AI Researcher specializing in multimodal learning and vision-language models, with 7 published papers on cross-modal representation learning and attention mechanisms
Demonstrated expertise in developing novel architectures that improve efficiency by 30% while maintaining SOTA performance on benchmark datasets
Proven track record of translating theoretical insights into practical implementations, with 3 papers accepted at top-tier venues (CVPR, NeurIPS, ICLR)
Research Experience
Meta AI Research
Senior Research Scientist
June 2022 - Present
Investigated fundamental limitations in vision-language alignment, developing a novel contrastive learning framework that improved zero-shot classification accuracy by 18% on ImageNet and 25% on domain-specific datasets
Designed and conducted large-scale experiments comparing 12 different attention mechanisms across multimodal architectures, resulting in 2 conference papers accepted at CVPR 2023 and NeurIPS 2023
Led cross-functional collaboration with 8 researchers across 3 institutions, coordinating a reproducibility study of SOTA multimodal models that identified previously unreported failure modes in medical imaging applications
Mentored 4 PhD interns and 2 postdoctoral researchers, with 3 resulting in first-author publications at major conferences
Stanford University - Computer Vision Lab
Postdoctoral Researcher
September 2020 - May 2022
Developed theoretical foundations for efficient transformer architectures, proving convergence guarantees for sparse attention patterns and reducing computational complexity from O(n²) to O(n log n)
Created novel benchmark dataset for few-shot multimodal learning with 50K carefully curated image-text pairs, now used by 15+ research groups and cited in 120+ papers
Investigated robustness properties of vision transformers under adversarial attacks, discovering 3 new vulnerability patterns and proposing defense mechanisms that improved certified accuracy by 12%
Collaborated with Google Research on federated learning for computer vision, resulting in joint publication at ICLR 2022 and $75K follow-up grant
MIT - Computer Science and Artificial Intelligence Laboratory (CSAIL)
Graduate Research Assistant
September 2016 - August 2020
Pioneered research on attention mechanisms for multimodal fusion, developing hierarchical attention networks that achieved 15% improvement on VQA benchmarks and became foundation for 3 follow-up studies
Designed and implemented novel neural architecture search algorithms for vision-language tasks, reducing search time by 40% while discovering architectures that outperformed hand-crafted designs
Led reproducibility initiative for major computer vision papers, implementing and evaluating 25 published methods, identifying discrepancies in 8 papers and contributing to improved research standards
Teaching assistant for "Deep Learning for Computer Vision" (6.819), mentoring 150+ students and developing new curriculum modules on attention mechanisms
Education
Massachusetts Institute of Technology
PhD in Computer Science (Machine Learning and Computer Vision)
September 2016 - August 2020
GPA: 3.95/4.0 Dissertation: "Attention Mechanisms for Cross-Modal Learning: Theory and Applications" Advisor: Prof. Regina Thompson (Computer Vision and Learning Group) Committee: Prof. David Kim (MIT), Prof. Sarah Chen (Stanford), Dr. Alex Rivera (Google Research) Relevant Coursework: Advanced Machine Learning, Probabilistic Graphical Models, Optimization for Machine Learning, Information Theory
Carnegie Mellon University
MS in Computer Science (Artificial Intelligence Track)
September 2014 - May 2016
GPA: 3.9/4.0 Thesis: "Deep Learning Approaches for Visual Question Answering" Advisor: Prof. Michael Torres (Language Technologies Institute) Relevant Coursework: Deep Learning, Natural Language Processing, Computer Vision, Statistical Machine Learning
University of California, Berkeley
BS in Mathematics with Computer Science Minor
September 2010 - May 2014
GPA: 3.8/4.0, Summa Cum Laude Senior Thesis: "Optimization Methods in Machine Learning" Relevant Coursework: Linear Algebra, Real Analysis, Probability Theory, Algorithms and Data Structures
Publications and Conference Presentations
Hierarchical Cross-Modal Attention for Vision-Language Understanding
NeurIPS 2023
2023
N. Rodriguez, A. Chen, M. Kumar. (Oral presentation, top 1% of submissions) [450+ citations] [Outstanding Paper Award nominee] [Code: github.com/nrodriguez-ai/hcma]
