Find Academic Supervisors

Laurie Gaskins BaiseView profile

Professor

• Geotechnical Earthquake Engineering
• Seismic Hazard Mapping
• Natural Hazards
• +3 more

Laurie Gaskins Baise is a Professor and Chair of the Department of Civil and Environmental Engineering at Tufts School of Engineering, Tufts University. She leads the Geohazards Research Lab and directs the BSCE degree program. Her expertise lies in geotechnical earthquake engineering, seismic hazard mapping, and natural hazards mitigation. Education: Ph.D., University of California, Berkeley (2000) M.S., University of California, Berkeley (2000 and 1997) B.S.E., Princeton University (1995) Research Interests: Dr. Baise focuses on integrating predictive models with observational data to address geohazards like earthquakes and extreme wind events. Her work emphasizes regional seismic hazard mapping, liquefaction risk assessment, and rapid damage detection via remote sensing. She develops machine learning frameworks for post-disaster damage mapping and geospatial models for site response analysis. Labs/Teams: She directs the Geohazards Lab at Tufts, which collaborates on projects involving satellite imagery, geostatistical methods, and deep learning for hazard response. Recent work includes liquefaction modeling in Türkiye and Ukraine, wildfire spread forecasting, and ensemble-based seismic hazard assessments. Grants/Advising: Her research is supported by national and international grants. She mentors students in developing predictive models and remote sensing techniques for disaster resilience. Notable collaborations include work with the U.S. Geological Survey and global seismic networks.

Kayo IdeView profile

Associate Professor

• Data Assimilation
• Atmospheric Dynamics
• Oceanography
• +3 more

Dr. Kayo Ide is an Associate Professor at the University of Maryland's Department of Atmospheric and Oceanic Science, within the College of Computer, Mathematical, and Natural Sciences. Her research focuses on dynamics of atmosphere and oceans, with expertise in data assimilation, scientific prediction, transport/mixing processes, and climate variability. She contributes to NOAA's operational systems and collaborates with teams like the UFS Coastal Applications Team. Her work emphasizes integrating advanced observational technologies (e.g., satellite data from CrIS, Aeolus) into numerical weather prediction and ocean modeling frameworks. Key projects include optimizing data assimilation algorithms, evaluating new sensor constellations (e.g., CubeSats), and improving forecast initialization techniques. Dr. Ide also develops software tools like the System for Analysis of Wind Collocations (SAWC) to intercompare multi-platform wind observations. Publications highlight innovations in satellite data utilization, ensemble-based methods, and the impact of novel observing systems on operational forecasting. Her research bridges computational methods, environmental science, and applied meteorology, addressing challenges in global climate monitoring and predictive modeling.

Irina PanovskaView profile

Associate Professor

• Macroeconomics
• Econometrics
• Time Series Analysis
• +4 more

Irina Panovska is an Associate Professor of Economics at the University of Texas at Dallas (UT Dallas), affiliated with the School of Economic, Political, and Policy Sciences. Her research focuses on macroeconomic policy responses, business cycle dynamics, and economic recoveries. She teaches macroeconomics, forecasting, and business cycles at both undergraduate and graduate levels. Education includes a PhD (2013) and MA (2009) in Economics from Washington University in St. Louis, and a BS in Economics and Mathematics from Ohio University (2007). She holds visiting scholar positions at the University of Zagreb (2023–2026) and has collaborated with institutions in Australia and Croatia. Research interests emphasize modeling policy impacts on economies, labor market dynamics, and time series analysis. Recent work addresses business cycle synchronization in the EU, jobless recoveries, and the effects of the pandemic on financial markets. She has presented at conferences including the Society for Economic Measurement and the Southern Economic Association. Professional activities include serving as Treasurer of the Society for Nonlinear Dynamics and Econometrics (2019–2024) and organizing academic sessions. In 2023, she received the School’s Teaching Comet Award for excellence in instruction. Her work bridges theoretical frameworks with policy relevance, addressing issues like fiscal policy effectiveness, inflation dynamics, and maternal labor force participation. Current projects include keynote talks on commercial real estate and mentoring initiatives for junior economists.

