Bahman Moraffah

Bahman Moraffah Faculty Machine, Algorithm, and Design Lab (MAD-Lab) Goldwater Center School of Electrical, Computer, and Energy Engineering Arizona State University Tempe, AZ 85287 Email: Bahman.Moraffah@asu.edu

Research

My interests span probabilistic inference, stochastic processes, Bayesian inference, Bayesian nonparametrics, Bayesian reinforcement learning, approximate inference (variational and MCMC), statistical signal processing, and information theory. In particular, I am interested in approaches to learning and decision making based on Bayesian inference in machine learning. The core areas of expertise are:

• Probabilistic Inference: Bayesian Inference, Bayesian Nonparametrics, Scalable Bayesian Inference, Unsupervised learning, Probabilistic Graphical Model

• Statistical Signal Processing: Multiple Object Tracking, Dynamic Systems, Coexistence, Pattern Recognition, Segmentation

• Information Theory: Privacy on High-dimensional Dataset, Source Coding

• Health: DNA Structure, Treatment and Prevention of Diseases, EEG Data Analysis, Causal Inference

News

• Invited to serve on the program committee for ICLR 2021.

• Po-Kan Shih has successfully defended his master's thesis.

• Invited to serve on the program committee for NeurIPS 2021.

• Invited to serve on the program committee for ICML 2021.

• I'll be serving on the program committee ISIT 2021.

• Sinjini Mitra has successfully defended her master's thesis.

• I'll be giving a talk on Modern Bayesian Inference: Models, Algorithms, and Applications at UCSB.

• Our paper "CAN: A Causal Adversarial Network for Learning Observational and Interventional Distributions" is now on arXix.

• I am selected to be the program chair for 54th Annual Asilomar Conference on Signals, Systems, and Computers.

• Our new paper "METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics" is accepted to the 54th Annual Asilomar Conference on Signals, Systems, and Computers.

• Invited to serve on the program committee for NeurIPS 2020.

• Our new paper on Bayesian Reinforcement Learning and its application in spectrum sharing is on arXiv.

• Our Metric Bayes paper on arXiv, May 2020.

• Our new papers on arXiv, April 2020.

• EEE 202 has moved online. We are using ZOOM. The link is provided on Canvas, March 2020.

• Giving a talk at URI on the applications of nonparametric modeling in multiple object tracking, February 2020.

• I'll be teaching EEE 202 in Spring 2020.

• I'll be giving a talk on feature allocation and paint box at U of H.

• I'll be attending NeurIPS, December 2019.

• I'll be presenting our works at Asilomar, November 2019.

• Two papers are accepted at Asilomar Conference on Signals, Systems, and Computers 2019.

• I'll be teaching EEE 554 in Fall 2019.

• I'll be teaching EEE/CSE 120 in Fall 2019.

• I'll be giving a talk on Bayesian nonparametric modeling and multiple object tracking at ASU, July 2019.

• I'll be giving a talk on Bayesian nonparametrics at ASU, June 2019.

Service and leadership

Conference Program Committee

• International Conference on Learning Representations (ICLR) (2020-)

• Conference on Neural Information Processing Systems (NeruIPS) (2019-)

• International Conference on Machine Learning (ICML) (2019-)

• International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2019-)

• IEEE International Symposium on Information Theory (ISIT)(2015-2020)

• Asilomar Conference on Signals, Systems, and Computers (2017-2019)

• Association for the Advancement of Artificial Intelligence (AAAI) (2017-2018)

• International Conference on Bayesian Nonparametrics (BNP)(2017)

• International Joint Conference on Artificial Intelligence (IJCAI)(2015)

Journal Reviewer

• MDPI (Sensor)

• MDPI (Symmetry)

• IEEE Transactions on Image Processing (TIP)

• IEEE Transactions on Information Forensics and Security (TIFS)

• IEEE Transactions on Signal Processing (TSP)

• IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI)

• IEEE Transactions on Aerospace and Electronic Systems (TAES)

• Journal of Machine Learning Research (JMLR)

• Machine Learning Journal (MLJ)

• Pattern Recognition (PR)

• Journal of Healthcare Informatics Research (JHIR)

Education

• Ph.D., Electrical Engineering

• M.Sc., Electrical Engineering

• B.Sc., Mathematics and Statistics

Affiliations

• School of Electrical, Computer, and Energy Engineering, Arizona State University