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Song Kim is an Associate Professor of Political Science at the Massachusetts Institute of Technology (MIT) and a Faculty Affiliate at the Institute for Data, Systems, and Society (IDSS). He holds a Ph.D. in Politics from Princeton University, where he was awarded the Harold W. Dodds Fellowship (2012-2013). His research focuses on International Political Economy, Formal and Quantitative Methodology, and Big Data analysis of international trade. He is particularly known for his work on firm-level political incentives in trade liberalization, which earned him the 2015 Mancur Olson Award and the 2018 Michael Wallerstein Award for best published article in political economy. Kim develops computational methods for analyzing trade data, including dimension reduction and visualization techniques. He maintains two key databases: LobbyView (tracking firm lobbying efforts) and TradeLab (for trade policy analysis). His research has been published in top journals such as the American Political Science Review, American Journal of Political Science, and International Organization. Educations : Ph.D. in Politics (Princeton University), B.A. not explicitly stated. His research interests include the dynamical evolution of lobbying networks, strategic links between political donations and lobbying, and the political origins of trade regulations. He also contributes methodological innovations, such as two-way fixed effects models and matching methods for causal inference with panel data. Awards : Mancur Olson Award (2015) Michael Wallerstein Award (2018) Harold W. Dodds Fellowship (2012-2013) Advising & Grants : No listed advisees. His work is supported by MIT’s IDSS and institutional funding. He collaborates on software tools like the 'wfe' and 'concordance' R packages, advancing computational social science. Labs/Teams : Associated with MIT’s Political Science Department and IDSS, focusing on interdisciplinary projects in trade, lobbying, and quantitative methods.

• International Political Economy
• Formal and Quantitative Methodology
• Big Data Analysis
• +5 more

Jan Skaloud serves as an Adjunct Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC). He holds positions across multiple departments including SSIE (Institute of Earth Surface Dynamics), EDCE (Doctoral Program in Environmental Sciences and Engineering), and leads the Earth Sensing and Observation (ESO) Lab. His office is located at GC C2 397 in the EPFL campus in Lausanne, Switzerland. Dr. Skaloud's research expertise spans satellite positioning, inertial and integrated navigation systems, sensor orientation and calibration, attitude determination, mobile mapping, airborne laser scanning, and Kalman filtering techniques. His work bridges theoretical development with practical applications in UAV navigation, photogrammetry, and remote sensing. He teaches across three EPFL sections and two faculties, demonstrating his interdisciplinary approach to education. His publication record shows consistent contributions to the field, with recent work (2023-2025) focusing on vehicle dynamic model-based navigation for various UAV platforms, including delta-wing and fixed-wing drones. His research demonstrates a clear trajectory toward increasingly sophisticated navigation systems that integrate aerodynamic modeling with traditional sensor fusion approaches. This trend reflects the growing importance of model-based navigation in achieving higher precision and autonomy in UAV operations. 2021: Samuel Gamble Award for career contribution in photogrammetry & sensing (ISPRS) 2020: U.V. Helava Award for best paper in ISPRS Journal (2016-2019) 2017: Best Demo Award at IEEE International Workshop on Metrology & Aerospace 2014: Hansa Luftbild Award for best paper in PFG journal 2012: Karl Kraus Medal for best textbook in Photogrammetry 2009: GNSS Leader to Watch Innovation Award (GPS World) Dr. Skaloud has supervised numerous PhD students whose work focuses on advanced navigation systems, sensor calibration, and UAV applications. His research has received funding for projects involving direct georeferencing, mobile mapping systems, and UAV-based search and rescue operations. The ESO lab he directs serves as a hub for cutting-edge research in Earth observation technologies. The Earth Sensing and Observation Lab under Dr. Skaloud's direction brings together researchers working on navigation systems, sensor integration, and data processing techniques for geospatial applications. The lab maintains strong connections with industry partners and international research organizations, facilitating technology transfer and collaborative research projects.

