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
I am always recruiting for graduate students. Research Assistantship (RA) positions will be offered to outstanding students who are hard-working and motivated. Interested candidates should email their CVs, transcripts, and brief research statements.
[Note] I apologize but I may not have time to reply to all the inquiry emails. However, I do reply to the students who have applied for admissions to our department if you have all the requested documents in your inquiry emails.
News & Research Updates
- 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 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.
- Congratulations to Ehsan Hajiramezanali, who has received the department Outstanding Graduate Student Award.
- 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 "Adaptive activity monitoring with uncertainty quantification in switching Gaussian Process models" with Randy Ardywibowo, Guang Zhao, Atlas Wang, Bobak Mortazavi, and Shuai Huang is accepted in AISTATS 2019.
- The paper "Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data" with Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, and Mingyuan Zhou is in NeurIPS 2018.
- 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.
- The paper "Unsupervised CNN-based co-saliency detection with graphical optimization" with Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, and Yung-Yu Chuang is published in ECCV 2018.
- 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.
© May 2020 Xiaoning Qian