Check out our @adjiboussodieng’s Op-Ed published by @AJEnglish: 📝 “AI fears are being weaponized” #AI #AIGovernance #AIRisk @PrincetonCS

🚀 We are hiring! Join Vertaix at @PrincetonCS for Fall 2027! Full funding available. 🔬 Are you passionate about the intersection of machine learning and the natural sciences? I am looking for highly motivated PhD students to join my research group, Vertaix, to build the foundations of Investigative Intelligence. ☀️ We are moving AI beyond simple predictive pattern matching and building systems capable of autonomous scientific discovery. We will tackle critical challenges like exploring high-dimensional spaces, mitigating mode collapse, and leveraging spectral diversity to create trustworthy AI systems that actually discover. Full funding from an NSF CAREER Award and other sources available. ⁉️ How to Apply: Apply directly through the Princeton Computer Science department application process (see links below). In your Statement of Purpose, explicitly mention your interest in working with Prof. Adji Bousso Dieng (@adjiboussodieng) and the Vertaix lab. Deadline is December 15, 2026. 🐯 If you are a PhD student already at Princeton (in any department, not only in CS) and want to be advised by @adjiboussodieng, please feel free to email her with your interests and CV. 🖇️ Link 1: cs.princeton.edu/grad/admiss… 🖇️ Link 2: gradschool.princeton.edu/adm… 🖇️ Link 3: cs.princeton.edu/news/dieng-… Let’s advance AI for scientific discovery together. Please share this with anyone who might be a great fit! #PhDposition #MachineLearning #AI #ScientificDiscovery #InvestigativeIntelligence #AI4Science #PrincetonUniversity #Vertaix

#NewPaper accepted in #EMNLP findings! 🔥 Clustering is a fundamental task in machine learning and science. In our new paper, we show you how you can do clustering using diversity as your mathematical object! We refer to this new algorithm as *Vendi Clustering*. 🧠 Instead of asking which two data points are closest, Vendi Clustering asks: which merge of datapoints causes the smallest loss in the diversity of the whole dataset? We optimize that global objective using the Vendi Score. When applied naively, the idea incurs an O(n^6) cost, which is prohibitive. Fortunately, we were able to derive an efficient version that reduces the naive intractable complexity of Vendi Clustering from O(n^6) to O(n^3). 📚 Vendi Clustering finds a natural application to the task of topic reduction. Embedding-based topic models like BERTopic often produce hundreds of fine-grained, overlapping topics. Topic reduction decides what structure survives in the final topic model. Poor topic reduction can merge genuinely distinct themes, preserve redundant ones, or discard useful data as noise. Existing topic  reduction methods either make local pairwise merge decisions, require trial-and-error retuning of the clustering, or rely on expensive stochastic LLM prompting. ☀️ Vendi Clustering provides an efficient way to do topic reduction while preserving topic diversity. Some highlights from the results: 📌 0.65 -> 0.88 topic diversity (+35%) over topic reduction assisted by the Gemma-3-12b-it LLM on 20 Newsgroups. 📌 Over 40x faster than the LLM baselines in our experiments. 📌 Consistently improves coherence and diversity over standard agglomerative topic reduction across benchmarks 🖇️ Check the paper out! openreview.net/pdf?id=gpecgW… 💻 Open Source Code: github.com/vertaix/Vendi-Clu… ✒️ Authors: Sean Kim (an amazing undergrad student from Brown University) and @adjiboussodieng. #VendiScore #VendiClustering #Diversity #Clustering #AI #Science