Speaker: Monther Abu-RemailehVenue: Room 205, Floor 1, Complex Building, School of Medicine, Zijingang CampusAbstract: Monther Abu-Remaileh is an Assistant Professor of Chemical Engineering and Genetics and an Institute Scholar of ChEM-H at Stanford University. He grew up in Palestine and earned his B.Sc. in Genetics from the Jordan University of Science and Technology before completing his Ph.D. at the Hebrew University of Jerusalem in 2014. He then conducted postdoctoral research at the Whitehead Institute at MIT before starting his lab at Stanford in 2019.His lab investigates pathways that enable cellular and organismal adaptation to metabolic stress and environmental changes, making fundamental discoveries in lysosome biology and neurodegenerative diseases while developing widely used tools for probing subcellular metabolism.Dr. Abu-Remaileh has received numerous prestigious awards, including Walter A. Shaw Young Investigator Award in Lipid Research (2025), the Sloan Research Fellowship (2025), Günter Blobel Early Career Award (2024), R.R. Bensley Award in Cell Biology (2024), Pew-Stewart Scholar Award (2022), NIH Director's New Innovator Award (2021), and Stanford Cancer Institute's Cancer Innovation Award (2020). He was also named an MIT Technology Review Innovator Under 35.

Speaker: Alfonso Martinez AriasVenue: Lecture Hall, Floor 1, School of Medicine, Zijingang CampusAbstract: Alfonso Martinez Arias is currently ICREA Research Professor at the Universitat Pompeu Fabra in Barcelona. He obtained a PhD from the University of Chicago (1983) and after a postdoc at the MRC Lab of Molecular Biology in Cambridge (UK) became affiliated with the University of Cambridge as a Wellcome Trust Senior Fellow and, from 2001, as professor of Developmental Mechanic. In 2021 he moved to Barcelona. He is an elected member of the European Molecular Biology Organization and of the Academia Europea and has received the Waddington medal of the British Society of Developmental Biology. Currently his work is at the interface between stem cell, developmental and systems biology. Alfonso has published over 190 articles in his field and coauthors the classic text book in the field Wolpert's Principles of Development (OUP). In 2023 he published “The Master Builder”(Basic Books) where he suggests a cell's eye view of biology from the perspective of development.

Speaker: Raphaël RodriguezVenue: Lecture Hall, Floor 1, School of Medicine, Zijingang CampusAbstract: Raphaël Rodriguez is a Research Director at the CNRS and a Principal Investigator at Institut Curie where he holds the Sklodowska-Curie Chair of Chemical Biology. He acquired the knowledge of chemistry and biology from several mentors in the United Kingdom. He leads the Biomedicine Laboratory at Institut Curie and investigates the molecular basis of cancer metastasis. Together with lab co-workers, they discovered the role of iron as a driver of cell adaptation and cell death. He has received a few accolades including the CNRS Silver Medal, the Liliane Bettencourt Prize for Life Sciences, the Klaus Grohe Prize, the Tetrahedron Young Investigator Award and the Charles Defforey Prize. He is a Fellow of the Royal Society of Chemistry and a Knight of the National Order of Merit.

Speaker: Peng XiongVenue: Room 215, No. 8 Haina Building, Zijingang CampusAbstract: This talk reviews several key historical milestones in conceptual development and experimental demonstration of the phenomena of quantum mechanical tunneling, culminating in the experiments by Martinis, Devoret, and Clarke in the mid-1980s that, for the first time, clearly demonstrated quantum tunneling at macroscopic scale. The talk highlights the equal importance of being theoretically adventurous and experimentally rigorous in producing scientific breakthroughs.

Speaker: Zhang HuaiVenue: Room A423, School of Management, Zijingang CampusAbstract: Exploiting the introduction of China’s 2018 Northbound Investor Identification System (NIIS) as an exogenous shock to insider trading incentives, we examine the impact of managerial insider trading incentives on firms’ disclosure quality. We find that when managers have lower incentives to engage in insider trading, they provide higher-quality disclosures, evidenced by more precise and accurate management earnings forecasts. The effect is more pronounced for firms with high exposure to regulatory changes, low litigation risk, poor corporate governance, and opaque information environment. Our study highlights the role of insider trading incentives in shaping corporate disclosure.