Michael MorrisView profile

Professor

• Human Centered Leadership
• Human Centered Design
• Strategic HR
• +5 more

Michael Morris is the Associate Dean of Undergraduate Studies and Student Affairs and the John W. Fisher Professor of Innovative Learning at the Haslam College of Business , University of Tennessee, Knoxville. His career includes leadership roles such as Director of the Smith Global Leadership Scholars program and department head at Haslam, alongside corporate experience as a metallurgical R&D special projects engineer at Teledyne Firth Sterling. His research spans Human Centered Leadership , Strategic HR , Leadership Development , and Work-Life Harmony . His work, appearing in top-tier journals like Organizational Research Methods and Family Relations , integrates machine learning for disease forecasting, HRD theories, and interdisciplinary approaches to workplace wellness. His recent publications focus on infectious disease modeling , fMRI reliability , and socioemotional wealth in family firms. He serves on editorial boards for Academy of Management Learning & Education and contributes to certifications from the National Council on Family Relations and IDEO. Past President, Academy of Human Resource Development

Alain BensoussanView profile

Professor

• Operations Research
• Stochastic Control
• Mathematical Finance
• +3 more

Alain Bensoussan is the Lars Magnus Ericsson Chair Professor of Operations Management at the University of Texas at Dallas and Director of the International Center for Decision and Risk Analysis. His work spans stochastic control, mathematical finance, and mean field games. He holds a PhD from the University of Paris (1969) and advanced degrees from École Polytechnique (1962) and École Nationale de la Statistique et de l’Administration Economique (1965). Research interests include inventory control under uncertainty, risk management frameworks, and applications of mean field theory to control problems. Recent work focuses on stochastic control in financial systems, machine learning integration with control theory, and optimal policies in dynamic environments. Notable awards: Legion d’Honneur (Officier), NASA Distinguished Public Service Medal, Member of French Academies of Sciences/Technology, and SIAM Charter Fellowship. Key grants: NSF-funded projects on mean field control theory (2016–2019) and mean field games (2023–present). Teaches advanced courses: Game Theory, Risk Analysis, Stochastic Dynamic Programming. His 2023–2025 publications emphasize theoretical advancements in stochastic control, mean field games, and machine learning applications. Ongoing work addresses infrastructure investment, wind farm optimization, and multi-agent system dynamics.

Noel CressieView profile

Professor

• Spatial Statistics
• Environmental Informatics
• Bayesian Statistics
• +3 more

Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.

Alexandros TaflanidisView profile

Professor

• Uncertainty Quantification
• Disaster Risk Reduction
• Bayesian Model Updating
• +3 more

Professor Alexandros Taflanidis holds a concurrent faculty position as Professor in the Department of Civil and Environmental Engineering and Earth Sciences and the Department of Aerospace and Mechanical Engineering at the University of Notre Dame's College of Engineering. He serves as the Director of Graduate Studies for CEEES. His research focuses on uncertainty quantification, disaster risk reduction, Bayesian model updating, and enhancing the sustainability and resilience of civil infrastructure systems, particularly in natural hazard contexts like hurricanes and earthquakes. His work integrates computational statistics and surrogate modeling to improve real-time emergency response and long-term risk mitigation strategies. Prof. Taflanidis earned a Ph.D. from the California Institute of Technology (2007), and M.S. and B.S. degrees in Civil and Environmental Engineering from Aristotle University of Thessaloniki (2003 and 2002). He leads projects such as the Coastal Hazards System (CHS) for Louisiana and Puerto Rico, advancing probabilistic coastal hazard analysis frameworks. His research also explores storm surge emulation, seismic response estimation, and innovative protective device designs for structures. He won the ASCE Huber Prize for his contributions to community resilience through scientific computing. His collaborative efforts include advancing machine learning for data imputation in coastal hazards and developing lifecycle assessment workflows for resilient buildings. Current research trends in his publications emphasize computational efficiency, multi-fidelity modeling, and adaptive strategies for real-time predictions. Prof. Taflanidis's work bridges academic and practical domains, addressing challenges such as climate change impacts on coastal regions and earthquake early warning systems. His lab focuses on integrating interdisciplinary approaches to create actionable solutions for infrastructure resilience.

