Find Academic Supervisors

Liu YongView profile

Assistant Professor

• Applied Econometrics
• Bayesian Econometrics
• Machine Learning
• +4 more

Yong Liu is an Assistant Professor in the Department of Agricultural Economics at Texas A&M University. His research focuses on applied econometrics, Bayesian methodologies, machine learning, and agricultural policy with emphasis on crop insurance and risk management. He holds a Ph.D. in Agricultural Economics from the University of Guelph (2016), preceded by dual M.S. degrees in Economics and Agricultural Economics from Yunnan University and the University of Guelph, respectively, and a B.S. in Chemistry from Yunnan University. His research explores econometric techniques for agricultural policy evaluation, including causal inference methods and behavioral economics applications. Recent work analyzes climate change impacts on crop yields, the efficacy of insurance subsidies, and the integration of weather data into risk assessment models. Liu’s publications demonstrate a strong focus on improving crop insurance mechanisms through statistical innovations like Bayesian model averaging and spatial-temporal forecasting. His academic contributions span theoretical advancements in density estimation and practical applications in agricultural finance. While no specific awards or grants are listed, his active publication record indicates sustained research engagement in agricultural economics and quantitative methods.

Ji ShuiwangView profile

Professor

• Machine Learning
• Artificial Intelligence for Science and Engineering
• Language Models and Agents
• +3 more

Shuiwang Ji is a Professor and Truchard Family Endowed Chair in the Department of Computer Science & Engineering at Texas A&M University, where he also holds Presidential Impact Fellow and Chancellor EDGES Fellow titles. He specializes in machine learning, AI for science/engineering, and language models/agents. His research bridges theoretical advances and practical applications in materials science, quantum chemistry, and biomedical engineering. Education: Ph.D. in Computer Science from Arizona State University (2010). Research focuses on equivaraint neural networks for symmetry-aware learning, graph-based molecular modeling, and generative AI for scientific discovery. He develops algorithms that integrate physics principles with deep learning, addressing challenges in materials design, PDE solving, and biomolecular structure prediction. Publications emphasize symmetry-aware architectures (e.g., equivariant Fourier neural operators), efficient interatomic potential computations, and diffusion models for protein/DNA design. Recent work explores trustworthiness in LLMs and causal reasoning in graph neural networks. Awards include NSF CAREER Award (2014), IEEE Fellow (2023), and Texas A&M teaching excellence awards. His work has been recognized in top venues like NeurIPS, ICML, and ICLR. His research group collaborates on projects funded by NSF, NIH, and industry partners, advancing AI applications in healthcare, robotics, and environmental science.

Jim PfaendtnerView profile

Professor

• Computational Chemistry
• Molecular Simulations
• Materials Science
• +4 more

Jim Pfaendtner is a Professor in the Department of Chemistry and serves as the Louis Martin-Vega Dean of Engineering at North Carolina State University. His research focuses on computational molecular science and engineering, with particular emphasis on biomimetic materials, nanoparticle self-assembly, and machine learning applications in chemistry. He leads interdisciplinary projects integrating molecular simulations, advanced materials design, and catalytic processes. Education details are not explicitly provided in the text, but his academic roles suggest advanced training in chemical engineering or chemistry. His work spans theoretical and experimental collaborations, including studies on peptoid-based materials, quantum dot superlattices, and enzyme engineering. Key research themes include: (1) biomimetic mineralization using sequence-defined polymers, (2) computational design of corrosion inhibitors and sustainable materials, (3) machine learning models for interatomic potentials and molecular design, and (4) electrochemical systems in energy storage. His team employs advanced simulation techniques like metadynamics and molecular dynamics to probe complex systems. Notable recent achievements include the FOMMS Medal Lecture (2024), leadership in developing predictive models for silica nanoparticle assembly, and contributions to chemical recycling of plastics via novel catalytic methods. His group actively publishes in high-impact journals like Journal of Physical Chemistry and Nano Letters . Current projects include: (1) hierarchical materials from high-information macromolecules, (2) AI-driven retrosynthetic pathway analysis, and (3) dynamic control of bioinspired nanomaterials. He collaborates with industry on sustainable chemical processes and biocatalyst development.

