CHRISWTANNER.

Research

Describes Chris Tanner’s research interests in Machine Learning, Deep Learning, and Natural Language Processing (NLP). Harvard University. Brown University, Spotify, IBM Research, IBM Watson, Johns Hopkins HLT COE, MIT Lincoln Laboratory, MITLL, Department of Defense, Google, Florida Tech, UCLA.

cwt at mit dot edu

Hi, I’m Chris Tanner.

I research and teach Natural Language Processing (NLP) and Machine Learning.

I serve as the Head of R&D at Kensho, a 120-person ML/NLP company headquartered in Harvard Square. Kensho is owned by S&P Global and independently operates as S&P's tech "innovation hub". I created the research lab in 2022, and we continue to grow. Additionally, I hold a joint faculty appointment at MIT, where I teach NLP in the Fall and Machine Learning in the Spring.

Currently, my Kensho lab's research mostly concerns LLMs and includes tokenization, long-document QA, building challenging evaluation tasks, and alignment. If you are interested in working together at Kensho, please see our current openings. Research Scientist roles are for my lab; all other positions report to other teams.

Before joining Kensho and MIT in 2022, I spent three wonderful years teaching full-time at Harvard’s Institute for Applied Computational Science (IACS), which centers around two Master’s programs: Data Science; and Computational Science and Engineering. Brief application advice. I am not accepting PhD students, and I tend to only advise 1 Master's Thesis from Harvard or MIT each year.

I received my PhD in Computer Science (NLP) at Brown University and was fortunate to be Eugene Charniak’s final student. My "grand-adviser" is Marvin Minksy (me --> Eugene --> Marvin). Before then, I worked at MIT Lincoln Lab as an Associate Staff NLP researcher from 2009-2012.

Current hobbies include woodworking, designing and sewing hiking gear, hi-fi audio, and going on challenging hikes. My fiancée and I enjoy the outdoors together. She's set a bunch of world records for speed-hiking trails in the shortest amount of time (mostly self-supported, but some supported hiking records, too), including the Appalachian Trail, Swiss Via Alpina, The Vermont Long Trail, etc.

FALL 2025

  1. Natural Language Processing (6.4610) — formerly 6.861 (400+ students). Topics include text classification, language modelling, seq2seq models, transformers, and structured models. Students will also work on a significant research project.

PAST STUDENTS

NOTE: Most of my formal advising was during my time at Harvard (2019-2022). While at Kensho+MIT, I still collaborate with many students but due to time constraints, I rarely serve as a formal thesis adviser.

  • Haoran Zhang (currently Harvard Master’s)

  • Xiaohan Yang (Harvard Master’s Thesis 2022 -> Apple)

  • Anita Mahinpei (Harvard Master’s Thesis 2022)

  • Xin Zeng (Harvard Master’s Thesis 2022)

  • Jack Scudder (Harvard Master’s Thesis 2022 -> West Point Instructor)

  • Xavier Evans (Harvard Undergrad Independent Study)

  • Ning Hua (Smith x Harvard Independent Study)

  • Jie Sun (Harvard Independent Study -> Co-founded basys.ai)

  • Yoel Zweig (ETH Zurich Master’s Thesis ‘21)

  • Ali Hindy (High School -> Stanford CS)

  • Thomas Fouts (High School -> University of Michigan ME)

  • Mingyue Wei (Harvard Master's 2021 -> Amazon)

  • Alessandro Stolfo (ETH-Zurich Master’s ‘21 -> PhD program)

  • Brendan Falk (Harvard ‘20 -> CEO @ Fig)

EXPERIENCE

During my career within academia, industry, and government, significant projects (1-5 years) have concerned:

  • training LLMs (upwards of 30B params)

  • long-document QA

  • tokenization

  • developing evaluation benchmarks

  • coreference resolution

  • sign language classification

  • natural language understanding (NLU)

  • entity linking

  • citation prediction

  • face recognition

  • topic modelling

  • machine translation

  • streaming algorithms for NLP

  • anomaly detection

  • adaptive web personalization

  • speech recognition via active learning

  • error-correcting codes

  • social network analysis

  • 2D pattern recognition

  • animats-based learning (swarm intelligence)

INVITED TALKS

2024

  • Nov 8 — Eugene Charniak Academic Memorial/Tribute (Eugene passed June 2023)

2022-2023

  • Mostly internal presentations to C-levels, Executive Committees, and Board of Directors at S&P Global and Kensho.

2021

2020

  • November 20 — Research Talk @ Florida Institute of Tech.

  • October 15 — Career Advice @ Florida Institute of Tech.

  • May 19 — Open Data Science Conference (ODSC)

  • January 23 — Sequential Data @ Harvard ComputeFest

2019

  • September 27 — PhD Alumni Panel @ Brown

  • October 27 — RDMeetsIT Panel @ MIT Media Lab + Mercedes Benz

  • March 11 — Coreference Resolution @ Invitae

  • April 1 — MIT

  • March 15 — University of Washington

  • March 6 — CMU

  • February 21 — Brown

  • February 15 — Harvard