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Douglas W DiamondView profile

Professor

• Financial intermediaries
• Financial crises
• Liquidity
• +3 more

Douglas W. Diamond is the Merton H. Miller Distinguished Service Professor of Finance at the University of Chicago's Booth School of Business, where he has been on the faculty since 1979. He received the Nobel Memorial Prize in Economic Sciences in 2022 for his groundbreaking research on banks and financial crises. Diamond is also a research associate of the National Bureau of Economic Research and a visiting scholar at the Federal Reserve Bank of Richmond. His research focuses on financial intermediaries, financial crises, and liquidity. Diamond's work has established the theoretical foundations for understanding why banks exist, how they create liquidity, and why they are vulnerable to runs. His seminal papers with Philip Dybvig on bank runs and deposit insurance form the basis of modern bank regulation. Diamond's extensive publication record shows a consistent focus on banking theory, financial intermediation, and financial stability. His research has evolved from foundational theoretical work to more recent analyses of liquidity, pledgeability, and the role of banks in the financial system. His work has become increasingly relevant following the 2008 financial crisis and the more recent banking turmoil. Nobel Memorial Prize in Economic Sciences (2022) Onassis Prize in Finance (2018) CME Group-Mathematical Sciences Research Institute Prize (2016) Morgan Stanley-American Finance Association Award (2012) Member of the National Academy of Sciences Fellow of the Econometric Society Fellow of the American Academy of Arts and Sciences Diamond has served as president of both the American Finance Association and the Western Finance Association. He has advised policymakers during financial crises, including providing analysis of the Silicon Valley Bank collapse in 2023. His research has directly influenced financial regulation and central bank policy regarding bank stability and liquidity requirements.

Stephen LichtView profile

Associate Professor

• Marine Robotics
• Biologically Inspired Propulsion
• Nonlinear Attitude Control
• +5 more

Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.

Robert JOLIETView profile

Associate Professor

• Finance
• Portfolio Management
• SRI (Socially Responsible Investing)
• +3 more

Robert JOLIET is an Associate Professor of Finance at IÉSEG School of Management in France. He holds a Ph.D. in Economics and Management from HEC-ULg Management School (Belgium) and has held various academic leadership roles, including Director of Academic Development and Academic Director of the PGE (MSc in Management). His research focuses on finance, portfolio management, SRI (Socially Responsible Investing), and corporate finance, with a particular emphasis on international expansion, capital structure, and sustainable investment strategies. Education: 2008: Ph.D. in Economics and Management, HEC-ULg Management School, Belgium 2004: Master of Advanced Studies (DEA), Management Sciences (Finance), University of Liège, Belgium 2002: Master of Science in Business Engineering (Finance), University of Liège, Belgium Research interests span SRI fund dynamics, corporate social responsibility valuation, international debt policies, and market liquidity. Notable contributions include analyzing the impact of SRI mutual funds on corporate governance and environmental practices, and exploring the stability of sovereign debt structures in the Eurozone. Awards include the 2005 FORTIS Award (Third Prize) and the 2003 E.G. de Barsy Doctoral Fellowship. He has published extensively in journals like Economics Letters and Journal of Banking & Finance . Academic leadership roles include directing programs and departments at IÉSEG, alongside coordinating the Finance Track and managing academic development initiatives. His teaching focuses on financial data analysis, active portfolio management, and investment strategies in capital markets. He is a member of the LEM (Laboratoire d'Économie Moderne) research group and contributes to interdisciplinary research in finance and economics.

Kenji KawaguchiView profile

Assistant Professor

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +12 more

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

Sean CaoView profile

Associate Professor

• Artificial Intelligence in Finance
• Machine Learning Applications
• Capital Market Research
• +5 more

Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.