• Quality Management
• Organizational Culture
• Agility
• +6 more

André M. Carvalho is an Assistant Professor of Quality Engineering and Management at the NOVA School of Science and Technology, NOVA University Lisbon, Portugal. He holds a PhD (2020) in Engineering Design and Advanced Manufacturing from the University of Minho/MIT Portugal Program. His career includes postdoctoral research at the Technical University of Denmark (2020) and visiting roles at MIT (2018–2020) and Northeastern University (2019). His research focuses on sociotechnical systems, quality management, organizational culture, agility, and sustainability. He explores how organizations adapt to global challenges through methodologies like Lean Six Sigma and Industry 4.0 frameworks. Education: PhD in Engineering Design and Advanced Manufacturing (2020) – University of Minho/MIT Portugal Program Research Interests: Carvalho’s work bridges quality management with organizational agility, emphasizing digital transformation, Industry 4.0/5.0, and sustainable practices. He investigates frameworks for operational excellence, such as the Quality 4.0 Roadmap and the QOE-SME model for small/medium enterprises. His studies address challenges like supply chain quality, data-driven decision-making, and organizational culture’s role in innovation. Article Trends: His recent publications (2022–2025) highlight themes like Lean Six Sigma applications in healthcare, AI-driven quality forecasting, and Industry 4.0 integration. He also explores ESG factors in performance optimization and the evolving profile of quality leaders in the digital era. Awards and Recognition: Acknowledged by the Industrial Engineering and Operations Management (IEOM) Society, International Academy for Quality (IAQ), and American Society for Quality (ASQ) for research contributions. Grants and Advising: While specific grants are not detailed, his postdoctoral and visiting roles suggest institutional support. Advising focuses on doctoral/master’s students (not listed explicitly in the text).

Etienne MéminView profile

Professor

• Stochastic Modeling
• Geophysical Flows
• Fluid Mechanics
• +3 more

Etienne Mémin is a Research Director (Full Professor status) at Inria and leads the Odyssey research group, which is affiliated with multiple institutions including University of Rennes, IRMAR, Ifremer, LOPS, UBO, IMT Atlantique, and Lab-STICC. He serves as a Visiting Professor at the Department of Mathematics, Imperial College London (2020–2026) and is the Principal Investigator of the ERC STUOD grant. His research spans the intersection of geophysical sciences, fluid mechanics, computational sciences, and applied mathematics, focusing on stochastic modeling of fluid flows, data assimilation, and uncertainty quantification. He has developed frameworks for stochastic geophysical flows, coarse-scale simulations, and robust motion estimation techniques. Recent publications highlight his work on stochastic Navier-Stokes equations, ensemble forecasting, and data assimilation for ocean and atmospheric models. He has applied these methods to numerical weather prediction, turbulence analysis, and real-time flow reconstruction using sparse measurements. Scientific Awards: ERC STUOD grant PhD Students: Francesco Tucciarone (ERC STUOD, NEMO code) Benjamin Dufée (Ensemble Kalman filters, ATER position) Berenger Hug (Stochastic Navier-Stokes analysis, teaching) Antoine Moneyron (Stochastic ocean models) Collaborations: Imperial College London (D. Crisan, S. Laizet), Zhejiang University (S. Cai, C. Xu), MétéoFrance (P. Arbogast, O. Pannekoucke), Ifremer (B. Chapron), IRSTEA Lyon (L. Pénard), IRMAR (R. Lewandovsky), University of Buenos Aires (G. Artana), ISSI Beijing (T. Corpetti).

• International Trade
• Economic Policy Analysis
• Digital Transformation
• +7 more

Elif Nuroğlu is a Professor at the Department of Economics, Faculty of Economics and Administrative Sciences at Türk-Alman Üniversitesi. She holds a PhD in Economics from Universität Wien (2010) and multiple master's degrees in Business Administration from Wirtschaftsuniversität Wien. Her academic career includes roles like Dozent (2014) and Assistenzprofessor at Türk-Alman and international universities. Her research focuses on international trade dynamics, digital transformation in industry, EU-Turkey-Germany geopolitical economics, climate change impacts, and econometric modeling. She has authored/co-authored over 30 peer-reviewed articles in journals like Insight Turkey , Empirical Economics , and Zbornik radova Ekonomskog fakulteta u Rijeci . Research Highlights: Analysis of Industry 4.0 implications for Turkish exports Gravity model applications in trade flow estimation Comparative studies on Turkey-Almanya digitalization strategies Post-pandemic capitalism evolution studies Balkans geopolitical economic rivalry analysis Environmental Kuznets curve validations for developing nations Her work intersects economic theory with applied policy analysis, particularly focusing on Turkey's position in global and regional markets. Current research trends emphasize digital transformation's role in trade competitiveness and green economic transitions under EU frameworks.

