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Christoph SchultView profile

Researcher

• Dynamic Macroeconomics
• Energy Economics
• Forecast Accuracy Analysis
• +5 more

Dr. Christoph Schult is a researcher at the Halle Institute for Economic Research (IWH) , specializing in dynamic macroeconomics and energy economics . He joined the Department of Macroeconomics in July 2016. Education: Bachelor's from Martin Luther University Halle-Wittenberg; Master's from Humboldt-Universität zu Berlin; PhD (2021). His research analyzes macroeconomic forecasting methodologies, fiscal policy responses to crises, and energy market dynamics. Recent work employs advanced econometric techniques and novel models like the Ten-Agents New-Keynesian (TENK) framework to study Germany's energy crisis and climate policy impacts. Key publication themes include: Improving forecast accuracy via double machine learning and partial linear instrumental variable models Evaluating targeted fiscal measures for consumption stabilization Quantifying DSGE model limitations in energy shock scenarios Assessing coal phase-out implications for German regions He collaborates with institutions like the German Federal Ministry for Economic Affairs and Climate Action, contributing to policy evaluations for the InvKG and STARK-Bundesprogramms .

Wolfgang UtschickView profile

Professor

• Signal Processing
• Wireless Communications
• Radar Technology
• +3 more

Prof. Wolfgang Utschick is a full Professor at the Technische Universität München (TUM), holding the professorship for Methods of Signal Processing since 2002. He is affiliated with the TUM School of Computation, Information and Technology and the Department of Electrical Engineering and Information Technology (EI). Since 2017, he has served as Dean of the EI Department, overseeing academic and research activities. His research integrates applied mathematics into signal processing applications across wireless communications, radar technology, vehicle safety, and machine learning. Prof. Utschick earned his doctorate after industry experience and completed postdoctoral studies at TUM and ETH Zurich. He has led numerous industrial and DFG-funded projects, resulting in patents in signal processing and over 150 peer-reviewed publications. Notable contributions include advancements in channel estimation, MIMO systems, and quantum radar technology. His research interests emphasize the intersection of mathematical theory and practical engineering, with a focus on optimizing communication systems and safety-critical applications. Awards include IEEE Fellow (2021), TUM Sabbatical for Teaching Excellence (2014), and multiple IEEE publication awards. Prof. Utschick’s work bridges academia and industry, addressing challenges in 5G/6G networks, radar innovation, and machine learning-driven signal processing. He remains active in curriculum development and departmental leadership at TUM.

Deniz GunduzView profile

Professor

• Wireless Communications
• Information Theory
• Machine Learning
• +3 more

Deniz Gündüz is a Professor of Information Processing at Imperial College London's Electrical and Electronic Engineering Department, leading the Information Processing and Communications Lab. He also serves as Deputy Head of the Intelligent Systems and Networks Group and holds a part-time faculty position at the University of Modena and Reggio Emilia. His research focuses on wireless communications, information theory, machine learning, and privacy, with significant contributions to semantic communication systems and AI-native networks. He has held visiting roles at Princeton University and the University of Padova. Dr. Gündüz is an Area Editor for IEEE Transactions on Communications and IEEE JSAC, and a former Distinguished Lecturer of the IEEE Information Theory Society. His awards include the IEEE Communication Society Early Achievement Award (2017), ERC Starting Grant (2015), and multiple best paper recognitions. He has organized major conferences and workshops, including the first MLCOM workshops on machine learning for communications. Education: B.S. (2002) from METU, Turkey; M.S. and Ph.D. (2004, 2007) from NYU Polytechnic School of Engineering. Prior roles include Research Associate at CTTC Barcelona, Consulting Assistant Professor at Stanford, and postdoctoral positions at Princeton. His work spans theoretical foundations and practical implementations, emphasizing interdisciplinary solutions for next-generation networks. Research Interests: Semantic communications, distributed learning, 6G systems, privacy-preserving techniques, and AI integration in communication networks. His recent work explores neural compression for cloud RAN, over-the-air computation, and federated learning optimizations. Awards and Roles: IEEE JSAC Series Editor (Machine Learning in Networks), IEEE Transactions on Wireless Communications Editor, and organizer of major conferences. His team's achievements include breakthroughs in joint source-channel coding and adversarial jamming defenses.

