Research project that investigated how different NLP models perform on social media data, finding that domain-specific approaches often outperform large pre-trained models. Includes PyConversations, a Python module for analyzing conversations across social media platforms.
We demonstrate that universal adversarial triggers can control both the topic and stance of GPT-2’s generated text, revealing security vulnerabilities in deployed language models and proposing constructive applications for bias auditing.
Key dimensions that have helped me understand multi-arm bandit problems: action space, problem structure, external information, reward mechanism, and learner feedback.
We introduce NewsTweet, a dataset and pipeline for studying embedded tweets in digital journalism, revealing that 13% of Google News articles incorporate tweets and providing insights into how social media becomes newsworthy.
We developed high-frequency monitoring tools to detect coordinated manipulation on Twitter, documenting anomalous follower patterns including sub-second spikes, sawtooth waves, circulating accounts, and dormant pre-2010 accounts resurfacing in bulk on high-profile political accounts.
We present an unsupervised algorithm for inducing semantic networks from Wiktionary’s crowd-sourced data, creating a WordNet-like resource more than 5x larger than Princeton WordNet with over 344,000 linked example sentences.
Discover how NEAT and HyperNEAT changed neuroevolution by automatically designing neural network architectures and scaling them through geometric patterns.
Understand the pattern recognition behind Netflix recommendations, email spam filters, and game-playing AI through three core machine learning approaches.
Explore the building blocks of classic AI reasoning, from knowledge bases and logic to how systems draw new conclusions from existing knowledge.
A 2018 exploration of evolutionary algorithms applied to Atari games, forking Dennis Wilson’s d9w/CGP.jl, where my contribution was the Atari reinforcement-learning experiment layer (experiment variants, fitness functions, hyperparameter tuning) on top of the upstream Cartesian Genetic Programming framework and its NEAT-inspired speciation.
QuAC introduces a conversational QA dataset that models student-teacher interactions, creating context-dependent questions that test systems’ ability to understand dialogue and resolve references.
CoQA extends question answering beyond isolated questions to conversations that require context and reference understanding.