About me
I am a technical and scientific leader with expertise building and leading large, high impact machine learning research and product teams in the areas of science, technology and quantitative trading. I am currently leading a team of applied research scientists, working closely with our engineering and product partners, to identify and solve large scale machine learning, natural language understanding and time series problems for our customers at Amazon Web Services (AWS) AI Labs in New York City. I am also an Adjunct Professor in NYU's Department of Finance and Risk Engineering, lecturing on natural language processing and machine learning applied to quantitative trading and finance. I received my Ph.D. in Machine Learning from Carnegie Mellon University, and my BA in Computer Science and Artificial Intelligence from Columbia University.
With expertise in machine learning, natural language processing and quantitative trading, my personal research interests are in robust machine learning, developing models and features that are robust to:
- extremely low signal to noise ratio and low sample size regimes
- changes in the distribution of features and labels across train and test sets (transfer learning)
- extracting features from unstructured data
I am particularly interested in applications of robust machine learning to time series and natural language processing models in financial and other domains.
Professional
Current:
Former:
Teaching
- News Analytics and Machine Learning (NYU FRE GY 7871, Fall 2020) (Fall 2019, Fall 2018)
This course introduces students to the topics of machine learning (ml) and natural language processing (nlp), in particular, as used to develop quantitative trading strategies. Students learn the mathematical fundamentals underlying many of the latest ml and nlp techniques (including deep neural networks, embeddings, and sentiment models), along with the basics of developing practical quantitative trading strategies based on these insights (such as quantifying the positive or negative sentiment of text, determining the relevance of text to particular stocks or classes of stocks, and the amount of novelty contained in textual content).
Publications
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Shen Wang, Xiaokai Wei, Cicero Nogueira dos Santos, Zhiguo Wang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang, Isabel F. Cruz and Philip S. Yu
Mixed-Curvature Multi-relational Graph Neural Network for Knowledge Graph Completion
The Web Conference (WWW), 2021. [to appear]
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Cheng Tang and Andrew O. Arnold
Neural document expansion for ad-hoc information retrieval
arXiv:2012.14005, 2020. [paper]
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Yifan Gao, Henghui Zhu, Patrick Ng, Cicero Nogueira dos Santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold and Bing Xiang
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction
arXiv:2011.13137, 2020. [paper]
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Haitian Sun, Andrew O. Arnold, Tania Bedrax-Weiss, Fernando Pereira and William W. Cohen
Faithful Embeddings for Knowledge Base Queries
Neural Information Processing Systems (NeurIPS), 2020. [paper]
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Andrew O. Arnold and William W. Cohen
Instance-based Transfer Learning for Multilingual Deep Retrieval
arXiv:1911.06111, 2019. [paper]
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Andrew O. Arnold
Exploiting Domain and Task Regularities for Robust Named Entity Recognition
Ph.D. Thesis, Carnegie Mellon University (CMU), 2009. [paper, slides, proposal, proposal slides]
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Amr Ahmed, Andrew O. Arnold, Luis Pedro Coelho, Joshua Kangas, Abdul-Saboor Sheikh, Eric Xing, William Cohen and Robert F. Murphy
Structured Literature Image Finder
ISMB BioLINK Special Interest Group (BioLINK), 2009. [paper]
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Andrew O. Arnold and William W. Cohen
Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection
International AAAI Conference on Weblogs and Social Media (ICWSM), 2009. [paper, extended version, poster]
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Andrew O. Arnold and William W. Cohen
Intra-document Structural Frequency Features for Semi-supervised Domain Adaptation
Conference on Information and Knowledge Management (CIKM), 2008. [paper, Slides]
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Andrew O. Arnold, Ramesh Nallapati and William W. Cohen
Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition
Association for Computational Linguistics: Human Language Technologies (ACL:HLT), 2008. [paper, slides]
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Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew O. Arnold, Hang Li and Harry Shum
