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Brandon M. StewartView profile

Associate Professor

• Quantitative Methods
• Statistical Methods
• Text Analysis
• +5 more

Brandon M. Stewart is an Associate Professor of Sociology at Princeton University with extensive interdisciplinary affiliations. He serves as Director of the Statistics Core at the Office of Population Research and maintains formal connections with the Politics Department, Princeton Institute for Computational Science and Engineering, Center for Information Technology Policy, and Center for the Digital Humanities. Stewart holds editorial leadership as Co-Editor-in-Chief of Political Analysis and Associate Editor at Sociological Methods & Research . His educational background includes: Ph.D. in Government from Harvard University (2015) Master's degree in Statistics from Harvard University (2014) Stewart's research pioneers innovative quantitative methods for social science applications, specializing in automated text analysis and modeling complex heterogeneity in regression. His methodological frameworks enable researchers to uncover hidden structures in large datasets that were previously too costly or impossible to analyze. While his recent work has focused on using newspaper archives to study propaganda mechanisms in contemporary China, his tools are deliberately designed for broad applicability across diverse domains including education, human trafficking, forced migration, international relations, constitutional law, and psychology. His publication record demonstrates consistent innovation at the intersection of statistics, machine learning, and social inquiry. Stewart's work shows a clear trajectory from foundational methodological development to practical implementation across numerous substantive areas, with recurring themes of enhancing causal inference with textual data, developing robust topic modeling techniques, and creating accessible computational tools for social scientists. Stewart's scholarly excellence has been recognized through multiple prestigious awards: 2024 Leo Goodman (Early Career) Award from the Methodology Section of the American Sociological Association 2023 Emerging Scholar Award from the Political Methodology Society Edward R Chase Dissertation Prize Gosnell Prize for Excellence in Political Methodology Political Analysis Editor's Choice Award Recognition for Excellence in Mentoring Graduate Students As a mentor, Stewart has guided several successful graduate students to faculty positions at institutions including UCLA and Georgetown. His collaborative approach is evident in numerous multi-author projects spanning disciplines from political science to computational linguistics. His leadership extends to the Sociology Statistics Reading Group, which he founded to foster interdisciplinary methodological exchange, and his summer methods camp that trains social scientists in advanced quantitative techniques.

Joshua D. RabinowitzView profile

Professor

• Cellular Metabolism
• Metabolomics
• Systems Biology
• +5 more

Joshua D. Rabinowitz is a Professor of Chemistry and the Lewis-Sigler Institute for Integrative Genomics at Princeton University, where he also serves as Director of the Ludwig Princeton Branch. His research focuses on achieving a quantitative, comprehensive understanding of cellular metabolism, with applications in both basic science and medical research. Dr. Rabinowitz's research interests span multiple areas of metabolism and systems biology: Quantitative analysis of metabolic networks and regulation Metabolomics and measurement of metabolite concentrations and fluxes Cancer cell metabolism and therapeutic targeting Metabolic regulation in microbes (E. coli, Saccharomyces cerevisiae) Biofuel production (focusing on Clostridium acetobutylicum) Metabolic impact of pathogen infection (viral infection of human cells) His laboratory has developed innovative methods for measuring cellular metabolites using state-of-the-art mass spectrometry technology and approaches for quantitating metabolic fluxes through isotope-labeling data interpretation. Analysis of recent publications reveals a strong focus on NAD+ metabolism, cancer metabolism, metabolic adaptations in disease states, and the intersection of metabolism with immunology and neuroscience, particularly in areas like T cell metabolism, Alzheimer's disease, and cardiac function. Dr. Rabinowitz has received recognition as a Highly Cited Researcher by Web of Science, indicating significant impact in his field. He advises several graduate students and has mentored numerous alumni, including Michel I. Nofal, Edmundo Leiva III, and Sean Hackett. His research is supported by multiple programs including NIH NHGRI Training Program and QCB Graduate Program. The Rabinowitz Lab operates at the intersection of chemistry, biology, and computational science, with all projects involving a mix of biological experiments, metabolomics, and computation to achieve their goal of a holistic understanding of cellular metabolism.

