About

Christian Borgs is a professor of Computer Science at the University of California, Berkeley, with a focus on the science of networks, including mathematical foundations, graph limits, graph processes, graph algorithms, and applications in economics, systems biology, and epidemiology. He has also contributed to mathematical statistical physics and recently to aspects of responsible AI, differential privacy, and AI for material science.

Research topics

• Computer Science
• Combinatorics
• Mathematics
• Information Retrieval
• Chemistry
• Artificial Intelligence
• Physics
• Medicine
• Organic chemistry
• Engineering
• Database
• Anatomy
• Biochemistry
• Statistical physics
• Algorithm
• Programming language
• Statistics
• Discrete mathematics
• Data science

Selected publications

• Auditing the Auditors: Does Community-based Moderation Get It Right?

  arXiv (Cornell University) · 2026-03-17

  preprintOpen access

  Online social platforms increasingly rely on crowd-sourced systems to label misleading content at scale, but these systems must both aggregate users' evaluations and decide whose evaluations to trust. To address the latter, many platforms audit users by rewarding agreement with the final aggregate outcome, a design we term consensus-based auditing. We analyze the consequences of this design in X's Community Notes, which in September 2022 adopted consensus-based auditing that ties users' eligibility for participation to agreement with the eventual platform outcome. We find evidence of strategic conformity: minority contributors' evaluations drift toward the majority and their participation share falls on controversial topics, where independent signals matter most. We formalize this mechanism in a behavioral model in which contributors trade off private beliefs against anticipated penalties for disagreement. Motivated by these findings, we propose a two-stage auditing and aggregation algorithm that weights contributors by the stability of their past residuals rather than by agreement with the majority. The method first accounts for differences across content and contributors, and then measures how predictable each contributor's evaluations are relative to the latent-factor model. Contributors whose evaluations are consistently informative receive greater influence in aggregation, even when they disagree with the prevailing consensus. In the Community Notes data, this approach improves out-of-sample predictive performance while avoiding penalization of disagreement.

  PublisherDOI

• MOFGen database

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-02

  datasetOpen accessSenior author
  PublisherDOI

• Synthesis of Highly Crystalline Covalent Organic Frameworks Using Large Language Models

  Journal of the American Chemical Society · 2026-02-23 · 5 citations

  article

  Crystallizing covalent organic frameworks (COFs) remain a central challenge in reticular chemistry, as achieving long-range order typically requires extensive trial-and-error optimization over many months or years. Here, we demonstrate that by integrating a deep research agent within ChatGPT, this process can be markedly accelerated, reducing the crystallization timeline to less than one month. Our approach, termed the LLM For Accelerated Synthesis Technique (LFAST), operates through two interlinked cycles. In the first, we formulated a structured, multistep prompt to guide the deep research agent in mining, correlating, and validating synthesis parameters from the relevant chemical literature. This yielded an expanded and refined design space for reaction condition screening. In the second, these conditions were executed by using an automated synthesis platform coupled with high-throughput powder X-ray diffraction analysis. Using a widely reported β-ketoenamine-linked COF, TpPa-SO3H, as a benchmark, LFAST produced frameworks with diffraction peaks corresponding to a 350% increase in crystallinity index (CI) relative to prior reports. The same protocol enabled the synthesis of an unreported β-ketoenamine-linked COF-2000 with an even higher structural order. To ensure reproducibility and data accessibility, we further introduce a standardized metadata format encompassing synthesis and PXRD data sets. This data-driven methodology transforms the way that COFs are crystallized and significantly accelerates the pace of materials discovery.

  PublisherDOI

• Auditing the Auditors: Does Community-based Moderation Get It Right?

  ArXiv.org · 2026-03-17

  articleOpen access

  Online social platforms increasingly rely on crowd-sourced systems to label misleading content at scale, but these systems must both aggregate users' evaluations and decide whose evaluations to trust. To address the latter, many platforms audit users by rewarding agreement with the final aggregate outcome, a design we term consensus-based auditing. We analyze the consequences of this design in X's Community Notes, which in September 2022 adopted consensus-based auditing that ties users' eligibility for participation to agreement with the eventual platform outcome. We find evidence of strategic conformity: minority contributors' evaluations drift toward the majority and their participation share falls on controversial topics, where independent signals matter most. We formalize this mechanism in a behavioral model in which contributors trade off private beliefs against anticipated penalties for disagreement. Motivated by these findings, we propose a two-stage auditing and aggregation algorithm that weights contributors by the stability of their past residuals rather than by agreement with the majority. The method first accounts for differences across content and contributors, and then measures how predictable each contributor's evaluations are relative to the latent-factor model. Contributors whose evaluations are consistently informative receive greater influence in aggregation, even when they disagree with the prevailing consensus. In the Community Notes data, this approach improves out-of-sample predictive performance while avoiding penalization of disagreement.

