation, generalization, and interpretability. Grants & Projects: Principal Investigator for the AHRC-funded Digital Music Lab (2012–2017) and Integrated Audio-Symbolic Model of Music Similarity (2017–present). Co-investigator in Innovate UK and EPSRC projects on safer gambling ( Advancing Consumer Protection , 2015–2018) and Raven (2012–2021). Collaborations: Affiliated with the Institute of Cognitive Science (Osnabrück), Intelligent Systems Research Laboratory (Reading), and the MPEG Ad-Hoc Group on Symbolic Music Representation. Awards: Co-author of the 2000 Comenius Medal-winning educational software Computer Courses in Music Ear Training and co-editor of the Osnabrück Series on Music and Computation . Publications: Over 150 peer-reviewed works including conference papers, journal articles, and book chapters, focusing on interdisciplinary applications of machine learning in music, health, and finance. Students: Supervised 13 PhD students across topics like grammar bias in neural networks, emotion recognition from audio, extrapolation behavior in neural networks, relation-based patterns, legal text parsing, and more.

Daniel BarbaráView profile

Professor

• Data Mining
• Machine Learning
• Databases
• +3 more

Daniel Barbará is a Professor in the Department of Information and Software Engineering at George Mason University since 1997. He holds a PhD and MS in Computer Science from Princeton University, and a BS in Electrical Engineering from Universidad Metropolitana. Research Interests: Data mining, machine learning, cybersecurity, mobile computing, and fractal-based data analysis. His work focuses on clustering algorithms, intrusion detection, data compression, and handling high-dimensional datasets. Grants: Multiple NSF and Air Force grants including "Clustering by Impact" (2002-2005) and "Data Mining Support for NIAC" (1999-2000). Scientific Awards: Outstanding Researcher, CS Department, George Mason University Best Paper Finalist at WWW 2002 Projects: Developed ADAM (Intrusion Detection), COOLCAT (Categorical Clustering), Fractal Mining, and Quasi-Cubes (Approximate Query Processing).

Jeongsub ChoiView profile

Assistant Professor

• Machine Learning
• Data Mining
• Data Analytics
• +6 more

Jeongsub Choi is an Assistant Professor in the Department of Management Information Systems at West Virginia University's John Chambers College of Business and Economics. His work bridges machine learning, data mining, and business intelligence with applications in strategic management, patent analysis, and advanced manufacturing. Ph.D. in Industrial and Systems Engineering, Rutgers University M.S. in Statistics, Rutgers University M.S. in Industrial and Systems Engineering, Rutgers University Choi's research focuses on sparse learning models, network analysis, and virtual metrology systems for semiconductor manufacturing. His work spans predictive maintenance, competitor detection, and anomaly identification in dynamic networks. Recent publications highlight trends in sensor optimization, fault diagnosis, and technology lifecycle modeling. Applications span semiconductor manufacturing, financial transaction networks, and patent citation analysis.

Peyman Afzali GorouhView profile

Researcher

• Power Engineering
• Smart Grids
• Renewable Energy Systems
• +5 more

Peyman Afzali Gorouh is a Postdoctoral Researcher in the Applied Power Electronic Systems group within the Faculty of Engineering and Science at Aalborg University, Denmark. His research focuses on developing innovative models for energy communities, smart grids, and renewable energy integration. Dr. Afzali's research interests span power engineering, smart grid technologies, renewable energy systems, and energy communities. His work particularly emphasizes prosumer economics, peer-to-peer energy trading, risk modeling in power systems, and energy democracy frameworks. He has developed novel approaches for optimizing energy communities while considering socio-economic-environmental factors, demand response, and uncertainty management. His recent publications (2020-2024) demonstrate a consistent focus on energy community modeling, with particular emphasis on peer-to-peer trading mechanisms, risk-constrained optimization, and multi-objective planning. His work bridges technical power system challenges with socio-economic considerations, creating integrated models that address both engineering and human aspects of modern energy systems. Dr. Afzali maintains an active research profile with numerous publications in high-impact journals including IEEE Transactions on Engineering Management, Energy and Buildings, Applied Sciences, and Sustainable Cities and Society. His research shows strong international collaboration, particularly with researchers from Iranian institutions.