Tim BaldeniusView profile

Professor

• Managerial Accounting
• Performance Measurement
• Corporate Governance
• +2 more

Tim Baldenius serves as the Paul M. Montrone Professor of Private Enterprise in the Accounting Division of Columbia Business School at Columbia University. He rejoined Columbia in 2017 after serving as Chair of NYU Stern's Accounting Department (2011-2016), and previously held faculty positions at Columbia Business School (1998-2011) where he chaired the Accounting Division (2009-2011). His teaching portfolio includes MBA courses in Financial Planning & Analysis, PhD seminars on managerial accounting, and executive education programs. His academic credentials include a Diploma in Business Administration from the University of Hamburg and a PhD in Accounting from the University of Vienna. Baldenius maintains editorial leadership as Associate Editor of Management Science and serves on the editorial boards of The Accounting Review , Review of Accounting Studies , and Review of Managerial Sciences . Professor Baldenius's research centers on managerial accounting systems, performance measurement frameworks, and corporate governance structures. His work examines how accounting information drives organizational decision-making, incentive design, and resource allocation. Key themes include transfer pricing mechanisms, cost allocation methodologies, and the interplay between managerial and tax objectives within multidivisional firms. His theoretical models often integrate contract theory with organizational economics to address real-world coordination challenges. His publication record spans top-tier journals in accounting, finance, and economics, revealing consistent focus on performance measurement systems (35%), transfer pricing (25%), corporate governance (20%), and incentive design (20%). The research demonstrates strong methodological diversity, employing game theory (40%), mathematical modeling (30%), and empirical analysis (30%) to investigate organizational decision processes. Baldenius has held significant leadership roles including Accounting Division Chair at Columbia (2009-2011) and Accounting Department Chair at NYU Stern (2011-2016). His teaching spans MBA, MS, PhD, and executive education programs with courses covering Financial Planning & Analysis, Accounting for Consultants, and Analytical Models in Accounting. His case studies on Sub-Micron Devices Inc. and Beanie Kids Holiday Camp are widely used in accounting pedagogy.

Leona WiegmannView profile

Associate Professor

• Management Control
• Digitalisation in Accounting Practices
• Performance Management
• +3 more

Leona Wiegmann is an Associate Professor of Management Control at ESCP Business School in Paris. Her research examines how digitalization reshapes accounting practices, focusing on tensions between technology and human actors, evolving roles of management accountants, and external stakeholder engagement. She employs qualitative methods like field studies, vignettes, and netnography to explore forecasting, performance management, and accountability. Previously, she held faculty positions at WHU – Otto Beisheim School of Management (Germany) and Monash University (Australia). Open to supervising PhD students, her work appears in journals such as Contemporary Accounting Research and Accounting, Auditing & Accountability Journal . Education: PhD from WHU – Otto Beisheim School of Management Key Research Themes: Digitalization in Accounting, Organizational Dynamics, Qualitative Methodologies Her articles analyze topics ranging from proxy advisor accountability to information search during crises, reflecting her focus on real-world organizational challenges. While no scientific awards are listed, her contributions to accounting and organizational studies literature highlight her scholarly impact.

