Lijun Qian

Regents Professor, AT&T Endowed Professor & Director (CREDIT)

ECE, CREDIT & CCSB

Email
liqianobfuscate@pvamu.edu

Dr. Lijun Qian is AT&T Endowed Professor of Electrical and Computer Engineering at the Prairie View A&M University. He received B.S. from Tsinghua University, China, M.S. from Technion-Israel Institute of Technology, and Ph.D. from Rutgers University. He was with Bell-Labs Research and a visiting professor of Aalto University, Finland, before joining Prairie View A&M University to help develop the new Ph.D. program in Electrical Engineering. He has given lots of his efforts in developing and growing the PhD program. Dr. Qian has supervised ten PhD students, including the first and second to graduate. He is also the PI of the recently awarded NSF HBCU-RISE grant.

He is the founder and director of the Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT Center), and his research projects have been supported by multiple government agencies and industry. His research interests are in the areas of big data processing, wireless communications and mobile networks, network security, and computational systems biology. Dr. Qian is a research mentor and a dedicated educator, and engaged many students in cutting-edge research.

Recent Papers

Efficient Privacy Preserving Edge Computing Framework for Image Classification

Semi-supervised Learning for COVID-19 Image Classification via ResNet

Robust Face Mask Detection using Deep Learning on IoT Devices

Data Driven Network Monitoring and Intrusion Detection using Machine Learning

Inference Performance Comparison of Convolutional Neural Networks on Edge Devices

Two-Path Deep Semisupervised Learning for Timely Fake News Detection

Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media

Effective covid-19 screening using chest radiography images via deep learning

Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN

A multitask bi-directional RNN model for named entity recognition on electronic medical records

Closed loop control of blood glucose level with neural network predictor for diabetic patients

Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach

Review of stochastic hybrid systems with applications in biological systems modeling and analysis

Time-Based Switching Control of Genetic Regulatory Networks: Toward Sequential Drug Intake for Cancer Therapy