CV

Education

• B.S. in Physics, Georgia Tech, 2016
• M.S. in Informatics, Indiana University 2019
• Ph.D in Informatics - Complex Systems, Indiana University, 2022 (expected)

Work experience

• 2021: Data Scientist
  • GeniusMesh
  • Duties included: Data science, NLP, recommender systems. Working with development team to deploy a client-facing dashboard to production.
• 2019-present, 2016-2017: Assistant Instructor
  • Indiana University. See Teaching Experience section below
• 2021-present, 2017-2019: Research Assistant
  • Indiana University
  • Duties included: Modeling and simulating online social systems to forecast user activity, spread of information, and market manipulation (2017-2019). Analyzing social media data and UK Parliamentary data (2021-Present).
  • Supervisors: Fil Menczer 2017-2019, John Bryden 2021-Present
• Summer 2014, Summer 2015: Radiological Surveyer and CAD Technician
  • USA Environment LP
  • Duties included: Assessing land for possible radiological contamination. Making and maintaining hardware packages to perform these tasks. Making maps of surveyed land and 3D models of future excavation plans.

Ongoing Research

• Measuring memetic impacts of interacting with social media content
  • Measuring changes in topical attention (using spacy models)
  • Measuring changes in sentiment toward topics (using transformer models)
  • Processing a ~20TB json.gz dataset of twitter data
• COVID vaccine hesitancy
  • Collecting social media data on covid vaccine hesitancy
  • Analyzing its relationship to public health behaviors and outcomes
  • Classifying tweets as Antivax or not, examining relationship between antivax content and vaccine uptake in the USA
• UK Parliament and Social Media
  • Analyzing Facebook, Twitter, New Media, and UK Parliementary speeches data.
  • Measuring impacts of media sources on UK Parliamentary discourse

Skills

• Data Science and Machine Learning
  • Python fluency. pandas, scikit-learn, spacy, etc.
  • Classical machine learning. Clustering, classification, regression, collaborative filtering, anomaly detection, feature engineering, etc.
  • Natural Language Processing (NLP). Text cleaning, NER and keyword extraction, targeted sentiment analysis, text classification and embedding, etc. Efficient text labeling with Prodigy.
  • Deep learning. PyTorch, HuggingFace Transformers. Mainly NLP, some experience in vision.
  • Big Data. pySpark, Koalas.
  • High dimensional data methods. Dimensionality reduction, manifold learning, graph embeddings, etc.
  • Network science. Community detection, diffusion modeling, graph characterization, etc.
  • Signal processing. Denoising, source separation.
  • Time Series. ARMAX, VAR, and related models.
  • Point process models. Hawkes processes, etc.
  • Causal inference. Granger causal modeling for time series and point processes.
  • Geospatial data science. Spatial regression, clustering, etc.
  • Model selection and model validation, Feature abalation studies.
  • Data visualization. Seaborn, matplotlib, Atlair, plotly.
  • High Performance Computing (HPC) for data science and simulation.
  • Matlab, Mathematica, R. Non-expert, but some experience.
  • SQL, MongoDB, Neo4j
  • Unix systems, bash, etc.
  • Git fluency.
• Modeling and Simulation
  • Focus especially on complex systems and network systems, e.g. social systems, ecological systems, economic networks.
  • Agent based models, Bayesian models, machine learning models, point process models.
  • Large scale simulation and HPC
  • Model validation
• Communications
  • Technical writing. Academic publicaiton, grants, public-facing reports, etc.
  • Interfacing with clients/funding agencies
  • Presentations, demos, etc.

Publications

See Google Scholar page

Conference Presentations

• “Forecasting Vaccine Refusal Rates by Modeling Social Influence,” NetSci, Full Talk, September 2020.
• “The empirical limits of prediction of microscopic dynamics of online conversation,” NetSci, e-Poster Session, September 2020.
• “Forecasting Vaccine Refusal Rates by Modeling Social Influence,” IC2S2, e-Poster Session, June 2020.
• “The empirical limits of prediction of microscopic dynamics of online conversation” IC2S2, e-Poster Session, June 2020.

Other Projects

• Comparison of spectrogram generation methods for audio classification using CNN classifiers, 2019.
  • Used CNN classifiers (i.e. ResNet) to classify audio spectrograms
  • Generated and compared audio spectrograms
• Forecasting Yellowstone Visitor Traffic, 2019.
  • Used ARMAX-like methods to forecast Yellowstone National Park visitor traffic
  • Performed feature ablation study on rich feature set
• Predicting Academic Citations with Collaborative Filtering, 2018.
  • Used collaborative filtering methods to forecast academic citations
• Radon Decay Chain, 2015.
  • Modeled concentrations of radioactive decay products over time for risk assessments
• Modeling Gravitational Wave Detection, 2014.
  • Used MCMC methods to fit model parameters for gravitational wave detection events
• Observations of the Faraday Instability 2014.
  • Used particle image velocimetry to measure internal fluid flows of Faraday waves
  • Wrote related software tools to enable data processing

Teaching Experience

• 2019-2021: Assistant Instructor for I-453, an ethics of technology/computing course.
  • Helped design curricula, including assignments, tests, and lectures; Occasionally lectured; Graded coursework.
• 2016-2017: Assistant Instructor for I-201, a discrete math course.
  • Managed lab sessions and taught course material; Graded coursework.

References

Contact me for a current list of references.