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Heather D. BakerView profile

Associate Professor

• Ancient Near East Assyriology
• Mesopotamian social and economic history
• Babylonian Urbanism
• +4 more

Heather D. Baker is an Associate Professor in the Department of Near and Middle Eastern Civilizations at the University of Toronto (St. George Campus). Her research focuses on 1st millennium BC Mesopotamia, particularly Babylonian urbanism, Neo-Assyrian royal households, and integration of textual/archaeological data. She holds a DPhil from the University of Oxford and led the SSHRC-funded MTAAC project (2017–2019) on cuneiform machine translation. Education: DPhil, University of Oxford Key Projects: Principal Investigator for Machine Translation and Automated Analysis of Cuneiform Languages (MTAAC) project Her research interests include Mesopotamian socio-economic history, Babylonian urban morphology, and digital humanities applications. She combines archaeological fieldwork with cuneiform text analysis to study domestic spaces, urban development, and state administration. Recent work explores the built environment’s role in political and social structures. Awards: SSHRC Grant for MTAAC Project (2019) Teaching/Outreach: Taught courses on Assyrian/Babylonian history at Charles University (2019) and contributed to public lectures on Babylonian housing and urbanism via podcasts (e.g., Thin End of the Wedge). Currently on leave until December 2025. Her publications bridge archaeology and textual analysis, emphasizing quantitative methods in studying domestic architecture, household organization, and urban socio-economic dynamics.

David KofkeView profile

Professor

• Statistical Physics
• Molecular Modeling and Simulation
• Software Engineering
• +3 more

Dr. David Kofke is a SUNY Distinguished Professor in the Department of Chemical and Biological Engineering at the School of Engineering and Applied Sciences, University at Buffalo . He holds the Walter E. Schmid Chair and has been a faculty member since 1989. His research focuses on molecular simulation, free-energy calculations, and the development of object-oriented software for education. Education PhD in Chemical Engineering, University of Pennsylvania (1988) BS in Chemical Engineering, Carnegie-Mellon University (1983) Dr. Kofke’s research interests span statistical physics , molecular modeling , and software engineering , with a focus on virial coefficients, crystal-phase calculations, and simulation method development. His work bridges theoretical and applied chemistry, emphasizing computational efficiency and accuracy. The 15 most recent articles highlight his expertise in virial equations of state , molecular simulation methods , and thermophysical data analysis . Topics include quantum fluids, polymer thermodynamics, and machine learning integration, reflecting his commitment to advancing computational chemistry through interdisciplinary approaches. Scientific Awards Presidential Young Investigator (1990) SUNY Chancellor’s Excellence in Teaching (1994) John M. Prausnitz Award (2012) Jacob F. Schoellkopf Medal (2007) Himmelblau Award (2012) AIChE Fellow (2014) AAAS Fellow (2015) Dr. Kofke has served as Associate Editor of the Journal of Chemical & Engineering Data since 2016 and led CACHE as President (2010-2012). His software development includes the Etomica simulation framework and the pyHMA post-processor for anharmonic properties.

Yaakov MalinovskyView profile

Professor

• Decision theory
• Stochastic ordering
• Sequential selection
• +6 more

Yaakov Malinovsky is a Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on decision theory, stochastic ordering, sequential selection methods, group testing, and nonparametric methods. He has held editorial roles at journals including Enumerative Combinatorics and Applications , Methodology and Computing in Applied Probability , and The American Statistician . His work bridges theoretical probability and applied statistics, with contributions to group testing optimization, sequential analysis, and combinatorial probability. Malinovsky earned his Ph.D. in Statistics from The Hebrew University of Jerusalem in 2009. He has secured research funding from the United States-Israel Binational Science Foundation for projects on minimax online learning policies in stochastic sequential selection. His teaching portfolio includes courses such as Probability Theory, Mathematical Statistics, and specialized topics in stochastic processes and nonparametric methods. His recent research explores round-robin tournament models, prime number distribution via dice rolls, and optimal stopping rules. He has published extensively in top-tier journals like Statistica Sinica , Biometrics , and Sequential Analysis , demonstrating expertise in statistical inference and combinatorial problems. His work often addresses practical applications in epidemiology and algorithm design while advancing foundational probability theory.

