Donald Goldfarb is the Alexander and Hermine Avanessians Professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research spans optimization algorithms, network flows, and large-scale computational methods, with applications in finance, image processing, and machine learning. Research Focus: Goldfarb specializes in developing efficient algorithms for convex and nonconvex optimization problems, including linear, quadratic, semidefinite, and second-order cone programming. His work extends to robust optimization for financial modeling and innovative methods for image restoration using total variation techniques. Publication Trends: His recent research emphasizes optimization in computational imaging (e.g., image decomposition and restoration) and finance (e.g., robust portfolio management). Algorithmic contributions include interior-point methods, Bregman iterations, and large-scale convex optimization techniques.
Changbo Zhu is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, part of the College of Science. He is also a Fellow of the Lucy Family Institute for Data & Society. His office is located at 101H Crowley Hall, and he can be reached at czhu4@nd.edu. Dr. Zhu holds a Ph.D. from the University of Illinois at Urbana-Champaign (2020), an M.S. and B.S. from the National University of Singapore (2016 and 2014, respectively). He completed a postdoctoral fellowship at the University of California, Davis from 2020 to 2022. His research focuses on the intersection of statistics, geometry, and optimization, with key interests in optimal transport, functional and object data analysis, time series analysis, high-dimensional statistical inference, and statistical machine learning. His work addresses challenges such as longitudinal data analysis, change point detection, and distance covariance methodologies. Zhu’s recent publications emphasize advancements in optimal transport theory, including barycenter optimization and autoregressive models, as well as applications in neuroimaging and high-dimensional data analysis. He collaborates with leading researchers such as Hans-Georg Müller and Jane-Ling Wang on topics ranging from spherical autoregressive models to brain volume trajectory studies. He advises Kaheon Kim, a Ph.D. candidate focusing on statistical optimal transport. His teaching portfolio includes courses on optimization algorithms for machine learning, computational statistics with R, and statistical computing methods. Zhu is affiliated with the Lucy Family Institute for Data & Society, contributing to interdisciplinary data science initiatives.
Alexander Chinco is an Assistant Professor at the Bert W. Wasserman Department of Economics and Finance, Baruch College, City University of New York. He holds a PhD in Finance from New York University. His academic appointment is full-time with no indication of retirement or former staff status. Research Focus: Professor Chinco's work spans three interconnected domains: Behavioral Finance : Examining psychological influences on market participants and asset pricing anomalies Asset Pricing : Developing models to understand risk factors and market efficiency Machine Learning Applications : Implementing advanced computational techniques to solve financial problems His research consistently bridges theoretical frameworks with empirical market analysis. Publication Trends: Chinco's 15 most recent articles (2014-2025) demonstrate a methodological evolution toward integrating machine learning with traditional finance. Early work focused on market microstructure and housing mispricing, while recent publications explore algorithmic trading, corporate decision-making, and novel econometric approaches. His scholarship consistently addresses both theoretical models and practical market applications.
Jean-charles Garibal is an active Assistant Professor in the Department of Accounting, Law & Finance . His academic work focuses on financial systems and quantitative methodologies. Education : PhD in Economics from the University of Orléans His research explores intersections of Financial Econometrics , Systemic Risk , and Portfolio Management , with publications analyzing systemic financial institutions and risk modeling frameworks. Key trends in his work include: Systemic risk quantification Portfolio optimization techniques ESG ranking adjustments Financial alert systems
Heikki Haario is a Professor in Computational Engineering at the LUT School of Engineering Sciences, LUT University, Lappeenranta. His research focuses on robust Bayesian inference, parameter estimation, and uncertainty quantification in chaotic and stochastic systems. Broad research areas: Bayesian Statistics, Chaotic Dynamical Systems, Gaussian Processes, Machine Learning The 15 most recent publications (2025-2023) demonstrate expertise in computational modeling, data-driven methods, and interdisciplinary applications spanning finance, biology, and engineering. Articles emphasize Bayesian techniques, kernel flows, and neural network integration for solving inverse problems and optimizing predictions in uncertain environments.