• Corporate Finance
• Mergers and Acquisitions
• Law and Finance
• +3 more

Azizjon ALIMOV is a Full Professor at IÉSEG School of Management (University of Lille, France), specializing in Corporate Finance, Mergers and Acquisitions, and Law and Finance. He holds a Ph.D. in Finance from the University of Oregon (2007) and an MBA from Central Michigan University (2001). His HDR (Habilitation) in Management Sciences was awarded by the University of Lille in 2023. His research focuses on corporate governance, cross-border M&A dynamics, and the interplay between legal frameworks and financial decision-making. Key themes include intellectual property rights' impact on corporate debt costs, product market competition effects on corporate cash holdings, and government borrowing influences on acquisition strategies. His work frequently examines global contexts, with studies spanning North America, Asia, and Europe. Notable publications include analyses of IPO staging mechanisms (2024), managerial discipline through trade liberalization (2023), and the role of labor protection laws in loan contracting (2015). His recent work emphasizes reproducibility in management science methodology (2023) and cross-border regulatory challenges in M&A. No scientific awards are explicitly listed; however, his extensive publication record reflects sustained academic contributions. He has advised students across institutions including City University of Hong Kong and the Sauder School of Business, though specific advisee names are not documented here. His professional experience includes roles at the University of Arizona (2008–2010), California State University (2007–2008), and as an HSBC Visiting Assistant Professor at the University of British Columbia (2016–2018). He currently leads the Finance Track at IÉSEG, contributing to programs like the Grande École Master’s in Finance and MSc Corporate Finance courses.

• Environmental Health Engineering
• Wastewater-Based Epidemiology
• Bioremediation
• +7 more

Rolf Halden is a Professor and Center Director at Arizona State University, holding appointments in the School of Sustainable Engineering and the Built Environment, the Biodesign Center for Environmental Health Engineering, and the Global Futures Scientists and Scholars program. He is a leading expert in environmental health engineering, wastewater-based epidemiology, and bioremediation. Education: Ph.D. in Civil (Environmental) Engineering, University of Minnesota (1997) M.S. in Civil (Environmental) Engineering, University of Minnesota (1994) M.S. in Biology, Technical University of Braunschweig, Germany (1992) Postdoctoral Fellow, Environmental Science, Lawrence Livermore National Laboratory (1998) Research Focus: Halden’s research integrates environmental chemistry, public health, and sustainability to understand how anthropogenic chemicals affect ecological and human health. His lab uses advanced mass spectrometry (LC-MS/MS, MALDI-TOF) to track pollutants such as PPCPs, microplastics, pesticides, and dioxins across air, water, soil, and biological systems. He pioneered the use of wastewater treatment plants as public health observatories, enabling real-time tracking of disease, drug use, and environmental exposures. Scientific Awards & Recognition: Leadership Award, Arizona State University (2018) Rocky Mountain Emmy Award (2018) Leroy E. Burney Lecturer, Johns Hopkins School of Public Health (2011) ACS Expert Program Member (2014–present) NIEHS Superfund R01 Working Group National Leader (2012–2014) Labs & Teams: Halden is the Founding Director of the Biodesign Center for Environmental Health Engineering at ASU, where he leads a multidisciplinary team focused on environmental proteomics, bioremediation, and diagnostic tool development. He also co-leads the Human Health Observatory , a national wastewater monitoring network. Grants & Funding: Halden has secured major funding from NSF, NIH, EPA, DoD, and private industry. Notable projects include the development of in-situ groundwater monitoring devices, national wastewater surveillance of SARS-CoV-2, and studies on microplastics and neurodegenerative disease.

