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Yubai YuanView profile

Assistant Professor

• Complex Network Analysis
• Causal Inference
• Optimal Transport
• +7 more

Yubai Yuan is an Assistant Professor of Statistics at the Pennsylvania State University, affiliated with the Department of Statistics within the Eberly College of Science. He holds a PhD from the University of Illinois Urbana-Champaign (2020) and completed postdoctoral research at UC Irvine. His research focuses on network science, causal inference, and statistical machine learning, with applications to neuroscience and social systems. Notable awards include the 2022 NSF-Simons Center Fellow Award and the 2019 ASA Student Paper Award. Education: PhD in Statistics (UIUC, 2020), MS in Statistics (Sun Yat-sen University, 2016), BS in Mathematics (Shandong University, 2012). Research interests span complex network analysis, optimal transport, active learning, and mediation analysis. Current projects include de-confounding causal inference and hypergraph modeling. Teaching includes courses on probability theory and statistical modeling at Penn State. Advises PhD students Yuanchen Wu (active learning on graphs) and Siyu Huang (latent network structures). Collaborates with the Center for Social Data Analytics and organizes workshops in statistical network science. Publications emphasize methodological advancements in network analysis, causal pathways, and data integration. Recent work addresses disaster response via social media data and neuronal activity analysis using optimal transport frameworks.

Xin GaoView profile

Professor

• Artificial Intelligence
• Machine Learning
• Statistical Genetics
• +5 more

Xin Gao is a Professor in the Department of Mathematics and Statistics at York University, Toronto. His research focuses on Artificial Intelligence , Machine Learning , and Statistical Genetics , with applications in biomedical data analysis and planetary science. He leads the Artificial Intelligence and Machine Learning Lab , which has developed impactful tools like an online Type 2 Diabetes risk predictor using logistic regression and Mars rock composition analyzers for NASA. His methodological work includes penalized composite likelihood and multi-task feature learning , implemented in R packages FusionLearn and lassoGEE . Scientific Awards NSERC Discovery Acceleration Award ($120,000, 2018-2020) Key Software Contributions FusionLearn : Correlated multi-task feature learning lassoGEE : High-dimensional clustered/longitudinal data analysis Notable Collaborations Vector Institute (AI scholarship mentoring) Fields Institute (committee roles) International genomic data integration projects

• Financial Econometrics
• Statistics
• Empirical Asset Pricing
• +5 more

Dacheng Xiu is the Joseph Sondheimer Professor of Econometrics and Statistics at the Booth School of Business , University of Chicago, and an Affiliated Faculty in the Department of Statistics. He serves as a Research Associate at the National Bureau of Economic Research and holds editorial roles at journals like Journal of Business & Economic Statistics and Journal of Financial Econometrics . PhD and MA in Applied Mathematics from Princeton University BS in Mathematics from University of Science and Technology of China His research focuses on statistical methodologies for financial data , including risk measurement , portfolio management , and empirical asset pricing using high-frequency data and machine learning . Recent work analyzes text data and large language models for economic forecasting. Editorial leadership includes Co-Editor and Associate Editor roles at top journals like Journal of Finance and Annals of Statistics . His lab ( Risk Lab ) specializes in systemic risk assessment through transaction-level data analysis. 2024 Dimensional Fund Advisors Prize 2023 GSU-RFS FinTech Conference Best Paper Award 2022 Society for Financial Econometrics Fellow 2018 Swiss Finance Institute Outstanding Paper Award

Samarjit ChakrabortyView profile

Professor

• Distributed Embedded Systems
• Hardware/Software Co-design
• Embedded Control Systems
• +9 more

Samarjit Chakraborty is the William R. Kenan, Jr. Distinguished Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. He previously held the Chair of Real-Time Computer Systems at the Technical University of Munich (2008–2019) and was an assistant professor at the National University of Singapore (2003–2008). His research spans real-time embedded systems, cyber-physical systems (CPS), and automotive security. Research Interests include distributed embedded systems, hardware/software co-design, low-power systems, energy storage, electromobility, and sensor network-based information processing. His work addresses challenges in scheduling algorithms for autonomous vehicles, timing predictability in automotive networks, and safety-critical controller implementations. Recent Publications highlight advancements in Timing analysis for automotive networks Energy modeling of Bluetooth Low Energy Autonomous vehicle perception computing Security in automotive systems Flexible manufacturing with process dynamics Scientific Awards include the ETH Medal, European DAAD Outstanding Doctoral Dissertation Award, multiple best paper awards at conferences (ISLPED, ICCD, RTCSA, etc.), the 2023 Humboldt Professorship, and IEEE Fellowship. Advising active PhD students: Clara Hobbs, Shengjie Xu, Sharmin Aktar, and postdoc Enrico Fraccaroli. His research is supported by NSF grants and industry partnerships with General Motors, Intel, Google, BMW, Audi, Siemens, and Bosch.

