I completed my PhD in Computer Science at Harvard University in 2019. My advisor was Prof. H.T. Kung. Currently, I am a research scientist at MIT Lincoln Laboratory. I am also an associate in Computer Science for Harvard John A. Paulson School of Engineering and Applied Sciences. My research centers around representation learning and its applications in Computer Vision, Natural Language Processing and Audio Video Processing.



Selected Publications

Multimodal Sparse Representation Learning and Cross-modal Synthesis
Miriam Cha
PhD Dissertation, Harvard University, Cambridge, MA, May 2019
Advisor: H.T. Kung
Committee: David Parkes, Yue Lu

Adversarial learning of semantic relevance in text to image synthesis
Miriam Cha, Youngjune Gwon, H.T. Kung
AAAI 2019, [pdf]

Improving SAR automatic target recognition using simulated images under deep residual refinements
Miriam Cha, Arjun Majumdar, H.T. Kung, Jarred Barber
IEEE ICASSP 2018, [pdf]

Language modeling by clustering with word embeddings for text readability assessment
Miriam Cha, Youngjune Gwon, H.T. Kung
ACM CIKM 2017, [pdf]

Adversarial nets with perceptual losses for text-to-image synthesis
Miriam Cha, Youngjune Gwon, H.T. Kung
IEEE MLSP 2017, [pdf]

Detecting depression using vocal, facial and semantic communication cues
James R Williamson, Elizabeth Godoy, Miriam Cha, Adrianne Schwarzentruber, Pooya Khorrami, Youngjune Gwon, H.T. Kung, Charlie Dagli, Thomas F Quatieri
ACM MM AVEC 2016, [pdf]

Multimodal Sparse Representation Learning and Applications
Miriam Cha, Youngjune Gwon, H.T. Kung
arXiv 2015, [arxiv]

Two-Stage Change Detection for Synthetic Aperture Radar
Miriam Cha, Rhonda Phillips, Patrick Wolfe, Christ Richmond
IEEE Transactions on Geoscience and Remote Sensing 2015, [pdf]

Twitter Geolocation and Regional Classification via Sparse Coding
Miriam Cha, Youngjune Gwon, H.T. Kung
AAAI ICWSM 2015, [pdf] [slides]

Multimodal Sparse Coding for Event Detection
Youngjune Gwon, William Campbell, Kevin Brady, Douglas Sturim, Miriam Cha, H.T. Kung
NIPS MMML 2015, [pdf] [slides]

Test Statistics for Synthetic Aperture Radar Coherent Change Detection
Miriam Cha, Rhonda Phillips, Patrick Wolfe
IEEE SSP 2012, [pdf]

Robust Periocular Biometric Recognition using Multi-level Fusion of Various Local Feature Extraction Techniques
Felix Juefei-Xu, Miriam Cha, Marios Savvides, Saad Bedros, Jana Trojanova
IEEE DSP 2011

Robust Local Binary Pattern Feature Sets for Periocular Biometric Identification
Juefei Xu, Miriam Cha, Joseph L Heyman, Shreyas Venugopalan, Ramzi Abiantun, Marios Savvides
IEEE BTAS 2010, [pdf]


Patents

United States Patent No. US10037477, Combined Intensity and Coherent Change Detection for Synthetic Aperture Radar.
International Patent (pending). Combined Intensity and Coherent Change Detection for Synthetic Aperture Radar.


Awards and Honors

National Science Foundation Graduate Research Fellowship Program (NSF GRFP)
National Defense Science and Engineering Graduate Fellowship (NDSEG)
Lincoln Scholarship
Harvard Distinction in Teaching Award, 2016 (Q Awards)