• Digital Communications
• Wireless Communications
• Satellite Communication Systems
• +7 more

George P. Efthymoglou is a Professor at the Department of Digital Systems, University of Piraeus, Greece, since 2002. He currently serves as Department Chair and teaches courses including Advanced Topics in Wireless Communications, Signals and Systems, and Digital Signal Processing. His research focuses on digital communications with an emphasis on cellular and satellite systems, interference management, and network performance analysis. Education: B.S. in Physics, University of Athens (1991) M.S. & Ph.D. in Electrical Engineering, Florida Atlantic University, USA (1993 & 1997) Research Interests: His work spans wireless communication systems, UAV-assisted networks, 5G/6G technologies, and signal processing for interference-limited environments. He has contributed to advancing techniques for mobility management, energy efficiency, and performance evaluation in heterogeneous networks. Recent Contributions: His recent work explores joint sensing-communication in UAV systems, beacon-assisted wireless power transfer, and stochastic analysis of fading channels. His studies often bridge theoretical models with practical implementations in satellite and vehicular networks. Advising & Grants: Actively seeks student interns for research projects. Has led collaborative initiatives with industry partners like Motorola and Cadence Design Systems, focusing on 3G/5G system modeling and performance evaluation. Labs & Teams: Engages in interdisciplinary projects at the University of Piraeus, collaborating with experts in medical informatics and computer science through initiatives like the Health CASCADE Study.

James S. LehnertView profile

Professor

• Wireless Communication Networks
• Spread Spectrum Communications
• Signal Processing
• +5 more

James S. Lehnert is a Professor at the School of Electrical and Computer Engineering , Purdue University, where he has been since 1984. He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign (1978, 1981, 1984). His research focuses on spectrum management , CDMA systems , channel estimation , and spread spectrum communications . He has led projects for DARPA, NSF, and the Air Force Office of Scientific Research, collaborating with institutions like the University of Michigan and Ohio State University. He has authored numerous articles in top journals like IEEE Transactions on Communications and IEEE Journal on Selected Areas in Communications . His awards include the IEEE MILCOM Lifetime Achievement Award (2009) and recognition as a Highly Cited Researcher (2000-2010). He is also a Fellow of the IEEE for contributions to spread-spectrum communications. Dr. Lehnert has advised over 30 graduate students, many of whom now hold prominent roles in academia and industry. He teaches advanced courses such as ECE544: Digital Communications and ECE639: Error Control Coding . His research group operates the Spread Spectrum and Satellite Communications Research Laboratory (S3CRL).

Francis X. DieboldView profile

Professor

• Dynamic Predictive Modeling
• Financial Econometrics
• Macroeconomic Forecasting
• +4 more

Francis X. Diebold is Paul F. Miller and Warren S. Miller Professor of Economics, Professor of Finance, and Professor of Statistics and Data Science at the University of Pennsylvania, and a current Janeway Institute visitor at the Faculty of Economics, University of Cambridge (15–18 August 2024). Education & Appointments: Paul F. Miller and Warren S. Miller Professor of Economics, University of Pennsylvania Professor of Finance, University of Pennsylvania Professor of Statistics and Data Science, University of Pennsylvania Janeway Institute Visitor, Faculty of Economics, University of Cambridge (Aug 2024) Research Focus: His research centers on dynamic predictive modeling of financial markets, macroeconomic aggregates, and the intersection with climate change—particularly the forecasting of Arctic sea-ice evolution. Methodologically, he integrates econometric theory, high-frequency finance, machine-learning techniques, and climate data analytics. Recent Publication Trends: Between 2021 and 2025, Diebold has published extensively on Arctic sea-ice forecasting , financial-network connectedness , yield-curve machine-learning models , and forecast-combination theory . These works appear in flagship journals such as Journal of Econometrics , Energy Economics , The Econometrics Journal , and the Bulletin of the American Meteorological Society , reflecting a cross-disciplinary reach from econometrics to climate science. Scientific Awards: No specific awards are listed in the provided text. Research Teams & Resources: Diebold maintains an active web repository of replication files, open course materials, and a long-running blog No Hesitations , underscoring his commitment to open science and scholarly communication.

