About Me
Deep (artificial) neural networks researcher (PhD) working on representation learning and multimodality, employing transformers and other neural network architectures, as well as various learning algorithms. Currently focusing on (deep) neural networks for multimodal algorithmic reasoning and math AI.
Having trained in both the theoretical and the computational domains, I am interested in (deep) neural networks, including the building blocks and fundamental aspects of deep learning, such as architecture design, learning algorithms, compression, and reasoning.
Have published in relevant conferences and journals (eg. ICML'24 AI4MATH workshop, NeurIPS'24 Multimodal Algorithmic Reasoning workshop, European Conference on Information Retrieval, etc.). TensorFlow, MXNet, and HuggingFace Transformers open source contributor of deep learning research.
Drawn to interesting / open problems related to (deep) neural network architectures and related machine learning algorithms.
My PhD research (probabilistic statistics, Virginia Tech) had been in algorithms related to maximum likelihood estimation and Kullback-Leibler divergence. At Virginia Tech, I also had the honor to briefly collaborate with I.J.Good, Alan Turing's colleague at Bletchley Park.
Majored in computer science for my undergraduate degree (when first introduced to artificial neural networks) and in high school. Math and physics olympic (silver medal, Romania nationals).
Lives and works in New York City.
Research highlights on X(Twitter):
* (deep) neural network architecture for representation learning in search
* (deep) neural networks for visual representation learning