Rianne de HeideView profile

Assistant Professor

• Machine Learning
• Statistics
• Hypothesis Testing
• +7 more

Rianne de Heide is an Assistant Professor in the Statistics group (STAT) within the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. She maintains collaborative arrangements with LUXs Data Science in Leiden, CWI, and VU Mathematics in Amsterdam as a guest researcher while working partly remotely during her family's relocation. Her academic journey includes a previous position as Assistant Professor at Vrije Universiteit Amsterdam. PhD Dissertation: 'Bayesian Learning: Challenges, Limitations and Pragmatics' (2020) MSc Thesis: 'The Safe-Bayesian Lasso' (2016) De Heide's research spans multiple interconnected domains within statistics and machine learning, with particular emphasis on developing mathematically rigorous frameworks that remain accessible to diverse audiences. Her work bridges theoretical foundations with practical applications, focusing on hypothesis testing with e-values, Bayesian learning methodologies, and best-arm identification problems in multi-armed bandit settings. She demonstrates exceptional interdisciplinary range, connecting statistical theory with philosophical inquiry and even theological discussions as evidenced by her publications on biblical authorship verification and mathematical beauty. Analysis of her publication trajectory reveals a clear evolution toward developing anytime-valid statistical methods, particularly through e-values and e-processes for multiple testing scenarios. Her recent work shows increasing focus on foundational questions in statistical inference while maintaining strong connections to practical machine learning applications. The 2024 'Safe Testing' paper in the Journal of the Royal Statistical Society represents a significant contribution that generated a formal discussion meeting. VENI project 'E-values for Multiple Testing' NWO M2 grant of €742,708 with Jelle Goeman (funding 2 PhD students and a scientific programmer) 2025 Bernoulli Society New Researcher Award De Heide actively supervises research through her VENI project and the NWO M2 grant, while also contributing to broader academic service through the 'Kindness and Excellence in Academia' initiative she co-founded. This initiative addresses critical cultural issues in academic environments through opinion pieces, resources, and community building around compassionate academic practices. She has organized specialized events like the E-Day meet-up for e-value researchers at CWI in Amsterdam, demonstrating leadership in her niche research community. Her research activities are centered around the Statistics group at the University of Twente, with significant external collaborations through the E-mailing list for e-value researchers and partnerships with institutions including CWI, VU Amsterdam, and Leiden's LUXs Data Science. The interdisciplinary nature of her work creates connections across mathematics, computer science, philosophy, and even religious studies.

Daniel BaumView profile

Research Professor

• Scientific Visualization
• Computational Biology
• Image Analysis
• +6 more

Daniel Baum is a Research Professor and Head of the Visual Data Analysis research group at the Zuse Institute Berlin (ZIB), which is affiliated with Freie Universität Berlin. His work spans across scientific visualization, computational biology, and image analysis, with a particular focus on developing methods for analyzing complex biological structures and neural circuits. He is actively involved in multiple interdisciplinary research projects including HFSP Chitons, Geometric Learning for Single-Cell RNA Velocity Modeling, and RobustCircuit. Dr. Baum's research interests center on visual and data-centric computing approaches to solve complex problems in biology and medicine. His work bridges the gap between computational methods and biological applications, with significant contributions to cryo-electron tomography analysis, neural circuit mapping, and geometric morphometrics. He develops innovative algorithms for 3D reconstruction, image segmentation, and visualization of biological structures, from molecular to organismal scales. His publication record demonstrates consistent contributions to visualization techniques applied to biological problems, with recent work focusing on neural circuit analysis in zebrafish and Drosophila, biomechanical studies of animal structures, and advanced methods for analyzing ancient artifacts. The research shows a clear trajectory toward increasingly sophisticated multimodal data integration and machine learning approaches. Dr. Baum leads a productive research group with several key collaborators who frequently appear as co-authors on his publications, indicating a strong mentoring relationship. His projects involve substantial funding from various sources supporting interdisciplinary collaborations across biology, computer science, and engineering. His laboratory at ZIB focuses on visual data analysis for complex biological systems, with particular strength in developing computational methods for neuroscience applications and biomaterial analysis. The group maintains strong collaborations with multiple institutions working on cutting-edge imaging technologies and biological model systems.