• Political Economy
• Media Economics
• Information Economics
• +7 more

Julia Cagé is a Full Professor of Economics at Sciences Po Paris since 2024, having joined the Department of Economics in 2014 and receiving tenure in 2021. She is affiliated with the Laboratory for Interdisciplinary Evaluation of Public Policies (LIEPP), the Centre for Economic Policy Research (CEPR) where she leads the CEPR Research and Policy Network on 'Media Plurality,' and the CESifo Research Network. Her research spans political economy, media economics, and economic history with a focus on democracy, information economics, and political participation. Cagé's research interests center on the intersection of media, politics, and economics, particularly examining how media structures influence democratic processes, political participation, and information provision. Her work combines historical analysis with contemporary economic methods to understand the relationship between media markets, campaign finance, and political representation. She has pioneered research on media ownership models, the economics of digital journalism, and the long-term effects of historical institutions on contemporary political behavior. Her publications reveal a consistent focus on the economics of information and democracy, with particular attention to media markets, campaign finance, and electoral systems. Her work spans both theoretical and empirical approaches, often using historical data to inform contemporary policy questions. The research demonstrates increasing interdisciplinary integration, connecting economics with political science, history, and media studies to address fundamental questions about democratic representation and information markets. European Research Council Starting Grant (2021) for the PARTICIPATE project Best Young French Economist Award (ex aequo) by Le Cercle des économistes and Le Monde (2023) Yrjö Jahnsson Award (2025) Special Jury Prize for Best Book on Media (2016) Prix Pétrarque de l'essai by Le Monde and France Culture (2019) Cagé has supervised numerous PhD students and early-career researchers through her ERC grant and other research projects. She has secured substantial funding including the European Research Council Starting Grant and McCourt Institute grants in 2022 and 2023. Her research group at LIEPP has produced influential policy recommendations on media pluralism and democratic representation. She has also been involved in practical applications of her research through her board membership at Agence France Presse (2015-2022) and her policy engagement with French and European institutions. Cagé leads the PARTICIPATE research project funded by the ERC, which studies campaign finance, information, and political influence using individual-level data and computer science tools. Her work connects with broader research initiatives at Sciences Po focused on democratic innovation and the political economy of information. She frequently collaborates with interdisciplinary teams including economists, political scientists, historians, and computer scientists to address complex questions about democracy in the digital age.

• Complex Dynamics
• Biophysics
• Statistical Physics
• +2 more

Jeff Urbach is a Professor in the Department of Physics at Georgetown University and Vice Provost for Research. He earned a B.A. in Physics from Amherst College (1985), a Ph.D. from Stanford University (1993), and completed a postdoctoral fellowship at the University of Texas at Austin (1993-1996). He joined Georgetown in 1996, advancing to Professor in 2006, and held leadership roles including Department Chair (2000-01, 2004-07, 2016-20) and Director of the Institute for Soft Matter Synthesis and Metrology (2011-15). Education: B.A. in Physics (Amherst College, 1985); Ph.D. in Physics (Stanford University, 1993) His research focuses on complex dynamics and biophysics , applying statistical physics, nonlinear dynamics, and advanced imaging to systems like granular materials, cytoskeletal proteins, and neuronal migration. Current work emphasizes quantitative modeling of multifaceted, interacting systems through computer simulations and experimental analysis. Scientific awards include the Sloan Foundation Fellowship and the Presidential Early Career Award for Scientists and Engineers . Research funding has been secured from the National Science Foundation, National Institutes of Health, NASA, Air Force Office of Scientific Research, NIST, and other foundations.

• Autonomous Driving
• Digital Twin
• Human-Machine Interaction
• +3 more

Ziran Wang is an Assistant Professor in the Department of Civil Engineering at Purdue University's College of Engineering, appointed as new faculty in 2022. His research bridges digital twin technologies, autonomous driving systems, and human-machine interaction to advance intelligent transportation solutions. Ph.D. in Mechanical Engineering, University of California, Riverside Prior role: Principal Researcher at Toyota North America His work focuses on creating personalized autonomous driving experiences through machine learning, emphasizing safety and efficiency in real-world applications. Key areas include multimodal large language model integration, federated learning for privacy-preserving data sharing, and cooperative perception frameworks. He develops novel approaches for digital twin-based traffic simulation, medical emergency detection in vehicles, and human behavior modeling in complex urban environments. Analysis of his 2024-2025 publications reveals a dominant trend toward generative AI applications in autonomous driving, particularly for perception-prediction-planning integration and real-world validation. His research increasingly incorporates digital twins for safety-critical testing and explores medical applications through in-vehicle health monitoring systems. Dr. Wang advises graduate students including Wenhui Huang and leads the Purdue Digital Twin Lab, which develops advanced simulation and testing platforms for autonomous systems. His lab maintains strong industry partnerships with Toyota for real-world deployment and validation of research成果.