Tao HongView profile

Professor

• Energy Forecasting
• Power Systems Operations
• Renewable Integration
• +7 more

Dr. Tao Hong is the Duke Energy Distinguished Professor and NCEMC Faculty Fellow at the Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte. He directs the Big Data Energy Analytics Laboratory (BigDEAL) and has been a Founding Chair of the IEEE Working Group on Energy Forecasting (2011-2019). Ph.D., Electrical Engineering & Operations Research (2010), NC State University M.S., Operations Research & Industrial Engineering (2008), NC State University B.Eng., Automation (2005), Tsinghua University His research focuses on Energy Forecasting with applications in power systems operations, renewable integration, risk management, and cross-sector forecasting for healthcare, transportation, and sports. He has led major Delivery point level load analysis (2017-present) Short-term probabilistic forecasting (2016) Demand response modeling using smart meter data (2014-2015) Dr. Hong's scientific contributions include 9+ journal articles on energy forecasting methodologies and 3 major forecasting competitions (GEFCom2012-2017, BigDEAL Challenge 2022). His work has been cited in leading journals like International Journal of Forecasting and IEEE Transactions on Smart Grid . Charlotte Business Journal Energy Education Leader of the Year (2017) IEEE PES PSPI Technical Committee Prize Paper Award (2016) As a dedicated educator , Dr. Hong has advised multiple PhD and Master's students including Shreyashi Shukla (2023), Yike Li (2022), and Jordan McCorey (2021). He teaches specialized courses in energy systems planning and computational intelligence.

Tharindu P. De AlwisView profile

Assistant Professor

• High-dimensional Multivariate Statistics
• Sufficient Dimension Reduction
• Dimension Reduction
• +9 more

Dr. Tharindu P. De Alwis is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is actively engaged in teaching and research, with a focus on high-dimensional data analysis and machine learning applications. Ph.D. in Mathematics (Statistics), Southern Illinois University Carbondale M.S. in Mathematics, Southern Illinois University Carbondale B.Sc. in Statistics and Operations Research, University of Peradeniya, Sri Lanka His research centers on dimension reduction techniques, particularly Sufficient Dimension Reduction (SDR), envelope methods, and their applications in multivariate time series and spatial-temporal data. He integrates deep learning and neural networks into statistical modeling, with recent work on stacking-based deep neural networks and Fourier-based SDR methods. His work bridges statistical theory with practical machine learning applications in complex datasets. His recent publications (2021–2024) demonstrate a strong focus on developing innovative statistical and machine learning methods for time series and high-dimensional regression. Key themes include nonlinear modeling, dimension reduction, R package development, and AI-augmented reliability analysis. These works reflect interdisciplinary applications in engineering, data science, and systems safety. Dr. De Alwis has presented his research at national academic conferences in the USA and has publications in peer-reviewed journals such as Statistical Methods & Applications and Reliability Engineering & System Safety , as well as preprints on arXiv and software on CRAN. He teaches a variety of courses including Precalculus with Trigonometry, Linear Algebra, Applied Statistics, and Data Science. While no formal advisees or grants are mentioned, his active publication record suggests ongoing research mentorship and scholarly engagement. He previously served as a post-doctoral scholar at Worcester Polytechnic Institute before joining UWF.

• Hydrogen Energy Storage
• Negative Emissions Technologies
• Life Cycle Assessment
• +7 more

Hanna Breunig is a Research Scientist at Lawrence Berkeley National Laboratory and Deputy of the Sustainable Energy & Environmental Systems Department. She holds a secondary appointment in the Earth Systems and Society Domain within the Climate and Ecosystem Science Division. Her work focuses on validating emerging energy technologies through process design, techno-economic modeling, and market analysis. Education: PhD in Civil and Environmental Engineering from UC Berkeley (2015), MSc (2011), and BSc from Cornell University (2010). Her research spans hydrogen storage , CO2 capture/utilization , brine management , and technoeconomic modeling for systems in data centers, hospitals, mines, aviation, and ports. She co-leads the DOE HyMARC consortium and the systems analysis team for the ARCHES California Hydrogen Hub. Recent publications highlight advancements in metal-organic frameworks (MOFs) for hydrogen storage, solar hydrogen production , and high-temperature thermal energy storage . She has received accolades like the 2023 Energy & Environmental Science Lectureship and 2014 Schmidt MacArthur Fellowship. Scientific Awards: 2023 Energy & Environmental Science Lectureship 2021 Early Scientific Career Award 2019 CEC Bioenergy Project Leadership 2017 LDRD Early Career Award 2014 Schmidt MacArthur Fellowship Hanna mentors graduate and undergraduate students and contributes to tools like the BioSiting v2 software for U.S. bioeconomy resource analysis.