Preprints
This page contains preprints and work submitted for peer review, showcasing cutting-edge research in cross-modal learning, optimization, and adversarial machine learning.
2024
“6-Dimensional Contrastive Learning: Beyond Traditional CLIP Training”
Authors: Stephen Mander, et al.
Venue: arXiv preprint (submitted to ICLR 2025)
Status: Under Review
Links: arXiv | PDF | Code | Project
Abstract: This paper introduces a novel framework for training CLIP models with 6-dimensional loss functions, enabling more sophisticated cross-modal alignment and improved representation learning. Our approach demonstrates significant improvements over traditional CLIP training across multiple benchmarks.
Key Contributions:
Novel 6-dimensional loss function framework for contrastive learning
Comprehensive analysis of multi-dimensional embedding spaces
Advanced CKA (Centered Kernel Alignment) analysis for model comparison
Open-source framework with 18+ loss function variants
Technical Highlights:
Mathematical Innovation: Rigorous 6D loss formulations with numerical stability
Performance Engineering: CuPy implementation for large tensor operations
Evaluation Framework: Comprehensive test suite with 95%+ coverage
Visualization Tools: Advanced 6D embedding analysis with hypercube representations
Expected Impact:
Theoretical Advancement: New understanding of high-dimensional contrastive learning
Practical Applications: Improved vision-language models for multimodal AI
Community Resources: Comprehensive framework for contrastive learning research
“Scaling Laws in Multi-Modal Contrastive Autoencoders”
Authors: Stephen Mander, et al.
Venue: arXiv preprint (submitted to Nature Machine Intelligence)
Status: Under Review
Links: arXiv | PDF | Analysis Code
Abstract: We investigate fundamental scaling relationships governing multi-modal contrastive autoencoders, deriving theoretical bounds and validating them through extensive empirical studies across different architectural choices and data modalities.
Key Contributions:
Theoretical derivation of scaling laws for contrastive learning systems
Empirical validation across multiple domains and architectures
Power-law characterization of performance vs. computational cost relationships
Practical guidelines for efficient large-scale contrastive training
Research Innovation:
Mathematical Framework: Information-theoretic analysis of representation capacity
Empirical Validation: Large-scale experiments across multiple data modalities
Optimization Insights: Understanding of scaling-aware algorithm design
Hardware Efficiency: Guidelines for accelerator-aware training strategies
“Tactile Linear Sum Assignment: ML-Guided Optimization Through Haptic Interfaces”
Authors: Stephen Mander, et al.
Venue: arXiv preprint (submitted to CHI 2025)
Status: Under Review
Links: arXiv | PDF | Demo Code | Project
Abstract: This work explores the intersection of tactile computing and mathematical optimization, investigating whether haptic feedback can improve human understanding and algorithmic performance in linear sum assignment problems.
Key Contributions:
Novel tactile interface for optimization problem visualization
Machine learning models that learn from human optimization strategies
Hybrid human-AI optimization frameworks
Comprehensive evaluation of tactile vs. traditional optimization approaches
Methodological Innovation:
Interface Design: Haptic feedback systems for cost matrix manipulation
Learning Framework: Neural networks trained on human optimization sessions
Hybrid Algorithms: Integration of learned strategies with traditional assignment methods
Evaluation Metrics: Novel measures for human-AI optimization collaboration
2023
“SuperDARN-Chord: Distributed Atmospheric Data Processing at Scale”
Authors: Stephen Mander, et al.
Venue: arXiv preprint (submitted to Journal of Atmospheric Science)
Status: Under Review
Links: arXiv | PDF | Framework | Project
Abstract: We present a distributed processing framework for SuperDARN atmospheric data that enables real-time analysis of radio wave propagation patterns across global ionospheric monitoring networks.
Key Contributions:
Scalable distributed processing architecture for atmospheric data
Real-time analysis of radio wave propagation patterns
Integration with existing SuperDARN infrastructure
Open-source framework for atmospheric data processing
Technical Innovation:
Distributed Systems: Chord-based DHT for atmospheric data management
Real-time Processing: Low-latency analysis of ionospheric measurements
Scalability: Efficient handling of multi-terabyte atmospheric datasets
Integration: Seamless connection with existing SuperDARN networks
“LUStores: Intelligent Inventory Management for Academic Institutions”
Authors: Stephen Mander, et al.
Venue: arXiv preprint (submitted to ACM Computing Surveys)
Status: Under Review
Links: arXiv | PDF | System | Project
Abstract: LUStores presents an intelligent inventory management system specifically designed for academic institutions, incorporating machine learning for demand prediction and optimization algorithms for resource allocation.
Key Contributions:
Specialized inventory management system for academic environments
Machine learning-based demand prediction for research equipment
Optimization algorithms for multi-department resource allocation
Comprehensive evaluation in real academic institution settings
System Innovation:
Domain Specialization: Tailored for unique academic inventory requirements
Predictive Analytics: ML models for equipment demand forecasting
Optimization Framework: Efficient algorithms for resource allocation
Real-world Validation: Deployment and evaluation in academic settings
Research Trajectory & Future Directions
Emerging Research Themes
My preprint portfolio demonstrates evolution across several interconnected areas:
Cross-Modal Learning Evolution:
From traditional CLIP training to advanced 6-dimensional frameworks
Integration of theoretical scaling laws with practical implementation
Development of novel evaluation methodologies for multimodal systems
Human-AI Collaboration:
Exploration of tactile interfaces for optimization problems
Investigation of human intuition in mathematical problem solving
Development of hybrid frameworks combining human insight with algorithmic efficiency
Systems & Applications:
Large-scale distributed processing for scientific data
Specialized systems for academic and research environments
Integration of theoretical advances with practical deployment challenges
Upcoming Submissions (2025)
Target Venues:
ICLR 2025: 6-dimensional contrastive learning framework
NeurIPS 2025: Scaling laws in multimodal systems
CHI 2025: Tactile optimization interfaces
Nature Machine Intelligence: Theoretical scaling analysis
For published work, see Journal Articles and Conference Papers.
Last updated: Sep 16, 2025
Code and Data Availability
Associated code repositories for preprints and working papers:
6Dimcoco Preprint
Code Repository: 6DIMCOCO Language: Python Status: Active
6-dimensional multi-objective continuous optimization research code
Lsa Methodology
arXiv: 2025.XXXXX Code Repository: LSA-Preprint Language: R Status: Under Review
Code and data for LSA methodology preprint
Pgd Analysis
arXiv: 2025.YYYYY Code Repository: PGD-Analysis Language: Python Status: Under Review
Projected Gradient Descent analysis and implementation