• Geomechanics
• Geophysics
• Numerical Modeling
• +5 more

Dr. Moritz Ziegler is a geomechanics researcher affiliated with the Technical University of Munich (TUM) and the Assistant Professorship of Geothermal Technologies . He previously worked at GFZ Potsdam (2017–2023) and earned his PhD in Geophysics from the University of Potsdam (2014–2017). His research focuses on geomechanical numerical modeling , uncertainty quantification , and 3D stress field analysis , with applications to geothermal energy, seismic hazard, and rock mechanics. Education: Bachelor of Science in Geosciences (2008–2011), Freie Universität Berlin Master of Science in Geophysics (2011–2014), University of Potsdam PhD in Geophysics (2014–2017), University of Potsdam & GFZ Potsdam His work integrates advanced computational methods ( Altair Hypermesh , Dassault Systèmes Abaqus , Python , Matlab ) to address complex geomechanical problems. Recent publications analyze stress gradients in the North Alpine Foreland Basin, fault-stress interactions, and physics-based machine learning in geomechanics. He has contributed to software development (DOuGLAS v1.0) and calibration tools (FAST Calibration v2.4). Scientific awards or honors are not explicitly mentioned in the provided text. However, his research has been published in leading journals such as Geophysical Journal International , Solid Earth , and Pure and Applied Geophysics .

Phillip B. GibbonsView profile

Professor

• Parallel Computing
• Distributed Systems
• Databases
• +4 more

Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty. His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations. Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field. He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory. IEEE Fellow (2014) - For contributions to parallel computing and databases ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions) Co-winner of the best paper award for NSDI '06 Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems. His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.

Cristina BicchieriView profile

Professor

• Game Theory
• Social Norms
• Decision Science
• +11 more

Cristina Bicchieri is the S. J. Patterson Harvie Professor of Social Thought and Comparative Ethics at the University of Pennsylvania , where she also holds appointments as Professor of Philosophy and Psychology and Professor of Legal Studies at the Wharton School. She founded and directs the Penn Center for Social Norms and Behavioral Dynamics , the Philosophy, Politics & Economics Program , and the Master of Behavioral and Decision Sciences . Education : PhD in Philosophy from Cambridge University and a Laurea (Summa cum Laude) from the University of Milano . Her research lies at the intersection of philosophy , game theory , and cognitive psychology , focusing on: Judgment and Decision-Making : Fairness, trust, cooperation, and how expectations influence behavior. Social Norms : Their nature, evolution, measurement, and application in policy (e.g., sanitation, corruption). Epistemic Foundations of Game Theory : Rational choices under bounded knowledge and computational models for game solutions. Her recent articles explore topics like norm stability , language framing effects , and sanitation interventions in India . These works emphasize behavioral dynamics , cognitive psychology , and public policy applications . Scientific Awards : Member, American Academy of Arts and Sciences (2021) Member, German Academy of Sciences (2021) Honorary Fellow, Wolfson College, Cambridge (2016) Pufendorf Medal (2015) Cavaliere Ordine al Merito della Repubblica Italiana (2007) She leads the Behavioral Ethics Lab and collaborates with organizations like UNICEF , the World Bank , and the Gates Foundation to implement norm-based interventions globally.

