So, where were we? 🤔
With the deadlines for #full and #short papers left behind, #UMAP2025 has still amazing calls to offer:
👩🏭 Industry (March 2)
👩🎓 DC* (March 7)
👩🏫 LBR & Demos (April 9)
*Hey, students, have you seen the grants and support opportunities? Take a look at: www.um.org/umap2025/gra...
13.02.2025 17:28 — 👍 7 🔁 6 💬 0 📌 0
Last but not least, I want to thank all my co-authors and all the people that inspired me along this path 💎
26.01.2025 11:13 — 👍 1 🔁 0 💬 0 📌 0
In my thesis,
‘Designing Secure and Knowledge-Aware Recommender Systems Leveraging Data Properties and Graph Structures,’
I explored #RecSys 💡, enhancing #Graph-based models 🕸️ with knowledge graphs and safeguarding data using #DifferentialPrivacy 🔒
26.01.2025 11:13 — 👍 2 🔁 0 💬 1 📌 0
Last Thursday, I successfully defended my thesis! 🎓
This PhD journey has been incredible, thanks to my amazing colleagues at #sisinflab and my supervisor #TommasoDiNoia 🫶.
A special shoutout to @neuralnoise.com for making my visiting period at the University of Edinburgh unforgettable! 🚀
26.01.2025 11:13 — 👍 7 🔁 0 💬 2 📌 1
https://www.um.org/umap2025/call-for-full-short-papers/
https://www.um.org/umap2025/call-for-workshop-proposals/
https://www.um.org/umap2025/call-for-tutorial-proposals/
Too many things to say, and very few available characters 🥲 (we might have wasted some 🫣)
🔷 The call for Workshops and Tutorials is officially out 👉 (TL;DR Jan 20, 2025)
🔷 Friendly reminder for the call for Full and Short Papers 👉 (TL;DR Jan 23, 2025)
#UMAP2025 #usermodeling #personalization
18.12.2024 06:51 — 👍 5 🔁 3 💬 0 📌 0
a city skyline with the empire state building in the middle
ALT: a city skyline with the empire state building in the middle
We've got a pocket full of dreams.
We are proud to announce the Final Call for Full and Short Papers for #UMAP2025, June 2025 in NYC. Please spread the word, as it promises to be a unique edition of our beloved conference.
Check out the details here: www.um.org/umap2025/cal...
03.12.2024 18:05 — 👍 14 🔁 7 💬 1 📌 0
Last Friday at Univaq, I had the opportunity to discuss how to rethink #recsys for software engineering.
It was a pleasure exchanging ideas with incredible SE researchers! 💡
If you want to join the discussion, DM me! 🖐️
Huge thanks to Davide Di Ruscio for organizing this event! 🙌
02.12.2024 09:36 — 👍 0 🔁 0 💬 0 📌 0
I would like to join :)
19.11.2024 01:40 — 👍 2 🔁 0 💬 0 📌 0
I’ll be travelling to London from Wednesday to Friday for an upcoming event and would be very happy to meet up! 🚀
I'd love to chat about my recent works (DeCoRe, MMLU-Redux, etc.). DM me if you’re around! 👋
DeCoRe: arxiv.org/abs/2410.18860
MMLU-Redux: arxiv.org/abs/2406.04127
18.11.2024 13:48 — 👍 11 🔁 7 💬 0 📌 0
The 34th ACM International Conference on User Modeling, Adaptation and Personalization (#UMAP2026)
June 8-11 2026
Gothenburg, Sweden
Website: https://www.um.org/umap2026/
Assistant Prof of CS at the University of Waterloo, Faculty and Canada CIFAR AI Chair at the Vector Institute. Joining NYU Courant in September 2026. Co-EiC of TMLR. My group is The Salon. Privacy, robustness, machine learning.
http://www.gautamkamath.com
Professor at Penn, Amazon Scholar at AWS. Interested in machine learning, uncertainty quantification, game theory, privacy, fairness, and most of the intersections therein
Computer science, math, machine learning, (differential) privacy
Researcher at Google DeepMind
Kiwi🇳🇿 in California🇺🇸
http://stein.ke/
Associate Professor of Computer Science at Northeastern University in Boston. Dad. Imposter.
Senior Lecturer #USydCompSci at the University of Sydney. Postdocs IBM Research and Stanford; PhD at Columbia. Converts ☕ into puns: sometimes theorems. He/him.
Associate professor at U of Toronto. Computer science and math research: (differentially) private data analysis, geometry, discrepancy, optimization.
Computer science professor at Carnegie Mellon. Researcher in machine learning. Algorithmic foundations of responsible AI (e.g., privacy, uncertainty quantification), interactive learning (e.g., RLHF).
https://zstevenwu.com/
Assistant Professor of Computer Science at the University of Virginia. I work on Responsible AI (differential privacy & fairness) and machine learning for science and engineering (differentiable optimization) | http://nandofioretto.github.io
Professor of computer science at Boston University. Not related to any economists, living or dead, as far as I know.
ML & Privacy Prof at the University of Melbourne, Australia. Deputy Dean Research. Prev Microsoft Research, Berkeley EECS PhD. @bipr on the X bird site. He/him.
Prof of machine learning at University of Helsinki. Interested in (differential) privacy and open source software.
Assistant professor at Georgia Tech in ISyE. I do mechanism design, differential privacy, fairness, and learning theory, mostly.
Postdoc @Penn; Ph.D. @Caltech; MSc @Columbia and @Supélec.
He/him.
🤖
new arXiv preprints mentioning "differential privacy" or "differentially private" in the title/abstract/metadata
+ updates from https://differentialprivacy.org
[Under construction.]
Algorithms, predictions, privacy.
https://theory.stanford.edu/~sergei/
Differential Privacy. Machine Learning. Apple.
Assistant Professor @Dept. Of Computer Science, University of Copenhagen, Ex Postdoc @MPI-IS, ETHZ, PhD @University of Oxford, B.Tech @CSE,IITK.
Postdoc at UW CSE. Differential privacy, memorization in ML, and learning theory.