  PublisherOA PDF

• MOFGen database

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-02

  datasetOpen accessSenior author
  PublisherDOI

• Algorithmic iterative reticular synthesis of zeolitic imidazolate framework crystals

  Nature Synthesis · 2025-11-25 · 7 citations

  articleOpen access

  Abstract The discovery of crystalline reticular materials remains largely trial-and-error despite their societal importance. We introduce our algorithmic iterative reticular synthesis (AIRES) cycle, which integrates automated synthesis, image recognition, single-crystal X-ray diffraction and, crucially, customized algorithmic decision-making, to maximize distinct crystal discoveries rather than optimizing single targets. Demonstrated on zeolitic imidazolate frameworks (ZIFs), AIRES achieves twice the discovery rate of random exploration, crystallizing 10 new linkers into diverse ZIF topologies and expanding the single-linker Zn-ZIF library by one-third. By transforming reticular synthesis from an empirical process to a systematic exploration, AIRES provides a scalable and efficient blueprint for accelerating materials discovery.

  PublisherOA PDFDOI

• Comparison of LLMs in extracting synthesis conditions and generating Q&A datasets for metal–organic frameworks

  Digital Discovery · 2025-01-01 · 11 citations

  articleOpen access

  Spoiler alert: Claude and Gemini did better than GPT-4 in extracting information from chemistry literature.

  PublisherOA PDFDOI

• An automated evaluation agent for Q&A pairs and reticular synthesis conditions

  Digital Discovery · 2025-11-18

  articleOpen accessCorresponding

  QAutoEval is an automated evaluation agent for Q&A datasets and reticular synthesis conditions, enabling reproducible benchmarking and transparent assessment of LLM driven workflows in reticular chemistry.

  PublisherOA PDFDOI

• Manifold-constrained nucleus-level denoising diffusion model for structure-based drug design

  Proceedings of the National Academy of Sciences · 2025-10-06 · 4 citations

  articleOpen accessCorresponding

  AI models have shown great potential in structure-based drug design, generating ligands with high binding affinities. However, existing models have often overlooked a crucial physical prior: Atoms must maintain a minimum pairwise distance to avoid atomic collision, a phenomenon governed by the balance of attractive and repulsive forces. To mitigate such atomic collisions, we propose NucleusDiff. It enforces spatial distance constraints between atomic nuclei and auxiliary mesh points placed on a spherical surface around each atom, approximating van der Waals boundaries to reduce atomic collisions. We quantitatively evaluate NucleusDiff using the CrossDocked2020 dataset and a COVID-19 therapeutic target, demonstrating that NucleusDiff reduces collision rate by up to 100.00% and enhances binding affinity by up to 22.16%, surpassing state-of-the-art models for structure-based drug design. We also provide qualitative analysis through manifold sampling, visually confirming the effectiveness of NucleusDiff in reducing atomic collisions and improving binding affinities.

  PublisherOA PDFDOI

• Travel Bans vs. Other Disease Mitigation Measures: A Mathematical Analysis

  ArXiv.org · 2025-10-10

  preprintOpen access1st authorCorresponding

  As the world grows increasingly connected, infectious disease transmission and outbreaks have become a pressing global concern for public health officials and policymakers. While policy interventions to contain and prevent the spread of disease have been proposed and implemented, there has been little rigorous quantitative analysis of the effectiveness of such interventions. In this paper, we study the susceptible-infected-recovered (SIR) infection process on a dynamic network model that models two communities with travel between them with the infection starting in one of them. In particular, we consider two Erdős--Rényi graphs where edges are dynamically changing based on node travel between the graphs. We characterize the time evolution of the outbreaks in both communities and pin down the time for when the infection first reaches the second community. Finally, we analyze two types of interventions--travel bans and intra-community interventions in the second community--and prove that travel bans are not effective, while the second type are effective even without travel bans, provided they sufficiently reduce the effective reproduction number. We complement our analytic results by numerical simulations on large networks with realistic degree distributions and disease recovery times, showing that these results are robust, and hold for settings that model actual contact networks and disease spread more closely.

  PublisherOA PDFDOI

Frequent coauthors

• Jennifer Chayes

  381 shared

• Omar M. Yaghi

  King Abdulaziz City for Science and Technology

  45 shared

• Béla Bollobás

  43 shared

• Oliver Riordan

  39 shared

• Riccardo Zecchina

  31 shared

• Nakul Rampal

  Kavli Energy NanoScience Institute

  30 shared

• Zhiling Zheng

  University of California, Berkeley

  28 shared

• Shang‐Hua Teng

  University of Southern California

  23 shared

Education

• B.S., Physics

  University of Munich

• Ph.D., Mathematical Physics

  University of Munich and Max-Planck-Institute for Physics

• Other, Mathematical Physics

  Free University in Berlin

Awards & honors

• Karl-Scheel Prize
• Heisenberg Fellowship
• Fellow of the American Mathematical Society
• Fellow of the Association of the Advancement of Science