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.

• 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), leading a team of applied research scientists, working closely with our product and engineering partners, to build novel AI-enabled cloud services driven by large scale machine learning and natural language understanding research:
• Amazon Kendra: An intelligent search service powered by machine learning.
• Contact Lens for Amazon Connect: Understanding the sentiment and trends of customer conversations in real-time.
• Amazon Connect Voice ID: Real-time caller authentication using ML-powered voice analysis.
• Amazon QuickSight Q: Natural language business intelligence querying.
• Amazon Lookout for Metrics: Automatic time series anomaly detection and diagnosis.
• Adjunct Professor at New York University lecturing on natural language processing and machine learning applied to quantitative trading and finance. (Press)
• Area Chair at North American Chapter of the Association for Computational Linguistics (NAACL), 2021.

• 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.
• Quantitative research intern at Merrill Lynch.
• Machine learning research intern at Microsoft Research.
• Machine learning research intern at IBM Research.
• Software engineer at 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

• 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]

• Cheng Tang and Andrew O. Arnold
  Neural document expansion for ad-hoc information retrieval
  arXiv:2012.14005, 2020. [paper]

• 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]

• 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]

• Andrew O. Arnold and William W. Cohen
  Instance-based Transfer Learning for Multilingual Deep Retrieval
  arXiv:1911.06111, 2019. [paper]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• 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]

• Andrew O. Arnold and Andrew Howard
  Reinforcement Learning in the Presence of Hidden States
  Computer Science Department, Columbia University, 2002. [paper]

Invited Talks

• 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

• Network-based Information Extraction System (NIES), part of the The Querendipity Project.