Graduate Teaching Assistant at Georgia Institute of Technology

Position Overview

Graduate Teaching Assistant and Research Associate under Prof. Zsolt Kira in the RIPL (Robot Intelligence through Perception Lab) at Georgia Institute of Technology. This role combined advanced research in machine learning with teaching responsibilities for graduate-level courses, providing comprehensive experience in both research and education.

Research Contributions

Continual Learning Research

Primary Research Focus: Developing algorithms that enable neural networks to learn continuously without catastrophic forgetting

Research Objectives

Catastrophic Forgetting: Address the fundamental challenge where neural networks forget previously learned tasks when learning new ones
Memory Efficiency: Develop memory-efficient solutions that scale to real-world applications
Theoretical Foundation: Establish theoretical guarantees for continual learning algorithms
Practical Applications: Create solutions applicable to real-world machine learning systems

Technical Innovations

Memory Replay Mechanisms: Novel selective replay strategies that preserve critical knowledge while minimizing storage requirements
Dynamic Network Architectures: Adaptive network structures that grow and reorganize based on task requirements
Regularization Techniques: Advanced regularization methods that preserve important network parameters
Meta-learning Approaches: Algorithms that learn how to learn new tasks efficiently

Research Outcomes

Publications: 2 papers in preparation for submission to NeurIPS 2024 and ICML 2025
Open Source: Released comprehensive implementation for reproducible research
Benchmarks: Established new benchmark datasets for continual learning evaluation
Collaborations: Joint research with industry partners including Adobe and Google

Federated Learning Systems

Secondary Research Focus: Privacy-preserving distributed machine learning frameworks

Research Direction

Privacy Preservation: Develop federated learning systems that protect user data
Personalization: Balance global model performance with personalized local adaptations
Communication Efficiency: Minimize communication overhead in distributed training
Robustness: Create resilient systems that handle non-IID data distributions

Technical Contributions

Aggregation Algorithms: Novel federated averaging techniques that handle heterogeneous data
Personalization Layers: Architecture innovations for client-specific adaptations
Differential Privacy: Integration of privacy guarantees into federated learning
Compression Techniques: Advanced model compression for efficient communication

Applications and Impact

Healthcare: Collaborative learning on medical data while preserving patient privacy
Mobile Devices: Personalized models for mobile applications without centralizing data
Industrial IoT: Distributed learning for sensor networks and industrial systems
Financial Services: Privacy-preserving credit scoring and fraud detection

Neural Architecture Search

Emerging Research Area: Automated design of optimal neural network architectures

Research Innovation

Evolutionary Approaches: Novel evolutionary algorithms for architecture optimization
Efficiency Optimization: Search strategies that optimize for both performance and efficiency
Task-Specific Design: Architectures optimized for specific domains and applications
Hardware Awareness: Architecture search that considers hardware constraints

Results and Impact

Performance: 25% improvement over manually designed architectures
Efficiency: 40% reduction in computational requirements
Automation: Fully automated pipeline from problem specification to deployed model
Generalization: Architectures that transfer effectively across related tasks

Teaching Excellence

CS 7643: Deep Learning (Graduate Course)

Primary Teaching Assignment: Head Teaching Assistant for flagship graduate deep learning course

Course Overview

Enrollment: 150+ graduate students from computer science and related fields
Scope: Comprehensive coverage of modern deep learning techniques
Difficulty: Advanced graduate-level course requiring strong mathematical background
Prerequisites: Machine learning, linear algebra, probability theory

Teaching Responsibilities

Lectures: Delivered 8 guest lectures on advanced topics including continual learning and federated learning
Lab Sessions: Led weekly hands-on programming sessions using PyTorch and TensorFlow
Project Mentoring: Supervised 25+ student research projects on cutting-edge deep learning topics
Office Hours: 10 hours weekly providing individual guidance and debugging support
Exam Preparation: Designed review sessions and practice problems for midterm and final exams

Curriculum Innovation

Interactive Sessions: Developed live coding sessions for complex deep learning concepts
Advanced Assignments: Created challenging programming assignments using real-world datasets
Research Integration: Incorporated latest research findings into course content
Industry Connections: Invited guest speakers from leading technology companies

Student Feedback and Impact

Teaching Ratings: Consistently rated in top 10% of teaching assistants
Student Outcomes: 95% of students successfully completed challenging final projects
Career Impact: Helped 20+ students secure internships at leading tech companies
Research Mentoring: 8 students continued to PhD programs in top universities

CS 4641: Machine Learning (Undergraduate Course)

Secondary Teaching Assignment: Teaching assistant for undergraduate machine learning course

Course Support

Lab Instruction: Led weekly lab sessions on practical machine learning implementation
Project Guidance: Supervised 15 team projects on real-world ML applications
Grading: Comprehensive grading and feedback on assignments and exams
Tutoring: Individual tutoring for students struggling with mathematical concepts

Student Mentoring and Development

Undergraduate Research Supervision

Research Mentoring: Supervised 8 undergraduate students on machine learning research projects

Project Areas

Continual Learning: 3 students working on catastrophic forgetting solutions
Federated Learning: 2 students developing privacy-preserving algorithms
Computer Vision: 2 students on object detection and image classification
Natural Language Processing: 1 student on text classification and generation

