About
I work on theoretical research in machine learning and generative modeling.
Past research includes other machine learning areas, such as sampling with diffusion, reasoning, representation learning, multimodality, backpropagation, statistical learning etc., employing objects such as transformers and other neural network architectures, graphs, signals, the frequency domain, etc. Having trained in both the theoretical and the computational domains, I am interested in problems related to the building blocks and theoretical aspects of machine learning, such as theoretical understanding, model design, and learning algorithms.
​
Have published in relevant conferences and journals (eg. ICML'24 AI4MATH workshop, NeurIPS'24 Multimodal Algorithmic Reasoning workshop, ECIR, SIGIR-AP'25, SIAM's Algo Diff.'24, NeurIPS'18 Spatiotemporal etc.). TensorFlow, MXNet, and HuggingFace Transformers open source contributor of deep learning research.
​
My PhD research (mathematical statistics, Virginia Tech) had been in algorithms related to estimation, divergence, and non-Gaussian processes (funded exclusively by grants). During my MSc at VT, I had the honor to briefly collaborate with I.J.Good.
Majored in computer science and applied cybernetics/math (N. Wiener style) for my undergraduate degree, when I was first introduced to artificial neural networks.
Theoretic highschool -CS track. Math and physics (thermodynamics) olympic (silver medal, Romania nationals).
​
Research highlights on X(Twitter) during my tenure as Staff ML Engineer / Applied Scientist / Machine Learning Researcher @ NYC Tech Industry (ex- ASAPP, Amazon, Reddit, Etsy):
* Designed and trained a (deep) neural network architecture for representation learning in personalized sponsored search and recommendations
*Advocated for more DL theory research (2017)
​​
US and EU dual citizen. Located in NYC.
​
​
​
​
​
​
​
​