• Signal Processing
• Wavelet Analysis
• Image Processing
• +3 more

Prof. Dr. Ing. Alexandru Isar is a Full Professor at the Department of Communications, Faculty of Electronics and Telecommunications, Politehnica University of Timișoara. He holds academic ranks since 1990 (Adjunct Professor), 1995 (Associate Professor), and 1999 (Full Professor). His research focuses on signal processing, wavelet analysis, and medical imaging. He has advised 5 Ph.D. students and currently supervises 2 doctoral candidates. Isar has published extensively in IEEE journals and authored books on time-frequency representations and network security. **Education**: Ph.D. in Engineering (1993), supervised by Prof. Eugen Pop. Completed postdoctoral research at Telecom Bretagne, France (2003). **Grants**: European Space Agency-funded projects (2013-2017) and national grants (IDEI 2009-2011). **Research Directions**: Introduced time-frequency representations and wavelet theory applications. **Notable Contributions**: Inventions in ultrasonic control systems (1987), development of the Hyperanalytic Wavelet Transform (2008), and SAR/SONAR image denoising algorithms. **Administrative Roles**: Deputy Dean (2001-2004), Department Director (2012-2020). **Collaborations**: European Space Agency projects, international conferences (Brest, Bordeaux), and peer reviews for IEEE journals. His work bridges theoretical signal processing with practical applications in healthcare and telecommunications.

• Statistical Pattern Recognition
• Machine Learning
• Computer Vision

Dr. Jing-Hao Xue is a Professor of Statistical Pattern Recognition at the Department of Statistical Science, University College London. He is an active editorial board member for several IEEE journals, including Senior Area Editor for IEEE Transactions on Circuits and Systems for Video Technology (2025-), and has received awards like the Outstanding Associate Editor (2022) and Best Associate Editor Award (2021). His research focuses on statistical pattern recognition, machine learning, and computer vision, with contributions to few-shot learning, deep learning, and medical imaging applications. Education: Alumni of Tsinghua University, KU Leuven, and the University of Glasgow. He has mentored numerous PhD graduates, including Dr. Zhu Rui (now Senior Lecturer at City University) and Dr. Yang Xiaokang (Lecturer at the University of Glasgow). As an Alan Turing Institute Fellow (2018-2023), he contributes to foundational AI research. His work spans theoretical advancements and practical applications in computer vision, medical diagnostics, and federated learning systems. Research trends in his publications emphasize few-shot learning innovations, robust classifier design for imbalanced data, and interdisciplinary applications in healthcare and security. Recent articles highlight advancements in neural network architectures, anomaly detection, and text-driven panorama generation.

Shizhe ChenView profile

Assistant Professor

• Statistics
• High-Dimensional Data Analysis
• Point Processes
• +3 more

Shizhe Chen is an Assistant Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional data, point processes, network reconstruction, and machine learning applications in neuroscience and biostatistics. He holds a Ph.D. and teaches advanced statistical courses such as STA 290. His work emphasizes flexible modeling approaches for complex systems and nonparametric methods for independence testing. Key research interests include: High-dimensional statistical inference and graphical models Point process modeling with applications to neuroscience and social networks Development of assumption-light screening techniques for Hawkes processes Network reconstruction using ordinary differential equations Recent work explores temporal dynamics in hippocampal sequences and distribution-free statistical tests for modern data structures. His methods bridge theoretical advancements with practical applications in computational biology and econometrics.

