I am currently looking for new opportunities as Data Scientist / ML Scientist. Please feel free to contact me at scott.cheng.hsin.yang[AT]gmail[DOT]com. I was the Lead Machine Learning Scientist at Redpoll. Redpoll offers efficient, humanistic tabular data analysis with a core technology based on Bayesian non-parameterics. My focus at Redpoll was to expand the technology's applications to agriculture data analysis and synthetic data evalutaion. Prior to this, I was a Research Associate in the Math & Computer Science Department at Rutgers University–Newark working with Patrick Shafto. My research focused on the learning and teaching between humans and machines. Projects that I worked on include: DARPA XAI, DARPA ASIST, human-AI cooperative inference, acitve and pedagogical learning, and human-recommender-system interaction. Prior to that, I was a post-doc with Daniel Wolpert and Máté Lengyel in the CBL Lab at the University of Cambridge. There I studied active sensing by tracking human eye movement and quantifying sensing efficiency with Bayesian active learning. Prior to that, I did my PhD with John Bechhoefer in the Physics Department at Simon Fraser University. There I modelled the DNA replication as a stochastic process and applied the theory to analyze several types of replication experiment. Contact: scott.cheng.hsiny.ang[AT]gmail[DOT]com

Publications

	Scott Cheng-Hsin Yang, Baxter Eaves, Patrick Shafto (2024) A Machine-learning Perspective on Crop Security from the Genetic to the Landscape Scale To be submitted to Current Opinion in Plant Biology
	Scott Cheng-Hsin Yang, Baxter Eaves, Michael Schmidt, Ken Swanson, Patrick Shafto (2024) Structured Evaluation of Synthetic Tabular Data arXiv:2403.10424
	Scott Cheng-Hsin Yang, Chirag Rank, Jake A. Whritner, Olfa Nasraoui, Patrick Shafto (2023) Human Variability and the Explore–Exploit Trade‐Off in Recommendation Cognitive Science 47:e13279
	Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto (2023) The Inner Loop of Collective Human–Machine Intelligence Topics in Cognitive Science
	Harshit Bokadia, Scott Cheng-Hsin Yang, Zhaobin Li, Tomas Folke, Patrick Shafto (2022) Evaluating perceptual and semantic interpretability of saliency methods: A case study of melanoma Applied AI Letters 3:e77
	Scott Cheng-Hsin Yang, Tomas Folke, and Patrick Shafto (2022) A Psychological Theory of Explainability The 39th International Conference on Machine Learning (ICML 2022) [arXiv] [poster] [slides] [5-min talk] [20-min talk]
	Scott Cheng-Hsin Yang, Tomas Folke, and Patrick Shafto (2021) Abstraction, Validation, and Generalization for Explainable Artificial Intelligence Applied AI Letters 2:e37 [arXiv]
	Tomas Folke, Scott Cheng-Hsin Yang, Sean Anderson, and Patrick Shafto (2021) Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462J [arXiv] [doi]
	Tomas Folke, Scott Cheng-Hsin Yang, ZhaoBin Li, Ravi B. Sojitra, and Patrick Shafto (2021) Explainable AI for Natural Adversarial Images ICLR-21 Workshop on Responsible AI [workshop site]
	Scott Cheng-Hsin Yang, Sean Anderson, Pei Wang, Chirag Rank, Tomas Folke, and Patrick Shafto (2021) Inferring Knowledge from Behavior in Search-and-rescue Tasks Proceedings of the 43rd annual conference of the Cognitive Science Society [poster] [slides]
	Scott Cheng-Hsin Yang*, Wai Keen Vong*, Ravi B. Sojitra*, Tomas Folke, and Patrick Shafto (2021) Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching Scientific Reports 11:9863 [data & code] [arXiv]
	Scott Cheng-Hsin Yang, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto (2020) Unifying recommendation and active learning for information filtering and recommender systems Under review.
	Libby Barak, Scott Cheng-Hsin Yang, Chirag Rank, and Patrick Shafto (2020) Replicating L2 learning in a Computational Model Proceedings of the 42nd annual conference of the Cognitive Science Society
