جست‌وجوی استاد و پژوهشگر

Chris Adolphمشاهده پروفایل

استاد

• Political Science
• Statistics
• Data Visualization
• +۱۰ مورد دیگر

Chris Adolph is a Professor of Political Science at the University of Washington, Seattle. He specializes in quantitative political methodology, data visualization, and the intersection of political economy with public health policy. His work bridges statistics and social science to address pressing policy questions. University: University of Washington Department: Political Science Email: cadolph@uw.edu His research focuses on political methodology, public health policy, and comparative political economy. Recent work analyzes pandemic response policies, health service coverage in Nigeria, and the role of partisanship in state-level health mandates. He employs advanced statistical techniques and machine learning to model complex policy outcomes. Key trends in his publications (2020–2024) include: Systematic analysis of COVID-19 policy across U.S. states and globally Machine learning applications in health service mapping in developing nations Cross-national studies of health equity and mental health impacts Long-standing contributions to maximum likelihood estimation and data visualization tools At UW, he co-founded the Center for Statistics and the Social Sciences and teaches courses like Advanced Quantitative Political Methodology and Visualizing Data .

Lachlan Astfalckمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Spectral Analysis
• Oceanography
• +۳ مورد دیگر

Lachlan Astfalck is a Research Fellow at the University of Western Australia's School of Physics, Mathematics and Computing, working on the TIDE ARC ITRH project. He specializes in statistical methodologies for oceanography, hydrodynamics, and paleoclimate modeling. His research focuses on spectral analysis, uncertainty quantification, and developing statistical methods for environmental applications. He holds a PhD (2019) and Bachelor of Engineering in Mechanical Engineering (2014) from the University of Western Australia. Dr. Astfalck's publications demonstrate a strong focus on developing novel statistical approaches for environmental systems. His recent work spans spectral analysis techniques, ocean turbulence modeling, ice sheet reconstruction, and Bayesian methods for climate applications. He actively develops open-source statistical tools and maintains several GitHub repositories related to spectral estimation methods. He is accepting PhD students in statistics and oceanography and has capacity for research collaborations.

Fotis Papailiasمشاهده پروفایل

مدرس ارشد

• Time Series Econometrics
• Financial Forecasting
• Macroeconomic Analysis
• +۴ مورد دیگر

Fotis Papailias is a Senior Lecturer in Banking & Finance at King’s Business School and Deputy Director of the Data Analytics for Finance and Macro (DAFM) Research Centre. His research focuses on time series econometrics, financial and macroeconomic forecasting, resampling procedures, portfolio selection, and technical trading strategies. He has consulted for hedge funds, Oxford Economics, and the European Commission, applying his academic work to investment strategies via the quantf research website. Education: Advanced qualifications in economics and econometrics (specific details not publicly disclosed). Affiliations: King’s Business School, DAFM Research Centre. His research interests emphasize the development of methodologies for analyzing financial and macroeconomic data, including big data applications for nowcasting, volatility discovery, and structural break analysis. Key contributions include enhancing forecasting accuracy through novel econometric techniques and improving portfolio allocation via covariance averaging. Recent publications highlight trends in leveraging alternative datasets (e.g., Airbnb) and central bank communication for real-time economic assessment. His work bridges academic rigor and practical investment strategies, with a focus on trend-following strategies and hedging mechanisms. Grants & Consulting: Collaborations with the European Commission, Oxford Economics, and small hedge funds. Labs/Teams: Leads the DAFM Research Centre, focusing on quantitative finance and macroeconomic research.

Laurent Gerbaudمشاهده پروفایل

استاد

• Computer Aided Design
• Modelling
• Dynamic simulation
• +۲ مورد دیگر

Laurent Gerbaud is a Professor at the ENS de Grenoble (Grenoble Institute of Technology) , affiliated with the G2Elab research laboratory and the MAGE team. His expertise lies in computer-aided design (CAD) of electrical systems, with a focus on electrical drives and power electronics. Research Interests: Analytical modeling, time/frequency simulation, gradient-based optimization, and CAD tool development. Teaching Activities: Computer-aided design, energy conversion, numerical methods, and optimization techniques. Research Trends across his publications highlight the application of advanced optimization methods (e.g., SQP, automatic differentiation) to complex electromagnetic and electrical systems, emphasizing electromagnetic compatibility (EMC) in aerospace, hybrid train energy management, and thermal modeling for multiphysics systems. His work bridges analytical modeling with practical implementation in renewable energy and transportation domains. Laboratory Affiliation : G2Elab (Grenoble Electrical Engineering Laboratory), located at Grenoble Cedex 1, France.