Shakoor PoosehView profile

Researcher

• Mathematical Psychology
• Computational Neuroscience
• Value-based Decision-making
• +5 more

Shakoor Pooseh is a Postdoctoral Research Fellow at the Freiburg Center for Data Analysis and Modeling (FDM) , affiliated with the Institute of Physics at the University of Freiburg. His research focuses on computational approaches to decision-making, resilience dynamics, and neuropsychopharmacology. He leads the Value-Based Decision-Making Battery project, a Bayesian adaptive assessment tool for impulsive and risky behavior, and contributes to the EU Horizon 2020 DynaMORE initiative modeling psychological resilience. Education: Completed a PhD in Computational Methods in the Fractional Calculus of Variations at the University of Aveiro, Portugal (2013), with prior work in fractional optimal control problems. His interdisciplinary background bridges mathematics, neuroscience, and clinical psychology. Research Interests: Explores mathematical psychology, computational neuroscience, and statistical modeling of decision processes. Key topics include serotonin/dopamine modulation of impulsivity, resilience dynamics under stress, and translational research in addictive disorders. He develops software tools for behavioral experiments and machine learning models. Grants/Projects: Principal investigator for the DynaM-OBS longitudinal study on resilience, and collaborates on interventions like DynaM-INT . His work integrates clinical data with computational models to understand mental health outcomes. Labs/Teams: Active in the FDM interdisciplinary group and the Institute of Physics’ neuroscience unit. Collaborates with psychiatric research teams at the University Medical Center Freiburg.

• Econometrics
• Statistics
• Statistical Inference
• +4 more

Rami Tabri is a Senior Lecturer in the Department of Econometrics & Business Statistics at Monash University. His research focuses on econometric methodologies, statistical inference, and their applications in poverty analysis, health economics, and social sciences. He has contributed to advancements in stochastic dominance testing, moment inequality models, and bootstrap methods. His work often addresses challenges in survey nonresponse and data imputation. Key collaborations include studies on public institutions in Lebanon and poverty evaluation in Australia. He has published in top journals such as the Journal of Econometrics and Health Economics . Notable contributions include frameworks for testing health concentration curves and methodologies for restricted stochastic dominance. Tabri’s media engagement includes an article analyzing the 2022 Lebanese Elections, which reached multiple platforms. His research aligns with UN Sustainable Development Goals, particularly addressing inequality and economic prosperity.

Stavros TavoularisView profile

Professor

• Fluid Mechanics
• Heat Transfer
• Turbulence
• +7 more

Stavros Tavoularis is a Professor in the Department of Mechanical Engineering at the University of Ottawa, cross-appointed in the Department of Physics. He holds the HPCVL-Sun Microsystems Chair in Computational Science and Engineering. His academic journey includes degrees from the National Technical University of Athens (Dipl.Eng., 1973), Virginia Tech (M.Sc., 1974), and Johns Hopkins University (Ph.D., 1978). He has served as Department Chair and Director of the Ottawa-Carleton Institute for Mechanical and Aerospace Engineering. Currently, he leads the uOttawa Fluid Mechanics Laboratory, supervising a multidisciplinary team of students and researchers. Education: Dipl.Eng., National Technical University of Athens (1973) M.Sc., Virginia Polytechnic Institute & State University (1974) Ph.D., The Johns Hopkins University (1978) Research interests span fluid mechanics, turbulence, vortex dynamics, aerodynamics, nuclear thermal hydraulics, and biofluid dynamics. His work emphasizes experimental and computational studies, including turbulence modulation, supercritical fluid heat transfer, and flow instrumentation. Notable projects involve CANDU reactor thermal hydraulics, mechanical heart valve fluid dynamics, and flow instability in eccentric channels. He has secured grants from NSERC, NRC, DND, and industry partners like Pratt & Whitney Canada. Publications reflect expertise in supercritical fluids, two-phase flow, and turbulence. Recent trends focus on flow instability mitigation, scalar dispersion optimization, and computational benchmarking for industrial flows. His work bridges fundamental fluid mechanics with engineering applications in energy systems and biomedical devices. Awards include Fellowships from the Canadian Academy of Engineering, Engineering Institute of Canada, and the American Physical Society, alongside the George S. Glinski Award. His advisory roles include supervision of over 50 graduate students and postdoctoral fellows. Current projects involve CFD simulations of nuclear reactor headers and biofluid dynamics in cardiovascular systems. Labs/Teams: Director of the uOttawa Fluid Mechanics Laboratory, collaborating with industry and government on experimental and computational flow analysis. Key facilities include supercritical fluid test loops and advanced PIV/PLIF measurement systems.

