جست‌وجوی استاد و پژوهشگر

Florentina Buneaمشاهده پروفایل

استاد

• Statistical machine learning theory
• High-dimensional statistical inference
• Optimal transport
• +۶ مورد دیگر

Florentina Bunea is a Professor in the Department of Statistics and Data Science at Cornell University’s Bowers College of Computing and Information Science, and an active member of the Graduate Fields of Statistics, Applied Mathematics, and Computer Science. She also serves on the Diversity and Inclusion Council of her college, championing workforce diversity in data-science disciplines. Education & Institutional Roles Professor, Department of Statistics and Data Science, Cornell University Member, Graduate Fields of Statistics, Applied Mathematics, Computer Science Member, Diversity and Inclusion Council, Bowers College of Computing and Information Science Research Interests Professor Bunea’s research lies at the intersection of statistical machine-learning theory and high-dimensional inference. She develops rigorous methodology supported by sharp theoretical guarantees to tackle core problems in modern data science. Recent themes include: Soft-max mixtures for understanding large-language-model/AI algorithms Optimal transport for high-dimensional mixture distributions Wasserstein-distance inference for sparse mixing measures in topic models Latent-space clustering and cluster-based inference in high dimensions Network modeling and hidden-structure inference Applications spanning genetics, systems immunology, neuroscience, sociology, and economics Research Funding & Awards Her work is supported by grants from the National Science Foundation (NSF-DMS). She is a Fellow of the Institute of Mathematical Statistics and a recipient of the IMS Medallion Award. Editorial & Service Contributions Associate Editor: Annals of Statistics, Bernoulli, JASA, JRSS-B, EJS, Annals of Applied Statistics Co-Editor: Chapman & Hall/CRC Statistics and Applied Probability Monograph Series Advising & Collaboration Professor Bunea has mentored numerous doctoral and post-doctoral researchers, including Xin Bing, Shuyu Liu, Seth Strimas-Mackey, and Yang Ning, among others. Collaborative projects extend across Cornell and external institutions, producing widely-used software packages and high-impact publications. Contact Office: 1184 Comstock Hall, Cornell University Email: fb238@cornell.edu Phone: (607) 255-8449

Katie Bylمشاهده پروفایل

دانشیار

• Robotics
• Control Systems
• Robot Dynamics and Control
• +۷ مورد دیگر

Katie Byl is an Associate Professor in the Departments of Electrical and Computer Engineering and Mechanical Engineering at the University of California, Santa Barbara (UCSB). Her research focuses on robot dynamics and control, particularly in locomotion and manipulation, with applications in rough terrain legged locomotion, supervised autonomy, and flapping-wing flight. She holds B.S., M.S., and Ph.D. degrees in Mechanical Engineering from MIT. Affiliations: Center for Control, Dynamical Systems and Computation (CCDC) Mechanical Engineering Department Research Interests: Byl’s work emphasizes modeling and control of underactuated systems, stochasticity in real-world environments, and the development of robust control principles for dynamic systems. Her applied projects include exoskeletons, autonomous robots like RoboSimian (part of the DARPA Robotics Challenge), and flapping-wing microrobotics. Scientific Awards: NSF Early Career Development Award Hellman Faculty Fellowship Alfred P. Sloan Foundation Fellowship in Neuroscience Regents’ Junior Faculty Fellowship Teaching & Advising: Byl teaches courses in control systems (e.g., ECE 147B, ECE 179D) and robotics. She advises students in UCSB’s Robotics Lab, emphasizing a mix of control theory, mechanical design, and algorithm development. Undergraduate researchers are also recruited annually for summer projects. Labs & Teams: Her Robotics Lab focuses on hardware implementation of control ideas, maintaining a high robot-to-student ratio. Collaborations include the Army’s Institute for Collaborative Biotechnologies and the DARPA Robotics Challenge with JPL.

Qianwen Wangمشاهده پروفایل

استادیار

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +۵ مورد دیگر

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

Elmira Hassanzadehمشاهده پروفایل

دانشیار

• Hydrology
• Water Resources Engineering
• Climate Change Impact Assessment
• +۴ مورد دیگر

Elmira Hassanzadeh is an Associate Professor at the Department of Civil, Geological and Mining Engineering , Polytechnique Montréal, with a focus on integrated water resource modeling and management under climate and anthropogenic changes. She is affiliated with the Global Institute for Water Security (University of Saskatchewan) and the Experimental and Digital Water Flow Engineering Group (GENIE EAU) . Education: PhD in Civil Engineering, University of Saskatchewan Postdoctoral Fellow, McGill University Research interests include climate change impact assessment, socio-hydrological modeling, stakeholder engagement in water management, and sustainable development of water systems in cold regions. Her work emphasizes decision-making under uncertainty and adaptive strategies for ungauged basins. Recent publications (2024–2020) span coastal flood hazard modeling, climate-driven hydrological changes in tropical and Canadian watersheds, evaluation of agricultural practices under warming climates, and integrated approaches to water quality and system dynamics. Keywords include Hydrology , Climate Change , Water Security , and Socio-Hydrology . Supervision: She has mentored 15+ graduate students in projects related to Lake Urmia, Saskatchewan River Basin, and climate adaptation in Africa and the Pacific.