Sonia Yeh is a Full Professor of Transport and Energy Systems at the Department of Space, Earth and Environment at Chalmers University of Technology in Gothenburg, Sweden. She serves as Deputy Head of Chalmers' Energy Area of Expertise and holds an adjunct professorship at the Department of Engineering and Public Policy, Carnegie Mellon University. Professor Yeh has established herself as a leading expert in energy economics, energy system modeling, alternative transportation fuels, sustainability standards, technological change, and consumer behavior and mobility, with extensive experience advising U.S. state and international policymakers on climate strategies to reduce environmental impacts and greenhouse gas emissions from transportation. Professor Yeh's research spans multiple interconnected domains of transportation and energy systems: Energy system modeling and policy analysis for transportation decarbonization Electric vehicle infrastructure planning and optimization Alternative fuel pathways and sustainability standards Socio-spatial dimensions of mobility and transportation equity Integration of big data analytics in transportation planning International collaboration on climate policy development Her recent publications reveal a clear trajectory toward increasingly interdisciplinary research that bridges transportation engineering, energy systems analysis, urban planning, and social science. Professor Yeh's work increasingly focuses on integrating transportation electrification with renewable energy systems, examining the socio-spatial implications of mobility patterns, and developing sophisticated modeling tools to support evidence-based policy making for low-carbon transitions in the transport sector. Professor Yeh has received significant recognition for her contributions to the field: Fulbright Distinguished Chair Professor in Alternative Energy Technology (2016-2017) Håkan Frisinger Award by Volvo Research and Educational Foundations (2019) Professor Yeh maintains extensive international collaborations with major organizations including the International Energy Agency (IEA), United Nations Framework Convention on Climate Change (UNFCCC COP), International Transport Forum-OECD, World Bank, and Asian Development Bank. She serves as Senior Editor for Energy Policy journal and contributed as a lead author to the Transport chapter in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. As co-leader of the International Transportation Energy Modeling (ITEM) project (https://transportenergy.org), she coordinates research among academic institutions, energy agencies, oil companies, and NGOs on future scenarios, big data, and policy needs for low-carbon transitions in transport. Her current research portfolio includes 13 significant projects including BluePortLab (Multi-use Infrastructure Spatial Concepts for the Blue Economy, 2025-2028), POTENT-X (Ports as energy transition hubs, 2024-2027), and Using urban big data to redefine experienced social segregation (2023-2026), demonstrating her commitment to addressing complex, real-world challenges at the intersection of transportation, energy, and urban systems.
Marc Tommasi is a Professor in Computer Science at the University of Lille, affiliated with the CRIStAL laboratory where he leads the Magnet research team. Previously, he was a founding member of the Mostrare project team at INRIA Lille. His academic career spans research in machine learning foundations and applications to structured data. His research focuses on: Machine learning theory and algorithms Structured prediction for trees and graphs Tree automata and set constraints Decentralized learning systems Sparse representations for structured data Natural language processing techniques Information extraction from semi-structured data His publication portfolio demonstrates consistent focus on spectral methods for graph learning, probabilistic tree modeling, and conditional random fields, with applications spanning network analysis, XML processing, and natural language tasks. Recent work emphasizes large-scale network modeling and spectral clustering techniques. He has supervised 13 PhD students including ongoing work in decentralized machine learning (Mahsa Asadi), privacy-preserving speech recognition (Brij Srivastava), and multilingual dependency parsing (Mathieu Dehouck), with completed theses covering graph construction, spectral clustering, and tree transducer learning. Significant research projects under his coordination include Pamela (decentralized ML), Lampada (structured data representations), Marmota (statistical ML for trees), Crotal (CRFs for NLP), ATASH (document transformations), and WebContent (information extraction). He maintains active involvement in the Magnet team at CRIStAL focusing on machine learning for graphs and NLP.
Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.
Phil Maguire is an Associate Professor in the Department of Computer Science at Maynooth University, part of the Faculty of Science & Engineering. He joined the university in 2006 and currently directs the BSc in Computational Thinking program. His educational background includes a BAI in Engineering from Trinity College Dublin (TCD), followed by an MSc and PhD in Cognitive Science from University College Dublin (UCD). He also holds a DipPysch in Psychology from the Open University (OU) and a PGDHE from Maynooth University. Maguire's research focuses on cognitive science and theoretical computer science, with particular emphasis on representation, uncertainty, measurement standards, and mathematical foundations applied to fintech. His work bridges computational models of human cognition with practical applications in decision-making, education, and financial systems. His publications span cognitive modeling, educational technology, and financial engineering. Recent trends include computational models of surprise, automated assessment in programming education, and robust financial indices using sparse data. His work often integrates interdisciplinary approaches, combining cognitive science principles with real-world applications in economics and education. Maguire has contributed to conferences such as the IEEE Conference on Computational Intelligence for Financial Engineering and the Cognitive Science Society meetings. He has explored portfolio optimization, risk-adjusted algorithms, and authorship attribution through grammatical models. His educational research includes studies on collaborative learning techniques like pair programming and the use of technology-enhanced tools in higher education. No scientific awards are explicitly listed in the provided information. His advising and grant activities are not detailed here. Maguire’s involvement with interdisciplinary labs or teams is not specified in the text.