Hanwen HuangView profile

Associate Professor

• High-dimensional Data Analysis
• Statistical Machine Learning
• Computational Methods for Dynamical Models
• +4 more

Hanwen Huang is an Associate Professor in the Department of Biostatistics and Data Science at the Medical College of Georgia, Augusta University. He holds a PhD in Statistics from the University of North Carolina at Chapel Hill (2011). His research focuses on high-dimensional data analysis, statistical machine learning, and computational methods for dynamical models in infectious diseases. He has developed Bayesian frameworks for ordinary differential equations and computational approaches for HIV recombination regions. Education: PhD (Statistics), University of North Carolina at Chapel Hill, 2011. Research interests include Bayesian statistical methods, high-dimensional inference, and interdisciplinary collaborations. His work bridges theory and application, addressing challenges in epidemiology, oncology, and nutritional studies. Recent publications explore topics such as robust L1-penalized estimators, influenza dynamics modeling, and the impact of vitamins on COVID-19 severity. Teaching areas include Introductory Biostatistics, Linear Models, and Statistical Machine Learning. His research has been applied to diverse fields, including cancer metabolism and public health interventions. Collaborative projects highlight his commitment to solving real-world problems through statistical innovation.

David SpielerView profile

Professor

• Machine Learning
• Stochastic Systems
• Biochemical Systems Modeling
• +3 more

David Spieler is a Professor of Machine Learning at the University of Applied Sciences Munich since September 2018. Previously, he served as a product owner for big data topics at Audi AG (2015–2018), a system developer at Bosch SoftTec (2014–2015), and completed his PhD in Modeling and Simulation at Saarland University (2009–2014). His research focuses on stochastic systems, biochemical modeling, parameter estimation, and oscillatory behavior analysis in Markovian systems. He has contributed to numerical methods for steady-state analysis, formal verification techniques, and applications in systems biology. Research interests include stochastic hybrid systems, sensitivity analysis of biochemical networks, geometric bounds for CTMC steady-state distributions, and oscillatory dynamics in chemical reaction networks. He has published extensively on topics like model checking, parameter estimation, and algorithmic solutions for stochastic processes. His work bridges theoretical computer science with practical applications in bioinformatics and engineering. Spieler has taught courses on data networks, quantitative model checking, and stochastic simulation techniques. He has reviewed manuscripts for over 20 conferences including CAV, CMSB, HSCC, and QEST, contributing to the advancement of formal methods and computational systems biology.

Steffen EickemeyerView profile

Adjunct Professor

• Lean Management
• Operations Management
• Supply Chain Management
• +3 more

Prof. Steffen Eickemeyer is an Adjunct Professor of Lean Management at the School of Business, Social & Decision Sciences at Constructor University Bremen gGmbH. His research focuses on capacity planning, regeneration of complex capital goods, supply chain optimization, and blockchain applications in logistics. He has contributed to the Belt and Road Initiative through studies on material flow security and has validated mathematical models for demand-driven capacity planning using case studies in global MRO companies. His academic work integrates data mining, statistical analysis, and mathematical modeling to address challenges in industrial asset management, maintenance strategies, and sustainable manufacturing processes. Notable contributions include optimizing regeneration workflows, reducing uncertainty in disassembly processes, and designing decision models for dynamic demand environments. Publications highlight his expertise in blockchain technologies for logistics security, fuzzy data management in maintenance systems, and availability optimization in capacity planning. His research bridges theoretical frameworks with practical applications in global industries, emphasizing cross-regional validation and real-world implementation. Prof. Eickemeyer collaborates with global enterprises to apply lean principles in complex industrial settings, focusing on lifecycle assessment, resource recovery, and cross-functional data integration. His work aligns with industry needs for reliable capacity solutions in fluctuating demand scenarios.

