Data Science 🌱 Neuromorphic Computing 🌱 AGI

I am a Data Scientist and AI researcher at NASA’s Space Mission Analysis Branch within the Systems Analysis and Concepts Directorate, and a PhD student in Data Science at NU studying neuromorphic computing and cooperative game theory for AI. I combine rigorous financial and systems analysis for multi-billion-dollar aerospace programs with research-driven AI and ML engineering to advance robust, scalable intelligence for high-consequence domains.

At NASA I architect data pipelines, real-time decision analytics, and technical risk models that protect mission integrity across procurement, schedule, and operations. I apply modern ML methods from probabilistic modeling and uncertainty quantification to deep scalable learning and neuromorphic accelerators to convert static financial artifacts into dynamic, explainable decision systems. I also serve as an agency-level AI advisor, bridging research, systems engineering, and strategic policy across NASA and industry partners.

My research agenda focuses on practical and theoretical pathways toward safe, cooperative, and generalizable AI. I study neuromorphic hardware-software co-design to reduce latency and energy cost for continual online learning, and I use cooperative game theory to formalize multi-agent credit assignment, incentive alignment, and distributed value learning. I seek architecture and evaluation paradigms that are both scientifically audacious and immediately useful for aerospace and other safety-critical systems.

Core strengths

1. Research domains: Neuromorphic computing; cooperative game theory; multi-agent systems; continual and meta learning; credit assignment and alignment.
   
2. ML & AI methods: Deep learning, Bayesian inference, reinforcement learning (policy gradients, off-policy), causal discovery, probabilistic programming.
   
3. Systems & hardware: Neuromorphic accelerators, model-hardware co-design, GPU/TPU pipelines, distributed model serving, on-edge continual learning.
   
4. Engineering stack: Python, PyTorch, JAX, TensorFlow; AWS, Snowflake; dbt; SQL; containerization and orchestration (Docker, Kubernetes).
   
5. Domain expertise: Aerospace systems analysis, financial modeling and risk quantification, explainability, safety-critical verification.
   
   

Research

1. Cooperative game theoretical credit assignment for multi-agent learning
   o Formalize Shapley-inspired credit allocation methods that scale to temporally extended tasks.
   o Apply to multi-vehicle coordination and distributed resource allocation, optimizing both team reward and individual incentive compatibility.
   

2. Modular, composable AGI curricula with provable transfer bounds
   o Design architectures of specialized modules (perception, planning, memory) and protocols for gating and composition.
   o Prove and empirically validate transfer generalization bounds under curriculum distributions, measure catastrophic forgetting.
   

3. Causal representation learning for robust generalization
   o Learn disentangled causal factors using interventional-style data augmentation and invariance objectives.
   o Integrate learned causal units into decision-making pipelines to improve OOD performance in mission scenarios.
   

4. Hybrid symbolic-neural meta-reasoning for long-horizon planning
   o Combine symbolic planning primitives with neural heuristics and learned value functions for reasoning under partial observability.
   o Benchmark on hierarchical mission planning tasks with sparse rewards.
   

✨ Facts you might be wondering about

🔭 Currently working on: Deep Study of Statistics and Regression algorithms.
⚡ Getting better at: Machine Learning, Neural Networks, Big Data.
🌱 Discovering queue: Statistics and Stochastic Differential Equations.
💬 Ask me about: Python, Physics, Guitar.
☕ Learn more about me: linkedin.com/in/DataDaimon
📫 How to reach me: knock! knock! at datadaimon@outlook.com
💜 Personal Interest: Programming, Astronomy, Cooking, Guitar.