PhD candidate in Probabilistic Machine Learning @ TU/e

Hi there! 👋🏼

I'm Gennaro, a deep learner working on tractable/self-organizing generative modelling. I have broad interests in (equivariant) deep learning and particularly like generative models as VAEs, GANs, flows, diffusion, tractable probabilistic models; and architectures as GNNs, transformers, and vector quantized models.

📧 gennarogala1396@gmail.com $\mathbb{X}$ gengala13 github.com/gengala

🎓 google scholar 📰 Curriculum Vitae

🎓 Education

PhD candidate at UAI group

Apr 2021 - Apr 2025, Eindhoven University of Technology (NL)

Master's Degree in Computer Science & Machine Intelligence

Oct 2018 - Dec 2020, University of Bari Aldo Moro (IT)

110/110 with honors (higher possible grade)

Bachelor's Degree in Computer Science

Oct 2015 - Oct 2018, University of Bari Aldo Moro (IT)

110/110 with honors (higher possible grade)

💼 Experience

Intern @ Italian National Research Council

Bari, Italy – (Mar 2018 - Sep 2018)

Intern researcher working on ML for marine biology 🐬

📚 Selected Publications

Scaling Continuous Latent Variable Models
as Probabilistic Integral Circuits

Gennaro Gala, Cassio de Campos, Antonio Vergari, Erik Quaeghebeur

TL; DR: We provide a comprehensive pipeline to build and scale hierarchical continuous latent variable models through the lenses of DAG-shaped probabilistic (integral) circuits

📄 arxiv preprint (under review)

Probabilistic Integral Circuits

Gennaro Gala, Cassio de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur

TL; DR: We add integral units to the language of tractable circuits to represent continuous latent variables and learn hierarchical tree-shaped neural continuous mixtures

📄 AISTATS paper gengala/pic

E(n)-equivariant Graph Neural Cellular Automata

Gennaro Gala, Daniele Grattarola, Erik Quaeghebeur

TL; DR: By replacing standard graph convolutions with E(n)-equivariant ones, we design and propose a class of isotropic automata that we call E(n)-GNCAs. These models are lightweight, but can nevertheless handle large graphs, capture complex dynamics and exhibit emergent self-organising behaviours.

📄 TMLR paper gengala/egnca

Continuous mixtures of tractable probabilistic models

Alvaro H.C. Correia*, Gennaro Gala*, Erik Quaeghebeur, Cassio de Campos, Robert Peharz

TL; DR: We investigate a hybrid approach: (small-dimensional) continuous intractable mixtures of tractable probabilistic models. While these models are analytically intractable, they are amenable to numerical integration schemes based on a finite set of integration points.

📄 AAAI paper AlCorreia/cm-tpm

Bayesian Structure Scores for Probabilistic Circuits

Yang Yang*, Gennaro Gala*, Robert Peharz

TL; DR: We develop Bayesian structure scores for deterministic PCs, i.e., the structure likelihood with parameters marginalized out, which are well known as rigorous objectives for structure learning in probabilistic graphical models.

📄 AISTATS paper

🛠️ Skills

• [Probabilistic Models] [Deep Learning] [Generative AI] [Neural Cellular Automata]

• [pytorch] [lightning] [python] [C/c++] [MATLAB] [TensorFlow] [sklearn]