• Distributions theory
• Bayesian statistics
• Robustness
• +8 more

Emilio Gomez Deniz is a Full Professor at the Universidad de Las Palmas de Gran Canaria, specializing in Bayesian statistics and decision-making techniques within economics and business contexts. His research focuses on distribution theory, actuarial statistics (credibility, ruin theory), and robust Bayesian analysis, with significant applications in tourism, insurance, and financial risk modeling. Key Research Areas: Bayesian statistics, asymmetric distributions, credibility theory, risk aggregation, and econometric modeling. Recent Publications: 2024–2025 work includes applications of the Gleser distribution to flood data, Bayesian credibility premiums, skew-unit models, and citation analysis using normalized Bayesian approaches. Projects: Collaborator on Spanish Ministry-funded projects related to Bayesian decision-making in health economics, risk modeling, and actuarial science. Collaborations: Works with interdisciplinary teams on topics like tourist expenditure, aircraft delay modeling, and social challenges. Books Authored: Textbooks on applied statistics for business, generalized beta distributions, and stochastic frontier models.

Matteo DettoView profile

Research Fellow

• Climate Science
• Ecology
• Environmental Science
• +3 more

Matteo Detto is a Research Fellow affiliated with the Department of Ecology and Evolutionary Biology and the High Meadows Environmental Institute (HMEI) at Princeton University. His research focuses on climate-forest interactions, particularly the spatial and temporal complexity of biosphere-atmosphere exchanges, including energy, water, CO₂, and greenhouse gases. He designs automated bio-environmental monitoring systems for diverse ecosystems, from Mediterranean regions to boreal forests, and develops statistical methods to analyze observational data across multiple scales. His work emphasizes understanding ecological complexity in natural and anthropogenic systems, with a focus on how climate change alters forest resource use and carbon dynamics. Detto collaborates with the Pacala Lab and contributes to the Carbon Mitigation Initiative (CMI) within HMEI. He has pioneered methods for analyzing forest structure using LiDAR and drones, and his research spans remote sensing applications, time-series analysis, and spatial modeling. Detto's publications address topics such as tropical forest responses to drought, liana-tree competition, and the integration of physiological data into ecosystem models. He actively engages in global initiatives like the ForestGEO network and has contributed to datasets like FLUXNET-CH4.

• Statistics
• Probability Theory
• Random Fields
• +4 more

Chunsheng Ma is a Professor of Mathematics at Wichita State University, affiliated with the Department of Mathematics, Statistics, and Physics within the Fairmount College of Liberal Arts and Sciences. His research focuses on advanced topics in probability theory, stochastic processes, and spatial statistics, with particular emphasis on random fields, covariance models, and their applications. He holds an office in Jabara Hall and can be reached via email at cma@math.wichita.edu. His teaching responsibilities include courses such as STAT370 and STAT774. Research Interests: Dr. Ma's work spans statistical methodology development, including studies on vector random fields, Lévy processes, and isotropic Gaussian fields. He explores theoretical properties like stochastic orderings, strong local nondeterminism, and covariance structures on complex spaces such as spheres and manifolds. His contributions address both foundational aspects (e.g., metric-dependent covariance functions) and applied challenges in spatial-temporal modeling. Key Trends in Articles: Recent publications emphasize non-Gaussian random field constructions, power-law processes, and stochastic comparisons. Themes include extending classical Brownian motion frameworks to non-Euclidean settings, analyzing covariance matrices on homogeneous spaces, and developing simulation techniques for isotropic fields. His work often bridges pure probability theory with computational methods for practical spatial and temporal data analysis. Funding & Professional Activities: Links to statistical societies (e.g., ASA, IMS) and grant opportunities suggest active engagement in academic networks. His webpage provides access to teaching materials, research resources, and collaborative initiatives in the mathematical sciences. No specific grants or awards are explicitly listed, though his extensive publication record reflects sustained scholarly activity.