Summary

Dr. Chen is a Machine Learning researcher with over 25 years of experience in developing and applying novel machine learning algorithms to a wide variety of applications including automatic speech recognition, image classification, and threat analysis and detection. He has deep expertise in Neural Networks, Random Forests, Graphical Models, Hidden Markov Models, and Support Vector Machines. Dr. Chen currently leads several research teams at LLNL focused on the development of new neural network learning algorithms that address recurring challenges in scientific and security applications including: the scarcity of labeled data, the multimodality of data types, and the scalability of large models on massive training datasets.

Dr. Chen chairs LLNL's Artificial Intelligence and Data Science LDRD Review Committee (https://ldrd-annual.llnl.gov) and serves on the senior council of LLNL's Data Science Institute (https://data-science.llnl.gov).

Ph.D. Electrical Engineering and Computer Science, University of California, Berkeley 2005

B.S. Electrical Engineering, University of Maryland at College Park 1997

Rafael Rivera Soto, Olivia Miano, Juanita Ordoñez, Barry Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews, “Learning Universal Authorship Representations”, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’21), 2021.

Barry Y. Chen, T. Nathan Mundhenk, Karl S. Ni. “Toward a Deep Learning System for Making Sense of Unlabeled Multimodal Data.” The Next Wave, 2019.

Guojing Cong, Giacomo Domeniconi, Joshua Shapiro, Chih-Chieh Yang, Barry Y. Chen. “Video Action Recognition with An Additional End-To-end Trained Temporal Stream.” Winter Conference on Applications of Computer Vision (WACV’19), 2019.

Yana Feldman, Margaret Arno, Carmen Carrano, Brenda Ng, Barry Chen. “Toward a Multimodal-Deep Learning Retrieval System for Monitoring Nuclear Proliferation Activities.” Journal of Nuclear Materials Management, 2018.

T. Nathan Mundhenk, Daniel Ho, and Barry Y. Chen. “Improvements to Context Based Self-Supervised Learning.” Proceedings of the Conference on Computer Vision and Pattern Recognition, 2018.

Guojing Cong, Giacomo Domeniconi, Joshua Shapiro, Fan Zhou, and Barry Y. Chen. “Accelerating Deep Neural Network Training for Action Recognition on a Cluster of GPUs.” International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2018.

Karl Ni, Kyle Zaragoza, Alexander Gude, Yonas Tesfaye, Carmen Carrano, Charles Foster, and Barry Chen. “Sampled Image Tagging and Retrieval Methods in User Generated Content.” Proceedings of the British Machine Vision Conference, 2017.

Michael B. Mayhew, Barry Chen, and Karl S. Ni. “Assessing Semantic Information in Convolutional Neural Network Representations of Images via Image Annotation.” Proceedings of the International Conference on Image Processing, 2016.

Brian Van Essen, Hyojin Kim, Roger Pearce, Kofi Boakye, and Barry Chen. “LBANN: Livermore Big Artificial Neural Network HPC Toolkit.” Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments (MLHPC '15), 2015.

Karl S. Ni, Roger A. Pearce, Kofi Boakye, Brian Van Essen, Damian Borth, Barry Y. Chen, and Eric X. Wang. “Large-scale deep learning on the YFCC100M dataset.” CoRR, abs/1502.03409, 2015.

Kush R. Varshney, Ryan J. Prenger, Tracy L. Marlatt, Barry Y. Chen, William G. Hanley, “Practical Ensemble Classification Error Bounds for Different Operating Points,” IEEE Transactions on Knowledge and Data Engineering, 11 Dec. 2012. IEEE computer Society Digital Library.

R. J. Prenger, K. R. Varshney, B. Y. Chen, T. D. Lemmond, W. G. Hanley, “Class-Specific Extensions of Error Bounds for Random Forest Classifiers,” Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, July 2010.

T. D. Lemmond, B. Y. Chen, A. O. Hatch, and W. G. Hanley, “An Extended Study of the Discriminant Random Forest,” Data Mining Special Issue in Annals of Information Systems, Vol. 8, 2010.

B. Y. Chen, T. D. Lemmond, W. G. Hanley, “Building Ultra-Low False Alarm Rate Support Vector Classifier Ensembles Using Random Subspaces,” Proceedings of  IEEE Symposium on Computational Intelligence and Data Mining, April 2009.

T. D. Lemmond, A. O. Hatch, B. Y. Chen, D. A. Knapp, L. J. Hiller, M. J. Mugge, W. G. Hanley, “Discriminant Random Forests,” Proceedings of the 2008 International Conference on Data Mining (DMIN'08), July 2008.