Ariel PakesView profile

Professor

• Econometrics
• Industrial Organization
• Economics of Technological Change
• +3 more

Ariel Pakes is the Thomas Professor of Economics at Harvard University, part of the Faculty of Arts and Sciences and the Department of Economics. He previously held the Charles and Dorothea Dilley Professorship at Yale University and tenured roles at institutions including the University of Wisconsin and the Hebrew University of Jerusalem. His research focuses on industrial organization (I.O.), the economics of technological change, and econometric theory. Pakes has advised notable students who now lead economics departments globally and has received prestigious awards including the Frisch Medal (1986) and election to the American Academy of Arts and Sciences (2002). Education: Pakes earned his Ph.D. in Economics from Harvard University in 1980. His academic journey includes roles as a lecturer at Harvard and positions at the University of Jerusalem. Research Interests: Pakes has pioneered methodologies for analyzing I.O. models, including demand and cost system estimation. Recent work examines AI’s impact on pricing strategies, antitrust policy implications of market dynamics, and healthcare cost structures. His theoretical contributions include simulation estimators and frameworks for dynamic oligopolies. Awards: Frisch Medal (Econometric Society, 1986) Fellow of the Econometric Society (1988) Fellow of the American Academy of Arts and Sciences (2002) Best Graduate Student Advisor Award (Yale University, 1996) Advisory and Grants: Pakes has served on NSF panels, including the Economics Advisory Panel and Presidential Fellow Advisory Board. His consulting work spans government agencies and large firms. He edited the RAND Journal of Economics and co-chaired the AEA Census Advisory Panel. Labs/Teams: Collaborates with NBER as a Research Associate and contributes to interdisciplinary projects like the PNAS issue on 'Science, Technology and the Economy.'

Joseph KableView profile

Professor

• Behavioral Neuroscience
• Cognitive Neuroscience
• Decision Processes
• +3 more

Joseph Kable is the Jean-Marie Kneeley President's Distinguished Professor of Psychology at the University of Pennsylvania's Department of Psychology within the School of Arts and Sciences. His research integrates experimental economics, cognitive neuroscience, and personality psychology to investigate the neural and psychological mechanisms underlying decision-making. He explores how subjective value is represented in the brain, deviations from rational choice theory, and individual differences in decision processes. His lab employs fMRI and interdisciplinary methods to study topics like risk tolerance, impulsivity, and neural plasticity. Education: BS in Chemistry from Emory University (undergraduate), PhD in Neuroscience from the University of Pennsylvania (doctoral). Research focuses on behavioral and cognitive neuroscience mechanisms of choice, including studies on temporal predictions, value signals, and brain structure-function relationships. His recent work examines how neural markers correlate with decision-making variability across individuals. He currently advises graduate students in Psychology and has post-doctoral researchers in his lab. Associated with MindCORE, Penn's hub for integrative mind research. Lab activities include undergraduate research programs and active studies exploring decision neuroscience and neuroeconomics.

Clifford SteinView profile

Professor

• Algorithms
• Combinatorial Optimization
• Operations Research
• +4 more

Clifford Stein is a Professor of Industrial Engineering and Operations Research (IEOR) and Computer Science at Columbia University, and Associate Director for Research at the Data Science Institute. He holds a Ph.D. (1992), M.S. (1989), and B.S.E. (1987) from MIT and Princeton University, respectively. His research focuses on algorithms, combinatorial optimization, operations research, scheduling, and computational biology. A co-author of the best-selling textbook Introduction to Algorithms , Stein has published widely in top venues and holds prestigious awards like ACM Fellow and NSF Career Award. His work includes foundational contributions to minimum cut algorithms, scheduling theory, and network optimization, supported by NSF and Sloan Foundation grants. Stein has advised over 40 graduate and undergraduate students, many now in academia and industry.