• Machine Learning
• Deep Learning
• Theoretical Foundations of AI
• +۱۱ مورد دیگر

Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.

Joan Brunaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Signal Processing
• +۵ مورد دیگر

Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.

Jianhua Guoمشاهده پروفایل

استاد

• Environmental Biotechnology
• Antimicrobial Resistance (AMR)
• Wastewater Treatment
• +۷ مورد دیگر

Professor Jianhua Guo is a faculty member at The University of Queensland (UQ), holding the position of Professor and UQ Amplify Fellow in the Australian Centre for Water and Environmental Biotechnology (ACWEB). He is also the ACWEB Deputy Director - Research. His research focuses on integrating process engineering, environmental microbiology, and biotechnology to develop sustainable solutions for contaminant removal in water and wastewater systems. He is a pioneer in studying the environmental dimension of antimicrobial resistance (AMR), particularly how non-antibiotic pharmaceuticals and chemicals contribute to AMR spread. His groundbreaking work has been featured in over 100 media outlets and has secured over $14M in research funding. He has published over 200 peer-reviewed articles in journals like Nature Microbiology , Water Research , and Environmental Science & Technology . His accolades include the 2013 DECRA and 2017 ARC Future Fellowship. He serves as an editor for Journal of Hazardous Materials and Water Science & Technology . Research Interests Environmental AMR and microbial ecology Methane-based biotechnology and bioconversion Nitrogen cycling microorganisms Bioremediation and biofilm reactors Microplastics and health risks Wastewater treatment innovations Grants & Funding $14M+ in ARC Discovery/Linkage grants and industry partnerships ARC Training Centre for Environmental and Agricultural Solutions to Antimicrobial Resistance (CEA-StAR) Dual-membrane upgrading and anaerobic alkane oxidation projects Awards 2013 DECRA 2017 ARC Future Fellowship Supervision Prof. Guo actively supervises PhD and master’s students in areas like AMR mitigation, methane-based bioreactors, and microbial ecology. Notable supervised projects include studies on antibiotic resistance in wastewater, novel disinfection processes, and microbial oxidation of gaseous alkanes. Labs & Teams He leads research teams within ACWEB and collaborates with the Queensland Alliance for Environmental Health Science. His lab focuses on cutting-edge technologies like membrane biofilm reactors and gas fermentation systems.

Neil Selwynمشاهده پروفایل

استاد

• Digital technology and society
• AI in education
• Digital sociology of education
• +۵ مورد دیگر

Neil Selwyn is a Professor in the Faculty of Education at Monash University with over 30 years of research experience focusing on the integration of digital technology into educational settings. His work centers on the 'real-life' constraints and problems faced when implementing technology-based education, with expertise in AI, automation, data-driven education, and digital degrowth. His primary research interests span digital technology and society, the use of digital technologies in educational settings, digital sociology of education, and environmental sustainability issues associated with digital technology use. Professor Selwyn is recognized internationally for his critical approach to educational technology and has led numerous nationally-funded projects examining educational data, learning analytics, and AI technologies. His recent publications reflect a growing focus on the environmental implications of AI in education, digital degrowth, and the practical challenges teachers face when implementing AI tools in classrooms. This work demonstrates a shift toward examining the sustainability and ethical dimensions of educational technology. Fellow of the Academy of Social Sciences in Australia (2024) Professor Selwyn serves as a media commentator for major outlets including the NYT, WSJ, CNN, Guardian, BBC, and ABC, and hosts the 'Education Technology Society' podcast. He maintains significant international collaborations, currently holding visiting positions at Lund University (2025), University of Oxford (2023-2027), and previously at the University of Gothenburg (2016-2017).

Shai Ben-Davidمشاهده پروفایل

استاد

• Machine Learning Theory
• Unsupervised Learning and Clustering
• Domain Adaptation and Transfer Learning
• +۳ مورد دیگر

Shai Ben-David is a Professor and University Research Chair at the Department of Computer Science, University of Waterloo. He is affiliated with the Cheriton School of Computer Science and can be reached at shai@uwaterloo.ca . His office is located in DC 2643. Education: Ph.D., Hebrew University, Jerusalem, Israel (1987) M.Sc., Hebrew University Jerusalem, Israel (1979) B.Sc., Hebrew University Jerusalem, Israel (1978) Research interests focus on foundational aspects of machine learning theory, including unsupervised learning (clustering), domain adaptation, fairness, interpretability, and alternative approaches to worst-case computational complexity. He also explores logic applications in computer science theory. His research trends emphasize theoretical challenges in machine learning, particularly clustering, fairness in representations, and the interplay between computational feasibility and learnability. He investigates how unlabeled data and sample compression techniques impact learning robustness and efficiency. No scientific awards are listed. His advising record shows no formal advisees listed here. Grants and funding details are not provided in the text. He has contributed to organizing events like the Dagstuhl Seminar on Foundations of Unsupervised Learning (2017) and co-edited MFCS 2016 proceedings. His work addresses both theoretical questions and practical gaps in ML implementation.

