Sarit Adhikari is a Postdoctoral Researcher and PhD in Computer Science with 11 years of experience blending multi-agent decision-theoretic research and practical data science/engineering. Her academic work produced scalable algorithms for planning under uncertainty among self-interested agents, and she now applies agent-based simulation to evaluate drug-intervention strategies for drug-resistant malaria at Temple University. She has industrial experience building recommendation and forecasting systems, EMV-enabled retail POS solutions, and NLP pipelines, and contributed machine-learning notebooks to the well-known CHAOSS/Augur open-source project for clustering and anomaly detection in repository discussions. Comfortable moving between theory and production, she teaches and mentors as well as ships data-driven features, having published peer-reviewed research and delivered course lectures on core AI topics. A detail that stands out: she combines advanced decision-theoretic methods with hands-on time-series, NLP, and clustering implementations that bridge research questions to deployable analytics.
11 years of coding experience
4 years of employment as a software developer
Deep Learning Specialization Deep Learning, Deep Learning Specialization Deep Learning at Coursera
Intermediate in Science Science, Intermediate in Science Science at St. Xavier's College
Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at University of Illinois Chicago
Bachelor's Degree Computer Engineering, Bachelor's Degree Computer Engineering at Institute of Engineering, Pulchowk Campus, Tribhuvan University
Python library and web service for Open Source Software Health and Sustainability metrics & data collection. You can find our documentation and new contributor information easily here: https://oss-augur.readthedocs.io/en/main/ and learn more about Augur at our website https://augurlabs.io
Role in this project:
Data Scientist
Contributions:24 commits, 12 PRs, 21 pushes in 2 months
Contributions summary:Sarit contributed a notebook for clustering comments and anomaly detection, indicating a focus on data analysis and model building within the project. The code involves importing libraries for text processing, machine learning, and data visualization. The user implemented TF-IDF vectorization, K-means clustering, and PCA for dimensionality reduction and visualization, demonstrating proficiency in applying these techniques to textual data.
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Sarit Adhikari - Postdoctoral Researcher at Temple University