Pavlos S. Georgilakis is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in modern techniques for power system analysis, optimization, and renewable energy integration. He holds a Diploma (1990) and PhD (2000) in Electrical Engineering from NTUA. His career includes roles as Lecturer (2009) and Associate Professor (2018–2023) at NTUA, and Assistant Professor at the Technical University of Crete (2004–2009). Research focuses on power transmission/distribution systems, transformer design, and applying AI/optimization for grid efficiency. He led 10 research projects, including Horizon 2020 initiatives SHAR-Q, WiseGRID, and NobelGrid. He authored 3 books and over 230 publications (SCOPUS citations: >5,500). Editor of IET Smart Grid, Energies, and Electricity journals; senior IEEE member. He supervised 4 doctoral, 9 master’s, and 76 diploma theses. Awards include the 2013 Best Reviewer Award from Electric Power Systems Research. Active in energy storage, smart grids, and decentralized energy resource integration.
Yulia Gel is a Professor in the Department of Statistics at Virginia Tech and serves as a Part-Time Program Director-Expert at the National Science Foundation (NSF). She holds a MSc (summa cum laude) and PhD in Mathematics from Saint Petersburg State University (Russia) and completed a postdoc in Statistics at the University of Washington. Her research focuses on uncertainty quantification in AI, statistical foundations of data science, spatio-temporal processes, and applications in climate science, healthcare, and blockchain analytics. She has received prestigious awards including the NSF Director’s Award (2023), ASA Distinguished Achievement Medal (2018), and TIES Abdel El-Shaarawi Award (2014). Gel has led grants on wildfire prediction, climate informatics, and blockchain data science. She serves on editorial boards of Statistica Sinica, Electronic Journal of Statistics, and Technometrics, and organizes workshops on AI for climate sustainability and fragile Earth systems. Her research group develops topological and geometric methods for graph neural networks, with applications to digital twins, environmental justice, and public health. Education: MSc (1997), PhD (2000) in Mathematics from Saint Petersburg State University; Postdoc in Statistics at University of Washington (2001–2003). Past roles include Professor at University of Texas at Dallas (2015–2024) and Associate Professor at University of Waterloo (2004–2014). Selected visiting positions include NASA Jet Propulsion Lab (2016–2017) and Isaac Newton Institute (2016–2017). She has pioneered statistical software packages like snowboot and funtimes for network inference and time-series analysis. Awards highlight her contributions to environmetrics and statistical methodologies. Current projects include NSF-funded research on AI-driven wildfire prediction and blockchain analytics for climate resilience. Her lab’s recent work emphasizes topological methods (e.g., zigzag persistence) for graph-based forecasting and adversarial robustness.
Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)
Stefano Noventa is a Research Fellow at the Methods Center, Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has held multiple postdoctoral positions at the University of Tübingen and previously at the University of Verona and the University of Padova. Education: Ph.D. in Cognitive Psychology, University of Padova (2011) M.Sc. in Physics, University of Padova (2006) Studies in Physics, University of Padova (1999–2006) International Visiting Graduate Student, University of Toronto (2009, 2010) Dr. Noventa's research lies at the intersection of mathematical psychology, psychometrics, and psychophysics, with a focus on developing and unifying quantitative models of human cognition and assessment. His work integrates Item Response Theory (IRT) and Knowledge Space Theory (KST) to create more robust frameworks for educational and psychological measurement. He investigates latent variable models, probabilistic knowledge structures, and the identifiability of complex psychometric models, often applying these to domains such as education, organizational psychology, and entrepreneurship. His recent publications (2020–2024) demonstrate a strong trend toward theoretical integration, particularly in bridging cognitive diagnosis models with traditional psychometric frameworks. The articles emphasize mathematical rigor, model generalization, and empirical validation, with applications in both cognitive science and applied psychology. Topics include the unification of assessment models, parameter estimation under local dependence, and the modeling of intuitive physical reasoning. Scientific Awards: No awards or honors listed in the provided text. Dr. Noventa has not been explicitly mentioned as an advisor to students, but he has served as a corresponding author and collaborator on multiple research projects, indicating a leadership role in research teams. He has been involved in a DFG-funded project (GLI NON-NORM) since 2019, suggesting active grant participation. His work is highly collaborative, involving researchers from Germany, Italy, Austria, and Canada. Labs and Research Groups: Methods Center, University of Tübingen Hector Institute of Education Science and Psychology, University of Tübingen Center of Assessment, University of Verona Department of General Psychology, University of Padova
