causal inference , Jax, Pytorch, Neural ODEs, Bayesian deep learning, Foundation model fine-tuning, Graph neural networks, structural causal models, PINNs methodology, Bayesian experimental design, RL for scientific discovery, causal discovery, active learning acquisition function design, diffusion models

Machine Learning, Cnn, Deep Learning, Tensorflow, Rnn, Pytorch, Python, Transformer, LSTM, ViT, MxNet, Caffe, Model Compression

Python, Pytorch, Cuda, LLM and foundation models

Machine Learning, Unsupervised Learning, Python, agentic AI, adversarial machine learning, debugging complex ML failure modes, reverse engineering model behaviors, supervised learning, generative models, reinforcement learning, data poisoning, ML safety, evaluating frontier AI systems, prompt injection, ML interpretability, multi-object optimization, adversarial robustness, model backdoor detection, adversarial machine learning AI agent security, red-teaming frontier AI systems

alpha generation , Jax, Pytorch, Execution, pre-training large foundation models, transfer across instruments, self-supervised objectives, evaluation protocols, model-serving components, market making, data efficiency, large-scale market datasets, time-series foundation models, tokenization schemes, model architectures, data representations, distributed training recipes, risk-management tasks, scaling laws, regime robustness, training checkpointing, Pricing

Python, Pytorch, Tensorflow, Jax, Deep Learning, diffusion models, autoregressive generative models, video synthesis, generative AI

Pytorch, vision-conditioned neural control, multi-modal data pipelines, VLA architectures, multimodal sensor fusion, agent-based training paradigms, simulation-trained algorithms, multi-node GPU training, policy learning

Pytorch, Cuda, Python, LLM and foundation models