Efficient Transformers for Multimodal Learning: A Comprehensive Analysis
CVPR 2023
2023
N. Rodriguez*, S. Park*, J. Liu (*co-first authors). [320+ citations] [Best Paper Award - Multimodal Learning Track]
Rethinking Attention Mechanisms in Vision-Language Models
ICLR 2022
2022
N. Rodriguez, L. Zhang. [280+ citations] [Code: github.com/nrodriguez-ai/rethink-attention] [500+ GitHub stars]
Federated Learning for Multimodal AI: Challenges and Solutions
ICML 2022
2022
K. Johnson, N. Rodriguez (corresponding author), R. Patel. [190+ citations] [Tutorial presented at ICML 2023]
Foundation Models for Scientific Discovery: A Multimodal Approach
ArXiv (Under review at Nature Machine Intelligence)
2024
N. Rodriguez, et al. ArXiv:2024.03456 [150+ citations on ArXiv] [Featured in MIT Technology Review]
Technical Skills
Machine Learning Frameworks: PyTorch (expert-level, custom CUDA kernel development), JAX (advanced), TensorFlow (proficient with distributed training), Hugging Face Transformers (contributor)
Mathematical Foundations: Optimization theory, Information theory, Statistical learning theory, Linear algebra, Probability theory, Graph theory
Research Tools and Methodologies: Weights & Biases for experiment tracking, LaTeX for scientific writing, Jupyter for reproducible research, Docker for containerized experiments, Git for version control, SLURM for cluster computing
Programming Languages: Python (expert), C++ (proficient), CUDA (intermediate), R (statistical analysis), MATLAB (signal processing)
Awards and Honors
Meta AI Research Award
Meta AI
2023
$100,000 grant for "Next-Generation Multimodal Foundation Models" project - One of 25 recipients globally from 500+ applications
Outstanding Paper Award, NeurIPS 2023
NeurIPS
2023
"Hierarchical Cross-Modal Attention for Vision-Language Understanding" - Selected from 12,000+ submissions, top 0.1% recognition
NSF Graduate Research Fellowship
National Science Foundation
2017-2020
$138,000 award for doctoral research in multimodal AI - Competitive national fellowship, 16% acceptance rate
MIT EECS Outstanding PhD Thesis Award
MIT
2020
Awarded to top 3 PhD graduates in Electrical Engineering and Computer Science
Google PhD Fellowship in Machine Learning
Google
2019
One of 15 recipients globally, $75,000 research funding - Recognition for exceptional doctoral research potential
CVPR 2023 Best Paper Award - Multimodal Learning Track
CVPR
2023
"Efficient Transformers for Multimodal Learning: A Comprehensive Analysis" - Selected from 300+ track submissions
Additional Research Activities
Professional Service
Program Committee Member: NeurIPS (2022-2024), CVPR (2023-2024), ICLR (2023-2024)
Reviewer: ICML, AAAI, IEEE TPAMI, Journal of Machine Learning Research
Workshop Organizer: "Multimodal Learning in the Wild" - CVPR 2023 (200+ attendees)
Tutorial Speaker: "Attention Mechanisms for Multimodal AI" - ICML 2023
Mentoring and Teaching
Mentored 8 PhD students, 6 postdocs, and 12 research interns (2020-present)
Guest Lecturer: Stanford CS231N, MIT 6.819, CMU 11-777
Created open-source educational materials for multimodal learning (5K+ GitHub stars)
Community Engagement
Maintainer of popular ML blog "Multimodal Insights" (50K+ monthly readers)
Contributor to Hugging Face Transformers library (15+ merged PRs)
Speaker at AI research meetups and industry conferences (20+ presentations)
References
Prof. Regina Thompson
Professor of Computer Science, MIT CSAIL
PhD Advisor and research collaborator Email: [email protected] Research Area: Computer Vision and Machine Learning Notable: AAAI Fellow, former NeurIPS Program Chair
Dr. Sarah Chen
Principal Research Scientist, Google Research
Postdoc supervisor and co-author on 4 papers Email: [email protected] Research Area: Multimodal AI and Vision-Language Models Notable: 50+ publications at top venues, h-index: 45
Prof. Michael Torres
Director, Language Technologies Institute, Carnegie Mellon University
Master's thesis advisor Email: [email protected] Research Area: Natural Language Processing and Multimodal Learning Notable: ACL Lifetime Achievement Award recipient
Dr. Alex Rivera
Research Director, Meta AI
Current supervisor and research collaborator Email: [email protected] Research Area: Foundation Models and AI Safety Notable: Former OpenAI researcher, 100+ patents in AI
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