Song Kimمشاهده پروفایل

دانشیار

• International Political Economy
• Formal and Quantitative Methodology
• Big Data Analysis
• +۵ مورد دیگر

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.

Jorge Eduardo Pinto Santosمشاهده پروفایل

استاد

• General Relativity
• Quantum Gravity
• Gravitational Aspects of String Theory
• +۳ مورد دیگر

Professor Jorge Eduardo Pinto Santos holds the position of Professor of Theoretical Physics at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. His career includes roles as Reader (2018–2022), Lecturer (2013–2018), and a Junior Visiting Professorship at the Institute for Advanced Study in Princeton (2019–2020). He completed his Ph.D. at DAMTP between 2006 and 2010, followed by postdoctoral fellowships at Stanford University (2013–2014) and the University of California, Santa Barbara (2010–2013). His research focuses on general relativity, quantum gravity, and gravitational aspects of string theory. Key areas include numerical relativity and applications of general relativity to condensed matter physics via holography. Notable contributions involve studies on black hole binaries, extremal black holes, and the AdS/CFT correspondence. His recent work explores topics such as spinning black binaries in de Sitter space, gravitational instabilities in anti-de Sitter space, and entanglement islands in higher dimensions. He is affiliated with the High Energy Physics and Relativity and Gravitation research groups at DAMTP. His advising record includes seven doctoral students, with ongoing supervision of William Boyce (2024–present) and John Crump (2021–present). Publications highlight interdisciplinary advances, including investigations into black hole thermodynamics, cosmic censorship violations, and holographic models of condensed matter systems. His research bridges fundamental physics and numerical methods, addressing both theoretical and applied questions in gravitational physics.

Eva Viehmannمشاهده پروفایل

استاد

• Arithmetic Geometry
• Representation Theory
• Algebraic Geometry
• +۴ مورد دیگر

Prof. Dr. Eva Viehmann is a leading mathematician at the University of Münster within the Faculty of Mathematics and Computer Science and a key figure in the Mathematics Münster cluster. She was awarded the prestigious Gottfried Wilhelm Leibniz Prize 2024 for her groundbreaking work in arithmetic algebraic geometry and representation theory within the Langlands program . University: University of Münster Department: Mathematical Institute Her research focuses on the intersection of algebra , geometry , and analysis , particularly through the lens of Shimura varieties and moduli spaces of local G-shtukas . She has pioneered the study of affine Deligne-Lusztig varieties in equal and mixed characteristics, advancing understanding of their dimension , connectedness , and irreducible components . Recent publications highlight her work on Newton stratification , Harder-Narasimhan theory , and p-adic moduli spaces . Her scientific advisory contributions include mentoring former doctoral student Stefania Trentin and collaborating with Prof. Urs Hartl over 15 years. Awards and honors include the Leibniz Prize 2024 , reflecting her status as a trailblazer in arithmetic geometry and p-adic geometry . Her research projects span the CRC 1442 and EXC 2044 , aiming to unify Galois representations , automorphic forms , and geometric methods .

