Planned Work & Future Publications
This page outlines ongoing research projects and planned publications representing the next phase of my research in cross-modal learning, optimization, and human-AI collaboration.
Current Manuscripts in Development
2025 Target Submissions
“MCMC-Enhanced LSA: Reusable Kernels for Cross-Domain Semantic Analysis”
Authors: Stephen Mander, et al.
Target Journal: Computational Linguistics
Status: 60% complete, expected submission April 2025
Abstract: We present novel Markov Chain Monte Carlo approaches for Latent Semantic Analysis that create reusable computational kernels, enabling more robust semantic models with improved transferability across different text domains and languages.
Key Innovations:
MCMC sampling techniques for LSA parameter estimation
Reusable kernel architectures for cross-domain semantic analysis
Theoretical analysis of semantic space transferability
Efficient implementation for large-scale text processing
Current Progress:
✅ MCMC algorithm design and initial implementation
✅ Theoretical framework for semantic transferability
🔄 Large-scale empirical evaluation across domains
⏳ Optimization for high-performance computing deployment
“Radio Wave Propagation Modeling with SuperDARN: ML-Enhanced Atmospheric Analysis”
Authors: Stephen Mander, et al.
Target Journal: Journal of Atmospheric and Solar-Terrestrial Physics
Status: 50% complete, expected submission June 2025
Abstract: This work integrates machine learning techniques with SuperDARN atmospheric data to develop enhanced radio wave propagation models, enabling improved understanding of ionospheric dynamics and space weather prediction.
Key Innovations:
ML-enhanced models for radio wave propagation prediction
Integration with global SuperDARN radar network data
Real-time atmospheric analysis capabilities
Open-source framework for atmospheric data processing
Current Progress:
✅ SuperDARN data integration and preprocessing pipeline
🔄 ML model development for propagation prediction
⏳ Real-time analysis framework implementation
⏳ Validation with historical atmospheric events
Planned Research Extensions
Multi-Year Research Program (2025-2027)
“Scaling Laws in Cross-Modal Systems: A Unified Theory”
Target: Nature Machine Intelligence or Science Advances
Timeline: 18-month development project
Research Vision: Develop comprehensive theoretical framework connecting scaling laws in contrastive learning with cross-modal understanding, bridging my work on 6-dimensional CLIP training with fundamental scaling analysis.
Planned Contributions:
Unified mathematical framework for cross-modal scaling analysis
Empirical validation across vision-language model families
Resource-efficiency guidelines for large-scale multi-modal training
Open datasets and evaluation protocols for community use
“Human-AI Optimization: From Tactile Interfaces to Collaborative Intelligence”
Target: ACM Transactions on Interactive Intelligent Systems
Timeline: 24-month collaborative project
Research Vision: Expand tactile optimization work into comprehensive framework for human-AI collaboration in complex problem solving, integrating insights from haptic interfaces with advanced AI reasoning.
Planned Contributions:
Theoretical framework for human-AI collaborative optimization
Multi-modal interfaces combining tactile, visual, and AI reasoning
Evaluation protocols for human-AI team performance
Applications across multiple optimization domains
Future Research Directions
Emerging Research Themes
Cross-Modal Foundation Models
Building on current contrastive learning research to develop next-generation foundation models that understand relationships across vision, language, audio, and tactile modalities.
Efficient AI for Edge Computing
Extending 8-bit LSA and tiny contrastive learning work toward comprehensive edge AI frameworks that maintain performance while minimizing computational requirements.
AI Security and Robustness
Advancing PICTAR and adversarial attack research toward comprehensive security frameworks for multi-modal AI systems in critical applications.
Scientific AI Applications
Expanding SuperDARN and atmospheric modeling work toward general frameworks for AI-enhanced scientific discovery and analysis.
Collaboration Opportunities
Seeking Collaborations In:
Industry Partnerships: Edge AI deployment and optimization
Academic Collaborations: Cross-modal learning theory and applications
Interdisciplinary Projects: Human-computer interaction and tactile computing
Open Science Initiatives: Reproducible research and open-source frameworks
Publication Strategy & Targets
High-Impact Venues (2025-2026)
Nature Machine Intelligence: Scaling laws and theoretical frameworks
Science: Cross-modal foundation model breakthroughs
ICLR: Advanced contrastive learning methodologies
NeurIPS: Optimization and human-AI collaboration
CVPR: Vision-language model improvements
Specialized Journals
Computational Linguistics: Semantic analysis and NLP innovations
AI Magazine: Game AI and strategic reasoning applications
Journal of Atmospheric Sciences: Scientific AI applications
Conference Targets
ICLR 2025: 6-dimensional contrastive learning framework
NeurIPS 2025: Scaling laws in multi-modal systems
CHI 2025: Tactile optimization interfaces
CVPR 2025: Advanced adversarial robustness techniques
Open Science Commitments
Reproducibility & Accessibility
Open Source: All research software publicly available
Open Data: Datasets and benchmarks for community use
Documentation: Comprehensive tutorials and implementation guides
Education: Workshop and tutorial development for research community
Community Engagement
Code Reviews: Active participation in open-source ML projects
Mentorship: Graduate student and junior researcher guidance
Peer Review: Regular reviewer for top-tier conferences and journals
Outreach: Public talks and educational content creation
For completed work, see Journal Articles, Conference Papers, and Preprints.
Research software implementations available at Research Software.
Last updated: Sep 16, 2025