Rohit KannanView profile

Assistant Professor

• Learning + Optimization
• Optimization Under Uncertainty
• Global Optimization
• +2 more

Rohit Kannan is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. and M.S. in Chemical Engineering from MIT and a B.Tech. from IIT Madras. His research focuses on integrating machine learning with global optimization and optimization under uncertainty, emphasizing energy systems applications. Previous roles include postdoc positions at Los Alamos National Laboratory and the Wisconsin Institute for Discovery. Education: Ph.D., Chemical Engineering, Massachusetts Institute of Technology, 2018 M.S., Chemical Engineering Practice, MIT, 2014 B.Tech., Chemical Engineering, IIT Madras, 2012 Research Interests: Global optimization, optimization under uncertainty, computational optimization, energy systems, and machine learning integration. Recent Highlights: Recipient of the Excellence in Teaching Spotlight Award (2024) Lead researcher in stochastic optimization and energy systems (e.g., hybrid polygeneration systems) Developed algorithms for chance-constrained nonlinear programs and distributionally robust optimization Service & Leadership: Elected Vice-Chair of Global Optimization, INFORMS Optimization Society (2025–2026) Reviewer for top journals like Operations Research and Mathematical Programming Advisor to ISE InclusiveVT and Graduate Admissions Committee Labs & Collaborations: Directs a research group advancing optimization and machine learning for energy and engineering systems. Active in interdisciplinary projects with LANL and UW-Madison.

Ralph JimenezView profile

Adjunct Professor

• Quantum Science
• Spectroscopy
• Photophysics
• +3 more

Ralph Jimenez is an Adjunct Professor of Chemistry and Institute Fellow at JILA, University of Colorado Boulder. He holds a Ph.D. from the University of Chicago (1996) and completed postdoctoral work at the University of California, San Diego (1997-1998), followed by research at The Scripps Research Institute (1998-2003). His research focuses on quantum spectroscopy and photophysics of fluorescent proteins, leveraging quantum optics to enhance spectroscopic sensitivity and developing genetically encoded biomarkers with improved photophysical properties. Key achievements include fluorescence-lifetime-based methods to engineer brighter fluorescent proteins and machine-learning approaches to improve photostability. His awards include the Arthur S. Flemming Award (2017) and U.S. Department of Commerce Gold Medal (2017). His group's work integrates quantum engineering with biophysical studies, targeting real-world applications in molecular imaging and materials science. The Jimenez Group operates labs at JILA (B117, B119, B121) and collaborates on projects involving entangled photons, two-photon absorption, and ultrafast spectroscopy. Research themes include quantum-enhanced spectroscopy for complex systems and overcoming limitations in fluorescent protein imaging through physical chemistry strategies. His lab develops novel instrumentation, including microfluidic sorting systems and tabletop X-ray spectroscopy platforms, to advance biomarker engineering and environmental monitoring.

• Artificial Intelligence
• Computational Social Choice
• Computational Complexity
• +5 more

Edith Hemaspaandra is a Professor in the Department of Computer Science at the Rochester Institute of Technology (RIT), located in the Golisano College of Computing and Information Sciences. She holds a BS, MS, and Ph.D. in Computer Science from the University of Amsterdam (the Netherlands). Her research focuses on computational social choice, computational complexity theory, logic complexity, and formal methods. She teaches courses such as CSCI-262/263 (Introduction to Computer Science Theory) and CSCI-664 (Computational Complexity). Her work explores the algorithmic aspects of voting systems, including election manipulation, control, and bribery, with a focus on their computational complexity. She has also contributed to formal methods for automata theory, educational tools like JFLAP extensions, and the study of complexity classes such as LWPP and WPP. Her research bridges theoretical computer science with practical applications in social choice theory and algorithm design. Her grants include an NSF-funded project on computationally protecting elections from manipulation (2011). She actively publishes in top venues like STACS and ISAAC, addressing topics ranging from graph reconstruction to hybrid election models. Though no awards are explicitly listed, her extensive publication record highlights her contributions to theoretical computer science. Her advising and grant activities include collaborative research projects and educational tool development. She is affiliated with RIT’s Department of Computer Science and maintains a personal website and ORCID profile.

