Yaqing Wang

Computer Science · Purdue University West Lafayette

Early career · publishing since 2018Rising activity

Topic Modeling Natural Language Processing Techniques Sentiment Analysis and Opinion Mining Generative Adversarial Networks and Image Synthesis Online Learning and Analytics

Publications

Citations

136

Est. group size

—

Recurring co-author estimate

Active years

Publishing since 2018

Research summary

AI-generated

Yaqing Wang works in computer science, focusing on machine learning methods that make models more efficient and adaptable, including few-shot learning (training with limited labeled examples), parameter-efficient tuning of large models, generative models like diffusion models and GANs, and natural language processing tasks such as sentiment analysis and named entity recognition. Their work also touches on federated learning, meta-learning, and applications to healthcare data and large language models. This research combines methods development with applications in text and medical data analysis.

Efficient and parameter-light model trainingFew-shot and meta-learningNatural language processing (sentiment analysis, NER, in-context learning)Generative models (diffusion models, GANs)Federated and semi-supervised learning

Publication output was minimal or absent from 2017–2020 but has grown steadily since 2021, reaching a peak of 5 publications in 2025.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence

Publications per year over the last 10 years — averaging 2.8/year recently

Recent publications

Survey on Neural Network Parameter Acquisition: from Optimization to Generation
2025
RD-MCSA: A Multi-Class Sentiment Analysis Approach Integrating In-Context Classification Rationales and Demonstrations
2025
Spectral Alignment as Predictor of Loss Explosion in Neural Network Training
arXiv (Cornell University) · 2025
Language Model Networks: Supervision-Efficient Learning through Dense Communication
ArXiv.org · 2025
Semantic Distance Measurement based on Multi-Kernel Gaussian Processes
arXiv (Cornell University) · 2025
Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
2024
AdaDiff: Accelerating Diffusion Models Through Step-Wise Adaptive Computation
Lecture notes in computer science · 2024
Learning to Learn with Contrastive Meta-Objective
arXiv (Cornell University) · 2024
GraphIC: A Graph-Based In-Context Example Retrieval Model for Multi-Step Reasoning
arXiv (Cornell University) · 2024
Teach LLMs to Personalize -- An Approach inspired by Writing Education
arXiv (Cornell University) · 2023
Macedon: Minimizing Representation Coding Rate Reduction for Cross-Lingual Natural Language Understanding
2023
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
arXiv (Cornell University) · 2022
Meta Self-training for Few-shot Neural Sequence Labeling
2021
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning
2021 IEEE International Conference on Big Data (Big Data) · 2021
MedRetriever
2021

Publishes in

arXiv (Cornell University)×7
2021 IEEE International Conference on Big Data (Big Data)×1
Lecture notes in computer science×1
2022 IEEE International Conference on Data Mining (ICDM)×1
ArXiv.org×1

Similar researchers

By research-topic overlap

Rajkumar Pujari
Computer Science · Purdue University West Lafayette
Dan Goldwasser
Computer Science · Purdue University West Lafayette
Daniel Dakota
Computer Science · Indiana University
María Leonor Pacheco
Computer Science · Purdue University West Lafayette
He Zhou
Computer Science · Indiana University

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 20, 2026.

Claim or correct this profile