Surya Kasturi
Born and raised in Hyderabad, India
Living in Boston, MA
Interested in how several tiny things compose into something cool in a wide variety of contexts, such as human languages, software systems compilers, plants, cancer
Projects
- Florina, 2025 - present
Voice AI assistant that helps women passively track their cycles by journaling their lived experiences
- How Far Can One Example Go? Reinforcement Learning Across Logic, Math, and Language, Sundai Club, Jun 2025
Can reinforcement learning on a single high-variance summarization example improve performance on other, unseen instances within the same domain?
- Personal health assistant, Out of Pocket Hackathon, May 2025
Collaborated with operations, growth, and medical professionals in a one-day sprint, leveraging LLMs to automatically extract and identify available insurance perks from patient documentation
- Schema guided dialog state tracking, DSTC8, AAAI 2020
Developed dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs.
- Synthetic dataset for document visual question answering, Apr 2020
Developed a synthetic Visual Question Answering (VQA) dataset from existing Question Answering (QA) datasets to evaluate the cross-modality generalization capabilities of models.
- Machine Reading Comprehension, Dec 2019
Optimized Machine Reading Comprehension models on SQuAD, CoQA, and ShARC by analyzing and mitigating the impact of entity-type sampling bias leading to significant performance gains. Top results from PAII Labs/Gamma Lab on SQuAD.
- Knowledge-grounded conversation modeling, DSTC7, AAAI 2019
Trained BERT and Memory Networks to generate contextually rich responses by conditioning on conversation history and Wikipedia facts, with performance significantly boosted by normalizing special characters in the Reddit dataset. Top results in the competition.
- Synthetic generation of MNIST style dataset using Google Fonts, Jun 2018
Developed a synthetic MNIST style dataset using Google Fonts to measure out of distribution performance of convolutional neural nets
- C implementation of Python's string module, Nov 2017
Developed a pure C library mirroring Python's advanced string module to provide a lightweight, dependency-free, unlicensed alternative to massive GNU/GPL
- Knowledge distillation of time series forecast models, Aug 2017
Developed deep neural net models for forecasting and detecting anomalies in chiller plants performance using real-time sensors
Open source contributions
Links