Research

• 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

Professional

• Senior Science Manager, AI Labs at Amazon Web Services (AWS).
• Adjunct Professor at New York University lecturing on natural language processing and machine learning applied to quantitative trading and finance. (Press)

• Machine learning research at Google Research.
• Portfolio manager and research director at Cubist Systematic Strategies, applying machine learning to quantitative trading.
• Cofounder, Partner and Portfolio Manager at Ophir Partners.
• Chief Technology Officer at Trexquant.
• Quantitative portfolio manager at WorldQuant.
• Machine learning researcher at Microsoft Research and IBM Research.
• Software engineer at Merrill Lynch and Bloomberg.

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

• Haitian Sun, Andrew O. Arnold, Tania Bedrax-Weiss, Fernando Pereira and William W. Cohen (2020). "Guessing What's Plausible But Remembering What's True: Accurate Neural Reasoning for Question-Answering." arXiv preprint, arXiv:2004.03658.

• Andrew O. Arnold and William W. Cohen (2019). "Instance-based Transfer Learning for Multilingual Deep Retrieval." arXiv preprint, arXiv:1911.06111.

• Exploiting Domain and Task Regularities for Robust Named Entity Recognition (2009). Ph.D. Thesis, Carnegie Mellon University.
  • (Thesis slides) (Proposal) (Proposal slides)

• Amr Ahmed, Andrew Arnold, Luis Pedro Coelho, Joshua Kangas, Abdul-Saboor Sheikh, Eric Xing, William Cohen and Robert F. Murphy (2009). "Structured Literature Image Finder." In proceedings of the Annual Meeting of The ISMB BioLINK Special Interest Group (BioLINK), June 28-29, 2009, Stockholm, Sweden.

• Andrew Arnold and William W. Cohen (2009). "Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection." In proceedings of the AAAI Conference on Weblogs and Social Media (ICWSM), May 17-20, 2009, San Jose, CA. (Extended version) (Poster).

• Andrew Arnold and William W. Cohen (2008). "Intra-document Structural Frequency Features for Semi-supervised Domain Adaptation." In proceedings of the Association for Computing Machinery Conference on Information and Knowledge Management (CIKM), October 26-30, 2008, Napa Valley, CA. (Slides)

• Andrew Arnold, Ramesh Nallapati and William W. Cohen (2008). "Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition." In proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL:HLT), June 15-20, 2008, Columbus, OH. (Slides)

• Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li and Harry Shum (2008). "Query Dependent Ranking Using K-Nearest Neighbor." In proceedings of the 31st Annual International ACM SIGIR Conference, July 20-24, 2008, Singapore.

• Andrew Arnold, Ramesh Nallapati and William W. Cohen (2007). "A Comparative Study of Methods for Transductive Transfer Learning." In proceedings of the IEEE International Conference on Data Mining (ICDM) 2007 Workshop on Mining and Management of Biological Data, October 28, 2007, Omaha, NE. (Extended version) (Slides)

• Andrew Arnold, Yan Liu and Naoki Abe (2007). "Temporal Causal Modeling with Graphical Granger Methods." In proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 12-15, 2007, San Jose, CA. (Slides) (Video)

• Andrew Arnold, Joseph E. Beck and Richard Scheines (2006). "Feature Discovery in the Context of Educational Data Mining: An Inductive Approach." In proceedings of the AAAI2006 Workshop on Educational Data Mining, Boston, MA, 7-13.

• Andrew Arnold, Richard Scheines, Joseph E. Beck and Bill Jerome (2005). "Time and Attention: Students, Sessions, and Tasks." In proceedings of the AAAI2005 Workshop on Educational Data Mining, Pittsburgh, PA, 62-66.

• Eleazar Eskin, Andrew Arnold, Michael Prerau, Leonid Portnoy and Salvatore Stolfo (2002). "A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data." In Daniel Barbara and Sushil Jajodia (editors), Applications of Data Mining in Computer Security, Kluwer.