João Tomé Saraivaمشاهده پروفایل

استاد

• Power Systems
• Renewable Energy Integration
• Electricity Market Modeling
• +۳ مورد دیگر

João Tomé Saraiva is a Professor at the Faculty of Engineering (FEUP) , University of Porto, and Research Coordinator at INESC Porto since July 15, 1985. He holds a PhD and Agregado degrees in Electrical and Computer Engineering, with 50+ MSc and 10+ PhD students supervised. His career spans EU and national projects, and consultancy work with agencies like EDP Distribuição and REN. Education: MSc (1987), PhD (1993), Agregado (2002) in Electrical and Computer Engineering from FEUP Research Interests: Focus on power systems with renewable integration, electricity market modeling, transmission expansion planning, climate change impact, AI applications in energy forecasting, and regulatory frameworks for collective self-consumption Publications: 30+ international journal papers, 120+ conference papers, and 3 co-authored books Projects: Leadership in EU/national funded initiatives and consultancy contracts with Portuguese and international TSOs Contact: +351222094230 | joao.tome.saraiva@inesctec.pt His 2024 publications address energy allocation in collective self-consumption, NECP impacts on MIBEL, battery arbitrage economics, climate-resilient transmission planning, and AI-driven hydro forecasting. These works reflect his expertise in market regulations, grid modernization, and renewable integration.

Heino Falckeمشاهده پروفایل

استاد

• High-energy astrophysics
• Astroparticle physics
• Black hole astrophysics
• +۸ مورد دیگر

Heino Falcke is a Full Professor of Astroparticle Physics and Radio Astronomy at the Institute for Mathematics, Astronomy and Particle Physics (IMAPP) of Radboud University, Nijmegen. He is also a guest scientist at the Max Planck Institute for Radio Astronomy (MPIfR) in Bonn and chairs the science council of the Event Horizon Telescope (EHT) Collaboration. Ph.D. in Astronomy (1994, University of Bonn) Habilitation in Astrophysics (2000, University of Bonn) Spinoza Prize (2011), Einstein Medal (2020), Breakthrough Prize (2020) Falcke's research focuses on supermassive black holes, accretion physics, and cosmic rays. He pioneered the concept of imaging black hole shadows through millimeter-wave interferometry, leading to the first black hole image in 2019. His work bridges theoretical models with observational techniques, including the development of the LOFAR telescope and lunar-based radio experiments. The 10 publications listed span topics like jet-disk coupling, cosmic ray detection, pulsar magnetospheres, and black hole imaging. These studies employ radio astronomy, X-ray observations, and cosmic ray physics to explore extreme astrophysical environments. Scientific Awards: 2023 Balzan Prize 2021 Henry Draper Medal 2020 Breakthrough Prize in Fundamental Physics ERC Synergy Grant (2013) Spinoza Prize (2011) Falcke leads the BlackHoleCam project, co-founded the EHT Collaboration, and contributes to the LOFAR telescope and Pierre Auger Observatory. He advocates for lunar radio astronomy via the NCLE experiment and promotes science communication through bestselling books like Licht im Dunkeln and outreach initiatives.

Alexander Flueckمشاهده پروفایل

دانشیار

• Power System Stability
• Power System Control
• Computational Tools for Power Engineering
• +۴ مورد دیگر

Alexander Flueck is an Associate Professor of Electrical and Computer Engineering at Illinois Institute of Technology (IIT), part of the Armour College of Engineering. He earned his B.S., M.Eng., and Ph.D. in Electrical Engineering from Cornell University. His research focuses on power system stability and control, developing computational tools for large-scale power systems. He has been at IIT since 1996. Education: Ph.D., Electrical Engineering, Cornell University, 1996 M.Eng., Electrical Engineering, Cornell University, 1992 B.S., Electrical Engineering, Cornell University, 1991 Research interests include power system stability, numerical methods for power system analysis, and computational tools for system planning. His work emphasizes security and competitiveness in power systems. He has contributed to tools like CPFLOW for tracing power system steady-state behavior under varying conditions. Teaching emphasizes hands-on problem-solving and analytical skills, with a focus on reinforcing fundamentals through lab experiments and challenging assignments. Publications span topics like power flow equations, contingency ranking, and voltage stability assessments. His NSF CAREER Award (1999) supported research on transfer capability in deregulated power systems. Service activities include directing the IIT-sponsored American Power Conference program and leading the Chicago Chapter of the IEEE Power Engineering Society. He also contributes to smart grid initiatives, including distributed power flow solvers and agent-based control systems. Contact: flueck@iit.edu, Siegel Hall 224, Chicago, IL.

