About Me

Hi, I’m Alex Loftus. I am a textbook author, kaggle competition winner, and PhD student starting in the fall with David Bau’s group. I am interested in understanding how we can use techniques from interpretability to steer large models at inference time, reduce hallucinations, and create truly customized systems. I have worked as a data scientist, a machine learning engineer, and as a master’s student in biomedical machine learning at Johns Hopkins University.

I’ve been fortunate to work with a number of brilliant people over the last few years. Here are some fun projects which resulted:

• I worked with Professor Joshua Vogelstein in the Johns Hopkins Biomedical Engineering department on a pipeline to create graphs from MRI data, which led to a paper in-review at Nature Methods.
• I co-authored a textbook on spectral graph theory, which we are publishing with with Cambridge University Press. This link is an old draft version; the final version is dramatically improved, and proactive readers can find a link to it in my CV.
• We developed an open-source project, Graspologic, which was acquired by Microsoft and used to measure collaboration changes in their workforce during COVID.
• This year, I began giving talks for the San Diego Machine Learning meetup group, where I joined a team competing in the Vesuvius Ink Detection Kaggle Competition. We won 1st place against 1,249 teams for a competition prize pool of $100,000. Our work was featured in Scientific American!
• I made a linear algebra tutoring series for my friend, which builds up the mathematical machinery of neural networks starting from the absolute foundations: dot product geometry and linear algebra.

I have a number of academic side-interests, including spectral theory and the mechanics of linear representation spaces, the history of science and mathematics, the mechanics of the visual system, the statistics of chess openings, and various causal relationships between geography and history. I am an avid traveler and am (slowly) learning Spanish.

Talks & Publications

• NNsight and NDIF: Democratizing Access to Foundation Model Internals: First authorship. Explore large model internals easily.

• A Saliency-based Clustering Framework for Identifying Aberrant Predictions: NeurIPS Workshop Paper, 2023 - won best poster
• 1st Place Solution - Vesuvius Ink Competition: Presentation, 2023, for 60 people. Presenting on our winning solution to a $100,000 Kaggle competition.
• Hands-on Network Machine Learning with Scikit-Learn and Graspologic: Textbook, 2023, Cambridge University Press. Second author, contributed roughly half the pages and figures.
• A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis: Paper, 2022, Nature Methods, under review. Second author, wrote most of the infrastructure for the codebase.
• ICML Conference Highlights: Talk about new machine learning techniques in drug discovery and medicine presented at ICML
• Working with LLMs: Talk, 2023, for 100 people at the AIML San Diego meetup
• Role of CAMKII in Associative Conditioning and GLR-1 Expression in C. Elegans: Poster, presented at Society for Neuroscience, 2017, Washington, DC. Later author, conducted most of the later experiments.
• Effects of an unc-43 (CaMKII) Gene Deletion on Short-Term Memory for Associative Conditioning in C. elegans: Talk, presented at Psychfest, 2017, Bellingham, WA.

Misc

I grew up in Seattle, WA. I was a competitive Starcraft 2 player in high school (I hovered between the grandmaster and top master leagues!). I studied behavioral neuroscience during my undergraduate years, with a philosophy minor. I got interested in math and programming and started a computational neuroscience club, where I taught weekly seminars. I also spent a lot of time partner dancing and playing guitar at open mic nights!

My girlfriend, Aina, is also in machine learning and we sometimes do research together. You can learn about her on her website: ainatersol.github.io.