Cihan Bayındır is an Associate Professor at Istanbul Technical University's Faculty of Civil Engineering, Department of Civil Engineering, specializing in fluid mechanics, coastal engineering, and computational methods. His research integrates advanced mathematical techniques with practical engineering applications across multiple disciplines. His primary research areas include Fluid Mechanics, Coastal Sciences and Engineering, Numerical Modeling, Fluid Physics, Acoustics and Vibrations, Nonlinear Dynamics, and Quantum Hydrodynamics. Bayındır's work demonstrates a strong interdisciplinary approach, bridging traditional civil engineering with cutting-edge computational methods and quantum phenomena. His research has significant applications in coastal protection, renewable energy systems, disaster management, and infrastructure resilience. Analysis of his recent publications reveals a clear trend toward applying artificial intelligence and machine learning techniques to traditional civil engineering challenges. His work increasingly focuses on using compressive sensing, deep learning (particularly LSTM networks), and fuzzy logic systems to solve complex problems in wave dynamics, vibration analysis, and tsunami prediction. The integration of quantum computing concepts with classical fluid mechanics represents another distinctive aspect of his research portfolio. ITU Doctoral Special Award-2024-Advisor (2025) ITU 2023 Academic Performance Award (2024) Turkish Academy of Sciences (TUBA) Outstanding Young Scientist Award (GEBİP) (2022) Elsevier Frontiers Article Award (2020) Kaleidoscope Award (American Physical Society, 2016) Bayındır leads multiple research projects funded by ITU's Scientific Research Projects Unit (BAP), including studies on dam break wave propagation using fractional equations, optimization of wave energy concentrators with AI methods, and three-dimensional vibration control in marine structures. His work involves collaborations with international institutions, including previous engagement with CERN on accelerator design. He maintains an active research laboratory focused on hydraulics and coastal engineering, with specialized equipment for wave and vibration analysis in the Hydraulics Laboratory HL 216 at ITU.
Yini Gao is an Assistant Professor of Operations Management at the Lee Kong Chian School of Business, Singapore Management University. She holds a dual bachelor's degree in Business Administration and Chemical Engineering from the National University of Singapore (2012) and a Ph.D. in Decision Sciences from NUS (2017). Her research focuses on supply chain risk management, healthcare analytics, and distributionally robust optimization. She has received prestigious awards including the Lee Kong Chian Research Fellowship and multiple Dean's Teaching Honor Lists. Her work spans critical areas such as disruption risk mitigation in global supply chains, healthcare operations optimization (e.g., cancer screening strategies and pandemic response modeling), and strategic decision-making under uncertainty. She has published in top-tier journals like Management Science , Production and Operations Management , and Manufacturing & Service Operations Management . Education: B.A. Business Administration & Chemical Engineering, National University of Singapore (2008-2012) Ph.D. Decision Sciences, National University of Singapore (2013-2017) Dr. Gao’s research portfolio demonstrates expertise in applying advanced optimization techniques to real-world problems, with notable contributions to anti-counterfeiting strategies, inventory management systems, and public health policy design. Her recent work explores the intersection of technology flexibility and subscription-based business models in operations strategy.
W. John Braun is a Professor in the Department of Computer Science, Mathematics, Physics and Statistics at the University of British Columbia Okanagan. He holds the rank of Professor within the Irving K. Barber Faculty of Science. His academic roles include former Head of the Department (2014) and Deputy Director of the Canadian Statistical Sciences Institute (CANSSI). Braun specializes in computational statistics with applications to fire science, statistical process control, and statistical education. He has contributed to methodologies in data sharpening and visualization, including the development of stochastic fire spread models and tools like the Data Visualization Laboratory. His teaching focuses on courses such as Modelling and Simulation, Predictive Modelling, and Data Visualization. Braun earned his PhD in Statistics from the University of Western Ontario (1992), following MSc (1987) and BSc (1985) degrees from the University of Calgary. His research spans scientific problem-driven applications in psychology, biology, medicine, engineering, and physics, emphasizing smoothing and inference techniques in data visualization and process monitoring. His professional network includes leadership roles in statistical science networks, such as the Rocky Mountain Data Science Network. Braun’s work integrates interdisciplinary collaboration, exemplified by projects like the Data Sharpening techniques for interval-censored data and contributions to the PROMETHEUS fire growth simulation model. His academic contributions bridge theoretical statistics with practical applications in environmental science and medical imaging.
