Find Academic Supervisors

Armando RungiView profile

Professor

• International Economics
• Industrial Organization
• Applied Econometrics
• +5 more

Armando Rungi is a Professor of Economics at IMT School for Advanced Studies in Lucca, Italy. He teaches econometrics, international economics, and macroeconomics to PhD students. In addition to his academic role, he serves as a research fellow at the Observatory on Foreign Firms in Italy and has consulted for the European Commission, OECD, and UNCTAD on international trade and investment issues. His research focuses on international economics, industrial organization, applied econometrics, and statistical learning. Recent work emphasizes the organization of multinational enterprises, global value chains, labor markets, cyber-resilience of supply chains, and the integration of econometric and machine learning tools for policy evaluation and predictive analysis. His recent publications explore topics such as the impact of trade agreements, multinational enterprises' strategies, and the application of machine learning in predicting firm behaviors and evaluating economic policies. A common theme is the analysis of supply chain resilience, corporate ownership structures, and the effects of globalization on firms' competitiveness and productivity. No scientific awards are mentioned in the provided information. No advisees or grant details are listed in the text. His professional activities include consulting roles and research collaborations. He is affiliated with the Observatory on Foreign Firms in Italy, which evaluates the impact of multinational companies and strategies to attract foreign investment in Italy.

• Bayesian Statistics
• Ecological Statistics
• Capture-Recapture Modeling
• +5 more

Prof. Ruth King is the Thomas Bayes’ Professor of Statistics at the University of Edinburgh’s School of Mathematics. Her research focuses on applying Bayesian statistical methods to ecological and public health challenges, including population estimation for hidden groups (e.g., injecting drug users, modern-day slaves) and wildlife conservation. She develops computationally efficient techniques for analyzing large datasets, such as spatial capture-recapture models for animal populations and spatio-temporal abundance models for hidden human populations. Key projects include estimating survival rates of guillemots (30,000 individuals) and improving capture-recapture models to account for animal movement dynamics. Her work bridges statistical methodology with real-world applications, emphasizing rigorous inference and scalable algorithms. King’s academic contributions span Bayesian modeling frameworks, parameter clustering in neuroscientific data, and hierarchical centering in random effects models. She collaborates with biologists and policymakers to address conservation and public health issues. Notable recent projects include incorporating memory effects into spatial capture-recapture models and developing semi-complete data augmentation for state-space models. Her interdisciplinary approach addresses challenges in ecology, epidemiology, and computational statistics, with a focus on methodological innovation for large-scale data. Her scientific contributions are highlighted through over 100 peer-reviewed articles, including work on integrated population models, animal movement dynamics, and hidden Markov models for seabird behavior. King emphasizes the importance of statistics in uncovering hidden information within datasets, advocating for robust methodologies that ‘stand up in court’ when applied to critical real-world problems.

Shiqing MaView profile

Assistant Professor

• Security and Privacy
• Trustworthy Machine Learning
• Software Engineering
• +3 more

Shiqing Ma is an Assistant Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. Previously, he held a faculty position at Rutgers University from 2019 to 2023. He earned his Ph.D. in Computer Science from Purdue University (2019) and B.E. from Shanghai Jiao Tong University (2013). His research focuses on secure, intelligent, and transparent computing systems, particularly at the intersection of security, AI, and software systems. Key areas include integrating machine learning into software systems, ensuring algorithmic security through program analysis, and developing novel system architectures. Professor Ma's work has been recognized with prestigious awards, including the NSF CAREER Award (2023), and distinguished paper awards at USENIX Security (2017) and NDSS (2016). He actively contributes to the academic community through editorial roles and program committees in security, privacy, and software engineering. His research explores topics like backdoor attacks, AI safety, and bias mitigation in large language models. Recent articles emphasize defense mechanisms against adversarial attacks, watermarking techniques, and automated debugging systems for machine learning pipelines.

• Robotics
• Transfer Learning
• Neuro-fuzzy Systems
• +2 more

C. S. George Lee is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering, located in West Lafayette. His research focuses on Robotics, Transfer Learning, Neuro-fuzzy Systems, Automatic Controls, and Computer Engineering. He holds a BSEE (1973), MSEE (1974) from Washington State University, and a PhD (1978) from Purdue University. His work integrates computational intelligence with AI, robotics, and education technology, emphasizing human-machine co-learning models and bilingual systems. His contributions span domains like quantum computing, generative AI, and knowledge graph applications. He leads the Art Lab at Purdue and has published extensively on topics ranging from humanoid robotics to cross-cultural educational platforms. His research areas include developing intelligent agents for edutainment, robotic assistants for student learning, and advanced machine learning techniques. Notable trends in his publications involve computational intelligence applied to bilingual language models (e.g., Taiwanese/English co-learning), quantum-based AI systems, and human-centric robotics. He has explored applications in healthcare (e.g., blood donor analysis), autonomous navigation, and game AI (e.g., Go). His work often bridges theoretical advancements with real-world implementations, such as Java software tools for motor activity assessment (JKinect) and AI-driven platforms for skill evaluation. Lee's research emphasizes interdisciplinary collaboration, with contributions to IEEE conferences and cross-institutional projects. His lab develops tools for adaptive e-learning, robotic task performance evaluation, and human pose estimation using neural networks. Despite prolific publishing, no specific grants or awards are explicitly mentioned in the provided text. His work continues to explore the intersection of human intelligence and smart machines through platforms like Metaverse integration and BCI (Brain-Computer Interface) applications.