Bharath GanapathisubramaniView profile

Professor

• Experimental Fluid Mechanics
• Aerodynamics
• Hydrodynamics
• +5 more

Professor Bharath Ganapathisubramani is a faculty member in the Department of Aeronautics and Astronautics at the University of Southampton. He holds the title of Professor of Experimental Fluid Mechanics and leads research on aerodynamic/hydrodynamic phenomena relevant to transportation, energy, and autonomous systems. His work is supported by funding from EPSRC, EU programs, and industry partners like Rolls-Royce and Huawei. He is affiliated with the National Wind Tunnel Facility and serves as an Associate Editor for Experiments in Fluids and Flow . Education: B.Tech in Naval Architecture (IIT Madras, 1999), M.S. and Ph.D. in Aerospace Engineering (University of Minnesota, 2004), followed by postdoctoral research at the University of Texas at Austin. Administrative Roles: Head of Aero/Astro Department (2019–2022), Deputy Head of School for Research (2018–2019). Research focuses on experimental methods to predict/control fluid flows, including boundary layer manipulation, flow control for aerodynamics/aeroacoustics, and data-driven approaches using machine learning. Current projects explore rough wall turbulence, flapping foil energy harvesting, and advanced diagnostics like FoRMS facilities. Awards include the ERC Starting Grant (2012) and Fellowships from the Royal Aeronautical Society/AIAA. He advises ~6–7 PhD/undergraduate projects annually, emphasizing experimental design (wind/water tunnels) and CFD modeling in aerospace/energy sectors. External engagements include invited speaking roles at conferences like the International Symposium on PIV (2013) and advisory roles in international research boards.

Margaret KalacskaView profile

Associate Professor

• Remote Sensing
• Environmental Science
• Hyperspectral Imaging
• +7 more

Margaret Kalacska is an Associate Professor in the Department of Geography at McGill University, leading the Applied Remote Sensing Lab. Her research focuses on advancing remote sensing technologies like hyperspectral imaging, Remotely Piloted Aircraft Systems (RPAS), LiDAR, and thermal imaging for environmental science and natural hazard monitoring. She has pioneered the use of RPAS-HSI systems, including developing Canada’s first fully operational RPAS-HSI for the Canadian Airborne Biodiversity Observatory (CABO) since 2018. Dr. Kalacska holds a PhD and MSc in Earth and Atmospheric Sciences from the University of Alberta. Her interdisciplinary work spans Canada, Brazil, Tanzania, Ghana, the Peruvian Amazon, Panama, Madagascar, and Costa Rica. Notable achievements include being the first Canadian woman to lead an airborne hyperspectral mission (MAC-13) in Costa Rica (2013) and receiving the Silver Medal from the Canadian Remote Sensing Society (2018). Her lab specializes in integrating cutting-edge remote sensing tools for biodiversity conservation, ecosystem monitoring, and disaster response. Recent projects include the Fish + Forest initiative studying aquatic habitats in Brazil and advancing custom RPAS for hyperspectral imaging. She also collaborates with the National Research Council of Canada and international organizations like NATO. Awards: Fessenden Prize (2014), Silver Medal (2018), Steacie Prize Nomination (2020) Key Projects: CABO, Fish + Forest , RPAS-HSI System Development Technologies: UAV LiDAR, Structure-from-Motion Photogrammetry, Satellite Data Validation Her research bridges environmental science and technology, emphasizing global applications in conservation and climate resilience.

Chen XiaodongView profile

Professor

• Materials Science
• Flexible Electronics
• Nanotechnology
• +5 more

Professor Chen Xiaodong is a Distinguished University Professor at Nanyang Technological University (NTU), Singapore, holding primary appointment in the School of Materials Science & Engineering with courtesy appointments in the Lee Kong Chian School of Medicine and School of Chemistry, Chemical Engineering and Biotechnology. He serves as Deputy Director of the Institute for Digital Molecular Analytics and Science (IDMxS) and Director of both the Innovative Centre for Flexible Devices (iFlex) and Max Planck-NTU Joint Lab for Artificial Senses. His research spans mechanomaterials science and engineering, flexible electronics, sense digitalization, cyber-human interfaces and systems, and carbon-negative technology. Professor Chen's work focuses on developing methods for controlling materials architecture at 1-100 nm scale to solve fundamental and applied problems in energy, environment, and healthcare. His group integrates expertise from materials science, chemistry, biology, physics, and engineering to create innovative solutions. His scientific contributions have been recognized through numerous prestigious awards including the Singapore President's Science Award, National Research Foundation Investigatorship and Fellowship, Friedrich Wilhelm Bessel Research Award, Dan Maydan Prize in Nanoscience and Nanotechnology, and election to multiple national academies including Singapore National Academy of Science, Academy of Engineering Singapore, and German National Academy of Sciences Leopoldina. Professor Chen serves as Editor-in-Chief of ACS Nano and sits on editorial boards of numerous prestigious journals including Advanced Materials, Chemical Reviews, and Matter. He has mentored numerous PhD students and research fellows who have gone on to faculty positions at institutions worldwide. His laboratory develops cutting-edge technologies in flexible electronics, bio-inspired materials, and nano-bio interfaces, with strong industry collaborations and translational research focus.