• Satellite positioning
• Inertial navigation
• Integrated navigation systems
• +9 more

Chuck Fang is an Assistant Professor of Finance at Drexel University's LeBow College of Business. He holds a PhD in Finance from UPenn Wharton (2023) and BAs in Economics, Mathematics, and Statistics from UC Berkeley (2015). His research is centered on credit markets, monetary policy, financial innovations, and financial data infrastructure. PhD in Finance, UPenn Wharton, 2023 BAs in Economics, Mathematics, and Statistics, UC Berkeley, 2015 His research interests span credit markets , monetary policy transmission , financial innovations (including DeFi and automated market makers), and financial data linkage (e.g., Bond-Compustat-CRSP and DealScan-Compustat links). His work explores how monetary policy affects bond fund flows, debt structure changes, and sovereign restructuring. He emphasizes data quality and transparency, contributing open-source tools for empirical finance. The most recent articles reveal a strong focus on monetary policy amplification , debt market structure , and data infrastructure for empirical research. Keywords across publications include finance, monetary policy, asset pricing, fintech, and data linkage. Subfields consistently involve bond fund flows, syndicated loans, sovereign debt restructuring, DeFi mechanisms, and financial data validation. His research bridges macroeconomic policy with micro-level financial data, often using large-scale institutional holdings and transaction data. Scientific awards include: WFA Brattle Group PhD Candidate Award for Outstanding Research Chuck Fang has advised no students listed in the materials. He has secured research recognition through conference presentations and working paper awards. His work is supported by access to major financial databases and collaboration with leading scholars such as Kairong Xiao and Greg Nini. He actively disseminates findings through SSRN, Google Scholar, and academic conferences. He leads or contributes to several data infrastructure projects, including the Bond-Compustat-CRSP Link and DealScan-Compustat Link. These tools enhance empirical research in corporate finance and asset pricing by improving issuer identification and data accuracy. He also maintains a personal website and is active on professional platforms like LinkedIn and Twitter, promoting open science and financial research transparency.

• Credit Markets
• Monetary Policy
• Financial Innovations
• +7 more

Xuan Liang is a Lecturer in Statistics at the Research School of Finance, Actuarial Studies and Statistics (RSFAS), Australian National University. With a PhD from Peking University and postdoctoral experience at Monash University, his research focuses on spatial statistics, nonparametric modeling, and environmental data analysis. Education: PhD in Statistics (Peking University, 2017), BSc in Statistics (Zhejiang University, 2012) His work addresses methodological challenges in spatial panel data analysis, network modeling, and air pollution quantification. He has developed novel techniques for meteorological confounder adjustment in air quality assessments and contributed to distributed data analysis methods. Recent research trends include: Advancing quasi-score matching for spatial econometric models Improving subbagging algorithms for big data Creating robust distributed data aggregation frameworks Refining spatial autoregressive panel data methodologies Scientific contributions include: ANU Vice-Chancellor’s Citation for Outstanding Contribution to Student Learning (Early Career), 2022 CBE Teaching Commendation for Outstanding Teaching, 2020 Co-development of the ggmatplot R package for matrix visualization Co-inventor of Chinese patent 201811183512.0 for air quality assessment He teaches advanced courses in time series analysis, regression modeling, and mathematical statistics at ANU, while maintaining active research collaborations in econometrics and environmental statistics.

• Spatial Modelling
• Panel Data
• Nonparametric Statistics
• +3 more

Prof. Dr. Michael Klasen is a leading theoretical physicist at the Institute of Theoretical Physics at the University of Münster, where he heads his eponymous research group. His work bridges nuclear and particle physics, with significant contributions to quantum chromodynamics and physics beyond the Standard Model. His research focuses on Particle Physics , Quantum Chromodynamics , and Physics beyond the Standard Model , with particular emphasis on understanding the quark-gluon structure of atomic nuclei and dark matter phenomena. His innovative approach connects microscopic quark-gluon dynamics with nuclear binding phenomena, creating a crucial bridge between nuclear and particle physics. Prof. Klasen's recent work analyzing nucleon binding at the quark-gluon level was recognized as a "Breakthrough of the Year 2024" by Physics World. His research group's publication in Physical Review Letters demonstrated how quarks and gluons behave differently in nucleon pairs than in free nucleons, fundamentally advancing our understanding of nuclear binding. Breakthrough of the Year 2024 from Physics World Leadership of Research Training Group 2149 "Strong and weak interactions - from hadrons to dark matter" Supervision of award-winning doctoral research including the Infineon Dissertation Prize 2025 Prof. Klasen has successfully mentored numerous PhD students, with 20 of his group's graduates continuing their academic careers at prestigious institutions including CERN and Stanford University. His research has been supported by major funding bodies including the German Research Foundation (DFG), the Helmholtz Alliance for Astroparticle Physics, and BMBF collaborative research programs. The Klasen working group maintains active collaborations with international research networks including CTEQ, DM@NLO, and RESUMMINO.