	Chi-Ken Lu, Scott Cheng-Hsin Yang, Xiaoran Hao, and Patrick Shafto (2020) Interpretable deep Gaussian processes with moments Proceedings of the 23rd international conference on Artificial Intelligence and Statistics [arXiv]
	Scott Cheng-Hsin Yang, Wai Keen Vong, Yue Yu, and Patrick Shafto (2019) A unifying computational framework for teaching and active learning Topics in Cognitive Science 11(2):316-337 [pdf] [code] [slides] [15-min talk]
	Chi-Ken Lu, Scott Cheng-Hsin Yang, Patrick Shafto (2018) Standing Wave Decomposition Gaussian Process Physical Review E 98:032303 [code] [arXiv]
	Yue Yu, Patrick Shafto, Elizabeth Bonawitz, Scott Cheng-Hsin Yang, Roberta M. Golinkoff, Kathleen H. Corriveau, Kathy Hirsh-Pasek, and Fei Xu (2018) The Theoretical and Methodological Opportunities Afforded by Guided Play With Young Children Frontiers in Psychology 9:1152 [doi]
	Wai Keen Vong, Ravi B. Sojitra, Anderson Reyes, Scott Cheng-Hsin Yang*, and Patrick Shafto* (2018) Bayesian teaching of image categories Proceedings of the 40th annual conference of the Cognitive Science Society
	Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi, Pei Wang, Wai Keen Vong, and Patrick Shafto (2018) Optimal Cooperative Inference Proceedings of the 21st international conference on Artificial Intelligence and Statistics [arXiv] [poster]
	Scott Cheng-Hsin Yang and Patrick Shafto (2017) Explainable Artificial Intelligence via Bayesian Teaching NIPS 2017 workshop on Teaching Machines, Robots, and Humans [workshop site] [poster]
	Scott Cheng-Hsin Yang and Patrick Shafto (2017) Teaching versus active learning: A computational analysis of conditions that affect learning Proceedings of the 39th Annual Conference of the Cognitive Science Society [poster] [code]
	Scott Cheng-Hsin Yang, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto (2017) Unifying recommendation and active learning for human-algorithm interactions Proceedings of the 39th Annual Conference of the Cognitive Science Society [slides]
	Scott Cheng-Hsin Yang, Daniel Wolpert, and Máté Lengyel (2016) Theoretical perspectives on active sensing Current Opinions in Behavioural Neuroscience 11:100-108
	Scott Cheng-Hsin Yang, Máté Lengyel, and Daniel Wolpert (2016) Active sensing in the categorization of visual patterns eLife 5:e12215 [full content online] [powerpoint] [code] [20-min talk]
	Shankar P. Das, Tyler Borrman, Scott Cheng-Hsin Yang, Victor W. T. Lui, John Bechhoefer, and Nicholas Rhind (2015) Replication timing is regulated by the number of MCMs loaded at origins Genome Research 25:1886-1892
	Scott Cheng-Hsin Yang (2012) Modelling the DNA replication program in eukaryotes PhD Thesis
	Antoine Baker, Benjamin Audit, Scott Cheng-Hsin Yang, John Bechhoefer, and Alain Arneodo (2012) Inferring where and when replication initiates from genome-wide replication timing data Phyiscal Review Letter 108:268101
	Scott Cheng-Hsin Yang, Nicholas Rhind, and John Bechhoefer (2010) Modeling genome-wide replication kinetics reveals a mechanism for regulation of replication timing Molecular Systems Biology 6:404 [supplementary material] [slides] [code] Rated by Faculty of 1000 Biology as MUST READ [link]
	Nicholas Rhind, Scott Cheng-Hsin Yang, and John Bechhoefer (2010) Reconciling stochastic origin firing with defined replication timing Chromosome Research 18:35
	Scott Cheng-Hsin Yang, Michel Gauthier, and John Bechhoefer (2009) Computational methods to study kinetics of DNA replication Methods in Molecular Biology 521:555-573
	Scott Cheng-Hsin Yang and John Bechhoefer (2008) How Xenopus laevis embryos replicate reliably: Investigating the random-completion problem Physical Review E 78:041917 [slides] Selected for a Viewpoint article: Just-in-time DNA replication

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