Qiwei Yao is a Professor of Statistics at the Department of Statistics, London School of Economics (LSE), where he maintains an active research program in statistical methodology and applications. His office is located in Columbia House, Room 7.16 at LSE's Houghton Street campus in London. Professor Yao's research focuses on statistical inference for complex time series, with particular expertise in high-dimensional time series, dynamic networks, spatio-temporal processes, functional time series, nonlinear time series, and high-frequency data. His work bridges theoretical statistics with practical applications, especially in financial econometrics. He has developed innovative methodologies for dimension reduction, factor modeling, and network analysis that have become influential in the field. His recent publications reveal a strong trend toward developing statistical methods for increasingly complex data structures, particularly focusing on high-dimensional and network-based time series. His work integrates machine learning techniques with traditional statistical approaches, as evidenced by papers on deep learning for Markov property testing and tensor decompositions for matrix time series. There's also a clear emphasis on privacy-preserving methods and differential privacy in network analysis. Professor Yao has secured substantial research funding through multiple EPSRC Programme Grants and Research Projects, including the EPSRC Programme Grant for 'Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS)' and 'Network Stochastic Processes and Time Series (NeST)'. He also leads the EPSRC Research Project on 'Statistical Network Analysis: Model Selection, Differential Privacy, and Dynamic Structures' and has collaborated with industry partners like Andurand Capital Management on projects such as 'Forecasting Oil Prices Based on Quantitative Methods'. His research has significant applications across various domains, particularly in energy forecasting (electricity load prediction), financial modeling (volatility modeling, oil price forecasting), and ecological modeling (spatio-temporal population dynamics). Professor Yao maintains strong collaborative relationships with researchers across multiple institutions and disciplines.
Ken Hendricks is a Professor of Economics at the University of Wisconsin-Madison, with a primary research focus on industrial organization and auction theory. He has held the Laurits R. Christensen Distinguished Chair since 2012 and served as editor of the Journal of Industrial Economics (2000–2005). His career spans institutions like the University of British Columbia (1982–2001), University of Texas at Austin (2001–2010), and visiting roles at Princeton, Harvard, and University of Arizona. Education: B.A., Economics, University of British Columbia (1976) M.A., Economics, University of British Columbia (1977) Ph.D., Economics, University of Wisconsin-Madison (1982) Research interests center on industrial organization , empirical and theoretical auction studies , game theory , and network economics . His work analyzes bidding behavior in auctions, market design, and the economic impacts of mergers, oil drilling, and information asymmetry. Recent publications include studies on sequential auction markets (2024), mortgage supply chains (2023), and competitive dynamics in common value auctions (2022). His articles frequently intersect with fields like energy economics , consumer behavior , and market efficiency . Scientific Awards: Distinguished Fellow, Industrial Organization Society (2024) Fellow, Econometric Society (2004) UBC Killam Research Prize (1993) UBC Alumni Prize for Social Sciences (1992) National Fellow, Hoover Institution (1986–1987) Active in editorial roles (e.g., Journal of Industrial Economics , 2014–present), he has also led academic committees (2015–2020 President, Industrial Organization Society) and contributed to policy and industry grants, including NSF and SSHRC funding.
Maria Rita D’Orsogna is a Professor of Mathematics at California State University, Northridge (CSUN) and holds an Adjunct Associate Professor appointment in the Department of Computational Medicine at UCLA. She earned her PhD in Theoretical Physics from UCLA in 2003 and has since bridged mathematical modeling with interdisciplinary research in biology, social dynamics, and criminology. Her work utilizes statistical mechanics and applied mathematics to study collective behavior, viral dynamics, and societal challenges. Her research spans Biological swarming and self-organization Crime pattern modeling and policy analysis Drug addiction relapse dynamics Environmental activism against offshore oil drilling Recent publications focus on Medical decision-making optimization Age-specific overdose mortality forecasting Radicalization and social network dynamics Criminal career empirical studies Hematopoiesis modeling . She has secured funding from the NSF and Army Research Office. Teaching experience includes differential equations, multivariable calculus, and mathematical biology at CSUN and UCLA. She has mentored students through RIPS, IPAM, and PUMP programs. As Associate Director of UCLA’s Institute for Pure and Applied Mathematics (2018–2021), she promoted interdisciplinary research. Her environmental advocacy in Italy led to national policy changes banning coastal oil drilling, earning her recognition as the "Erin Brockovich of Italy".
