Dhia Naouali

Dhia naouali

Research interests: representation learning and vision, modeling and understanding emergent structure in learned representations (SSL, diffusion, ...), with broader interest in world models and RL.

I am currently a research intern working on representation learning for diffusion models with Prof. Qianqian Wang at Kempner Institute, Harvard University.

Looking for a Master's thesis research internship starting between February (preferred) and April 2027.

News

Oct-26.I am joining the Vision and Learning Lab, Harvard SEAS & Kempner Institute.
Sep-26.[arXiv]"Cross-Time Directional Selection in Diffusion Sampling" — D. Naouali [arXiv]
Accepted to the AI for Stochastic Dynamics Workshop @ NeurIPS'26.
Sep-26.Serving as a reviewer for the AI for Stochastic Dynamics workshop @ NeurIPS'26.
Aug-26. [arXiv]"VLCP: Vision Language Control Policy Closed-Loop Code Replanning
for Robot Manipulation" — D. Naouali, M. Wu, C. Wong, A. Puthran, O. G. Younis [arXiv]
Accepted to the EMR Workshop @ ECCV'26.
Jul-26.[poster]Presented "Perceptually Controversial Latents" (poster) at EEML'26 summer school. [poster]
Mar-26.[poster]Ranked 4th in the ICLR'26 Re-Align Challenge; solution presented as a poster. [poster]
Nov-25.Ranked 13th (top 1.1%) in the NeurIPS'25 EEG Foundations Challenge (team: return_sota).
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Blogs  & technical notes

Modeling the World

April-2026
This blog surveys the world modeling landscape: visual representation and latent imagination, inference-time planning and emerging directions in latent geometry, hierarchy and 3D structure.

A Decade of Residuals: History & Effects on modern ML

January-2026
This blog traces the evolution of residual connections and their influence on modern architectures, optimization dynamics, learned representations and the emergence of gating and hyper-connection mechanisms.

Learning Reinforcement Learning

December-2026 (in progress)
Notes on modern deep RL: policy gradients, actor–critic methods, Q-learning, exploration and practical training insights mainly from UC Berkley's CS285 Deep RL course.

A Gentle Introduction to Distributed Training

November-2025
In this blog post we discuss distributed training by examining how parallelism strategies shard and schedule data, parameters and intermediate activations to control memory usage and execution flow.

JAX: Jit Autograd XLA

October-2025
In-depth reference on JAX: design and programming philosophy, distributed / multi-device training, async dispatch via XLA and high-performance usage patterns.

Mechanistic Interpretability

August-2025
An overview of techniques for reverse-engineering features, circuits, and representations in vision models using probing, disentanglement, and adversarial analysis.

Generative Adversarial Networks

July-2025
A concise exploration of how GANs work focusing on training schemes, architectures, objectives and the Generator-Discriminator dynamics.

Transformers as a flock of tokens

April-2025
This blog post breaks down transformers into alternating phases of token-to-token communication and representation-space transformation, viewing tokens as a flock evolving across layers.

A Representation Space Interpretation of Neural Networks

March-2025
This blog post offers a geometric reframing of neural networks, describing how successive layers sculpt and reorganize the data manifold in representation space.