Jie Gao

About

Exploring enhanced light-matter interactions with optical, thermal and quantum nanomaterials, structures and devices. Our multidisciplinary research bridges the researchers across the fields of optical engineering, mechanical engineering, electrical engineering, applied physics, and materials science.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Combinatorics
• Metallurgy
• Materials science
• Mathematical optimization
• Discrete mathematics
• Geometry
• Theoretical computer science

Selected publications

• Maximizing Truth Learning in a Social Network is NP-hard

  arXiv (Cornell University) · 2025-02-18

  preprintOpen accessSenior author

  Sequential learning models situations where agents predict a ground truth in sequence, by using their private, noisy measurements, and the predictions of agents who came earlier in the sequence. We study sequential learning in a social network, where agents only see the actions of the previous agents in their own neighborhood. The fraction of agents who predict the ground truth correctly depends heavily on both the network topology and the ordering in which the predictions are made. A natural qu…

  PublisherOA PDFDOI

• Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures

  ArXiv.org · 2025-05-30

  preprintOpen access1st authorCorresponding

  Randomized dimensionality reduction is a widely-used algorithmic technique for speeding up large-scale Euclidean optimization problems. In this paper, we study dimension reduction for a variety of maximization problems, including max-matching, max-spanning tree, max TSP, as well as various measures for dataset diversity. For these problems, we show that the effect of dimension reduction is intimately tied to the \emph{doubling dimension} $λ_X$ of the underlying dataset $X$ -- a quantity measurin…

  PublisherOA PDFDOI

• Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees

  arXiv (Cornell University) · 2024-02-03

  preprintOpen access

  Active learning (AL) aims to improve model performance within a fixed labeling budget by choosing the most informative data points to label. Existing AL focuses on the single-domain setting, where all data come from the same domain (e.g., the same dataset). However, many real-world tasks often involve multiple domains. For example, in visual recognition, it is often desirable to train an image classifier that works across different environments (e.g., different backgrounds), where images from ea…

  PublisherOA PDFDOI

• Enabling Asymptotic Truth Learning in a Social Network

  arXiv (Cornell University) · 2024-10-06

  preprintOpen accessSenior author

  Consider a network of agents that all want to guess the correct value of some ground truth state. In a sequential order, each agent makes its decision using a single private signal which has a constant probability of error, as well as observations of actions from its network neighbors earlier in the order. We are interested in enabling \emph{network-wide asymptotic truth learning} -- that in a network of $n$ agents, almost all agents make a correct prediction with probability approaching one as…

  PublisherOA PDFDOI

• Obtaining Approximately Optimal and Diverse Solutions via Dispersion

  Research Square · 2023-12-13

  preprintOpen access1st author

  Abstract There has been a long-standing interest in computing diverse solutions to optimization problems. In 1995 J. Krarup posed the problem of finding $k$-edge disjoint Hamiltonian Circuits of minimum total weight, called the peripatetic salesman problem (PSP). Since then researchers have investigated the complexity of finding diverse solutions to spanning trees, paths, vertex covers, matchings, and more. Unlike the PSP that has a constraint on the total weight of the solutions, recent work ha…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: ATD: Theory and Algorithms for Discrete Curvatures on Network Data from Human Mobility and Monitoring

  NSF · $100k · 2017–2020

• NeTS: Small: Geometric and Topological Analysis on Trajectory Sensing: Collection, Classification and Anonymization

  NSF · $250k · 2016–2020

• Collaborative Research: From Brains to Society: Neural Underpinnings of Collective Behaviors Via Massive Data and Experiments

  NSF · $219k · 2019–2021

Frequent coauthors

• Bhaskar Krishnamachari

  130 shared

• Tarek Abdelzaher

  129 shared

• Sotiris Nikoletseas

  128 shared

• Luca Mottola

  Politecnico di Milano

  128 shared

• Magnús M. Halldórsson

  Reykjavík University

  128 shared

• Viktor K. Prasanna

  128 shared

• Andrea Roli

  64 shared

• Xianfeng Gu

  Stony Brook University

  29 shared

Labs

• Jie Gao LabPI

Similar researchers at Stony Brook University

• Anurag Purwarh-74
• Lifeng Wangh-49
• Nilanjan Chakrabortyh-48