Query Dependent Ranking Using K-Nearest Neighbor
Special Interest Group on Information Retrieval (SIGIR), 2008. [paper]
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Andrew O. Arnold, Ramesh Nallapati and William W. Cohen
A Comparative Study of Methods for Transductive Transfer Learning
International Conference on Data Mining Workshop on Mining and Management of Biological Data (ICDM), 2007. [paper, Extended version, slides]
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Andrew O. Arnold, Yan Liu and Naoki Abe
Temporal Causal Modeling with Graphical Granger Methods
International Conference on Knowledge Discovery and Data Mining (KDD), 2007. [paper, slides, video]
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Andrew O. Arnold, Joseph E. Beck and Richard Scheines
Feature Discovery in the Context of Educational Data Mining: An Inductive Approach
AAAI Workshop on Educational Data Mining (AAAI), 2006. [paper]
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Andrew O. Arnold, Richard Scheines, Joseph E. Beck and Bill Jerome
Time and Attention: Students, Sessions, and Tasks
AAAI Workshop on Educational Data Mining (AAAI), 2005. [paper]
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Eleazar Eskin, Andrew O. Arnold, Michael Prerau, Leonid Portnoy and Salvatore Stolfo
A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
Applications of Data Mining in Computer Security, 2002. [paper]
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Kristinn R. Thorisson, Hrvoje Benko, Denis Abramov, Andrew O. Arnold, Sameer Maskey, and Aruchunan Vaseekaran
Constructionist Design Methodology for Interactive Intelligences
AI Magazine (AAAI), 2004. [paper, abstract, article, video]
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Andrew O. Arnold and Andrew Howard
Reinforcement Learning in the Presence of Hidden States
Computer Science Department, Columbia University, 2002. [paper]
Invited Talks
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Recruiting for the Next Generation of Quant
Carnegie Mellon University's Master of Science in Computational Finance (MSCF) 25th Anniversary Celebration. Pittsburgh, PA (March 21, 2020) [postponed].
- Building the Optimal Data/ML Teams
AI & Data Science in Trading Conference. New York, NY (March 17-18, 2020) [postponed].
- Transfer Learning for Machine Learning and NLP: Adapting Models to Changing Markets
AI & Data Science in Trading Conference. New York, NY (March 17-18, 2020) [postponed].
- Navigating Data Challenges with the Latest ML/AI Applications
AI & Data Science in Trading Conference. New York, NY (March 17-18, 2020) [postponed].
- AI in the Workplace
CMU NYC Tech & Entrepreneurship Panel, with Manuela Veloso, Tom Doris and Evan Schnidman. Liquidnet, New York, NY (September 26, 2019).
- Machine Learning Developments and Applications to Quantitative Trading
AI & Data Science in Trading Conference. New York, NY (March 19-20, 2019) [invited].
- Transfer Learning for Quantitative Trading
machineByte 2018: The Global Machine Learning in Quantitative Investment Management Forum. Half Moon Bay, CA (December 13, 2018) [invited].
- Machine Learning and Trading
Career Speaker Series. Bendheim Center for Finance, Princeton University, Princeton, NJ
(March 29, 2017).
- Intra-document Structural Frequency Features for Semi-supervised Domain Adaptation
Association for Computing
Machinery Conference on Information and
Knowledge Management (CIKM), Napa, CA (October 29, 2008). [slides]
- Exploiting Document Structure and Feature
Hierarchy for Semi-supervised Domain Adaptation
Machine Learning
Lunch. Carnegie Mellon University, Pittsburgh, PA (September 29, 2008). [slides, video]
- Exploiting Feature Hierarchy for Transfer Learning in Named Entity
Recognition
46th Annual Meeting of the Association
for Computational Linguistics: Human Language Technologies (ACL:HLT), Columbus, OH (June 16, 2008). [slides]
- A Comparative Study of Methods for Transductive Transfer Learning
IEEE International Conference on Data Mining (ICDM)
2007 >Workshop on Mining and Management of Biological Data, Omaha, NE (October 28,
2007). [slides]
- Temporal Causal Modeling with Graphical Granger Methods
Thirteenth ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, San Jose, CA (August 13,
2007). [slides, video]
- A Comparison of Methods for Transductive Transfer Learning
Information Retrieval and Mining Seminar. Microsoft
Research Asia, Beijing, China (May 30, 2007). [slides]
- Feature Discovery in the Context of Educational Data Mining: An Inductive Approach
IBM Mathematical Sciences Department Seminar. IBM Watson Research, Yorktown Heights, NY (July 6,
2006). [slides]
- Causal Modeling for Anomaly Detection
IBM Mathematical Sciences Department 2006 Summer Student Seminar Series. IBM Watson Research, Yorktown Heights, NY (June 23, 2006). [slides]
Software