Vladimir Milovanovićمشاهده پروفایل

دانشیار

• Electrical Engineering
• Computer Science
• Signal Processing
• +۷ مورد دیگر

Dr. Vladimir Milovanović is an Associate Professor at the Faculty of Engineering, University of Kragujevac, Serbia, holding a position as Chair of the Department of Electrical Engineering and Computer Sciences. His academic career is anchored in interdisciplinary research at the intersection of electrical engineering, computer science, and biomedical applications. His research focuses on hardware design, radar sensor systems, biomedical signal processing, and machine learning applications. Notable areas include CMOS circuit design, FPGA-based implementations, and parameterizable hardware generators for signal processing tasks. He also explores medical imaging analysis using deep learning techniques for conditions like disc hernia diagnosis. Recent work emphasizes radar technology (FMCW transceivers, signal processing architectures), embedded systems (FPGA development, IP core utilization), and high-performance streaming data algorithms. His publications span over two decades, demonstrating expertise in analog/digital circuit design, comparative classifier analysis in biomedical contexts, and hardware-software co-design methodologies. Dr. Milovanović collaborates with industry on energy-efficient MIMO arrays and semiconductor gas sensors. His contributions include open-source HDL models for digital signal processors and comparator circuits optimized for low-power and high-speed applications.

Zhaosheng Fengمشاهده پروفایل

استاد

• Nonlinear Systems
• Differential Equations
• Dynamical Systems
• +۳ مورد دیگر

Zhaosheng Feng is the Carlos and Stephanie Manrique de Lara Endowed Professor at the School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley. His research focuses on nonlinear systems, differential equations, dynamical systems, computational methods, mathematical physics, and population dynamics. He teaches advanced courses like Math 8344 on Function Space Methods in System Theory. Research interests include analysis of ODEs/PDEs, bifurcation theory, and applications in epidemiology/physics. Editorial roles include serving on 9 journal editorial boards including Annals of Physics and Communications in Nonlinear Science. Active in organizing conferences and special issues, having edited proceedings of major AIMS conferences. Recent work emphasizes traveling wave solutions, fractional equations with critical exponents, and mathematical modeling of disease spread. His studies often involve stability analysis, bifurcation phenomena, and numerical simulations of complex systems.

Jakob Kjøbsted Huusomمشاهده پروفایل

استاد

• Process Control
• Model Predictive Control (MPC)
• Optimization
• +۷ مورد دیگر

Jakob Kjøbsted Huusom is a Professor at the Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU). His research emphasizes advanced process control, model development for the process industry, and sustainable technologies. He is affiliated with the KT Consortium PROSYS and the Process and Systems Engineering Centre (PROSYS), contributing to UN Sustainable Development Goals related to affordable energy and climate action. His work spans hybrid modeling approaches integrating AI with first-principles methods, optimization of industrial processes, and real-time adaptive systems. Current projects include MPC tuning algorithms, enzymatic biodiesel production control, energy-efficient distillation technologies, and electrification of industrial processes using renewable energy. As a supervisor, he guides multiple PhD students in topics such as risk monitoring, model-based digitalization, and Power-to-X applications. His research tools and methodologies address challenges in process identification, state estimation, and controller design, with a focus on scalability and real-world implementation. Collaborations include international teams in Julia-based simulation tools and hybrid neural network modeling. His contributions advance both theoretical frameworks and practical solutions for sustainable industrial systems.

Almir Maricمشاهده پروفایل

دانشیار

• Physical Channel Modeling
• Communication Channel Characterization
• Fading Channels
• +۵ مورد دیگر

Almir Maric is an Associate Professor at the Department of Telecommunications, Faculty of Electrical Engineering, University of Sarajevo. He holds a PhD in Telecommunication Engineering from the same university (2020), preceded by a Master’s (2013) and Bachelor’s (2010) in the same field. His research focuses on geometric-based stochastic channel modeling, communication channel characterization, and fading channel simulation. He has contributed to renowned journals like Wireless Networks and IEEE Communications Surveys & Tutorials , as well as conferences such as the International Conference on Information, Communication and Automation Technologies (ICAT). His work spans topics including composite fading models, error probability analysis in digital modulation, and quantum cryptography in 5G networks. Education: Bachelor’s (2010), Master’s (2013), PhD (2020) in Telecommunication Engineering, University of Sarajevo Maric’s research trends emphasize statistical channel modeling, stochastic processes in wireless systems, and network security in software-defined networks. Notable contributions include the Best Paper Award at the MCSS’17 conference for his work on quantum key distribution signaling protocols. Awards: Best Paper Award at MCSS’17 (2017) His publications reflect a blend of theoretical and applied research, with collaborations on books such as Zbirka zadataka iz mikrotalasnih komunikacija (2020). His work bridges fundamental channel modeling with practical applications in 5G and quantum communication systems.