LU Wen FengView profile

Adjunct Associate Professor

• Additive Manufacturing
• Robotics
• Smart Manufacturing
• +4 more

LU Wen Feng is an Adjunct Associate Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), affiliated with the College of Design and Engineering. His research focuses on advanced manufacturing technologies, including additive manufacturing, robotics, and AI-driven systems. He explores sustainable design methodologies, smart manufacturing innovations, and bioprinting applications. Key areas include optimizing material processes, enhancing mechanical properties of printed materials, and developing autonomous robotic solutions for industrial tasks. Contact: mpelwf@nus.edu.sg , located at E3-02-07. Research Interests : His work bridges AI and manufacturing, emphasizing Knowledge graph integration for additive manufacturing, Autonomous robotic systems in industrial settings, Bioprinting for tissue repair with smart bioinks, Topology optimization for lightweight and sustainable structures, Material characterization and process engineering for 3D-printed composites. Recent Article Trends : LU Wen Feng's 2025 articles highlight advancements in AI-augmented manufacturing systems (e.g., MaViLa, AutoMEX) and sustainable design workflows. His 2024 studies address material anisotropy, corrosion behavior, and topology optimization strategies for lattice structures. These trends reflect his interdisciplinary approach to solving challenges in additive manufacturing, robotics, and biomedical applications. Awards : No scientific awards explicitly mentioned. Advising & Grants : No current graduate students or grants listed. His research likely integrates industry-academia collaborations given the focus on applied manufacturing technologies. Labs/Teams : Not explicitly detailed, but his work suggests involvement in advanced manufacturing labs and AI-robotics teams at NUS.

• Digital Agriculture
• Machine Systems Engineering
• Data Science
• +4 more

Dr. Dennis Buckmaster is a Professor in Agricultural & Biological Engineering at Purdue University, serving as Dean's Fellow for Digital Agriculture. He holds a B.S. from Purdue University and M.S./Ph.D. from Michigan State University. His research focuses on digital agriculture, machine systems engineering, and data science applications in farming. He co-coordinates the Agricultural Systems Management program and teaches courses like Computing Technology with Applications and Ag Tech and Innovation. He leads the Open Ag Technology and Systems Center (OATS Center), advancing open-source solutions for agriculture through platforms like ISOBlue and OADA. His work integrates IoT, robotics, and machine learning to optimize crop production, livestock management, and farm decision-making. He has authored over 150 publications on precision agriculture technologies. Professional memberships include American Society of Agricultural and Biological Engineers and Fluid Power Society. He emphasizes data interoperability, edge computing, and bridging engineering with agricultural practices through collaborative frameworks like LATTICE and Meta Ag.

• Statistics
• Optimization
• Machine Learning
• +3 more

Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.

Pascale BironView profile

Professor

• Hydro-geomorphology
• River Restoration
• Floodplain Management
• +3 more

Pascale Biron is a Professor in the Department of Geography, Urban Planning and Environment at Concordia University, Montreal. She holds a Ph.D. in Geography from Université de Montréal (1995) and has been with Concordia since 1998. Her research focuses on river dynamics, stream restoration for fish habitat, flood modeling, and climate change impacts. She specializes in hydrogeomorphology, river management in agricultural watersheds, and numerical modeling of fluvial processes. Research Interests: Her work includes river restoration strategies, flood risk assessment using LiDAR technology, and the 'river freedom' concept promoting ecosystem resilience. She collaborates closely with government agencies to translate research into practical river management policies. Professional Affiliations: Canadian Geomorphology Research Group, Canadian Association of Geographers, American Geophysical Union, GRIL (Limnology Research Group), and RIISQ (Quebec Flood Risk Network). Publications & Research: Recent studies address global salmonid biomass patterns, fluvial hazard detection via machine learning, and large-scale flood modeling. She supervises 19 graduate students in Ph.D./M.Sc. programs in Geography and Environmental Studies, focusing on topics like river confluence hydraulics and agricultural stream restoration. Grants & Funding: Active projects include river dynamics in fish habitats, flood modeling for road infrastructure vulnerability, and computational fluid dynamics simulations of river flows. She also leads research on societal dimensions of river restoration and policy frameworks for flood resilience.

