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Rising Stars
2025

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Rising Stars

D-Index
42
Citations
8361
World Ranking
553
National Ranking
79

Computer Science

D-Index
34
Citations
4175
World Ranking
12276
National Ranking
4973

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Sijia Liu is affiliated with Michigan State University in the United States. The research contributions span primarily the field of Computer Science, with a focus on Artificial Intelligence and its intersecting subfields.

The main areas of study covered by their work include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Molecular Biology
  • Computational Mechanics

The scientific topics central to their research portfolio include:

  • Adversarial Robustness in Machine Learning
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Anomaly Detection Techniques and Applications
  • Machine Learning and ELM
  • Machine Learning and Data Classification
  • Stochastic Gradient Optimization Techniques

Frequent co-authors collaborating with Sijia Liu include:

  • Pin-Yu Chen
  • Yanzhi Wang
  • Shiyu Chang
  • Yihua Zhang
  • Xue Lin

The venues commonly chosen for disseminating research are varied but concentrated in notable outlets and preprint archives. These include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • E3S Web of Conferences
  • IEEE Signal Processing Magazine
  • Frontiers in Plant Science

Selected recent publications illustrate the scope of their research:

  • "A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications", 2020, IEEE Signal Processing Magazine
  • "4DNvestigator: time series genomic data analysis toolbox", 2021, Nucleus
  • "Enhancing the Phytoremediation of Heavy Metals by Combining Hyperaccumulator and Heavy Metal-Resistant Plant Growth-Promoting Bacteria", 2022, Frontiers in Plant Science
  • "StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs", 2021, IEEE Transactions on Neural Networks and Learning Systems
  • "MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge", 2021, arXiv (Cornell University)

Best Publications

  • Clinical information extraction applications: A literature review.

    Yanshan Wang;Liwei Wang;Majid Rastegar-Mojarad;Sungrim Moon

  • A clinical text classification paradigm using weak supervision and deep representation.

    Yanshan Wang;Sunghwan Sohn;Sijia Liu;Feichen Shen

  • A comparison of word embeddings for the biomedical natural language processing

    Yanshan Wang;Sijia Liu;Naveed Afzal;Majid Rastegar-Mojarad

  • AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks

    Chun-Chen Tu;Paishun Ting;Pin-Yu Chen;Sijia Liu

  • Topology attack and defense for graph neural networks: An optimization perspective

    Kaidi Xu;Hongge Chen;Sijia Liu;Pin Yu Chen

  • Adversarial T-shirt! Evading Person Detectors in A Physical World

    Kaidi Xu;Gaoyuan Zhang;Sijia Liu;Quanfu Fan

  • On the convergence of a class of Adam-type algorithms for non-convex optimization

    Xiangyi Chen;Sijia Liu;Ruoyu Sun;Mingyi Hong

  • Deep learning and alternative learning strategies for retrospective real-world clinical data

    David Chen;Sijia Liu;Paul Kingsbury;Sunghwan Sohn

  • A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery

    Caiping Zhang;Jiuchun Jiang;Linjing Zhang;Sijia Liu

  • Sensor Selection for Estimation with Correlated Measurement Noise

    Sijia Liu;Sundeep Prabhakar Chepuri;Makan Fardad;Engin Masazade

  • Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

    Tianlong Chen;Sijia Liu;Shiyu Chang;Yu Cheng

  • Clinical concept extraction: A methodology review

    Sunyang Fu;Sunyang Fu;David Chen;Huan He;Sijia Liu

  • A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications

    Sijia Liu;Pin-Yu Chen;Bhavya Kailkhura;Gaoyuan Zhang

  • Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation.

    Andrew Wen;Sunyang Fu;Sungrim Moon;Mohamed El Wazir

  • Learning sparse graphs under smoothness prior

    Sundeep Prabhakar Chepuri;Sijia Liu;Geert Leus;Alfred O. Hero

  • CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks.

    Akhilan Boopathy;Tsui-Wei Weng;Pin-Yu Chen;Sijia Liu

  • Adversarial Robustness vs. Model Compression, or Both?

    Shaokai Ye;Xue Lin;Kaidi Xu;Sijia Liu

  • Structured Adversarial Attack: Towards General Implementation and Better Interpretability

    Kaidi Xu;Sijia Liu;Pu Zhao;Pin-Yu Chen

  • Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases

    Ren Wang;Gaoyuan Zhang;Sijia Liu;Pin-Yu Chen

  • Optimal Periodic Sensor Scheduling in Networks of Dynamical Systems

    Sijia Liu;Makan Fardad;Engin Masazade;Pramod K. Varshney

  • The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models

    Tianlong Chen;Jonathan Frankle;Shiyu Chang;Sijia Liu

  • Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications

    Sijia Liu;Jie Chen;Pin-Yu Chen;Alfred O. Hero

  • Understanding and Improving Visual Prompting: A Label-Mapping Perspective

    Unknown

  • On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization

    Xiangyi Chen;Sijia Liu;Ruoyu Sun;Mingyi Hong

  • Sparsity-Aware Sensor Collaboration for Linear Coherent Estimation

    Sijia Liu;Swarnendu Kar;Makan Fardad;Pramod K. Varshney

  • On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method

    Pu Zhao;Sijia Liu;Pin-Yu Chen;Nghia Hoang

  • A Memristor-Based Optimization Framework for Artificial Intelligence Applications

    Sijia Liu;Yanzhi Wang;Makan Fardad;Pramod K. Varshney

  • ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

    Xiangyi Chen;Sijia Liu;Kaidi Xu;Xingguo Li

  • signSGD via Zeroth-Order Oracle.

    Sijia Liu;Pin Yu Chen;Xiangyi Chen;Mingyi Hong

  • Proactive Image Manipulation Detection

    Unknown

  • StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs.

    Tianyun Zhang;Shaokai Ye;Xiaoyu Feng;Xiaolong Ma

  • MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

    Geng Yuan;Xiaolong Ma;Wei Niu;Zhengang Li

  • 4DNvestigator: time series genomic data analysis toolbox.

    Stephen Lindsly;Can Chen;Sijia Liu;Scott Ronquist

  • Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing

    Sanghamitra Dutta;Dennis Wei;Hazar Yueksel;Pin-Yu Chen

  • Adversarial Robustness vs Model Compression, or Both?

    Shaokai Ye;Kaidi Xu;Sijia Liu;Jan-Henrik Lambrechts

Frequent Co-Authors

Pin-Yu Chen
Pin-Yu Chen IBM (United States)
Yanzhi Wang
Yanzhi Wang Northeastern University
Pramod K. Varshney
Pramod K. Varshney Syracuse University
Alfred O. Hero
Alfred O. Hero University of Michigan–Ann Arbor
Hongfang Liu
Hongfang Liu The University of Texas Health Science Center at Houston
Bin Ren
Bin Ren Xiamen University
Shiyu Chang
Shiyu Chang University of California, Santa Barbara
Mingyi Hong
Mingyi Hong University of Minnesota
Sunghwan Sohn
Sunghwan Sohn Mayo Clinic

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