• Corporate Finance
• Corporate Governance
• Risk Management
• +8 more

Dr Jiao Ji serves as Lecturer in Finance and Programme Director of Accounting, Governance, and Financial Management at the University of Sheffield Management School, where she actively contributes to the Accounting and Financial Management division and Centre for Research into Accounting and Finance in Context (CRAFiC). Her academic credentials include: BSc. in Accounting (CPA) from Southwestern University of Finance and Economics MSc. in Economics and Finance from Durham University Ph.D. in Finance and Accounting from University of Sheffield Specializing in empirical corporate finance and governance, Dr. Ji employs advanced big data analytics and textual analysis to investigate corporate risk, misconduct, and disclosure practices. Her research extends into banking, financial innovations, ESG/CSR initiatives, and behavioral finance, with particular emphasis on extracting insights from corporate disclosures and social media content through machine learning methodologies. Her publication record (2018-2025) reveals consistent focus on governance-risk relationships using innovative quantitative approaches. Recent work applies double machine learning to healthcare diversity studies, examines shareholder structures in stock crash risk, and investigates legal enforcement in fintech credit across international contexts. China-based evidence features prominently in her corporate governance research. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) Dr. Ji currently supervises three PhD candidates researching P2P lending dynamics, CEO organizational identification, and boardroom education impacts, while previously guiding research on bank CSR and governance. Her externally funded projects address critical industry issues: Exploring the Link Between Toxic Culture in Banks and Misconduct: Machine Learning Approach (British Academy/Leverhulme, 2023, £9,070) Enhancing Sustainable Supply Chain Management with Big Data Analytics (The Grantham Centre, 2024, £8,500) As an active member of CRAFiC, she collaborates on interdisciplinary finance research while serving as Associate Editor for Qualitative Research in Financial Markets and Committee Member for BAFA's Corporate Governance Special Interest Group.

Shuangning LiView profile

Assistant Professor

• Econometrics
• Statistics
• Machine Learning
• +5 more

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.

• Information Theory
• Active Learning
• Decentralized Learning
• +4 more

Tara Javidi holds the Jerzy (George) Lewak Endowed Chair and is a Professor in the Department of Electrical and Computer Engineering and Halicioglu Data Science at the University of California San Diego (UCSD). She leads multiple initiatives, including serving as Founding CTO of KavAI, Co-Director of the Center for Machine Intelligence, Computing and Security, and Co-Principal Investigator (CoPI) of the NSF AI Institute TILOS. Her research focuses on stochastic analysis, design, and control of information systems, emphasizing active learning, decentralized optimization, and wireless networks. Key areas include information acquisition/utilization, stochastic control, and AI-driven communication solutions. Her work bridges theoretical foundations and practical implementations, such as drone systems for information gathering (via detecdrone.ucsd.edu) and optical data center networking. Notable contributions include end-to-end scheduling for all-optical data centers and hybrid wireless-optical architectures. Javidi is an IEEE Fellow and has received significant grants, including leading UCSD’s Schmidt AI in Science Postdoctoral Fellowship program. She actively collaborates with industry and academia, with a focus on next-generation wireless networks and decentralized systems. Education: Ph.D. in Electrical Engineering (implied from title). Affiliations: IEEE Journal of Selected Areas in Information Theory (Editor-in-Chief), CALIT2, CNS, and TILOS. Grants: NSF AI Institute TILOS ($20M over 5 years), Schmidt AI Fellowship program. Her research group emphasizes both theoretical rigor (e.g., sequential hypothesis testing) and practical testing, with applications in service drones, cognitive networks, and federated learning. Recent articles highlight advancements in optical networking, secure communication, and distributed learning protocols. Awards: IEEE Fellow, Jerzy Lewak Chair. Labs/Teams: Center for Machine Intelligence, TILOS Institute, KavAI, and UCSD’s AI in Science initiatives.