Joshua Ott GundersenView profile

Professor

• Astrophysics
• Cosmology
• Intensity Mapping
• +3 more

Joshua Gundersen is a Professor in the Department of Physics at the University of Miami's College of Arts and Sciences. His research focuses on observational cosmology, particularly using intensity mapping techniques to study the distribution of molecular gas in the early universe. He is a key member of the CO Mapping Array Project (COMAP), which aims to map carbon monoxide emissions to trace galaxy evolution and probe the Epoch of Reionization (EoR). His work includes developing data analysis methods for large-scale structure observations and advancing instrumentation for submillimeter and radio astronomy. Key projects include the COMAP Pathfinder instrument and its successor phases (COMAP-EoR and COMAP-ERA), which enable high-precision measurements of CO(1-0) and CO(2-1) power spectra. Gundersen's research also involves cross-correlating CO intensity maps with galaxy surveys such as eBOSS and HETDEX to enhance detection sensitivity. His contributions span instrument design, signal processing, and theoretical modeling of molecular gas distributions in cosmic history. His publications highlight advancements in data calibration, systematic error mitigation, and forecasts for future experiments. Ongoing efforts aim to characterize the cosmic molecular gas content up to redshift z~8, providing insights into star formation rates and galaxy evolution during reionization. Collaborations include the Argus+ spectroscopic instrument at the Green Bank Telescope and the ASTHROS stratospheric observatory.

Yixiao JiangView profile

Assistant Professor

• Financial Economics
• Econometrics
• Credit Rating Models
• +4 more

Yixiao Jiang is an Assistant Professor of Finance at Western New England University, specializing in Financial Economics and Econometrics. Their research spans credit rating models, economic growth, income inequality, and international finance. Education : Ph.D. in Financial Economics (2019), B.A. in Mathematics and Economics (2013) from Rutgers University. Research Interests focus on econometric modeling of financial and macroeconomic phenomena, including semiparametric methods for censored data, credit risk analysis, and cultural impacts on economic outcomes. Recent work explores nonlinear fiscal and trade dynamics in Germany and Japan. Publications (2020–2023) reflect expertise in Financial Econometrics, Risk Analysis, and Applied Economics, with applications to credit ratings, economic growth, and income inequality. Key methodologies include decomposition analysis and nonlinear ARDL modeling.

Armeen TaebView profile

Assistant Professor

• Selective Inference
• Multiple Testing
• Graphical Models
• +7 more

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

• Numerical Analysis
• Partial Differential Equations
• Scientific Computing
• +3 more

Prof. Dr. Willy Dörfler is a full professor at the Institute for Applied and Numerical Mathematics within the Faculty of Mathematics at Karlsruhe Institute of Technology (KIT). He leads the research group on Numerical Methods for Partial Differential Equations and teaches advanced courses including Adaptive Finite Element Methods and Modeling and Simulation of Li-Ion Batteries . His work focuses on numerical analysis, scientific computing, and multi-physics simulations. His research spans Partial differential equations with adaptive discretization Wave propagation and space-time methods Battery modeling with chemo-mechanical coupling Lattice Boltzmann techniques for fluid dynamics Applications in mechanics, optics, and electrochemistry as evidenced by his 15 most recent publications. These works emphasize finite element methods, discontinuous Galerkin approaches, and high-performance computing for complex systems. Contact: willy.doerfler@kit.edu | Office hours: Mondays 14:30–15:30 during lecture periods

Florentina ToneView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Convergence Analysis
• +4 more

Dr. Florentina Tone is a Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. She has been a faculty member since 2006, contributing to both undergraduate and graduate education through classroom and online instruction. Her academic background includes a Ph.D. in Mathematics from Indiana University and both a Master’s and Bachelor’s degree in Mathematics from the University of Bucharest, Romania. Dr. Tone's research is centered on numerical analysis of partial differential equations , with a focus on convergence, stability, and error estimation of numerical schemes. Her work applies to fluid dynamics, phase field models, and coupled physical systems. She has published in leading journals such as SIAM Journal on Numerical Analysis and Numerische Mathematik , and has presented at numerous national and international conferences. The analysis of her recent publications reveals a consistent focus on rigorous mathematical validation of numerical methods, particularly finite element and finite difference schemes for time-dependent PDEs. Her work spans applications in fluid mechanics, heat transfer, chemotaxis, and electromagnetics, demonstrating strong interdisciplinary reach within applied mathematics. She has been recognized for her contributions with the Distinguished Research and Creative Activities Award from UWF. This honor reflects her sustained excellence in scholarly output. Dr. Tone actively supervises student research projects and teaches courses such as Complex Analysis, Numerical Analysis, Linear Algebra, and Calculus. While specific grant details are not listed, her sustained publication record and award suggest active research funding. She has mentored students through research advising, contributing to academic development within the department. She is affiliated with the Department of Mathematics and Statistics, which supports research through seminars, colloquia, and student associations such as the Math Association. The department also hosts technical reports and research labs, including the CSDA Lab, which may support data-intensive computational work.

