World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
12423
World Ranking
8615
National Ranking
3691

Overview

Yanjun Qi is affiliated with the University of Virginia in the United States and conducts research primarily in the field of Computer Science. Within this broad domain, their work focuses extensively on Artificial Intelligence, with a significant number of publications also spanning Molecular Biology, Computer Vision and Pattern Recognition, Computer Networks and Communications, and Infectious Diseases.

Their research topics cover a variety of areas, including:

  • Topic Modeling
  • Domain Adaptation and Few-Shot Learning
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Explainable Artificial Intelligence (XAI)
  • Genomics and Phylogenetic Studies

Yanjun Qi has contributed to multiple publication venues, with the majority of work appearing in:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Bioinformatics
  • mSystems

Recent research papers authored or co-authored by Yanjun Qi include:

  • Long-Range Transformers for Dynamic Spatiotemporal Forecasting (2021), published in arXiv (Cornell University)
  • Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management (2020), published in IEEE Transactions on Cloud Computing
  • TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2020), published in arXiv (Cornell University)
  • Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure (2021), published in arXiv (Cornell University)

The scientist frequently collaborates with other researchers, including:

  • Arshdeep Sekhon
  • Jake Grigsby
  • Vicente Ordóñez
  • Jack Lanchantin
  • Zhe Wang

Best Publications

  • Opportunities and obstacles for deep learning in biology and medicine.

    Travers Ching;Daniel S. Himmelstein;Brett K. Beaulieu-Jones;Alexandr A. Kalinin

  • Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.

    Weilin Xu;David Evans;Yanjun Qi

  • Random Forest for Bioinformatics

    Yanjun Qi

  • Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

    Ji Gao;Jack Lanchantin;Mary Lou Soffa;Yanjun Qi

  • TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP.

    John X. Morris;Eli Lifland;Jin Yong Yoo;Jake Grigsby

  • Evaluation of different biological data and computational classification methods for use in protein interaction prediction

    Yanjun Qi;Ziv Bar-Joseph;Judith Klein-Seetharaman;Judith Klein-Seetharaman

  • General Multi-label Image Classification with Transformers

    Jack Lanchantin;Tianlu Wang;Vicente Ordonez;Yanjun Qi

  • DeepChrome: Deep-learning for predicting gene expression from histone modifications

    Ritambhara Singh;Jack Lanchantin;Gabriel Robins;Yanjun Qi

  • A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.

    Lourdes Pena-Castillo;Murat Tasan;Chad L Myers;Hyunju Lee

  • Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers.

    Weilin Xu;Yanjun Qi;David Evans

  • Random forest similarity for protein-protein interaction prediction from multiple sources.

    Yanjun Qi;Judith Klein-Seetharaman;Ziv Bar-Joseph

  • Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning.

    Paola Cascante-Bonilla;Fuwen Tan;Yanjun Qi;Vicente Ordonez

  • Systems and methods for semi-supervised relationship extraction

    Yanjun Qi;Xia Ning;Pavel Kuksa;Bing Bai

  • Cas9-chromatin binding information enables more accurate CRISPR off-target prediction

    Ritambhara Singh;Cem Kuscu;Aaron Quinlan;Yanjun Qi

  • Sentiment classification based on supervised latent n-gram analysis

    Dmitriy Bespalov;Bing Bai;Yanjun Qi;Ali Shokoufandeh

  • Prediction of interactions between HIV-1 and human proteins by information integration.

    Oznur Tastan;Yanjun Qi;Jaime G. Carbonell;Judith Klein-Seetharaman

  • Protein complex identification by supervised graph local clustering

    Yanjun Qi;Fernanda Balem;Christos Faloutsos;Judith Klein-Seetharaman

  • Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins

    Yanjun Qi;Oznur Tastan;Jaime G. Carbonell;Judith Klein-Seetharaman

  • DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

    Jack Lanchantin;Ritambhara Singh;Beilun Wang;Yanjun Qi

  • Learning to rank with (a lot of) word features

    Bing Bai;Jason Weston;David Grangier;Ronan Collobert

Frequent Co-Authors

Judith Klein-Seetharaman
Judith Klein-Seetharaman Arizona State University
Jason Weston
Jason Weston Facebook (United States)
Ziv Bar-Joseph
Ziv Bar-Joseph Carnegie Mellon University
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Ronan Collobert
Ronan Collobert Facebook (United States)
Casey S. Greene
Casey S. Greene University of Colorado Denver
Jaime G. Carbonell
Jaime G. Carbonell Carnegie Mellon University
Alexander G. Hauptmann
Alexander G. Hauptmann Carnegie Mellon University
Marty Humphrey
Marty Humphrey University of Virginia
John Lach
John Lach George Washington University

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