• Particle Physics
• Quantum Chromodynamics
• Physics beyond the Standard Model
• +3 more

Mohammadtaghi Hajiaghayi is the Jack and Rita G. Minker Professor of Computer Science at the University of Maryland, College Park. He is affiliated with the Robert H. Smith School of Business and holds Research Affiliate positions at MIT CSAIL and the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). His research focuses on algorithms, game theory, and network design, supported by NSF, ONR, and industry grants. He has received prestigious awards including ACM Fellow (2018) and EATCS Nerode Prize (2015) for his work on bidimensionality theory. Education: PhD from MIT (2005), postdocs at CMU and MIT, MSc from University of Waterloo, and BSc from Sharif University. He teaches courses like Data Science and Algorithms at UMD. Industry experience includes roles at Amazon, Google, and AT&T Labs. Over 20 students have graduated under his advisement, many in academia and industry. His work spans approximation algorithms, game theory, and big data. Projects include BigDND with Erik Demaine. He serves on editorial boards of Algorithmica, SODA, and others. Awards also include IEEE Fellow (2020) and Blavatnik Honoree (2020).

• Design and Analysis of Algorithms
• Approximation Algorithms
• Fixed-Parameter Algorithms
• +8 more

Dr. Ameer Abdelhadi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on application-specific custom-tailored computer architectures, hardware-efficient deep learning, neurotechnology, and reconfigurable computing. He holds a PhD from the University of British Columbia and has held academic positions at the University of Toronto, Imperial College London, and Simon Fraser University, alongside industry experience in semiconductor design. Education: PhD in Computer Engineering (University of British Columbia, 2016). Research Interests: Hardware acceleration for machine learning and neurotechnology Reconfigurable computing and FPGAs/ASICs Asynchronous circuits and synchronization protocols VLSI physical design and CAD algorithms Publications span high-impact venues such as IEEE Journal of Solid-State Circuits, IEEE Hot Chips, and IEEE Micro. Notable achievements include the 2017 Best Paper Award at ASYNC for work on synchronization FIFOs. Teaching includes COMPENG 4DV4 (VLSI System Design) and ELECENG 4OI6B (Engineering Design). His lab focuses on advancing hardware systems for next-generation applications in AI and biomedical engineering.

• Hardware Acceleration
• Neurotechnology
• Reconfigurable Computing
• +4 more

Mohammad Hamdaqa is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads the Laboratory of Software and Emerging Technologies. His academic journey includes a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (2016), a Master's in Electrical and Computer Engineering from Concordia University, an MBA from the New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of software engineering and emerging technologies, particularly examining how software engineering approaches can be adapted for complex new platforms like cloud computing and blockchain. His work spans model-driven software engineering, cloud application architecture, smart contract development, and infrastructure as code. He investigates both how traditional software engineering practices can evolve to address the challenges of modern distributed systems and how emerging technologies can transform software development processes themselves. Analysis of his recent publications reveals a strong emphasis on blockchain technologies (particularly smart contracts), cloud-native applications, and the application of AI to software engineering tasks. His work shows a consistent thread of empirical research combined with practical tool development, with increasing focus on sustainability aspects of software systems in recent years. Much of his research bridges theoretical foundations with practical implementation concerns. Professor Hamdaqa serves as a thesis supervisor for multiple graduate students, with recent completed Master's theses focusing on smart contract auditing, prompt engineering for OCL generation, model-driven epidemiology, and security practices in infrastructure as code. He actively recruits students for research projects in his laboratory. He is a member of both the IEEE Computer Society and the Association for Computing Machinery (ACM), has served on program committees for major software engineering conferences, and is on the editorial board of Service Transaction on Internet of Thing. His laboratory, the Laboratory of Software and Emerging Technologies, serves as the hub for his research activities in blockchain, cloud computing, and model-driven engineering.

• Software Engineering
• Model-Based Software Engineering
• Cloud Computing
• +7 more

Professor Vincent Y. F. Tan holds dual appointments in the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS). His research focuses on Online Decision Making, Multi-Armed Bandits, Reinforcement Learning, Information Theory, and Statistical Signal Processing. Notably, he has been actively publishing in top-tier conferences like NeurIPS, ICML, and IEEE journals, with recent works exploring topics such as low-rank adaptation, off-policy evaluation, and queueing control. Professor Tan has advised numerous PhD students, including Fengzhuo Zhang, Yujun Shi, and Junwen Yang. He has received recognition for his teaching, including a 4.7/5.0 rating for EE5137 Stochastic Processes. His work has led to impactful publications, such as the best paper award at the ICML 2025 workshop on World Models and an oral presentation at ICLR 2025. He currently serves as a Senior Area Chair for NeurIPS 2025 and an Area Editor for the IEEE Transactions on Information Theory. His research group focuses on advancing theoretical and applied aspects of machine learning, with projects funded by grants in areas like distributed optimization and adversarial robustness. He collaborates widely, including with institutions like IIT Delhi and HKUST Guangzhou. Open positions are available for motivated postdocs and students in his research areas.

