Min Xu

About

Min Xu is an Associate Professor and co-Director of the M.S. in Computational Biology Program at the Ray and Stephanie Lane Computational Biology Department within the School of Computer Science at Carnegie Mellon University. His research focuses on developing computer vision and machine learning methods for the automatic structural analysis of cell systems at molecular resolution and in close-to-native states. Specifically, his work involves information extraction and modeling of the structures and spatial organizations of macromolecules and their interactions with organelles in single cells captured by cryo electron-tomography 3D images. This emerging research field aims to address fundamental biological questions using a wide range of state-of-the-art computational and mathematical techniques.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Computer Security
• Data Mining
• World Wide Web
• Mathematics
• Cognitive psychology
• Psychology
• Database

Selected publications

• Self-supervised Pretraining of Visual Features in the Wild

  arXiv (Cornell University) · 2021 · 139 citations

  Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image and from any unbounded dataset. In this work, we explore if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision. Our fi…

  DOI

• MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models

  ArXiv.org · 2025-09-09

  preprintOpen accessSenior author

  Neural Collaborative Filtering models are widely used in recommender systems but are typically trained under static settings, assuming fixed data distributions. This limits their applicability in dynamic environments where user preferences evolve. Incremental learning offers a promising solution, yet conventional methods from computer vision or NLP face challenges in recommendation tasks due to data sparsity and distinct task paradigms. Existing approaches for neural recommenders remain limited…

  PublisherOA PDFDOI

• Towards SISO Bistatic Sensing for ISAC

  ArXiv.org · 2025-08-18

  preprintOpen access

  Integrated Sensing and Communication (ISAC) is a key enabler for next-generation wireless systems. However, real-world deployment is often limited to low-cost, single-antenna transceivers. In such bistatic Single-Input Single-Output (SISO) setup, clock asynchrony introduces random phase offsets in Channel State Information (CSI), which cannot be mitigated using conventional multi-antenna methods. This work proposes WiDFS 3.0, a lightweight bistatic SISO sensing framework that enables accurate de…

  PublisherOA PDFDOI

• Bistatic Passive Sensing via CSI Power

  ArXiv.org · 2025-11-27

  preprintOpen access

  Passive object sensing with communication signals is a key enabler of perceptive mobile networks and integrated sensing and communication. In practical bistatic deployments, transmitter-receiver asynchrony and hardware impairments introduce time-varying random phase offsets in Channel State Information (CSI). Together with limited bandwidth and small antenna arrays, these effects degrade sensing accuracy. This work proposes a lightweight bistatic passive tracking and sensing framework that opera…

  PublisherOA PDFDOI

• Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation

  ArXiv.org · 2025-07-05

  preprintOpen access

  Accurate gland segmentation in histopathology images is essential for cancer diagnosis and prognosis. However, significant variability in Hematoxylin and Eosin (H&E) staining and tissue morphology, combined with limited annotated data, poses major challenges for automated segmentation. To address this, we propose Color-Structure Dual-Student (CSDS), a novel semi-supervised segmentation framework designed to learn disentangled representations of stain appearance and tissue structure. CSDS com…

  PublisherOA PDFDOI

Frequent coauthors

• Jinqiao Wang

  Peng Cheng Laboratory

  45 shared

• Changsheng Xu

  42 shared

• Zhenkun Lei

  Dalian University of Technology

  42 shared

• Chen Tang

  Sichuan Agricultural University

  41 shared

• Jesse S. Jin

  Tianjin University

  38 shared

• Xiangjian He

  29 shared

• Chunxiang Cao

  Tongji University

  29 shared

• Suhuai Luo

  Shenyang Aerospace University

  28 shared

Similar researchers at Carnegie Mellon University

• Kathryn Roederh-88
• Jian Mah-60
• Carl Kingsfordh-40