• Topological Data Analysis
• Dynamical Systems
• Machine Learning
• +7 more

Firas Khasawneh is an Associate Professor at the Department of Computational Mathematics, Science and Engineering at Michigan State University. His research focuses on the intersection of topological data analysis, dynamical systems, and machine learning with applications in engineering, neuroscience, and biological systems. Topological Data Analysis - Persistent Homology, Zigzag Persistence, Data Assimilation Dynamical Systems - Bifurcation Detection, Delay Differential Equations, Nonlinear Dynamics Machine Learning - Applications in Signal Processing, Surface Texture Analysis, Neural Activity Modeling His recent publications (2023-2025) demonstrate a strong emphasis on topological approaches for analyzing complex systems, including neural activity characterization, vibration analysis in manufacturing processes, and data assimilation techniques for forecasting. He has developed computational tools like the Teaspoon Python package for topological signal processing.

Dan GutfreundView profile

Researcher

• Machine Learning
• Natural Language Processing
• Computer Vision
• +3 more

Dan Gutfreund is a Principal Research Scientist and Senior Manager at the MIT-IBM Watson AI Lab, focusing on machine learning with applications to natural language processing and computer vision. He previously held managerial and technical roles at IBM's Haifa Research Lab and was involved in IBM Project Debater. Gutfreund earned his PhD in computer science from the Hebrew University in Jerusalem in 2005. His research spans Neuro-Symbolic AI , Computational Complexity , and Foundations of Cryptography , with notable contributions to datasets like Moments in Time and ObjectNet . His recent work includes multimodal models for the metaverse, generative AI for engineering design, and simulator-assisted training for interpretable systems. Gutfreund's publications reflect expertise in AI applications for supply chain prediction, avatar personalization, and reconciling virtual disputes. He has also explored evolutionary algorithms for software engineering and constraint-based generative models in design tasks.

Tsui-Wei WengView profile

Assistant Professor

• Neural Network Robustness
• AI Safety
• Deep Learning Theory
• +8 more

Tsui-Wei Weng is an Assistant Professor at the Halıcıoğlu Data Science Institute, affiliated with the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). Her research focuses on enhancing the robustness, reliability, and safety of AI systems and deep learning models. Education: Ph.D. in Electrical Engineering and Computer Science (EECS), Massachusetts Institute of Technology (MIT), 2020; M.S. in Communication Engineering, National Taiwan University, 2013; B.S. in Electrical Engineering, National Taiwan University, 2011. Her research interests span neural network robustness, AI safety, adversarial robustness certification, control policy verification, and theoretical machine learning. She has contributed foundational work on probabilistic and deterministic robustness certification frameworks like PROVEN and CNN-Cert, with a focus on improving the scalability and efficiency of verification methods. Her publications from 2018–2021 reveal a trajectory in adversarial robustness, randomized smoothing, deep reinforcement learning, and interpretable AI. Collaborative efforts with institutions like MIT-IBM Watson AI Lab, Google DeepMind, and IBM Research further underscore her interdisciplinary approach. Scientific awards include the Best Paper Award at IEEE Components, Packaging and Manufacturing Technology (2016). She actively collaborates with students and postdocs, emphasizing mathematical and machine learning rigor in their research contributions.

Chris J BuddView profile

Professor

• Nonlinear Mathematics
• Industrial Mathematics
• Numerical Analysis
• +7 more

Professor Chris J Budd OBE is a distinguished Professor of Applied Mathematics at the University of Bath's Department of Mathematical Sciences, where he serves as Director of Knowledge Exchange for the Bath Institute for Mathematical Innovation (IMI). He is also Professor of Mathematics at the Royal Institution of Great Britain and a former Gresham Professor of Geometry. His leadership extends to directing the Centre for Nonlinear Mechanics and serving as Super Champion of the KE Hub. His educational background includes a gap year with Marconi that profoundly shaped his career, followed by undergraduate studies at Cambridge and a DPhil at Oxford. This industry experience during his formative years established his lifelong commitment to industrial mathematics and knowledge exchange. Budd's research focuses on nonlinear mathematical problems with industrial applications, particularly adaptive moving mesh methods for meteorology and climate modeling, data assimilation, non-smooth dynamical systems, and the mathematics of machine learning. He approaches linear problems as 'for cissies,' preferring the challenges of nonlinear systems that better represent real-world phenomena. His work bridges theoretical mathematics with practical applications across meteorology, environmental science, and engineering. His recent publications reveal a strong trend toward integrating machine learning with traditional numerical methods, particularly in climate modeling and solving partial differential equations. This includes Fourier Neural Operators, adaptive mesh methods enhanced by graph neural networks, and mathematical frameworks for understanding climate tipping points through non-smooth dynamics. OBE for services to mathematics National Teaching Fellowship (NTF) Knowledge Transfer Award for work with the Met Office Fellow of the Institute of Mathematics and its Applications (FIMA) Chartered Mathematician (C Math) British Science Association award for best science festival (2009) As principal investigator of the £3.5M EPSRC Programme Grant 'Maths4DL' on the Mathematics of Deep Learning, Budd leads a major collaborative effort between Bath, Cambridge, and UCL. He actively supervises numerous PhD students across diverse projects including climate modeling, machine learning applications, and industrial mathematics problems. His commitment to knowledge exchange is exemplified through V-KEMS (Virtual Forum for Knowledge Exchange in the Mathematical Sciences), which he co-founded to address challenges like the COVID-19 pandemic through mathematical approaches. Budd directs the Centre for Nonlinear Mechanics at Bath, fostering interdisciplinary research through mathematical modeling of complex systems. He also leads the Bath Institute for Mathematical Innovation's knowledge exchange activities, connecting academic mathematics with industrial and societal challenges. His work with V-KEMS has proven particularly effective during the pandemic, mobilizing teams of mathematicians to address urgent real-world problems.

