Hi, I'm Arjun Suryawanshi!
I study CIS and Bioengineering at the University of Pennsylvania and am interested in Software Engineering and Quantitative roles, particularly involving Machine Learning. I enjoy solving hard problems and innovating new solutions, be it a custom computer vision model for classifying horse behavior or improved vehicle simulation tools, and strive to find such challenges to tackle.
Featured Projects!
FedCast
A central-bank policy-forecasting platform that infers Federal Reserve and ECB policymaker beliefs from unstructured communications data, then predicts upcoming rate decisions. Decomposes each meeting into five economic dimensions (employment, growth, inflation, financial conditions, uncertainty) with an importance weight and expected direction per dimension, combines them with realized macro data through estimated reaction functions, and forecasts the rate change (bps) and discrete action (hike/hold/cut).
- Team Project, repo private as private data was utilized. Created as part of Bridgewater Associates Immersion: AI for Innovation Hackathon Program.
- Built a data pipeline ingesting and cleaning ~5,000 central-bank speeches and ~5,200 policy documents across the Fed and ECB into a Parquet feature backbone (20,000+ dimension-feature rows, 10,000+ macro/market control series, 416 policy decisions) with automated QC reporting.
- Implemented a sequential Bayesian belief-updating model (pgmpy) that processes speeches chronologically — each posterior becomes the next prior — blending transcript, prior, and macro evidence into a five-dimension belief state.
- Estimated Ridge + multinomial-logistic reaction functions per institution, benchmarking macro-only, belief-only, and combined feature sets to quantify the marginal signal of transcript-derived beliefs; backtested over 2012–2025 on ~2,300 monthly snapshots, reaching up to 95% hike/hold/cut classification accuracy and ~5–12 bps rate-change MAE in-sample.
- Orchestrated multi-step reasoning with LangGraph/LangChain and live context retrieval via the Perplexity API, surfacing evidence traces, historical analogs, and skeptic flags for interpretability.
- Shipped a Dockerized FastAPI service with synchronous and async job-based forecast endpoints, progress polling, and on-disk forecast caching, plus an interactive JS dashboard featuring belief-history timelines and a rotatable 3D policy-dimension view.
Vanstagram
A scalable social media platform inspired by Instagram and Facebook. Implements core features such as user auth, posting/commenting on home and wall pages, friending, and settings modifications. Also includes personalized news feeds via a modified adsorption algorithm, live chat system using Socket.io, and profile picture customization.
- My Contributions - (repo private by course policy)
- Planned and implemented backend architecture and route/database helper functions using AWS DynamoDB & S3 to ensure scalability and reliability.
- Implemented a modified adsorption algorithm using Apache Spark/Livy & AWS EMR for personalized news feeds along with the article liking system to determine edge weights.
- Created a friend-friend graph visualization tool using vis.js to analyze social connections and network structure.
- Assisted in frontend page organization and styling using HTML, CSS, and JavaScript to enhance user experience.