Rico Angell

I am currently a postdoc in the Center for Data Science at New York University working with He He. My research interests are in AI safety and alignment. Specifically, I am interested in building a scientific understanding of where and why AI systems fail in order to inform the implementation of safety measures. Importantly, I believe AI has the potential to provide a great service to humanity, but on its current trajectory it has the potential to cause substantial harm.

I completed my PhD in Computer Science at University of Massachusetts Amherst working with Andrew McCallum on machine learning and natural language processing. During my PhD, I was generously supported by the NSF Graduate Research Fellowship and the Spaulding-Smith Fellowship.

If you are interested in a potential collaboration or just want to chat, please email me!

Publications

Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models [pdf] [code]
Adam Karvonen, Benjamin Wright, Can Rager, Rico Angell, Jannik Brinkmann, Logan Smith, Claudio Mayrink Verdun, David Bau, Samuel Marks
ICML 2024 Mechanistic Interpretability Workshop Oral Presentation
To appear NeurIPS 2024

Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching [pdf] [code]
Rico Angell, Andrew McCallum
ICML 2024

Fairkit, Fairkit, on the Wall, Who’s the Fairest of Them All? Supporting Data Scientists in Training Fair Models [pdf]
Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, Sam Witty, Stephen J Giguere, Yuriy Brun.
EURO Journal of Decision Processes, 2023

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization [pdf] [code]
Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum
EMNLP 2022

Entity Linking via Explicit Mention-Mention Coreference Modeling [pdf] [code]
Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum
NAACL 2022

Interactive Correlation Clustering with Existential Cluster Constraints [pdf] [code]
Rico Angell, Nicholas Monath, Nishant Yadav, Andrew McCallum
ICML 2022

Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions [pdf]
Nicholas Monath, Nishant Yadav, Rico Angell, Andrew McCallum
EMNLP/CRAC 2021

Clustering-based Inference for Biomedical Entity Linking [pdf] [code]
Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum
NAACL 2021

Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology [pdf]
Sunil Mohan, Rico Angell, Nick Monath, Andrew McCallum
BCB 2021

Relation-Dependent Sampling for Multi-Relational Link Prediction [pdf]
Arthur Feeney*, Rishabh Gupta*, Veronika Thost, Rico Angell, Gayathri Chandu, Yash Adhikari and Tengfei Ma
ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+)

Inferring Latent Velocities from Weather Radar Data using Gaussian Processes [pdf]
Rico Angell and Daniel Sheldon
Conference on Neural Information Processing Systems (NeurIPS) 2018

Themis: Automatically Testing Software for Discrimination [pdf]
Rico Angell, Brittany Johnson, Yuriy Brun and Alexandra Meliou
Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2018

Don’t Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization [pdf]
Rico Angell and Grant Schoenebeck
International Conference on Web and Internet Economics (WINE) 2017

A Topological Approach to Hardware Bug Triage [pdf]
Rico Angell, Ben Oztalay, Andrew DeOrio
Microprocessor and SOC Test and Verification (MTV) 2015

Mentoring

Independent Studies
  • Dhruv Agarwal (MS -> PhD, Spring 2021-Present) - Clustering for Entity Resolution
  • Sriharsha Hatwar (MS, Spring 2023) - Modeling Uncertainty of Human Feedback for Entity Resolution
  • Aneri Rana (Spring 2023) - Modeling Uncertainty of Human Feedback for Entity Resolution
  • Haritha Ananthakrishna (MS, Fall 2022) - Scalable Semidefinite Programming
  • Pragya Prakash (MS, Fall 2022) - End-to-end Supervised Correlation Clustering
  • Manay Patel (Undergrad, Fall 2021) - Entity Resolution for Patent Author Disambiguation
  • Shubham Shetty (MS, Fall 2021) - Lifelong Entity Linking and Discovery
  • Ronald Soeh (MS, Spring 2021) - Efficiently Updating Nearest Neighbor Indices During Model Training
  • Matt Pearce (Undergrad, Fall 2020) - Visualization of Discovered Biomedical Entities and Concepts

COMPSCI 696DS, Industry Mentorship Program Projects
  • Vijayalakshmi Vasudevan, Rishabh Garg, Sriharsha Hatwar (Spring 2024) - Investigations in Compressed Context Windows
  • Aditya Kuppa, Alexandra Burushkina, Yugantar Prakash (Spring 2023) - A Unified Natural Language Understanding Re-ranker with Deep Reinforcement Learning
  • Ruei-Yao Sun, Nilesh Khade (Spring 2021) - Non-Gradient Based Adversarial Attack and Defense for Sequence Labeling
  • Arthur Feeney, Yash Adhikari, Gayathri Chandu, Rishabh Gupta (Spring 2020) - Using Graph Neural Networks for Drug-Drug Interaction Detection

Teaching

  • COMPSCI 696DS (University of Massachusetts Amherst) - Industry Mentorship Program - Lead TA - Spring 2022
  • COMPSCI 696DS (University of Massachusetts Amherst) - Industry Mentorship Program - Spring 2021
  • EECS 280 (University of Michigan) - Programming and Introductory Data Structures - Winter 2015

Additional Interests

Outside of work, I enjoy skiing, hiking, and training Brazilian jiu-jitsu (I am a purple belt under John Clarke in the Carlson Gracie lineage). I train at both Broadway Jiu-Jitsu in Boston and Unity Jiu-Jitsu in NYC.

Contact

r [dot] angell [at] nyu [dot] edu