• Multidisciplinary design optimization
• Topology optimization
• Aeroelasticity
• +3 more

Kai A. James is an Associate Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in Computational Science and Engineering. He holds a Ph.D. (2012) and M.A.Sc. (2006) from University of Toronto, and B.A.Sc. (2004) in Engineering Science. His research focuses on multidisciplinary design optimization, topology optimization, aeroelasticity, and nonlinear mechanics, with applications to aerospace structures, additive manufacturing, and smart materials. He teaches courses such as Structural Design Optimization (AE 498), Nonlinear Solid Mechanics (AE 598), and Finite Element Analysis (ME 471). His honors include the NSF CAREER Award (2018), Scott White Aerospace Engineering Fellow (2020), and UIUC Teacher of the Year (2017). His work spans academic publications (over 50 journal/conference articles listed) and innovations in topology optimization frameworks for complex systems. Recent research emphasizes bi-stable structures (e.g., cardiovascular stents, morphing airfoils), thermomechanical design of shape-memory alloys, and spatial packing optimization for engineering systems. His lab, located in Talbot Laboratory, develops computational tools for multiphysics and multiscale design optimization.

• Tobacco Cessation
• Health Policy
• Health Insurance Benefit Mandates
• +3 more

Dr. Sara McMenamin is an Associate Professor at the Herbert Wertheim School of Public Health & Human Longevity Science at UC San Diego. She serves as the Associate Director of the MPH program and Interim Assistant Dean for Undergraduate Education . Her research focuses on state-level policies addressing tobacco use and preventive services, particularly health insurance coverage for tobacco dependence treatment in California and nationally. She also co-chairs the California Health Benefits Review Program , evaluating proposed health insurance legislation's medical, cost, and public health impacts. Her work spans public health policy analysis , health insurance mandate evaluations, and tobacco control strategies. Key areas include analyzing the effects of tobacco tax increases (e.g., Proposition 56), tracking smoking trends among youth and adults, and investigating the interplay between e-cigarette use and smoking cessation. She has contributed to studies on fertility preservation mandates, telemedicine for substance use disorders, and health equity in insurance access. Recent research highlights include assessing declines in youth smoking despite e-cigarette surges, evaluating tobacco policy coverage changes in California, and projecting future cigarette consumption trends. Her work often bridges policy implementation and population health outcomes, emphasizing evidence-based approaches. Dr. McMenamin collaborates with interdisciplinary teams and state agencies to translate research into actionable policies. Her contributions span over 150 peer-reviewed publications, with frequent media and policy engagement (e.g., cited in 14+ news outlets for key studies).

• Public Health
• Health Policy
• Organizational Change
• +3 more

Dr. Linda De Caestecker is a Visiting Professor at the School of Health & Wellbeing, Glasgow Caledonian University. Her work focuses on public health policy, healthcare management, and organizational change within clinical and public sector contexts. She has contributed to studies on smoking cessation interventions during pregnancy, the role of lay-workers in healthcare settings, and systemic organizational dynamics affecting public institutions. Her research spans disciplines including public health, clinical policy, and management theory, with notable contributions to understanding barriers to organizational change and healthcare delivery improvements. Dr. De Caestecker's publications highlight interdisciplinary approaches to complex health challenges, such as balancing economic incentives with health outcomes and addressing paradoxical tensions in institutional reform.

Xiaocheng ShangView profile

Associate Professor

• Mathematical Optimization
• Data Science
• Numerical Analysis
• +3 more

Xiaocheng Shang is an Associate Professor in Mathematical Optimisation and Data Science at the University of Birmingham's School of Mathematics. His research focuses on numerical methods for stochastic differential equations, with applications in computational mathematics, statistics, physics, and data science. He is affiliated with the Optimisation and Numerical Analysis Group, Statistics and Data Science Group, and the Institute for Data and AI. Shang holds a PhD in Applied and Computational Mathematics from the University of Edinburgh (2016) and completed postdocs at the University of Edinburgh, Brown University, and ETH Zurich before joining Birmingham in 2019. His academic achievements include fellowships from The Alan Turing Institute, the LMS Emmy Noether Fellowship, and the EUniWell Leadership Fellowship. He has secured funding from EPSRC, the Royal Society, and the Isaac Newton Institute. Shang is actively involved in supervising PhD students and co-organizing research initiatives such as the Data Science and Computational Statistics Seminar. Research interests include structure-preserving integrators, Bayesian sampling techniques, and machine learning applications in dynamical systems. His work bridges numerical analysis, probability theory, and multiscale modeling in materials science. Recent projects involve neural networks for complex dynamical systems and numerical algorithms for deterministic/stochastic systems.