Claudia R. Binderمشاهده پروفایل

استاد

• Urban Sustainability
• Transition Research
• Urban Metabolism
• +۵ مورد دیگر

Claudia R. Binder is Full Professor at EPFL's School of Architecture, Civil and Environmental Engineering, leading the Laboratory for Human-Environment Relations in Urban Systems (HERUS) since 2016. Previously, she held professorships at the University of Munich (2011-2016), University of Graz (2009-2011), and University of Zurich (2006-2009). She served as Dean of EPFL's ENAC School from 2020-2023 and holds advisory roles with Swiss federal institutions including the Mercator Foundation since 2024. Her academic foundation includes a Biochemistry degree and PhD in Environmental Sciences from ETH Zurich, followed by postdoctoral research at the University of Maryland. This interdisciplinary background underpins her research approach spanning natural and social sciences. Professor Binder's work centers on urban sustainability transitions, examining urban metabolism dynamics through systems science frameworks. She investigates energy-food-transport interdependencies in cities using transdisciplinary methods that integrate material flow analysis, spatial modeling, and socio-technical assessments. Her research particularly emphasizes regulatory mechanisms and transformation drivers in human-environment systems, with case studies across Swiss and global urban contexts. Recent publications reveal evolving focus from foundational urban metabolism studies toward actionable transition strategies. Her 2024-2025 work increasingly addresses social tipping dynamics, circular decarbonization, and spatially explicit waste management, demonstrating methodological innovation through geo-referenced material flow analysis and participatory backcasting frameworks. Key thematic clusters include energy innovation diffusion, plastic waste governance, and demand-side flexibility in residential systems. She actively mentors 7 current PhD candidates while supervising 11 graduates since 2018, with research spanning urban metabolism modeling, sustainability assessment, and transition governance. Her leadership extends to Swiss National Science Foundation committees and National Research Program 71 on energy consumption management. At EPFL, she directs the HERUS laboratory which develops the Sustainability Solution Space methodology for urban assessment. The lab operates at the intersection of data science, environmental engineering, and social theory, maintaining strong field connections in Switzerland, Indonesia, and Germany for empirical validation of transition models.

Witold J. Heniszمشاهده پروفایل

استاد

• Political Risk Management
• ESG (Environmental, Social, Governance) Impact Analysis
• Stakeholder Engagement
• +۵ مورد دیگر

Witold J. Henisz serves as Vice Dean and Faculty Director of the Impact, Value, and Sustainable Business Initiative at The Wharton School, University of Pennsylvania, holding the Deloitte & Touche Professorship in Management. His research centers on political and social risk identification, ESG impact materiality, and corporate diplomacy frameworks that transform stakeholder relationships into strategic assets. His educational background includes a Ph.D. in Business and Public Policy from UC Berkeley's Haas School and an M.A. in International Relations from Johns Hopkins SAIS. Henisz has served as Departmental Editor at Journal of International Business Studies and Associate Editor at Strategic Management Journal , reflecting his scholarly influence. Research interests converge on geopolitical risk quantification, stakeholder engagement optimization, and sustainable business model innovation. His work demonstrates how political hazards materially impact firm valuation and how corporate diplomacy can mitigate conflict through strategic stakeholder alignment. Recent publications leverage massive media datasets (4+ billion articles) to model populism dynamics, indigenous land conflicts, and ESG-credit risk linkages. Award highlights include the Aspen Institute Ideas Worth Teaching Award (2020), Iron Prof recognition (2019), and Academy of International Business Fellowship. His geostrategy framework Geostrategy by Design (2024) provides executives with actionable methods for navigating geopolitical volatility. Aspen Institute Ideas Worth Teaching Award (2020) Iron Prof recognition (2019) Academy of International Business Fellow (2014-present) Multiple Wharton Excellence in Teaching Awards (2006-2021) Industry Studies Association Best Paper Award (2019) Henisz consults through PRIMA LLC for multinational firms (Rio Tinto, Shell), financial institutions (Eaton Vance, World Bank), and NGOs. His executive education programs train leaders in ESG integration and geopolitical risk management, while his KEROVKA crisis simulation develops real-time strategic response capabilities. Media frequently cites his expertise on ESG controversies, with 50+ major publications featuring his analysis since 2022 including Financial Times , Bloomberg , and Wall Street Journal .

• Empirical Corporate Finance
• Banking Regulation
• Climate Finance
• +۳ مورد دیگر

Professor Duc Duy (Louis) Nguyen is Professor in Finance at Durham University Business School. Previously, he was Associate Professor at King's College London and Assistant Professor at University of St Andrews. His research has been featured in Forbes, Harvard Business Review, and BBC. He holds a BSc in Computing from National University of Singapore, MSc in Accounting, Finance, and Management from University of Bristol, and PhD in Finance from University of Edinburgh. Research foci include: Climate risk integration in mortgage markets and corporate finance Impact of social policies (e.g., marriage equality) on credit access Corporate governance and misconduct prevention in banking Local information environments and fraud detection Cross-cultural dimensions of executive decision-making His publication portfolio demonstrates consistent examination of how institutional frameworks (regulation, culture, governance) shape financial behaviors and market outcomes, with recent emphasis on climate finance and social equity impacts. Awards include: David Hume Publication Prize (2015) Semi-finalist, FMA Europe Best Paper Award (2018) Finalist, FMA Asia/Pacific Best Paper Award (2019) Best Registered Report on "Politics and Corporate Power" Vietnam Symposium in Climate Transition Best Paper Award (2024) He currently supervises PhD candidates Bingzhi Zhang and Jing Wei. His externally funded research includes grants from British Academy/Leverhulme Trust and Carnegie UK Trust. He serves as Associate Editor for European Journal of Finance and British Accounting Review. As frequent speaker at central banks and policy forums (Federal Reserve Banks of New York & St. Louis), he translates research insights into regulatory practice and policy development.