Bruce StephenView profile

Senior Lecturer

• Data-Driven Methods
• Power Systems
• Smart Grids
• +12 more

Bruce Stephen is a Senior Lecturer and Strathclyde Chancellor's Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, where he has been since 1999. His work lies at the intersection of data science and power systems engineering, with a strong focus on real-world industrial applications. His educational background includes a BSc in Aeronautical Engineering from the University of Glasgow (1997), an MSc from the University of Strathclyde (1998), and a PhD in Electronic and Electrical Engineering (2005) from the University of Strathclyde. Dr. Stephen's research centers on data-driven methodologies for solving complex engineering challenges in power systems, particularly under conditions of limited data or domain knowledge. His applications span the entire energy value chain—from generation (nuclear, wind, solar) to transmission, distribution, and end-use. He develops software solutions for condition assessment, anomaly detection, and predictive modeling to support asset management and future grid planning. Notably, he co-founded Silent Herdsman Ltd, a spin-out company applying intelligent systems to precision livestock farming. His recent publications highlight a strong trend toward advanced machine learning techniques such as transfer learning, surrogate modeling, and synthetic data generation (e.g., using CTGANs) to improve reliability and decision-making in power systems. These works emphasize explainability, uncertainty quantification, and scalability, particularly in renewable-rich and data-scarce environments. Dr. Stephen is currently the Principal Investigator on the EPSRC-funded Analytical Middleware for Informed Distribution Networks (AMIDiNe) project, aiming to identify barriers to Net Zero through improved data modeling of unmonitored networks. He has also contributed to major projects including EU FP7 ORIGIN, EPSRC APAtSCHE, AGILE, and Transactive Energy Supply Arrangements. He actively advises students and collaborates on interdisciplinary research. His professional activities include organizing the QFF Quarterly Forecasting Forum (2018) and delivering invited talks at industry workshops. He has supervised datasets and research involving structural health monitoring and industrial diagnostics. His work supports UN Sustainable Development Goals related to affordable and clean energy, industry innovation, and climate action.

Nelson Sepulveda AlancastroView profile

Professor

• Micro and Nanoscale Sensors/Actuators
• Smart Materials
• Multifunctional Materials
• +4 more

Nelson Sepulveda Alancastro is a Professor and Interim Chairperson of Electrical and Computer Engineering (ECE) at Michigan State University's College of Engineering, with a joint appointment in Mechanical Engineering (ME). He holds a Ph.D. (2005) and M.S. (2002) from Michigan State University, and a B.S. (2001) from the University of Puerto Rico-Mayaguez. His research integrates micro/nano sensors, smart materials, and energy harvesting, with applications in biomedical devices, environmental monitoring, and MEMS. Research Focus: Dr. Sepulveda's work centers on ferroelectret nanogenerators, vanadium dioxide-based reconfigurable devices, flexible sensors, and machine learning for sensor data analysis. His lab develops self-powered systems for biomechanical monitoring, invasive species detection, and concussion prediction. Awards and Honors: MSU Withrow Teaching Excellence Award (2018) MSU Withrow Diversity Excellence Award (2018) Michigan State University Teacher-Scholar Award (2015) NSF Career Award (2010-2015) IEEE Senior Member (2011) Students and Team: He advises Ph.D. candidates including Ian González-Afanador, Gerardo Morales-Torres, and Henry Dsouza. His Advanced Microsystems Group (AMG) focuses on interdisciplinary projects spanning materials science, MEMS, and embedded systems.

