About me#
Hello! I am a postdoctoral research fellow at Harvard University, specializing in representation learning methods for decision-making. My research interests focus on the following key areas:
Representations for counterfactual reasoning and planning: I aim to develop representations that provide reliable guarantees for counterfactual reasoning, particularly in offline settings. This research intersects with causal inference, offline reinforcement learning, and world models.
Representations in structured graph-like domains: My work involves learning representations in structured domains such as graph-based and temporal data. In addition to graph neural networks, I have adopted the topological deep learning paradigm, which allows for capturing higher-order interactions in the data.
Self-supervised learning: I am developing learning methods from unlabeled or partially labeled data across multiple modalities and tasks. My goal is to create foundation models that can be easily fine-tuned for counterfactual inference and sequential decision-making tasks, are robust to distributional shifts, and are adaptable to new features or variables. I am particularly focused on working with tabular and graph-structured data for which the current image-based self-supervised learning methods are not directly applicable.
Common-sense and external knowledge: I have been fascinated by AI systems that can leverage external common-sense reasoning to overcome tabula rasa learning. This is now possible more than ever with the advent of LLMs and foundation models. I seek to combine LLMs and other foundation models with learning embeddings of features to improve the generalization of models to new tasks and domains.
I am recognized as a principal investigator in federal grants by the National Science Foundation and the National Institutes of Health due to its implications on public health and climate change.
Our group is actively looking for motivated students, interns, and postdocs to join our research efforts. If you are interested in working with us, please reach out.
I earned my Ph.D. in Statistics from UT Austin, where I focused on statistical methods, reinforcement learning and computer vision for spatial domains. I held internships at Meta AI (FAIR) and Intel AI. I was a member of the UT Austin Villa Robot Soccer Team, where I developed vision systems for autonomous robots. Prior to my Ph.D., I completed a B.S. in Applied Mathematics at ITAM and an M.S. in Pure Mathematics at the University of Cambridge. Below is a photo of myself during the 2022 Robocup competition in Bangkok.
News#
[2024-06-15] ✨ New grant as co-PI funded by the NSF and NIH for “Synergizing Topological Deep Learning and Spatio-Temporal Causal Inference.” This methodological grant will enable new possibilities for (possibly multi-modal, multi-resolution) spatiotemporal data.
[2024-05-22] 📃 New ArXiV paper: E(n)-Equivariant Topological Neural Networks. Check out our blog post.
[2024-05-17] 🔥 Our paper Causal Estimation of Exposure Shifts with Neural Networks has been accepted to KDD 2024. See you in Barcelona!
[2024-02-01] 🔨 Our workshop Training Agents with Foundation Models in the Reinforcement Learning Conference (RLC) 2024 is to be held on August 9th, 2024. We will release the website and call for papers soon! Reach out to
tafm.rlc@gmail.com.[2024-02-01] 📃 New manuscript: Optimizing Heat Alert Issuance for Public Health in the United States with Reinforcement Learning. My first paper as senior author.
[2024-01-16] 🔥 New paper: SpaCE paper has been accepted to ICLR 2024. See you in Vienna!
[2024-01-15] 🤖 Created a LIVE CV, powered by retrieval augmented generation. Have fun asking your own questions about my research and work experience.
[2023-12-10] Started this new website based on Chris Holdgraf’s new blog template.
[2023-09-01] Promoted to Research Associate at Harvard University. I will continue my research as usual but take on more projects in a senior role and propose grant applications.
[2023-05-15] ✨ New grant as Co-PI awarded by the Harvard Chan-NIEHS to develop new computer vision architectures that are robust for prediction under covariate shift (with applications to projecting climate change’s health impacts).
[2023-03-01] 📃 New ArXiV paper [Causal Estimation of Exposure Shifts with Neural networks](https://arxiv.org/pdf/2302.02560.p df).
[2022-08-15] Started a postdoc at Harvard University, Department of Biostatistics.
CV#
Try my 🤖 Live CV Chatbot here, powered by LLMs and RAG. Have fun asking your own questions. You can also download an outdated pdf ⬇ here. Last updated: 2024-03-30.
My Family#