• Online Decision Making
• Multi-Armed Bandits
• Reinforcement Learning
• +3 more

Professor Christian Deutscher is a prominent sports economist at Bielefeld University's Faculty of Psychology and Sport Science, specializing in the Department of Sport Science within Division V - Sport and Business. As both Research Officer and Internationalization Officer, he leads significant work in sports economics, particularly focusing on betting markets, match-fixing detection, and sports integrity. His research has attracted funding from major institutions including the German Research Foundation. Deutscher's research interests span sports economics, betting market efficiency, match-fixing detection, sports management, and the business aspects of professional sports. His work combines advanced statistical modeling with practical applications in sports integrity monitoring. He has extensively analyzed live betting markets, Bundesliga economics, and the impact of technological innovations like VAR in football. His research often examines the intersection of sports performance, economic incentives, and market behavior. Analysis of his recent publications reveals a strong focus on empirical studies of sports betting markets, particularly live and in-play betting dynamics. His work demonstrates sophisticated use of state-space models and other advanced statistical techniques to detect market inefficiencies and potential integrity issues. Deutscher frequently collaborates with statisticians and economists to develop warning systems for match-fixing, with particular attention to German football leagues. His research bridges theoretical economics with practical applications in sports governance. Professor Deutscher actively contributes to the European Sport Economics Association (ESEA), having edited special issues of conference proceedings and serving in editorial capacities. His work has been published in leading journals including Journal of Sports Economics, Economic Inquiry, and Applied Stochastic Models in Business and Industry. As Research Officer for the Faculty of Psychology and Sport Science, Deutscher oversees research initiatives and international collaborations. His current projects include data-based fraud detection in live betting markets, funded by the German Research Foundation across multiple phases. He also serves on various university committees including the Quality Improvement Commission and Examination Board for the Department of Sport Science. Deutscher maintains strong connections with sports organizations and betting industry stakeholders, ensuring his research has practical relevance for sports integrity monitoring. His work on betting market inefficiencies has direct applications for regulatory bodies seeking to protect the integrity of sporting competitions.

• Sports Economics
• Betting Markets
• Match Fixing
• +7 more

Khurram K. Afridi is a Professor in the Electrical and Computer Engineering Department at Cornell University's College of Engineering. With a BS from Caltech and SM/PhD from MIT, he leads the High-Frequency Power Electronics and Control (HFPEC) research group. His work focuses on high-frequency power electronics, wireless power transfer systems, and electric vehicle charging infrastructure. Dr. Afridi's research spans capacitive wireless power transfer systems for electric vehicles, high-frequency power converters , impedance control networks , and power density optimization . His team has developed innovative approaches for multi-MHz wireless power transfer, reduced-fringing-field systems, and high-efficiency power conversion architectures. Recent work emphasizes practical implementation challenges including thermal management, pavement-embedded systems, and dynamic charging applications. His publications reveal a strong focus on high-power-density systems , wireless charging technologies , and advanced power conversion techniques . The research spans both theoretical modeling and practical implementation, with numerous papers in top conferences like APEC, ECCE, and IEEE journals. Key themes include improving power transfer efficiency at multi-MHz frequencies, developing novel matching network designs, and creating practical systems for electric vehicle charging infrastructure. First Place Prize Paper Award, IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023 First Place Best Contribution Award, IEEE WPTCE, 2023 Second Place Prize Paper Award, IEEE Transactions on Power Electronics, 2022 Distinguished Lecturer, IEEE Vehicular Technology Society, 2022 Cornell Engineering Research Excellence Award, 2021 NSF CAREER Award, 2016 Dr. Afridi has successfully advised numerous graduate students who have received multiple Outstanding Presentation Awards at major conferences. His research has been supported by significant funding including the NSF CAREER Award. The HFPEC group maintains strong industry connections and focuses on translating theoretical advances into practical power electronics solutions with real-world impact. Current research directions include electrified roadways, dynamic wireless charging systems, and high-power-density power conversion architectures for next-generation applications.