Vijay VaziraniView profile

Professor

• Algorithmic Game Theory
• Matching Markets
• Computational Complexity
• +6 more

Vijay Vazirani is a Distinguished Professor in the Department of Computer Science at the University of California, Irvine , where he directs the ACO Center @ UCI . He earned his Ph.D. in Computer Science from UC Berkeley and a S.B. from MIT. Vazirani is a Guggenheim Fellow , ACM Fellow , and 2022 INFORMS John von Neumann Theory Prize recipient. Research Areas: Algorithmic Game Theory, Matching Markets, Computational Complexity, Approximation Algorithms His groundbreaking work includes co-founding algorithmic game theory and solving a 30-year-old problem with an NC algorithm for perfect matching in planar graphs . Recent research focuses on matching-based market design, with a $500K NSF grant for advancing algorithms in matching and market equilibria. His 15 most recent papers explore topics like core imputations, stable matching lattices, and Nash bargaining solutions. Scientific Awards: Guggenheim Fellowship ACM Fellow 2022 INFORMS John von Neumann Theory Prize Vazirani advises numerous Ph.D. students and postdocs, including Tung Mai , Thorben Trobst , and Rohith Reddy Gangam . He contributes to major workshops and co-edited foundational texts like Algorithmic Game Theory and Online and Matching-Based Market Design .

Prakash MuraliView profile

Associate Professor

• Quantum Computing
• Quantum Architecture
• Resource Estimation
• +3 more

Prakash Murali is an Associate Professor in the Department of Computer Science at Cambridge University, specializing in quantum computing, quantum architecture, and resource estimation. He previously worked as a quantum architect at Microsoft, where he contributed to the Azure Quantum Resource Estimator. He earned his Ph.D. in Computer Science from Princeton University in 2021, with a dissertation recognized by the ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award. His research focuses on bridging the gap between quantum algorithms and hardware through compiler and architecture innovations. His awards include the ACM SIGARCH/IEEE CS TCCA Outstanding Dissertation Award (2022), Communications of ACM Research Highlights (2022), and the IBM PhD Fellowship (2021). He leads a research group comprising PhD students (e.g., Sanaa Sharma and Dmitry Filippov), MPhil candidates, and undergraduate researchers. Key contributions include the TriQ compiler framework for quantum systems and the TimeStitch technique for decoherence mitigation. His work has been adopted in industry compilers and has influenced quantum benchmarking practices.

Daniel SchneiderView profile

Professor

• Inequality
• Social Policy
• Work and Employment
• +6 more

Daniel Schneider is the Malcolm Wiener Professor of Social Policy at the Harvard Kennedy School (HKS) and a Professor of Sociology at Harvard University’s Faculty of Arts and Sciences (FAS). He holds a B.A. in Public Policy from Brown University (2003) and a Ph.D. in Sociology and Social Policy from Princeton University (2012). Prior to his current roles, he was an Assistant Professor of Sociology at UC Berkeley (2012–2020) and a Robert Wood Johnson Foundation Postdoctoral Scholar in Health Policy Research at Berkeley/UCSF. His research focuses on social demography, inequality, and the intersection of work and family life. Key themes include precarious employment, workplace scheduling instability, gender and racial inequities in labor markets, and the effects of policy interventions on worker well-being. As Co-Director of the Shift Project, Schneider leads research on unpredictable work schedules and their impacts on household economic security and health. Recent articles highlight his work on paid sick leave policies, job quality mobility during the “Great Resignation,” and the gender wage gap in the service sector. His research has been supported by grants from NIH/NIA, the Urban Institute, and the Annie E. Casey Foundation, among others. He currently serves as a W.T. Grant Foundation Faculty Research Scholar. Notable contributions include studies on the health consequences of employment shocks during the pandemic, the role of labor market conditions in job mobility, and the relationship between workplace technology and job retention for older workers. Schneider’s work bridges academic research with policy advocacy, emphasizing evidence-based solutions to reduce economic and social inequalities.

Sam KirshnerView profile

Associate Professor

• Behavioral Decision Making
• Operations Management
• Technology Management
• +6 more

Associate Professor Sam Kirshner is a faculty member at the University of New South Wales within the School of Information Systems and Technology Management . His research focuses on behavioral decision making , algorithmic impact on operations , and artificial intelligence applications in business contexts. PhD in Management Science from Queen’s University, Canada Teaching expertise in data visualization, predictive analytics, and AI ethics Co-author of Business Analytics: A Management Approach Member of the Ethical AI Advisory His research explores how psychological distance and construal level theory influence decisions in supply chains, technology management, and consumer behavior. Recent work examines ChatGPT's decision biases , algorithm aversion , and sustainable operations under financial constraints. Key publication trends show focus areas: AI ethics and human-AI collaboration Behavioral supply chain analysis Temporal/spatial psychological distance effects CO2 forecasting with sparse data Consumer behavior in digital platforms Virtual reality and cognitive processing Supervision roles include mentoring 2 PhD students and 6 honors students, contributing to the next generation of scholars in business analytics and technology management.