A. Stolcke, B. Chen,  H. Franco, V. R. R. Gadde, M. Graciarena, M.-Y. Hwang, K. Kirchhoff, A. Mandal, N. Morgan, X. Lei, T. Ng, M. Ostendorf, K. Sonmez, A. Venkataraman, D. Vergyri, W. Wang, J. Zheng, Q. Zhu, “Recent Innovations in Speech-to-Text Transcription at SRI-ICSI-UW,” IEEE Transactions on Audio, Speech and Language Processing, Vol. 14, No. 5, September 2006.

B. Y. Chen, L. M. Kegelmeyer, J. A. Liebman, J. T. Salmon, J. Tzeng, and D. W. Paglieroni, "Detection of Laser Optic Defects Using Gradient Direction Matching," SPIE Photonics West LASE Symposium: 8th International Workshop on Laser Beam and Optics Characterization, Proc. SPIE, Vol. 6101, January 2006.

B. Y. Chen and D. W. Paglieroni, “Using Gradients, Alignment and Proximity to Extract Curves and Connect Roads in Overhead Images,” SPIE Defense & Security Symposium: Optics and Photonics in Global Homeland Security II, Proc. SPIE, Vol. 6203, April 2006.

C. Pelaez-Moreno, Q. Zhu, B. Y. Chen and N. Morgan, “Automatic Data Selection for MLP-based Feature Extraction for ASR,” Proceedings of EUROSPEECH 2005, September 2005.

Q. Zhu, B. Y. Chen, F. Grezl and N. Morgan, “Improved MLP Structures for Data-Driven Feature Extraction for ASR,” Proceedings of EUROSPEECH 2005, September 2005.

Q. Zhu, A. Stolcke, B.Y. Chen and N. Morgan, “Using MLP Features in SRI's Conversational Speech Recognition System,” Proceedings of EUROSPEECH 2005, September 2005.

B. Y. Chen, Q. Zhu, and N. Morgan, "Tonotopic Multi-Layered Perceptron: A Neural Network for Learning Long-Term Temporal Features for Speech Recognition,” Proceedings of the International Conference on Acoustics Speech and Signal Processing, March 2005.

B. Y. Chen, Q. Zhu, and N. Morgan, "Learning Long-Term Temporal Features in LVCSR Using Neural Networks," Proceedings of International Conference on Spoken Language Processing, October 2004.

Q. Zhu, B. Y. Chen, N. Morgan, and A. Stolcke, "On using MLP Features in LVCSR," Proceedings of International Conference on Spoken Language Processing, October 2004.

N. Morgan, B. Y. Chen, Q. Zhu, and A. Stolcke, "Tandem Connectionist Feature Extraction for Conversational Speech Recognition," Proceedings of Joint AMI/PASCAL/IM2/M4 Workshop on Multimodal Interaction and Related Machine Learning Algorithms, June 2004.

B. Y. Chen, Q. Zhu, and N. Morgan, "Long-term Temporal Features for Conversational Speech Recognition," Proceedings of Joint AMI/PASCAL/IM2/M4 Workshop on Multimodal Interaction and Related Machine Learning Algorithms, June 2004.

N. Morgan, B. Y. Chen, Q. Zhu, and A. Stolcke, "TRAPping Conversational Speech: Extending TRAP/Tandem Approaches to Conversational Telephone Speech Recognition," Proceedings of International Conference on Acoustics Speech and Signal Processing, May 2004.

N. Morgan, B. Y. Chen, Q. Zhu, and A. Stolcke, "Scaling Up: Learning Large-Scale Recognition Methods From Small-Scale Recognition Tasks," Proceedings of Special Workshop in MAUI (SWIM), Jan 2004.

B. Y. Chen, S. Chang, and S. Sivadas, "Learning Discriminative Temporal Patterns in Speech: Development of Novel TRAPS-Like Classifiers," Proceedings of EUROSPEECH 2003, September 2003.

P. Somervuo, B. Y. Chen, and Q. Zhu, "Feature Transformations and Combinations for Improving ASR Performance," Proceedings of EUROSPEECH 2003, September 2003.

C. Benitez, L. Burget, B. Y. Chen, S. Dupont, H. Garudadri, H. Hermansky, P. Jain, S. Kajarekar, and S. Sivadas, "Robust ASR Front-End Using Spectral-Based and Discriminant Features: Experiments on the Aurora Tasks," Proceedings of Eurospeech 2001, September 2001.

• Lawrence Livermore National Laboratory, Global Security Gold Award, 2010
• UC Berkeley, Electrical Engineering Department, Best Graduate Student Instructor, 1998

Patent

B. Y. Chen, W. G. Hanley, T. D. Lemmond, , L. J. Hiller, D. A. Knapp, M. J. Mugge, "Discriminant Forest Classification Method and System," U.S. Patent 8,306,942 B2, Nov 2012.