Marco SeeberView profile

Professor

• Public Policy
• Higher Education Governance
• Research Evaluation
• +5 more

Marco Seeber is a Professor at the University of Agder's Department of Political Science and Management. He holds a PhD from the University of Chieti Pescara (Italy) and has previously worked at the University of Lugano (Switzerland) and Ghent University (Belgium). His research focuses on public policy, governance, and management with specialization in higher education systems, research evaluation, and science policy. Professor Seeber's research examines policy implementation, research metrics, peer review systems, academic careers, internationalization, and interdisciplinarity in higher education. He employs comparative and quantitative methodologies to analyze governance structures and policy impacts across different educational systems. He has received significant recognition including the Swiss Prize for Research in Education (2019) and fellowships from IEEE, AIUM, and AIMBE. As Co-Editor-in-Chief of the European Journal of Higher Education, he shapes scholarly discourse in his field. He currently coordinates the Horizon CSA project IANUS (2025-) and leads courses in organizational theory, research methodology, and leadership.

Lili QiuView profile

Professor

• Internet Networking
• Wireless Networking
• Mobile Systems
• +6 more

Lili Qiu is a Professor in the Department of Computer Science at The University of Texas at Austin, where she has been a faculty member since January 2005. She is an active member of the Wireless Networking and Communications Group (WNCG) and has made significant contributions to the field of networking research. Dr. Qiu previously spent 2001-2004 as a researcher at Microsoft Research in Redmond, WA, before joining UT Austin. Dr. Qiu's research spans Internet and wireless networking with a current focus on wireless network management and content distribution in mobile networks. Her work extends into diverse applications including acoustic imaging, metasurface applications, healthcare sensing technologies, and AI systems. She has pioneered research in areas such as acoustic motion tracking, passive RFID sensing, and wireless network optimization. Her research demonstrates a consistent pattern of innovation that bridges theoretical networking concepts with practical real-world applications, particularly in mobile and wireless systems. Her extensive publication record shows a clear evolution from fundamental networking research to increasingly interdisciplinary work that combines wireless systems with healthcare applications, AI, and novel sensing technologies. Recent publications demonstrate growing integration of machine learning techniques with traditional networking problems, as well as expansion into healthcare applications like Parkinson's disease modeling and non-invasive glucose monitoring. ACM Fellow IEEE Fellow National Academy of Inventors (NAI) Fellow ACM Distinguished Scientist NSF CAREER award Google Faculty Research Award Best paper award at ACM MobiSys'18 Best paper award at IEEE ICNP'17 Dr. Qiu has supervised numerous students, including a PhD dissertation that won the SIGMOBILE best dissertation award in 2020. She has served in significant leadership roles including chair of ACM SIGMOBILE, General co-chair for ACM MobiCom 2025, and various conference chair positions for IEEE ICNP, ACM CoNEXT, and other major networking conferences. Her research has been supported by substantial grants from NSF, Google, and other organizations, enabling her to lead cross-disciplinary research teams. As a member of the Wireless Networking and Communications Group at UT Austin, Dr. Qiu leads research efforts that combine networking expertise with innovations in sensing technologies, metasurfaces, and AI systems. Her lab has produced numerous influential results in mobile networking, wireless sensing, and network management, with applications spanning healthcare, consumer electronics, and communication infrastructure.

Seongjin ChoiView profile

Assistant Professor

• Urban Mobility Data Analytics
• Spatiotemporal Data Modeling
• Deep Learning & Artificial Intelligence
• +3 more

Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.

• Open Innovation
• Technology Transfer
• Food Industry Innovation
• +4 more

Barbara Bigliardi is an Associate Professor at the Department of Engineering and Architecture, University of Parma, with national scientific qualification for full professor in Economic-Management Engineering (SSD ING-IND/35). She serves as President of the Management Engineering Program at University of Parma and Director of Bachelor's/Master's programs at University of San Marino, co-leading double-degree initiatives between the two institutions. Over 100 publications (57 SCOPUS-indexed) with H-index=20 Editorial roles: Cambridge Scholars Publishing (2019), MDPI Sustainability, Sci, European Journal of Innovation Management Key research areas: Open Innovation, Technology Transfer, Food Industry Innovation, Industry 4.0, Supply Chain Sustainability Recent Publications (2024-2025) demonstrate leadership in: Industry 4.0 integration with circular economy AI applications in public administration and healthcare Digitalization of food supply chains Green startup resource orchestration Simulation-based optimization in remanufacturing Sustainable additive manufacturing Scientific Recognition : 2005 Emerald Highly Commended Award 2013 Most Cited Paper in Trends in Food Science & Technology 2019 Highly Cited Paper in Review of Policy Research Research Leadership includes: National Observatory on Start-ups (President since 2021) National Observatory on Reputation (Vice President since 2019) INAIL-funded mobile risk assessment systems (2018-2020) INAF space technology transfer projects (2018-present) Academic Contributions : Supervised over 300 theses Deputy Coordinator of Industrial Engineering Doctoral Program Director of Management Engineering Programs (Parma & San Marino) Founder of academic spin-offs: Sistemi per il marketing di contenuto S.r.l. (2016-present), Univenture SrL (2006-2010)

