I am an associate professor at Texas A&M University. I am with the Biomedical Imaging, Sensing, and Genomic Signal Processing Group in the Department of Electrical and Computer Engineering (ECE). I am also a faculty member in the TEES-AgriLife Center for Bioinformatics & Genomic Systems Engineering (CBGSE).

My research interests include machine learning, Bayesian methods, optimal experimental design, signal/image processing, and their applications in life and materials sciences.

To prospective Ph.D. students

News & Research Updates

• Our new work on Bayesian active learning finally got accepted by AISTATS 2021 and ICLR 2021. Congratulations, Guang and all the collaborators! Our group and collaborators also started a new project on automated feature engineering accepted by AAAI 2021, led by Ziyu and Mingzhou. A good way to start 2021 and say bye to 2020.
• Our paper on Bayesian relational learning with Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, and Krishna Narayanan is accepted by NeurIPS 2020.
• We have just launched a new website on Objective Uncertainty Quantification.
• Congratulations to Ehsan Hajiramezanali, who has received the Chevron Scholarship from our department for the coming academic year.
• Guang's recent paper on filtering and experimental design with stochastic differential equations is accepted by IEEE Transactions on Signal Processing. This work is related to our efforts in AEOLUS.
• Two papers on 1) Neural Architecture Distribution Search (NADS) with Randy Ardywibowo, Shahin Boluki, Xinyu Gong, and Atlas Wang; and 2) Graph connection dropout learning with Ehsan Hajiramezanali, Arman Hasanzadeh, Shahin Boluki, Nick Duffield, Krishna Narayanan, and Mingyuan Zhou are accepted by ICML 2020. Thanks to all the coauthors for the hard work!
• Our recent work on "Hashing using Bernoulli VAE" by Siamak, Shahin and Mingzhang is accepted by UAI 2020. Congratulations!
• Two papers, "Semi-Implicit Stochastic Recurrent Neural Networks" by Ehsan and Arman and "ARSM Gradient Estimator for Supervised Learning to Rank" by Siamak and Shahin are accepted by ICASSP 2020. Ehsan and Arman's paper has been selected in the final list of Best Student Paper Award. Congratulations!
• Two papers, including "Learnable Bernoulli dropout for Bayesian deep learning" by Shahin, Randy, and Siamak are accepted by AISTATS 2020.
• Two papers on dynamic graph analytics --- "Semi-Implicit Graph Variational Auto-Encoders" and "Variational Graph Recurrent Neural Networks" --- with Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, and Mingyuan Zhou are accepted by NeurIPS 2019.
• The paper "Autonomous efficient experiment design for materials discovery with Bayesian model averaging" with Shahin Boluki, Anjana Talapatra, Raymundo Arroyave, and Edward Dougherty is published in Physical Review Materials (PRM). The story was covered in Science Daily, Phys.ORG, and Analytics Insight.
• NSF proposal "Collaborative Research: Combinatorial Collaborative Clustering for Simultaneous Patient Stratification and Biomarker Identification" is funded by the Information Integration and Informatics (III) program.
• The paper "Safe feature screening for generalized LASSO," co-authored with Shaogang Ren, Shuai Huang and Jieping Ye, is published in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
• The paper "BNP-Seq: Bayesian nonparametric differential expression analysis of sequencing count data," co-authored with Siamak Dadaneh and Mingyuan Zhou, is published in Journal of the American Statistical Association (JASA). Using the gamma/beta negative binomial process, we remove sophisticated ad-hoc pre-processing steps commonly required in existing algorithms. Please take a look at our paper and code if you are interested in differential expression analysis of sequencing counts.




© January 2021 Xiaoning Qian