Scott WeinsteinView profile

Professor

• Logic
• Philosophy of Mathematics
• Cognitive Science
• +3 more

Scott Weinstein is a Professor of Philosophy, Mathematics, and Computer Science at the University of Pennsylvania, where he also serves as Director of the Logic, Information, and Computation Program. His academic appointments span multiple departments within the College of Arts and Sciences, reflecting his interdisciplinary expertise. He holds a Ph.D. from Rockefeller University. Education: Ph.D. in Philosophy, Mathematics, and/or Computer Science from Rockefeller University Research Interests: Dr. Weinstein’s work focuses on foundational aspects of logic, philosophy of mathematics, cognitive science, and formal learning theory. His research bridges theoretical computer science, mathematical logic, and ancient philosophical paradoxes such as Zeno’s. He explores topics like truth detection models, inductive inference systems, complexity in finite structures, and the philosophical implications of theoretical terms in scientific inquiry. Articles Trends: His publications reflect a sustained engagement with formal methods in science and philosophy. Recent work (e.g., 2022) re-examines classical paradoxes through modern mathematical lenses, while earlier contributions (e.g., 1988, 1989) investigate inductive reasoning and computational models of scientific discovery. Key themes include the interplay between logic and empirical inquiry, the structure of scientific knowledge, and foundational questions in mathematics and computing. Awards: No scientific awards are explicitly mentioned in the provided text. Advising & Grants: While no formal advisees or grant details are listed, his role as Director of the Logic, Information, and Computation Program indicates leadership in interdisciplinary academic initiatives. His publications often involve collaborations with colleagues like Daniel Osherson and Michael Stob. Labs/Teams: As Director of the Logic, Information, and Computation Program, he oversees a collaborative research environment at the University of Pennsylvania, integrating philosophical, mathematical, and computational disciplines.

Georgios FellourisView profile

Associate Professor

• Sequential hypothesis testing
• Quickest change detection
• Sequential parameter estimation
• +4 more

Georgios Fellouris is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign (UIUC), serving as Associate Chair since 2020. He holds additional affiliations with the Coordinated Science Laboratory (2015–present) and the Department of Electrical and Computer Engineering (2019–present). His research focuses on sequential hypothesis testing, quickest change detection, educational measurement, and decision-making under communication constraints. Education: PhD and M.Phil. in Statistics from Columbia University (2010), M.A. in Statistics (2006), M.S. in Mathematical Modeling (2005), and a Diploma in Applied Mathematics from the National Technical University of Athens (2004). Recent research emphasizes statistical methods for sequential analysis, with publications in Bernoulli and Annals of Statistics . His work bridges theoretical foundations with applications in signal processing and data streams. Key topics include asymptotic optimality, multistage testing, and anomaly detection in dependent data. Affiliations also include postdoctoral research at the University of Southern California (2010–2013). His interdisciplinary approach spans statistics, engineering, and education, with contributions to both theory and practical methodologies.

• Health Communication
• Public Health Messaging
• Media Effects
• +5 more

Laura Crosswell serves as Associate Professor of Health Communication and Director of the Center for Advanced Media Studies at the University of Nevada, Reno's Reynolds School of Journalism and Media Studies. She holds a joint appointment with the university's School of Medicine, reflecting her interdisciplinary approach to health communication research. Crosswell previously taught at the College of Charleston, Louisiana State University, and Arizona State University before joining UNR. Her research focuses on the intersection of consumerism, persuasive texts, and public health messaging, with particular attention to how commercial interests influence health communication. Crosswell frequently employs advanced eye-tracking technology to examine physiological and psychosocial responses to media content, especially in pharmaceutical advertising and public health campaigns. Her book Politics, Propaganda, and Public Health: A Case Study in Health Communication and Public Trust analyzes Merck's Gardasil vaccination campaign, exploring how corporate interests shape public health messaging. Crosswell's recent publications reveal consistent focus on contemporary health communication challenges, particularly surrounding the COVID-19 pandemic, vaccine hesitancy, and environmental health risks. Her work spans multiple disciplines including communication, public health, psychology, and medical education, often employing innovative methodologies like virtual reality simulations and biometric measurement. Recipient of Organizational Behavior Management (OBM) Innovative Research Award, 2023 Co-author of scholarly book on pharmaceutical marketing and public health trust Regular contributor to journals including Critical Public Health , Public Health Perspectives , and Journal of Environmental Studies and Sciences Crosswell has received recognition for her innovative research approaches that bridge communication theory with practical health applications. Her work examining virtual reality applications in healthcare education demonstrates her commitment to developing new methodologies for training healthcare professionals. With numerous forthcoming publications for 2025, she remains an active contributor to the field of health communication research.