Lisa Grant LudwigView profile

Professor

• Earthquake Science
• Seismology
• Geohazards
• +6 more

Lisa Grant Ludwig is a Professor in the Department of Population Health and Disease Prevention at the University of California, Irvine (UCI). She is a nationally recognized expert in earthquake science, focusing on translating geophysical research into policy for disaster risk reduction. Her work bridges seismology, public health, and policy implementation. PhD in Geology with Geophysics minor (Caltech) MS in Environmental Engineering Science (Caltech) BS in Applied Environmental Earth Science (Stanford) Dr. Ludwig's research centers on earthquake dynamics, particularly along the San Andreas Fault, advancing disaster resilience through innovative nowcasting techniques using AI and machine learning. Her publications demonstrate expertise in seismic hazard modeling, geodetic imaging, and community preparedness studies. Recent publications highlight AI-enhanced earthquake prediction (QuakeGPT), temporal-spatial nowcasting models, and applications of geodetic data for crustal deformation analysis. These works integrate machine learning with traditional seismological methods to improve hazard forecasting. President of Seismological Society of America NASA 2012 Software of the Year Medal Featured on Science magazine cover Congressional Testimony provider Active Federal Advisory Committee member Her interdisciplinary approach combines geophysics, public health policy, and computational science. Current projects focus on earthquake nowcasting, fault zone analysis, and developing accessible geospatial tools like GeoGateway for disaster response.

Somak DuttaView profile

Associate Professor

• Statistics
• Machine Learning
• Genomics
• +5 more

Somak Dutta is an Associate Professor at Iowa State University conducting interdisciplinary research at the intersection of statistical methodology, machine learning, and agricultural sciences. His work addresses critical challenges in plant breeding, genomics, and environmental modeling through innovative quantitative approaches. His academic foundation includes: PhD in Statistics, University of Chicago, 2015 M.Stat (First Division with Distinction), Indian Statistical Institute, Kolkata, 2010 B.Stat (Honours with Distinction), Indian Statistical Institute, Kolkata, 2008 Dr. Dutta specializes in developing advanced statistical frameworks for high-dimensional data analysis, Bayesian variable selection, and spatial modeling. His research directly impacts plant breeding programs through applications in phenotyping, haploid induction systems, drought response mechanisms, and genetic architecture studies across key crops including maize, soybean, and mungbean. He also contributes to meteorological science through machine learning applications for severe weather verification. Analysis of his 2023-2025 publications reveals two dominant research streams: (1) agricultural applications focusing on genomic selection, phenotyping technologies, and stress response modeling in crops, and (2) meteorological innovations applying machine learning to severe thunderstorm wind verification and precipitation forecasting. Both streams demonstrate his signature approach of developing novel statistical methodologies tailored to domain-specific challenges. No scientific awards were documented in the provided materials. Information regarding student advising, grant funding, laboratory infrastructure, or collaborative teams was not available in the source documentation.

• Maritime Risk Analysis
• Autonomous Ship Systems
• Safety Engineering
• +3 more

Børge Rokseth is an Associate Professor at the Department of Engineering Cybernetics , Norwegian University of Science and Technology (NTNU). His work focuses on integrating advanced methodologies for safety and risk control in autonomous maritime systems. He has held academic positions since at least 2014, with a consistent record of research collaboration and publication. Research Areas: Maritime risk analysis, autonomous ship systems, safety engineering, dynamic positioning systems, systems-theoretic process analysis (STPA) Key Publications: 15 most recent articles cover topics like trajectory prediction for autonomous vessels, hybrid power systems safety, machine learning in risk assessment, and dynamic positioning system reliability His publications (2014-2025) emphasize safety-critical systems in marine environments. Common themes include: Application of STPA for hazard analysis in autonomous shipping Development of risk-informed control systems Integration of machine learning with engineering risk assessment Comparative studies of different ship autonomy levels As a supervisor, Rokseth has guided master's students including Ane Joramo Stokke and Ludvig Vik Løite. His work has been presented at international conferences such as the European STAMP Workshop, International Conference on Conceptual Modeling, and the International Seminar on Safety and Security of Autonomous Vessels.