Biondo BiondiView profile

Professor

• Seismic Imaging
• Geophysics
• Computational Geosciences
• +6 more

Biondo Biondi is the Barney and Estelle Morris Professor of Geophysics at Stanford University, affiliated with the School of Earth Sciences. He leads the Stanford Exploration Project and holds roles such as Chair of the Geophysics Department (2019–2022) and Director of the Stanford Earth Imaging Project (1998–Present). His research focuses on seismic imaging algorithms, computational geophysics, and fiber-optic sensing technologies. He earned his Ph.D. (1990), M.S. (1987) in Geophysics from Stanford, and M.Sc. in Electrical Engineering from Politecnico di Milano (1984). Dr. Biondi's research emphasizes improving seismic data imaging through advanced computational methods. He pioneered urban seismic monitoring using preexisting telecommunication fibers, enabling cost-effective subsurface analysis. His work integrates machine learning and high-performance computing to address challenges in reservoir imaging, CO2 monitoring, and infrastructure health. Key research areas include distributed acoustic sensing (DAS), ambient noise tomography, and inverse theory applications. He has authored over 180 publications and received awards like the SEG Honorable Mention (2019, 2016, 2009) and the Distinguished Instructor Short Course (2007). His teaching includes courses like 3-D Seismic Imaging and Reflection Seismology, and he advises graduate students in geophysics and computational science. Collaborations span industry (e.g., Schlumberger, Saudi Aramco) and global institutions. Biondi’s administrative contributions include co-directing the Stanford Earth Sciences Algorithms and Architectures Initiative and serving on editorial boards like the SIAM Journal on Imaging Sciences. His lab’s innovations bridge geophysics with emerging technologies, advancing both academia and industry applications in energy, environment, and urban infrastructure.

Simon DobsonView profile

Professor

• Complex Systems
• Sensor Systems
• Data Analytics
• +3 more

Simon Dobson is a Professor of Computer Science and Deputy Head of the School of Computer Science at the University of St Andrews. His research focuses on complex systems, sensor analytics, computational tools for simulation, and data analytics. He leads grants exceeding EUR30M, including a £5M EPSRC-funded programme in Sensor Systems Software. He is a Fellow of the Royal Society of Edinburgh (2020) and advises the Scottish government. Education: BSc (University of Newcastle), DPhil (University of York), both in Computer Science. Professional: Chartered Engineer, Fellow of the British Computer Society. Research Interests: Complex systems, network science, higher-order networks, epidemiological modeling, and sensor data integration. Teaching: CS4203 (Computer Security), CS5728 (Complex Systems Modelling). Supervises PhD/MSc projects. Awards: Includes RSE Fellowship, BCS Fellowship, and multiple leadership roles in conferences and committees.

• Software Engineering
• Graph Transformation Systems
• Model-Based Development
• +5 more

Reiko Heckel is a Professor of Software Engineering at the University of Leicester, serving as Director of Postgraduate Teaching for Computing degrees and Data Analytics Lead at the Leicester Innovation Hub. She previously held academic roles at the Technical Universities of Dresden and Berlin before joining Leicester in 2004. Her research focuses on graph transformation systems, model-based development, stochastic modeling, and formal methods in software engineering. She earned her PhD (Dr.-Ing.) in Computer Science from TU Berlin in 1998. Her research interests span software engineering pedagogy, formal specification techniques, and applications of graph grammars in system modeling. Recent work explores stochastic graph transformations for social networks, transparency engineering in AI systems, and blockchain-based smart contract frameworks. Her contributions bridge theoretical foundations with practical applications in cybersecurity, data integration, and human-centric systems design. Key contributions include advancements in automated test case generation via graph transformations, visual contracts for software reverse engineering, and formal methods for complex system analysis. Her work frequently intersects with industry through collaborations via the Leicester Innovation Hub, emphasizing data analytics and technology transfer. Education: MSc Computer Science, Technical University of Dresden PhD (Dr.-Ing.), Computer Science, TU Berlin (1998) Leadership Roles: Head of Department (2014-2018) Director of Postgraduate Teaching (Ongoing) Research Themes: Model-Based Development Stochastic Systems Analysis Graph Neural Networks Trustworthy AI Her publications reflect a focus on formal methods, with recent trends in applying graph transformation techniques to social network modeling, blockchain smart contracts, and educational pedagogy.