Engineering, Systems & Data Science Projects:

📄 Cooperative Rule Caching for SDN Switches Ori Rottenstreich, Ariel Kulik, Ananya Joshi, Jennifer Rexford, Gábor Rétvári, Daniel Menasché, 2020 IEEE 9th International Conference on Cloud Networking (CloudNet).
📄 Data Plane Cooperative Caching With Dependencies Ori Rottenstreich, Ariel Kulik, Ananya Joshi, Jennifer Rexford, Gábor Rétvári, Daniel Menasché, 2020 IEEE Transactions on Network and Service Management.
📄 An open repository of real-time COVID-19 indicators Alex Reinhart, Logan Brooks… Ananya Joshi …, Roni Rosenfeld, Ryan J. Tibshirani, Proceedings of the National Academy of Sciences.

🛠️ NiChrome, Google : Intern project with Anthony Rolland, advised by Ron Minnich and Christopher Koch
🛠️ COVIDCast Engineering, Delphi: Contributor since 2020.
🛠️ Cases2Beds Project lead for a tool to provision hospital beds, developed in collaboration with the Allegheny County Health Department and shared with several other county public health departments during the COVID-19 Pandemic. Presented on 01/08/2020 at the COVID-19 Trends and Impact Surveys Data Users Meeting.
🛠️ Research at MIT Lincoln Laboratory on Probabiltiy of Cloud Free Line of Sight
📄 Urban Data Mining in Switzerland, ETH Zurich

Prepared Lectures

📕 Time Series Data: Clarifying Practical Approaches
This was a 80-minute active-learning lecture for students in Machine Learning in Practice. In a post-class anonymous survey (85% completion rate), 83% found the material to be at the right level (1 student found it too easy and 1 found it too difficult) and, across all tasks, identified in the learning objectives, students reported being able to increase their skills (e.g. from being unfamiliar with the task, being able to define the task, being able to classify tasks that belong to the component, knowing at least 2 ways to approach the task, and being familiar with some technical insights/nuances of the tasks).

📕 AI for Social Good in Public Health which used a follow-along Colab notebook to demonstrate statistical properties of data streams like nonstationarity, noisiness, and weekday effects and walkthrough methods to process data with these properties.

Please contact me if you would like to use these activities!

📕 MLCommons Medical Working Group Presentation: Monitoring for Health Events: Bridging Healthcare and Public Health Approaches.
Motivated by the goal of enhancing collaboration between these fields, the lecture proposes a foundational step in scoping opportunities for integrating machine learning orchestration in healthcare and public health. Details available upon request.

Recent Highlights

🌟 Best Paper Award (SIGCSE ‘23) [Group Award]
🌟 Carnegie Mellon University Graduate Student Service Award 2022 [Group Award]

🌟 Co-Organizer of the Delta Workshop for drift phenomena at KDD ‘24! Link
🌟 Co-Organizer of the Data Driven Public Health Workshop at AAAI ‘25 Link
🌟 Presenting at AAAI Doctoral Consortium 2025
🌟 Presenting at KDD Doctoral Consortium 2024

🌟 Gave a talk at CMU’s Parallel Data Laboratory, Systems Group (Dec 2024) 🌟 Had a great at at UIowa’s Rising Stars Event (Dec 2024) 🌟 Enjoyed Presenting at the Human-Computing Seminar at Ohio State! (Nov 2024) 🌟 Presented a talk at CMU Artificial Intelligence Seminar Series (Apr 2024).
🌟 Presented a poster at the first annual InsightNet meeting (Apr 2024).

● Co-Instructor for 15-996 (Spring 2022/23) Article & TA for Machine Learning in Practice (Spring 2023)
● Completed the Eberly Center’s Future Faculty Program
● Selected Courses: Grad AI (A+), Mobile & Pervasive Computing [IoT] (A+)
● Partial travel grant to KDD, fee waiver for NU’s Future Faculty Workshop.

● Joined the Council of State and Territorial Epidemiologists’ (CSTE) Peer-to-Peer Technical Assistance network as a mentor for the Pennsylvania Dept. of Health and Santa Clara County Public Health Department. On a biweekly cadence I prepare lectures and hand-on Colab notebooks for concepts relevant to mentees! (2024+)
● Lead of SCS Ph.D. Wellness Group (Sep 2020-May 2022)
● Joined AI/Healthcare panel from the Coding School as part of a free machine learning course for high school students (2024).

● Mentored students in Pittsburgh Girls-of-Steel for data science basics weekly (Sep 2022-Jun 2023).
● Mentored my amazing undergraduate student Tara Lakdawala on a changepoint detection project (Sep 2022-Aug 2023) [Blog]. She’s now at Goldman Sachs!
● Congratulations to my incredible masters student, Richa Gadgil for her graduation from the Machine Learning Masters Degree at CMU! Richa was a core contributor for the FlaSH project and we are excited to keep working at Delphi with her this summer! (May 2024)

Hobbies: Pittsburgh is a great place to explore new hobbies. I’ve enjoyed starting rock climbing and martial arts here! My views and opinions are my own!

Bridging Gaps in Research and Public Health: CSTE Conference Takeaways

A few weeks ago, I had the opportunity to attend the Council of State and Territorial Epidemiologists (CSTE) Annual Conference right here in Pittsburgh! Getting involved with CSTE this past year, especially through the forecasting and modeling workgroup calls, has been incredibly rewarding, and it was great to see some familiar faces in person.

Information on Moving to Nairobi

I arrived in Nairobi last week to start my summer internship with the AI team at IBM Research! I’m enjoying my project, and the team has a welcoming culture.

Comments on CMS proposed rule (CMS-1808-P)

The CMS proposed rule will impact how data related to public health will be collected. Two standout quotes related to monitoring large-scale systems are,

Teaching Hands-On/Practical ML Classes

This semester, I had the opportunity to put together two practical, active learning lectures related to AI x Public Health!

Takeaways from the first InsightNet Meeting

Last week, I attended the first annual InsightNet conference in North Carolina!

Getting Started with Public Health Monitoring Literature

Interesting Papers, Talks, Videos Related to Public Health Monitoring

Public Health Opportunities

If you are a CS/AI/ML student interested in getting exposure either with applying your methods to public health or are interested in developing methods for public health applications these opportunities/insights may be interesting to you:

AutoPrompt: Improving GPT Responses Using Personas and Synthetic Documents

Using Synthetic Persona Generation to Improve Prompt Responses

Initial Impressions of Epidemic Forecasting

Epidemic forecasting, by its very nature, is a direct application of computer science, math, and biology.

Shiftview, GPT2 for Moral Reframing

Deploying the Shiftview repo on Google Cloud creates a website that highlights my work on ShiftView, an ideological transformer based on the moral foundations theory. You can find a light demo of the work in the sidebar. Please be patient as the translations require time to generate. Additionally, because some of the data is uncensored, there may be cursing and confusing speech. I am not repsonsible for the translations generated. In its current state, ShiftView is simply a guide for what is possible with automated moral reframing.

Fulbright 2019-2020 Research

The following are from the bitsvbytes website that I could salvage - while this project was put on hiatus during COVID, I learned a lot about designing public health interventions using a data-driven approach!

Fulbright 2019-2020 Recap & Notes

In 2019-2020, I was a Fulbright research student in Singapore. I kept a weekly blog (formerly called bitsvbytes because I was using ML techniques to reduce mosquito bites) that turned into a daily blog at the start of COVID. Unfortunately, I kept my blog (with all my writing, pictures, models, and demos for Buzznet) on Heroku (with a Heroku database) with the hope of eventually restarting the service once I could shrink the footprint (and the cost) of hosting this resource.