Osman YağanView profile

Research Professor

• Machine Learning
• Data Science
• Network Science
• +7 more

Osman Yağan is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with affiliate faculty status in the School of Computer Science. He is also a core member of CyLab Security and Privacy Institute. Prior to joining CMU in 2013, he was a Postdoctoral Research Fellow at CyLab. He holds a Ph.D. in Electrical and Computer Engineering from the University of Maryland (2011) and a B.S. from Middle East Technical University (2007). His research focuses on modeling, analysis, and optimization of computing systems, leveraging applied probability, network science, data science, and machine learning. Key areas include multi-armed bandits, resilient machine learning, contagion processes in networks, and cybersecurity. Research Interests include Machine Learning, Data Science, Network Science, Cybersecurity, and Robustness in Cyber-Physical Systems. He has advised numerous students, including current PhD candidates Yurun Tian, Orkun İrsoy, and Ishank Juneja, as well as notable alumni such as Mansi Sood (now at MIT) and Jun Zhao (Assistant Professor at Nanyang Technological University). Key Awards include the CIT Dean's Early Career Fellowship, IBM Academic Award, and Best Paper Awards at ICC 2021, IPSN 2022, and ASONAM 2023. His work spans theoretical contributions (e.g., contagion models in multi-layer networks) and applied research (e.g., mitigating cascading failures in power systems). He leads or co-leads grants from ONR, NSF, and ARO, focusing on resilient machine learning, network robustness, and pandemic modeling.

• Industrial Organization
• Competition Policy
• Consumer Policy
• +3 more

Dr Luke Garrod is a Senior Lecturer in Economics at Loughborough University's Loughborough Business School. He holds a PhD, MPhil, and BA from the University of East Anglia. His research focuses on industrial organization, competition policy, consumer policy, and behavioral economics, with a particular emphasis on modeling firm incentives in imperfectly competitive markets. He received the International Journal of Industrial Organization Best Theoretical Paper Award 2019 for his work on collusion mechanisms. His book on hub-and-spoke cartels provides critical insights into antitrust enforcement strategies. Teaching responsibilities include Principles of Microeconomics (ECA002) and Industrial Economics (ECC005). He serves as an Associate Editor for the International Journal of the Economics of Business and contributes to economic consultancy through his role at Economic Insight. His research has explored topics such as cartel damages quantification, digital market regulation, and the interplay between consumer protection and competition policies. Key awards include the 2019 Best Theoretical Paper Award, reflecting his contributions to advancing antitrust and competition policy frameworks. His work bridges theoretical economic modeling with practical regulatory applications, addressing emerging challenges in global competition dynamics and digital economy policies.

Daniel Dominic Kaplan SleatorView profile

Professor

• Algorithms
• Data Structures
• Theoretical Computer Science
• +5 more

Daniel Dominic Kaplan Sleator is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. He maintains an office in the Gates-Hillman Center (7205 Gates-Hillman) and teaches various courses in algorithms and theoretical computer science. Professor Sleator's research spans several areas of theoretical computer science and algorithms. His primary interests include: Algorithms and Data Structures Amortized Analysis and Competitive Analysis Persistent and Self-Adjusting Data Structures Computational Geometry and Combinatorial Optimization Combinatorial Game Theory and Mathematical Games Music Analysis using Computational Methods His extensive publication record shows a consistent focus on efficient data structures and algorithms. Over the years, his work has evolved from foundational data structures like splay trees and skew heaps to applications in diverse areas such as music analysis and combinatorial games. A notable trend in his work is the development of self-adjusting data structures that achieve excellent amortized performance without maintaining explicit structural constraints. His papers on splay trees, skew heaps, and persistent data structures have become classics in the field. Professor Sleator has made significant contributions across multiple domains of computer science. His work on competitive algorithms for paging and list update problems has been particularly influential, establishing fundamental results in online algorithms. His research extends beyond traditional computer science into interdisciplinary areas like computational music theory, demonstrating the broad applicability of algorithmic thinking. He teaches a variety of courses including Algorithms 15-451/651, Competition Programming 15-295, and specialized topics like mathematical games.