Cihan Tepedelenlioglu is an Associate Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His work bridges wireless communications, statistical signal processing, and renewable energy systems, with a focus on photovoltaic array monitoring, fault detection, and optimization. PhD, MS, and BS in Electrical Engineering from University of Minnesota, University of Virginia, and Florida Institute of Technology 2001 NSF CAREER Award recipient Research interests span wireless communications , graph signal processing , stochastic optimization , and machine learning applications to solar energy systems . Key projects include quantum machine learning for PV topology optimization, consensus algorithms for distributed networks, and real-time fault detection using neural networks. Recent articles emphasize machine learning in energy systems (2023-2025), with 12 publications on photovoltaic monitoring and 3 on consensus algorithms. Earlier work focused on channel estimation in OFDM systems and fading models in wireless communications. Scientific awards : NSF CAREER Award (2001) Major grants include NSF funding for networked solar array management (2013-2016), nonlinear distributed consensus (2013-2016), and statistical processing of solar data (2009-2012). Teaching roles include EEE 350 Random Signal Analysis and graduate research supervision in signal processing and wireless communications. Collaborates extensively with Andreas Spanias, Mahesh Banavar, and other researchers on cyber-physical systems for energy applications.
Professor Amr Rizk is the Director of the Networks and Communication Systems (NCS) Lab at the University of Duisburg-Essen, where he has been serving as Professor since April 2021. Previously, he was Assistant Professor at Ulm University (2019-2021) and completed his habilitation at TU Darmstadt in 2019. His academic journey includes research positions at prestigious institutions including University of Massachusetts Amherst, University of Warwick, and TU Darmstadt where he was an Athene Young Investigator. Professor Rizk's research spans multiple aspects of networking and communication systems with a particular focus on network performance analysis, stochastic modeling, and practical implementations. His work bridges theoretical foundations with real-world applications, especially in content delivery, video streaming, and network protocols. He has made significant contributions to network calculus, quality of experience optimization, and novel approaches to congestion control and caching mechanisms. His publication record demonstrates consistent high-impact contributions across top networking conferences and journals. Recent work shows a growing emphasis on programmable data planes, AI/ML applications in networking, and advanced techniques for network measurement and performance prediction. His research group at Duisburg-Essen maintains strong connections with both academic and industrial partners in the networking ecosystem. Best Paper Award at ACM MMSys Conference (2023) Distinguished TPC Member for IEEE INFOCOM (2020, 2022) Best Paper Award at ACM/USENIX Middleware Conference (2017) Athene Young Investigator Award, TU Darmstadt (2017) Professor Rizk serves as Associate Editor for Elsevier Computer Communications and has extensive experience with research funding bodies as a reviewer. His leadership extends to conference organization, including roles as PC Co-Chair for IEEE MIPR (2023) and Steering Committee member for Workshop on Network Calculus (2022). He maintains active participation in numerous top networking conferences as Technical Program Committee member, reflecting his standing within the international networking research community.
Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com
Jia-Bin Huang is an Associate Professor in the Department of Computer Science at University of Maryland, College Park , with a secondary appointment at the University of Maryland Institute for Advanced Computer Studies . His work bridges computer vision , computer graphics , and machine learning . His research focuses on 3D scene reconstruction , neural radiance fields , generative models , and multimodal foundation models . He has made significant contributions to video super-resolution , text-driven 3D modeling , and inverse rendering techniques. 15 recent publications (2024-2025) at top venues: CVPR , NeurIPS , SIGGRAPH Asia , 3DV , and ECCV Pioneering work in Urban Scene Inverse Rendering , Generative Video Editing , and 3D Human Digitization He has received multiple awards including the 3M Non-Tenured Faculty Award , ETRA Best Paper , and NSF Grants . His lab trains 12 PhD students and has graduated 18 Masters/PhD students now at institutions like Stanford , Meta , and Google .