Xiongcai CaiView profile

Adjunct Associate Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Dr. Xiongcai Cai is an Adjunct Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW). With expertise in Artificial Intelligence , Machine Learning , and Computer Vision , he contributes to advancing Recommender Systems , Natural Language Processing , and Health Informatics . His work bridges theoretical and applied research in technology for human-centric applications. Current roles: Adjunct Associate Professor, UNSW School of Computer Science and Engineering Key research areas: Machine Learning, Recommender Systems, Computer Vision, Generative AI Dr. Cai's research portfolio demonstrates a consistent focus on recommender systems and machine learning over the past decade. His technical contributions span graph convolutional networks , temporal bilinear models , and embedding techniques for collaborative filtering. Recent work in 2025 addresses knowledge distillation for GCNs-based recommenders, while earlier studies tackled cold-start transitions and matrix factorisation boosting. His publication history (2 book chapters, 7 journal articles, and 36 conference papers) reveals a strong emphasis on real-time applications in domains like gait recognition (2020), health data analytics (2016), and social network recommendation (2010-2015). The research applies mathematical rigor to practical challenges in online dating platforms , medical decision support , and object tracking systems . Contact details: Email: x.cai@unsw.edu.au Phone: +61 2 9385 8858

• Machine Learning
• Statistics
• Model-Based Clustering
• +5 more

Brendan Murphy is a Full Professor in the School of Mathematical Sciences at University College Dublin since 2015, previously holding a Professor role at the same institution (2007-2015) and a Lecturer position at Trinity College Dublin (1999-2007). He is a Principal Investigator at the Insight Centre for Data Analytics and actively works in Machine Learning & Statistics. Research Focus: His work centers on Model-based clustering Mixture models Applications in sports analytics, food science, microbiome studies, and public health Bayesian statistical methods High-dimensional data analysis Article Trends: Murphy's recent publications (2024-2025) emphasize soft clustering techniques, Bayesian mixture models, and their applications across diverse domains including metabolomics, political science, and oceanography. His work addresses challenges in variable selection, robust classification, and multi-omics integration.

Emanuele BorgonovoView profile

Professor

• Sensitivity Analysis
• Decision Analysis
• Risk Analysis
• +3 more

Emanuele Borgonovo is a Full Professor and Director of the Department of Decision Sciences at Bocconi University. He serves as a Research Affiliate at MIT's Department of Nuclear Science and Engineering, co-editor-in-chief of the European Journal of Operational Research , and advisor for the Springer International Series in Management Science and Operations Research. His leadership roles include President of the Decision Analysis Society of INFORMS (2020-2022) and Co-Chair of the ESRA Technical Committee on Uncertainty Analysis. Affiliation: Bocconi University Collaboration: MIT, NASA, DARPA, Idaho National Laboratory, EDF His research focuses on Sensitivity Analysis, Decision Analysis, Risk Analysis, and Uncertainty Quantification. He pioneered techniques like the differential importance measure, now adopted in NASA's PRA Guide. Recent work explores explainable AI via Shapley values and optimal transport methods for sensitivity analysis. 2025: Explainable risk assessment modeling 2025: Sensitivity analysis of climate models 2024: AI impact on job markets Scientific honors include: Excellence in Research Award (2020) IBM Faculty Award (2015) Best Risk Analysis Paper Award (2014) Multiple Best Reviewer Awards (2006-2022) At SDA Bocconi, he leads the Artificial Intelligence and Data Science for Executives program and taught in MBA/DBA courses. His work has been supported by grants from US Department of Energy, NASA, and Electricité de France.