• Telecommunications
• Machine Learning
• Network Optimization
• +۳ مورد دیگر

Darijo Raca is a researcher in the Department of Telecommunications , focusing on advanced telecommunications systems, machine learning applications in network optimization, and adaptive video streaming technologies. He actively contributes to academic activities such as organizing workshops on optical fiber systems and fostering collaborations with institutions like TU Dublin. Research Interests: His work emphasizes telecommunications infrastructure , machine learning for network prediction , 5G and 4G LTE optimization , and quality of experience (QoE) in adaptive streaming . He has developed frameworks like JuliaGrid and DashReStreamer , and contributed to datasets like the 360 video DASH dataset . Key Contributions: Over 20 journal articles and conference papers highlight his expertise in throughput prediction, distributed state estimation, and video streaming algorithms. Notable trends include integrating AI/ML into 5G networks and improving real-world network performance through empirical testing. Labs/Teams: He is part of the initiative to establish a quantum cryptography laboratory within the Department of Telecommunications, pushing boundaries in secure communication systems.

Nelson Rodriguesمشاهده پروفایل

پژوهشگر

• Intelligent Manufacturing
• Reconfigurable Manufacturing Control Systems
• Artificial Intelligence
• +۵ مورد دیگر

Nelson Rodrigues is a Researcher at the Polytechnic Institute of Bragança (IPB) and a Ph.D. candidate at the University of Porto, conducting research since 2010 with involvement in H2020 PERFoRM and GO0D MAN projects, as well as FP7 ARUM and GRACE initiatives. His academic credentials include: Master of Science in Information Systems, Polytechnic Institute of Bragança His research centers on Intelligent and Reconfigurable Manufacturing Control Systems, investigating Artificial Intelligence applications for self-adaptive manufacturing evolution. Additional expertise spans Industry 4.0, Cyber-physical systems, Multiagent systems, Simulation, and Advanced Data Analytics, with emphasis on enhancing manufacturing flexibility through cutting-edge computational methods. Recent publication analysis reveals a strategic pivot toward energy systems optimization, particularly virtual power plants, utilizing high-performance computing, quantum annealing, and evolutionary algorithms. Concurrent research explores intelligent transportation frameworks, assistive robotics, and manufacturing business analytics, demonstrating interdisciplinary technical agility. He maintains active affiliations with LIACC - Artificial Intelligence and Computer Science Laboratory and the IEEE Technical Committee on Industrial Agents, contributing to over 29 publications with an h-index of 11. Research funding derives from European Union projects including H2020 PERFoRM and GO0D MAN, and FP7 ARUM and GRACE, where he developed intelligent manufacturing control architectures and service reconfiguration systems. Within LIACC and IEEE collaborations, he advances industrial agent technologies for real-time manufacturing adaptation, focusing on dynamic reconfiguration and human-in-the-loop cyber-physical production systems.

• Mobile Robotics
• Industrial IoT
• Cyber-Physical Systems
• +۲ مورد دیگر

José Luis Lima is Coordinator Professor and Head of Electrical Engineering at Polytechnic Institute of Bragança. As vice-coordinator of the Research Centre in Digitalization and Intelligent Robotics, he leads projects in industrial IoT and autonomous systems. His research focuses on: Mobile robot localization and navigation Energy-efficient robotic systems Human-robot collaboration frameworks Industrial cyber-physical systems He developed the RobotAtFactory competition platform to advance Industry 4.0 education. Current projects include EU Horizon initiatives on sustainable manufacturing (PRODUTECH 4 S&C) and AI-driven healthcare (SmartHealth). With 280+ publications, his work integrates battery management systems for robots and vision-based industrial inspection platforms.

Paromita Nathمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Additive Manufacturing
• Bayesian Inference
• +۴ مورد دیگر

Dr. Paromita Nath is an Assistant Professor in the Department of Mechanical Engineering at Rowan University's Henry M. Rowan College of Engineering. She holds a Ph.D. in Civil Engineering from Vanderbilt University and specializes in uncertainty quantification with applications in additive manufacturing and healthcare systems. Her research employs computational methods to develop probabilistic models for manufacturing process optimization. Her primary research integrates Bayesian inference and computational modeling to study process-structure-property relationships in additive manufacturing. Current projects focus on developing probabilistic digital twins for manufacturing systems and multi-fidelity modeling approaches. Research spans health diagnostics, sustainable materials development, and digital engineering solutions. Dr. Nath's publications demonstrate consistent focus on uncertainty management in manufacturing systems, with recent emphasis on surrogate modeling, probabilistic control systems, and multi-objective optimization. Her work frequently combines computational methods with practical manufacturing challenges.