Richard Martin Flores

Data Scientist

datadaimon@outlook.com

Systems Analysis and Concepts Directorate

Space Mission Analysis Branch

Langley Research Center

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“The people who are crazy enough to think they can change the world are the ones who do.” ~ Steve Jobs

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

• Research domains: Neuromorphic computing; cooperative game theory; multi-agent systems; continual and meta learning; credit assignment and alignment.
• ML & AI methods: Deep learning, Bayesian inference, reinforcement learning (policy gradients, off-policy), causal discovery, probabilistic programming.
• Systems & hardware: Neuromorphic accelerators, model-hardware co-design, GPU/TPU pipelines, distributed model serving, on-edge continual learning.
• Engineering stack: Python, PyTorch, JAX, TensorFlow; AWS, Snowflake; dbt; SQL; containerization and orchestration (Docker, Kubernetes).
• 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
• Formalize Shapley-inspired credit allocation methods that scale to temporally extended tasks.
• 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
• Design architectures of specialized modules (perception, planning, memory) and protocols for gating and composition.
• Prove and empirically validate transfer generalization bounds under curriculum distributions, measure catastrophic forgetting.
3. Causal representation learning for robust generalization
• Learn disentangled causal factors using interventional-style data augmentation and invariance objectives.
• 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
• Combine symbolic planning primitives with neural heuristics and learned value functions for reasoning under partial observability.
• Benchmark on hierarchical mission planning tasks with sparse rewards.

Prospective students and postdocs, please check out this page on how to get involved.

Recent Projects

• Document-Aware Vision-Language OCR with Normalized Relational Export: NASA, SMAB
• Ontology-Grounded NLP Synthesizer for Predevelopment-to-Retirement Mission Insights: NASA, SMAB
• Ensemble Prognosticator with Uncertainty-Aware GYR Decisioning for SMD Flights: NASA, SMAB
• Spatiotemporal Convolutional Prognostics for X-59 Near-Field Disturbance Detection: NASA, ASAB
• Scalable Corpus-Level Labeling and Hierarchical Folder Abstraction Engine: NASA, SACD
• Mass-Scale Multiformat Semantic Tagging and Contextual Archive Summarization: NASA, AETC
• High-Precision Invariant Mass Regression with Physics-Informed Networks: [WGU 2022]
• Zero-Shot Affective Inference via Prompt-Tuned Language Models: [WGU 2022]
• Generative Latent Modeling for Unlabeled Earth-Observation Feature Discovery: [WGU 2021]