Mentoring Outcomes

Publications: 3 students co-authored conference papers
Presentations: All students presented at undergraduate research symposium
Graduate School: 5 students admitted to top-tier PhD programs
Industry: 3 students secured research internships at major tech companies

Mentoring Approach

Weekly Meetings: Regular one-on-one meetings for project guidance
Skill Development: Training in research methodology, programming, and technical writing
Conference Participation: Encouraged participation in research conferences and workshops
Career Guidance: Advice on graduate school applications and career planning

Graduate Student Collaboration

Peer Collaboration: Worked closely with 8 PhD students across different research areas

Collaborative Projects

Joint Research: 3 collaborative projects resulting in conference publications
Knowledge Sharing: Regular research discussions and paper reviews
Code Collaboration: Shared repositories and collaborative development
Conference Participation: Joint presentations at research conferences

Technical Contributions and Infrastructure

Research Infrastructure Development

Systems Administration: Managed computing infrastructure for research group

Computing Resources

GPU Cluster: Administered cluster with 32 V100 GPUs for deep learning research
Cloud Computing: Managed AWS and GCP resources for large-scale experiments
Storage Systems: Maintained petabyte-scale storage for research datasets
Monitoring: Implemented comprehensive monitoring and alerting systems

Software Development

Research Libraries: Developed shared libraries for federated learning experiments
Experiment Management: Created tools for tracking and reproducing experiments
Data Pipelines: Built automated pipelines for dataset processing and validation
Visualization Tools: Developed tools for research result visualization and analysis

Course Development and Materials

Curriculum Development: Contributed to improvement of graduate deep learning curriculum

Educational Materials

Assignment Design: Created hands-on programming assignments with real datasets
Lab Materials: Developed interactive Jupyter notebooks for practical learning
Documentation: Wrote comprehensive tutorials and guides for complex topics
Assessment Tools: Designed fair and comprehensive evaluation methods

Technology Integration

Modern Frameworks: Updated course to use latest deep learning frameworks
Cloud Platforms: Integrated cloud computing resources for student projects
Collaboration Tools: Implemented collaborative development environments
Automated Grading: Developed automated testing frameworks for assignments

Research Outcomes and Publications

Publications in Preparation

Paper 1: “Continual Learning with Dynamic Memory Networks” (Under Review, NeurIPS 2024)
Paper 2: “Federated Learning with Personalized Adaptation” (Under Review, ICML 2024)
Workshop Papers: 3 papers submitted to major conference workshops

Open Source Contributions

FedPer Library: Python library for federated learning with personalization (500+ GitHub stars)
ContinualML: Framework for continual learning experiments (300+ GitHub stars)
Educational Resources: Open-source course materials adopted by other universities

Community Impact

Code Adoption: Research implementations used by 50+ research groups worldwide
Citations: Early work receiving 50+ citations from research community
Reproducibility: All research includes comprehensive reproducibility packages

Professional Development and Recognition

Conference Participation

NeurIPS 2023: Presented research poster and attended federated learning workshop
ICML 2023: Participated in continual learning workshop and tutorials
Local Conferences: Regular presentations at Georgia Tech research seminars

Skills Enhancement

Technical Skills: Advanced proficiency in PyTorch, CUDA programming, distributed systems
Research Skills: Grant writing, peer review, experimental design
Communication: Technical writing, presentation skills, teaching pedagogy
Leadership: Project management, team coordination, cross-functional collaboration

Recognition and Awards

Outstanding Graduate Teaching Assistant Award (Spring 2023)
Research Impact: Work featured in Georgia Tech research highlights
Peer Recognition: Collaborative research opportunities with multiple faculty
Industry Interest: Research attracted collaboration offers from major tech companies

Impact on Research Community

Knowledge Transfer

Workshop Organization: Co-organized workshop on continual learning at major conference
Review Activities: Served as reviewer for workshop papers and journal submissions
Technical Talks: Delivered invited talks at other universities and research labs
Collaboration: Established research collaborations with international researchers

Educational Impact

Curriculum Influence: Course improvements adopted by other institutions
Student Success: Strong track record of student placement in top PhD programs and industry
Teaching Innovation: Pedagogical approaches adopted by other teaching assistants
Mentoring Model: Mentoring approach serving as model for other graduate students

Future Research Directions

Immediate Objectives

Publication Goals: Complete submission of 2 major conference papers
System Development: Deploy continual learning systems in real-world applications
Collaboration Expansion: Establish partnerships with additional industry research labs
Open Source: Release comprehensive software frameworks for research community

Long-term Vision

Theoretical Advances: Develop theoretical foundations for continual learning guarantees
Practical Deployment: Create production-ready continual learning systems
Interdisciplinary Research: Apply continual learning to robotics and autonomous systems
Educational Innovation: Develop new pedagogical approaches for AI education

The Graduate Teaching Assistant position at Georgia Tech provided an exceptional combination of cutting-edge research opportunities and meaningful teaching experience. The role enabled contributions to both the advancement of machine learning research and the education of the next generation of AI researchers and practitioners.