James R. Williamson

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Thomas QuatieriMITVerified email at ll.mit.edu
Brian S. HelferAssociate Staff in Bioengineering Systems and Technologies, MIT Lincoln LaboratoryVerified email at ll.mit.edu
Daryush D. Mehta, PhDDirector of Voice Science and Technology Laboratory, Center for Laryngeal Surgery and VoiceVerified email at alum.mit.edu
Gregory CiccarelliApplied Scientist, AmazonVerified email at mit.edu
Stephen GrossbergWang Professor of Cognitive and Neural Systems, Boston UniversityVerified email at bu.edu
Bea YuSenior Data ScientstVerified email at voya.com
Adam LammertAssistant Professor, Biomedical Engineering, Worcester Polytechnic InstituteVerified email at wpi.edu
Christopher SmaltMIT Lincoln Laboratory; Human Health & Performance Systems GroupVerified email at mit.edu
Daniel W. BlissProfessor, Electrical, Computer, and Energy Engineering, Director WISCA, Arizona State UniversityVerified email at asu.edu
Rachelle Horwitz-MartinVerified email at mit.edu
Isao HayashiKansai UniversityVerified email at cbii.kutc.kansai-u.ac.jp
James MundtSand Ridge Secure Treatment CenterVerified email at dhs.wisconsin.gov
Tejash PatelMIT Lincoln LabsVerified email at ll.mit.edu
Ennio MingollaNortheastern University, Department of Communication Sciences and DisordersVerified email at neu.edu
Buller, mjBrown University / USARIEMVerified email at brown.edu
David PaydarfarDell Medical School, The University of Texas at AustinVerified email at austin.utexas.edu
Pooya KhorramiMIT Lincoln LaboratoryVerified email at ll.mit.edu
Youngjune GwonExecutive Vice President, Samsung SDSVerified email at samsung.com
Miriam ChaMIT Lincoln Laboratory, Harvard UniversityVerified email at g.harvard.edu
H. T. KungProfessor, Harvard UniversityVerified email at harvard.edu

James R. Williamson

Technical Staff in Human Health & Performance Systems, Lincoln Laboratory

Verified email at ll.mit.edu

biomedical prediction signal processing machine learning cortical processing


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Gaussian ARTMAP: A neural network for fast incremental learning of noisy multidimensional maps JR Williamson Neural networks 9 (5), 881-897, 1996	420	1996
A neural model of how horizontal and interlaminar connections of visual cortex develop into adult circuits that carry out perceptual grouping and learning S Grossberg, JR Williamson Cerebral cortex 11 (1), 37-58, 2001	241	2001
Seizure prediction using EEG spatiotemporal correlation structure JR Williamson, DW Bliss, DW Browne, JT Narayanan Epilepsy & behavior 25 (2), 230-238, 2012	224	2012
Vocal and facial biomarkers of depression based on motor incoordination and timing JR Williamson, TF Quatieri, BS Helfer, G Ciccarelli, DD Mehta Proceedings of the 4th international workshop on audio/visual emotion …, 2014	217	2014
Vocal biomarkers of depression based on motor incoordination JR Williamson, TF Quatieri, BS Helfer, R Horwitz, B Yu, DD Mehta Proceedings of the 3rd ACM international workshop on Audio/visual emotion …, 2013	202	2013
Detecting Depression using Vocal, Facial and Semantic Communication Cues JR Williamson, E Godoy, M Cha, A Schwarzentruber, P Khorrami, Y Gwon, ... AVEC '16 Proceedings of the 6th International Workshop on Audio/Visual …, 2016	165	2016
Synthetic aperture radar processing by a multiple scale neural system for boundary and surface representation S Grossberg, E Mingolla, J Williamson Neural Networks 8 (7-8), 1005-1028, 1995	152	1995
On the relative importance of vocal source, system, and prosody in human depression R Horwitz, TF Quatieri, BS Helfer, B Yu, JR Williamson, J Mundt 2013 IEEE international conference on body sensor networks, 1-6, 2013	65	2013
Classification of depression state based on articulatory precision. BS Helfer, TF Quatieri, JR Williamson, DD Mehta, R Horwitz, B Yu Interspeech, 2172-2176, 2013	57	2013
Tracking depression severity from audio and video based on speech articulatory coordination JR Williamson, D Young, AA Nierenberg, J Niemi, BS Helfer, TF Quatieri Computer Speech & Language 55, 40-56, 2019	56	2019
A constructive, incremental-learning network for mixture modeling and classification JR Williamson Neural Computation 9 (7), 1517-1543, 1997	55	1997
Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG JR Williamson, DW Bliss, DW Browne 2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011	53	2011
A self-organizing neural system for learning to recognize textured scenes S Grossberg, JR Williamson Vision Research 39 (7), 1385-1406, 1999	53	1999
Segment-dependent dynamics in predicting Parkinson's disease JR Williamson, TF Quatieri, BS Helfer, J Perricone, SS Ghosh, G Ciccarelli, ... Sixteenth annual conference of the international speech communication …, 2015	47	2015
A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data D Muchoney, J Williamson IEEE transactions on Geoscience and Remote Sensing 39 (9), 1969-1977, 2001	47	2001
Cognitive impairment prediction in the elderly based on vocal biomarkers B Yu, TF Quatieri, JR Williamson, JC Mundt Sixteenth Annual Conference of the International Speech Communication …, 2015	39	2015
Self-organization of topographic mixture networks using attentional feedback JR Williamson Neural Computation 13 (3), 563-593, 2001	38	2001
Generalized two-stage rank regression framework for depression score prediction from speech N Cummins, V Sethu, J Epps, JR Williamson, TF Quatieri, J Krajewski IEEE Transactions on Affective Computing 11 (2), 272-283, 2017	30	2017
Articulatory dynamics and coordination in classifying cognitive change with preclinical mTBI BS Helfer, TF Quatieri, JR Williamson, L Keyes, B Evans, WN Greene, ... Fifteenth Annual Conference of the International Speech Communication …, 2014	29	2014
Prediction of cognitive performance in an animal fluency task based on rate and articulatory markers B Yu, TF Quatieri, JR Williamson, JC Mundt Fifteenth Annual Conference of the International Speech Communication …, 2014	28	2014

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