Mohammad Saifur RahmanView profile

Professor

• Digitization Economics
• Omnichannel Retailing
• Innovations and Inequality
• +8 more

Mohammad Saifur Rahman is the Daniels School Chair in Management and a Professor of Management Information Systems at the Mitch Daniels School of Business, Purdue University. Previously, he was an Associate Professor at the Haskayne School of Business, University of Calgary. His academic journey includes significant leadership roles such as co-chairing the Conference on Information Systems and Technology (CIST) in 2013 and serving as president of the INFORMS eBusiness Society in 2014. Currently, he serves as an associate editor for both Management Science and Information Systems Research (ISR). Ph.D. in Management Information Systems, Krannert School of Management, Purdue University (2008) MBA in Management Information Systems, Southern Illinois University (2004) BS in Computer Science, Southern Illinois University (2002) Rahman's research primarily focuses on the economic implications of digital transformations. His work examines how digitization affects consumer behavior, market structures, and business strategies across various industries. He investigates the value of digital traces in driving consumer behavior and improving decision-making, particularly in omnichannel retail environments. His research on the sharing economy has revealed important insights about economic disparities, showing how platforms like Airbnb provide economic benefits to predominantly white neighborhoods while failing to generate similar spillover effects in predominantly Black or Hispanic neighborhoods. His recent publications reveal a consistent focus on the intersection of technology, economics, and social impact. Rahman's work frequently examines how local market structures interact with digital platforms to create or exacerbate economic inequalities. His research spans multiple disciplines including information systems, economics, marketing, and operations management, with a particular emphasis on empirical studies using real-world data from major companies like Walmart and Cisco. CICP Faculty Commercialization Award (2020) INFORMS Sandy Slaughter Early Career Award (2018) World's Top 40 Business School Professors Under 40 by Poets and Quants (2017) Jay N. Ross Young Faculty Scholar Award (2015) Dean's Award for Outstanding Research Achievement (University of Calgary, 2014) Rahman has secured substantial research funding from organizations including Social Sciences and Humanities Research Council (SSHRC), Adobe Systems ($50,000 grants in 2017 and 2020), Trask Innovation Fund ($21,218), and NSF I-Corps ($50,000). He has advised numerous PhD students and collaborated with industry partners to translate research into practical applications. Rahman co-founded RightFit Analytics, a precision health analytics solution that utilizes AI to learn success patterns in healthcare. He has organized significant academic events including the Krannert-Walmart Data Dive (believed to be the first data dive on a college campus) and the Dawn or Doom Data Dive in cooperation with Cisco. Rahman actively engages with industry through consulting and collaborative research projects. His work with Walmart, Cisco, and other major corporations demonstrates his ability to bridge academic research with practical business applications. He has been particularly active in studying how AI and big data analytics can be leveraged to improve business decision-making while addressing potential negative consequences like algorithmic bias and economic inequality.

Todd SorensenView profile

Academic

• Labor Market Structure
• Immigration
• Discrimination

Todd Sorensen is an Associate Teaching Professor at the University of California, Merced (UC Merced), affiliated with the School of Social Sciences, Humanities & Arts and the Economics and Business Management department. He holds fellowships at IZA (Institute of Labor Economics) and GLO (Global Labor Organization). Prior to UC Merced, he served as an Associate Professor at the University of Nevada, Reno (2014–2023) and an Assistant Professor at UC Riverside (2007–2014). His professional journey includes a visiting role as Visiting Associate Professor at UC Merced before his current appointment in 2022. Education: PhD in Economics, University of Arizona, 2007 Research Interests: Todd's work focuses on labor market structures , exploring topics like monopsony power and wage-setting dynamics; immigration , including its demographic and policy implications; and discrimination , particularly in criminal sentencing and labor markets. His research has been published in journals like ILR Review , Labour Economics , and the Journal of Population Economics , with findings cited in the U.S. Economic Report of the President. Scientific Awards: Fellow at IZA (2022–present) Fellow at GLO (2022–present) Advising & Grants: Todd has advised students across various levels but no specific advisees are listed. His grants and funding sources are not detailed in the provided text. He collaborates with interdisciplinary teams and maintains affiliations across UC campuses through his and his spouse’s academic careers. Labs/Teams: Engaged with research networks at IZA and GLO, focusing on labor market policies and migration studies. Active in collaborative projects analyzing immigration trends, labor market competition, and discrimination metrics.