• Wildlife Management
• Wildlife Ecology
• Wildlife Physiology
• +4 more

Paul Griesberger is a Senior Researcher at the Institute of Wildlife Biology and Game Management under the University of Natural Resources and Life Sciences, Vienna , where he has contributed to both basic and applied research since 2017. He completed his Master’s in Wildlife Ecology and Wildlife Management (2014–2017) and Bachelor’s in Biology (Zoology) (2010–2014). Key research areas: Wildlife Management, Wildlife Ecology, Wild Ungulates, Spatio-Temporal Modeling, GPS Telemetry His recent work integrates decision-support tools for sustainable ungulate management, mathematical approaches like Benford’s Law for telemetry data validation, and climate adaptation strategies for forest protection. Collaborations span EU institutions and national parks. His 14 publications (2018–2025) focus on wild ungulate behavior, interspecies competition, telemetry analytics, and canid morphology, with methodologies bridging ecology, statistics, and conservation policy. He has advised 4 Master’s students and contributed to practical guidelines for red deer and chamois management. Scientific awards: Granser-United Global Academy Forschungspreis für eine Nachhaltige Jagd (2022) YO Research Award by CIC (2020) Nationalpark Hohe Tauern stipend (2021)

• Wildlife Ecology
• Conservation Genetics
• Molecular Ecology
• +3 more

Marcia Sittenthaler is a researcher at the Institute of Wildlife Biology and Game Management , part of the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna . She holds a PhD (2020), MSc (2012), and BSc (2009) in wildlife and environmental sciences from the same institution. Doctoral studies (2015-2020): Natural Resources and Life Sciences, Vienna Master of Science (2009-2012): Wildlife Ecology and Management Bachelor (2006-2009): Environment and Bio Resources Management Her research focuses on conservation genetics, wildlife ecology, and environmental pollution , particularly microplastics in aquatic ecosystems. Key projects include non-invasive genetic census of otters , genetic differentiation of black grouse , and microplastic contamination studies . Her work integrates molecular ecology with field-based conservation strategies . Notable awards: Fritz-Frank Award (2021) Carl von Schreibers Forschungspreis (2015) She has supervised 3 theses, including Sophie Layendecker's (2023) Microplastic intake of otters and Florian Kunz's (2017) Black grouse genetic diversity . Active in scientific community service , she serves as a reviewer for journals like Mammalian Biology and Wildlife Biology , and is a member of the IUCN Otter Specialist Group (since 2012).

Olga MulaView profile

Researcher

• Optimal Transport
• Wasserstein Spaces
• Model Reduction
• +4 more

Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.

Andrew BuckView profile

Assistant Professor

• Machine Learning
• Intelligent Agents
• Autonomous Drones
• +2 more

Andrew Buck is an Assistant Research Professor in the Department of Electrical Engineering and Computer Science at the University of Missouri . His work focuses on machine learning , intelligent agents , autonomous drones , computer vision , and 3D visualization . Education: PhD, MS, BS in Computer Engineering, and BS in Electrical Engineering from the University of Missouri Andrew Buck's research involves the development of photorealistic synthetic datasets (e.g., HiFiAerial), explainable AI frameworks for drone autonomy, and human-agent teaming dynamics (e.g., trust modeling). He has pioneered multidisciplinary simulation platforms like Mizsim for AI training and evaluation, and contributed to real-time 3D reconstruction techniques combining classical algorithms with deep learning. His work on uncertainty quantification in depth estimation and fuzzy voxel maps demonstrates his interest in probabilistic approaches to perception. A key theme in his publications is the evaluation of AI performance in partially observable environments , including applications in explosive hazard detection and cooperative game scenarios.