• Wireless Power Transfer
• Power Electronics
• Electric Vehicle Charging Systems
• +5 more

Tao Zou is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans covariance regression modeling, network data analysis, and applications in financial and environmental statistics. He earned a Ph.D. in Statistics in 2016. Ph.D. in Statistics, 2016 Dr. Zou’s work pioneers covariance regression, where covariances are modeled as functions of covariiates. Key contributions include robust estimation techniques, spatio-temporal modeling, missing data imputation via semi-supervised learning, and distributed data aggregation. His methods address challenges in high-dimensional and non-Euclidean data analysis. Recent publications (2025–2023) explore quasi-score matching for spatial autoregressive models, regularization in network regression, functional principal component analysis for complex data, and environmental applications like PM2.5 pollution studies. These works emphasize robustness, scalability, and interdisciplinary relevance in economics, finance, and environmental science. Dr. Zou collaborates on projects like the 2023 Data Analysis App to Empower Assessment of Immunogenicity of Biologics (Co-Investigator). While his student supervision list isn’t explicitly provided, his methodological advancements influence big data and spatial statistics. He contributes to open-access software and continues expanding covariance regression for non-normal and functional data.

• Covariance regression modeling
• Network data modeling
• Financial statistics
• +3 more

Niklas Engbom is an Assistant Professor at the Stern School of Business of New York University , specializing in macroeconomics and labor economics. He holds affiliations with NBER, CEPR, IFAU, and UCLS as a researcher. His prior experience includes a Junior Scholar role at the Federal Reserve Bank of Minneapolis (2018–2019) and a PhD from Princeton University (2018) under Richard Rogerson's supervision. His research focuses on labor market dynamics, firm behavior, and their macroeconomic implications. Key themes include wage stagnation, job ladder decline, workforce aging effects, and minimum wage policies. He has contributed to understanding how labor market fluidity impacts skill accumulation and earnings inequality, with cross-country analyses in OECD nations and Brazil. Engbom's work integrates theoretical models with empirical data, such as employer-employee panels and Swedish labor market records. His findings highlight structural shifts in labor markets—such as reduced job mobility and increased employer concentration—as critical drivers of wage growth slowdowns. He also examines how demographic changes suppress entrepreneurship and firm dynamics, linking aging populations to reduced economic growth. His research has been featured in outlets like The Economist , MarketWatch , and the Economic Report of the President . Notable projects include analyzing Brazil's inequality decline through firm policies and evaluating the consequences of German labor market reforms.

• Macroeconomics
• Labor Economics
• Firm Dynamics
• +3 more

Christoph Breuer is a Professor at the Institute of Sport Economics and Sport Management at the Deutsche Sporthochschule Köln. His work focuses on sports economics, club management, and policy analysis. He leads research initiatives examining sports integrity, club ownership dynamics, and volunteer engagement. Breuer is actively involved in editorial roles for journals such as the Journal of Global Sport Management and Sport Management Review. His research spans over 800 publications, including studies on pandemic impacts on athletes and clubs, sponsorship strategies, and logistics in sports organizations. He has received the SMAANZ Best Conference Paper Award 2024. Key projects include analyzing multi-club ownerships and evaluating prevention measures against sports match-fixing. He advises PhD students like Thomas Breuer and Christoph Pott. His grants include projects funded by third parties, such as the 'Sportentwicklungsbericht' series and investigations into sports integrity. Breuer collaborates internationally, addressing topics like equipment logistics and athlete well-being. His work bridges academic research with practical applications for sports policy and management. Editorial roles: Journal of Global Sport Management, Sport Management Review Key projects: Sport Development Report (SEB 3.0), UEFA EURO 2024 evaluation Media engagements: Over 100 press contributions on topics like sports economics and club financing

• Sport Management
• Economics of Sport
• Sports Clubs Operations
• +3 more

Thomas Lemieux is a Professor at the Vancouver School of Economics within the Faculty of Arts at the University of British Columbia , where he has been affiliated since 1999. Previously, he taught at MIT and the Université de Montréal. Born in Quebec City, he earned his Ph.D. from Princeton University. Research Interests: His work focuses on labor economics and econometric methods , particularly analyzing earnings inequality , unionization effects , regression discontinuity designs , and educational returns . He employs advanced decomposition techniques to study wage dynamics across gender, immigration status, and sectoral divides. Scientific Awards: Fellow, Royal Society of Canada Fellow, Society of Labor Economists Research Fellow, Institute for the Study of Labor (IZA) Research Associate, National Bureau of Economic Research (NBER) Publications: He has published extensively in top journals like the Quarterly Journal of Economics , Econometrica , and Journal of Labor Economics , with recent work examining: Union wage premiums using matched employer-employee data Spillover effects of minimum wage policies Changes in task prices and occupational wages Top income dynamics in Canada Immigrant wage gaps across education sources Regression discontinuity identification challenges Canadian labor market responses to the Great Recession

• Labor Economics
• Econometrics
• Income Inequality
• +4 more