Dr Alex Gibberd is a Senior Lecturer (equivalent to Associate Professor) in Statistics within the School of Mathematical Sciences at Lancaster University. He has been a faculty member since 2018, following postdoctoral research at Imperial College London and a PhD in Statistics from University College London (UCL) in 2017. He also holds an MPhys in Astrophysics from the University of St Andrews (2012). Education: PhD in Statistics, University College London (2017) MPhys in Astrophysics, University of St Andrews (2012) Research Interests: Dr Gibberd's research focuses on high-dimensional time-series analysis, with methodological contributions in statistical modeling under non-stationarity and high-dimensionality. His work spans both theoretical developments and practical applications, particularly in neuroscience and finance. Key areas include: Sparse dynamic factor models and regularized estimation techniques Spectral analysis and locally-stationary wavelet models Optimization algorithms for model selection in high-dimensional settings Applications in brain connectivity analysis and economic forecasting Research Themes: His recent publications demonstrate a strong focus on developing interpretable statistical methods for complex systems. These include advances in principal component analysis with joint rank and covariance estimation, sparse dynamic factor models, and regularized spectral estimation for high-dimensional point processes. The applications range from neural connectivity modeling to energy efficiency policy analysis. Grants & Funding: Research in Paris Grant (2024) Support of Collaborative Research with Dr S. Roy at University of Bath (2021) Model Selection for High-Dimensional Temporal Disaggregation in Official Statistics (2021-2023) Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation (2021-2025) Wavelet Methods for Dependency Analysis in Multivariate Time Series (2020) STOR-i: Information Fusion for Non-homogeneous Panel and Time-series Data (2019-2025) PhD Supervision & Students: Dr Gibberd supervises PhD students at the intersection of high-dimensional statistics and time-series analysis. Current students include Carla Pinkney (STOR-i CDT), Ziyan Zhao, and Kai Zheng (Centre for Marketing Analytics & Forecasting). Research Affiliations: STOR-i Centre for Doctoral Training Centre for Marketing Analytics & Forecasting Changepoints and Time Series Research Group Data Science Institute - Foundations Social and Economic Statistics Group
Ruth Fong is a Teaching Professor in the Department of Computer Science at Princeton University since July 2021. Her academic journey includes a Ph.D. in Engineering Science (2020) and an M.Sc. in Neuroscience from the University of Oxford , where she was funded by the Rhodes Trust and Open Philanthropy . She completed her B.A. in Computer Science at Harvard University . Research Focus: Computer Vision, Machine Learning, Explainable AI (XAI), ML Fairness, Human-Computer Interaction (HCI) Key Techniques: Post-hoc model analysis, interpretable-by-design architectures, interactive visualization tools, concept-based explanations Her 15 most recent publications (2023-2025) span topics in interactive explainability, gender artifacts in datasets, concept-based explanation frameworks (UFO, ELUDE), and real-world AI trust dynamics. Collaborative work with Olga Russakovsky 's Visual AI Lab appears prominently. Scientific Recognition: Rhodes Scholarship (2015) Open Philanthropy AI Fellowship (2018) Princeton Engineering Council Teaching Award (2025) Keller Center Summer Course Development Grant (2025) CHI Honorable Mention (2023) As director of Princeton's Looking Glass Lab , she mentors students like Indu Panigrahi and Sunnie S.Y. Kim . Her teaching portfolio includes COS324 (Machine Learning) and COS126 (Intro CS) , where she implemented an open-ended final project gallery.
Ali Gurbuz is an Assistant Professor in the Department of Electrical and Computer Engineering at Mississippi State University's College of Engineering, specializing in smart sensing systems and machine learning applications. His research integrates signal processing with autonomous systems for environmental monitoring and medical imaging. His educational background includes a Bachelor's degree from Bilkent University (Turkey), and Master's/Doctoral degrees in Electrical and Computer Engineering from Georgia Institute of Technology. Since joining MSU in 2018 after a position at the University of Alabama, he has established himself as a leading researcher in sensing technologies. Gurbuz's research focuses on developing intelligent front-end sensing systems that optimize data acquisition using machine learning, addressing critical bottlenecks in processing capabilities for applications ranging from autonomous vehicles to precision agriculture. His work emphasizes efficient data collection through radar, lidar, and camera systems, with particular attention to soil moisture estimation and medical imaging applications. His publication portfolio demonstrates strong trends in UAS-based remote sensing, RF interference mitigation, and deep learning for signal processing. Key research areas include GNSS reflectometry for soil moisture mapping, radar-based sign language recognition, and seafloor gas seep detection using sonar data. NSF CAREER Award recipient (2021) for $500,000 to advance smart sensing systems research Gurbuz co-directs the Information Processing and Sensing (IMPRESS) research group at MSU, collaborating extensively with the Center for Advanced Vehicular Systems and Geosystems Research Institute. His work bridges theoretical signal processing with practical implementations in agricultural monitoring, environmental sensing, and medical applications, with several projects demonstrating hardware-software co-design approaches for next-generation sensing systems.