Dr. Dragan Doder is an Assistant Professor in the Intelligent Systems group within the Faculty of Science at Utrecht University. His office is located in the Buys Ballot Building at Princetonplein 5, Room 5.20, 3584 CC Utrecht, Netherlands. He is actively engaged in research and teaching within the domain of Artificial Intelligence, with a specific focus on logical frameworks for AI systems. Dr. Doder's research expertise spans several interconnected areas of theoretical and applied AI. His primary interests include: Artificial Intelligence with emphasis on logical foundations Argumentation theory and frameworks Probabilistic reasoning and temporal logic Deontic logic for normative reasoning Human-centered AI approaches His work bridges theoretical computer science with practical applications in multi-agent systems and decision-making frameworks. Analysis of Dr. Doder's recent publications (2020-2025) reveals a strong focus on the intersection of logic, probability, and AI. His research trajectory shows increasing sophistication in handling uncertainty through probabilistic temporal logics, while maintaining strong connections to practical applications in multi-agent systems and argumentation frameworks. A notable trend is his work on integrating causal reasoning with probabilistic models, particularly in multi-agent contexts where group responsibility and risk assessment become critical concerns. His publications demonstrate consistent contributions to top AI conferences including IJCAI, AAAI, and ECAI. Dr. Doder actively collaborates with researchers across multiple institutions. His collaborative network includes prominent researchers in AI and logic from institutions worldwide. His research has practical implications for developing AI systems that can reason under uncertainty, handle complex normative constraints, and make responsible decisions in multi-agent environments. Dr. Doder is affiliated with the Intelligent Systems research group at Utrecht University, which focuses on developing theoretically sound approaches to AI that can be applied to real-world problems. The group emphasizes human-centered AI approaches that consider ethical implications and practical usability alongside technical excellence.
Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.
Fei He is an Associate Professor at Tsinghua University's School of Software, where he leads the THUFV research lab focused on formal verification and program analysis. His research spans formal methods, automated reasoning, and program verification, with applications in concurrent systems, networking (P4 programs), and probabilistic systems. Education & Employment: PhD from Tsinghua University (2008) Visiting Scholar at Carnegie Mellon University (2010-2011) and Politecnico di Milano (2006-2007) Faculty positions at Tsinghua since 2008 (Assistant Professor 2008-2011, Associate Professor 2011-present) Research: He's developed innovative techniques in SMT solving for concurrency verification, termination analysis, and regression verification. His tools like Deagle have won gold medals at SV-COMP. Current work focuses on probabilistic program verification and network program analysis. Publications: His 80+ publications demonstrate consistent contributions across formal methods (PLDI, OOPSLA, ICSE), networking (NSDI, INFOCOM), and software engineering (TSE, TOSEM), with recent emphasis on data-driven verification and automated invariant inference. Awards: Gold Medals in SV-COMP ConcurrencySafety (2022, 2023, 2025) Best Paper Awards at PPoPP 2022 and SETTA 2022 Advising: Mentors 13 PhD/Master's students in THUFV lab, with graduates joining Huawei, MPI-SP, and research institutions. Secured multiple NSF China grants for trustworthy software research. Service: Associate Editor for Theory of Computing Systems, program committees for PLDI/ICSE/OOPSLA, and former Local Chair for ISSTA 2019.
Richard Kempter is a Full Professor at the Humboldt-Universität zu Berlin, where he leads the Theoretical Neuroscience research group within the Institute for Theoretical Biology, Department of Biology. His research focuses on the neural basis of learning and memory through computational and mathematical modeling of synapses, neurons, and neural networks. He is affiliated with several major research centers including the Bernstein Center for Computational Neuroscience, the Einstein Center for Neurosciences Berlin, and the CRC 1315 Memory Consolidation. Professor Kempter's research interests span theoretical and computational neuroscience with a particular focus on the neural mechanisms underlying learning and memory. His work employs biophysical modeling and mathematical analysis to study synaptic short- and long-term plasticity, the dynamics of single neurons, and the interaction of neurons in recurrently coupled networks. A key aspect of his research investigates how neural systems maintain a balance between learning susceptibility and stability against pathological activity patterns, with model systems including the hippocampus and early auditory system. His research group has made significant contributions to understanding hippocampal sharp wave-ripple events, phase precession in spatial navigation, auditory processing in barn owls, and memory consolidation mechanisms. The group's work combines theoretical approaches with computer simulations to unravel the computational principles of neural circuits, showing particular interest in how neural tissue remains susceptible to learning while maintaining robust stability against pathological activity patterns. Scholarship of the State of Bavaria (03/1994-12/1995) Emmy Noether Fellowship Part I (09/1999-08/2001), funded by the Deutsche Forschungsgemeinschaft Emmy Noether Fellowship Part II (01/2003-09/2008) Guest Professor , HU Berlin, Department of Biology (10/2008-03/2010) Professor Kempter has advised numerous PhD and Master's students throughout his career, with many continuing in neuroscience research. His group maintains strong connections with experimental laboratories to bridge computational models with empirical findings, particularly in hippocampal function and auditory processing. The Theoretical Neuroscience Lab participates in collaborative projects investigating memory consolidation and neural coding principles, contributing significantly to our understanding of how neural circuits implement computational principles underlying learning and memory.