Lorenzo De StefaniView profile

Lecturer

• Algorithms
• Big Data
• Statistical Learning
• +3 more

Lorenzo De Stefani serves as an Assistant Teaching Professor (formally titled Lecturer) in Brown University's Department of Computer Science, teaching core courses including Theory of Computation (CSCI1010), Design and Analysis of Algorithms (CSCI1570), and Data Science (CSCI1951A). His academic credentials feature dual doctoral degrees: a PhD in Computer Science from Brown University (2020) under Eli Upfal, and a PhD in Computer Engineering from the University of Padova (2016) under Gianfranco Bilardi. Additional qualifications include a Master of Science (2012) and Bachelor of Science (2009) in Computer Engineering from the University of Padova. PhD, Brown University, 2020 PhD, University of Padova, 2016 MSc, University of Padova, 2012 BSc, University of Padova, 2009 De Stefani's research centers on algorithmic innovation for large-scale data challenges, with emphasis on memory-efficient graph stream processing, statistical learning for multiple hypothesis testing, and Byzantine-resilient computation. His work bridges theoretical computer science and practical data analysis through rigorous probabilistic frameworks. Publication trends reveal consistent contributions to graph algorithm optimization, particularly in single-pass triangle counting for dynamic networks and I/O complexity bounds for fundamental operations like matrix multiplication. His methodologies prioritize computational efficiency while maintaining statistical validity in massive datasets. Scientific recognition includes: KDD Best Student Paper Award (2016) for Tríest streaming algorithms While advising PhD candidates during his doctoral studies at Brown and Padova, current student mentorship details remain unspecified. No major grant awards are documented in available materials. He actively participates in Brown's computer science research ecosystem through collaborative networks focused on algorithmic foundations and data science applications.

Hyungsik Roger MoonView profile

Professor

• Econometrics Theory
• Applied Econometrics
• Labor Economics
• +5 more

Hyungsik Roger Moon is Professor of Economics in the Department of Economics at the University of Southern California's Dornsife College of Letters, Arts and Sciences, where he has served since 2000 after beginning his career at UC Santa Barbara. His academic trajectory progressed from Assistant Professor (2000) to Associate Professor (2004) and full Professor (2008), reflecting sustained contributions to econometric methodology. His educational foundation includes: Ph.D. in Economics, Yale University, 1998 M.A. in Economics, Yale University, 1995 B.A. in Economics, Seoul National University, 1989 Moon's research centers on econometric theory development and applied methodology, with particular expertise in panel data analysis, dynamic modeling, and high-dimensional estimation. His theoretical innovations address complex challenges in interactive fixed effects, unit root testing, and heterogeneity modeling, while applied work spans labor economics (income dynamics), health economics (pancreatic cancer trials), and macroeconomics (Covid-19 forecasting). This dual focus bridges rigorous mathematical frameworks with real-world policy applications across multiple economic subfields. Analysis of recent publications reveals an intensifying focus on robust estimation techniques for dyadic data, Bayesian approaches to sparse heterogeneity, and methodological innovations in forecasting with censored panel data. His work increasingly integrates machine learning concepts with traditional econometrics, particularly in high-dimensional seemingly unrelated regression systems and network-based peer effect modeling. His distinguished scientific contributions have been recognized through: Fellow of the Econometric Society (2023) Fellow of the Journal of Econometrics (2019) RK Cho Economics Award (2018) Maekyung/KAEA Economist Award (2012) Econometric Theory Multa Scripsit Award (2006-2007) Korea-America Economic Association Young Scholar Award (2005) Moon has secured significant research funding including an NSF grant of $180,675 for 'Forecasting with Dynamic Panel Data Models' (2016-2020) and $68,000 for 'Asymptotic Analysis of Panel Regression Models' (2009-2010). His academic leadership extends to editorial roles at the Journal of Business and Economic Statistics, Econometric Theory, and Journal of Econometrics, plus administrative service as Director of Graduate Studies for USC's Economics Ph.D. program (2018-2021) and Associate Director of USC Dornsife INET (2015-2017). Through his position at USC Dornsife INET and graduate program leadership, Moon actively shapes research directions in new economic thinking while mentoring future econometricians through advanced courses like Big Data Econometrics.

• Econometrics
• Financial Econometrics
• Time Series Analysis
• +5 more

Skrobotov Anton Andreevich is a Professor at the Faculty of Economic Sciences and Director of the Center for Big Data in Economics and Finance at the National Research University Higher School of Economics (HSE). With 15 years of scientific and teaching experience, he joined HSE in 2024 and focuses on econometrics, financial econometrics, and non-stationary time series analysis. His research emphasizes robust statistical methods. Education: Doctor of Economics (2024) Candidate of Economic Sciences (2018), Saint Petersburg University Master's degree in Economics (2013), Russian Presidential Academy of National Economy and Public Administration (RANEPA) Skrobotov specializes in econometrics, time series analysis, and robust testing. His recent publications address financial bubbles, volatility clustering, and structural shifts in economic data. Scientific incentives: High Professional Potential Group (HSE Personnel Reserve) Category 'New Teachers' (2025) He has led courses in Econometrics at RANEPA and HSE, and secured multiple grants from the Russian Science Foundation and Russian Foundation for Basic Research. His work involves collaborations with institutions like the Gaidar Institute and Saint Petersburg State University.