Speaker: Prof. Yu JunVenue: Room B212 School of Management, Zijingang CampusAbstract: This paper proposes discrete-time approximations to rough continuous-time models of realized variance (RV). The leading rough models can be viewed as autoregressive processes driven by fractional Gaussian noise. We show that the Wold representation of this noise concentrates its dependence at the first lag when the Hurst parameter is below one half. Augmenting the autoregressive (AR) and heterogeneous autoregressive (HAR) models with a first-order moving-average (MA(1)) component therefore approximates the roughness, and the MA coefficient maps almost linearly into the Hurst parameter. We refer to these extensions as the ``rough'' AR and ``rough'' HAR models. Estimating them on the log RV of ten ETFs, we find negative MA coefficients for every asset, and the implied Hurst parameters align closely with the estimates from the continuous-time models. In the HAR literature, the negative MA(1) component is a significant feature that has been largely overlooked. In out-of-sample comparisons, the ``rough'' models outperform their classical counterparts for nearly every asset and horizon, with the largest gains at short horizons, and their accuracy is comparable to that of the rough continuous-time models but much easier to estimate by standard off-the-shelf software.

Speaker: Buhui Qiu Venue: Room 530, No. 7 Chengjun Building, Zijingang CampusAbstract: We examine whether specialized investment social media shapes firm boundaries by promoting the formation of strategic alliances. Using Seeking Alpha coverage initiation as a shock to a firm’s information environment, we find that social media coverage significantly accelerates corporate cooperative activities: alliance intensity increases by 35% and the annual probability of forming any alliance rises by 4.1 percentage points. This real-economy effect is concentrated in information opaque firms under severe financial constraints and larger industry shocks. Covered firms subsequently raise more debt capital and attract more sophisticated investors and monitors. Alliances formed post-initiation exhibit superior long-run operating and stock performance. Taken together, our results suggest that non-traditional information intermediaries influence the real allocation of resources by simultaneously improving capital market access and closing the visibility gap.

Speaker: Li MingVenue: Room 718 School of Economics, Zijingang CampusAbstract: We decode China’s industrial policies from 2000 to 2022 by employing large language models (LLMs) to extract and analyze rich information from a comprehensive dataset of 3.3 million documents issued by central, provincial, and city-level governments. Through careful prompt engineering, multistage extraction and refinement, and rigorous verification, we use LLMs to classify the industrial policy documents and extract structured information on targeted industries, policy tones (supportive or regulatory/suppressive), implementation tools, etc. Combining these newly constructed industrial policy data with micro-level firm data, we explore the political economy of industrial policy, focusing on the economic and political foundations of sector choice and its evolution over time. We find 2013 as a turning point for policy recentralization, both in the central policies' coverage and strength and in the local governments' compliance. We document spatial inefficiencies and industry-wide overcapacity as potential downsides of industrial policies.

Speaker: Xinyu CaoVenue: Room 718 School of Economics, Zijingang CampusAbstract: Many online shopping platforms allow buyers to upload photos with their product reviews, giving prospective consumers access to visual information beyond seller-provided product images. Yet it remains unclear whether such buyer-generated images help or hurt subsequent sales. We examine the demand consequences of buyer review photos and how their effects depend on the visual information they provide relative to seller images. Using item-day data from apparel products on a large e-commerce platform, we exploit the arrival of the first buyer review containing photos and implement a matched event-study design. We find that the arrival of buyer review photos leads to an average 2.8% decline in subsequent sales. Importantly, this negative effect is larger when there is a larger visual gap between buyer- and seller-provided images. We further examine specific visual characteristics underlying this discrepancy. Differences in visual appeal account for part of the negative effect associated with the seller-buyer model-image gap, whereas differences in technical image quality and scene complexity play comparatively limited roles. Together, our findings show that buyer-generated product photos do not necessarily facilitate sales. Instead, their demand consequences depend critically on whether the visual information they reveal confirms or conflicts with the product presentation created by sellers.