CV
- jlu414@gatech.edu
- +1 470-929-4016
- Atlanta, GA
- Google Scholar
Ph.D. candidate in Machine Learning at Georgia Tech advised by Shihao Yang. I work on how sequence models read context, store knowledge, and rewrite their own weights, toward self-improving LLMs and agents. My architectures make LLMs and time-series models more expressive under practical compute constraints; I now study parametric memory as a route to continual learning. Since 2024, I have published 10 papers as the main contributor at ICLR, ICML, and NeurIPS.
Education
Research interests
Selected publications
All publicationsNextGen sequence modeling
LLM pretraining, efficient and expressive attention, and dynamic MLPs
HyperMLP: An Integrated Perspective for Sequence Modeling
ICML 2026
Free Energy Mixer
ICLR 2026
ZeroS: Zero-Sum Linear Attention for Efficient Transformers
NeurIPS 2025
Spotlight
Attention meets dynamical systems
VAR/ARMA attention and symplectic position encoding beyond RoPE
Forecasting for real-world decisions
In-context, multivariate, and distribution-valued time series
CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series
NeurIPS 2026
In-context Time Series Predictor
ICLR 2025
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
ICML 2024
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
ICLR 2024
* Corresponding author.
Research translation
Submit weekly influenza hospitalization forecasts for all U.S. states to the CDC FluSight forecasting hub (entry name: Gatech-ensemble), translating time-series forecasting research into public-health decision support.
Co-author of Multi-season evaluation and analysis of categorical trend forecasts of influenza hospital admissions in the United States (medRxiv preprint, 2026; submitted to Nature Communications).
Research leadership and mentoring
Since Fall 2024, I have organized a Georgia Tech undergraduate Sequential AI team, leading each project through the full research cycle, from idea to publication.
Teaching and service
Independent instructor for ISyE 4031 Regression and Forecasting at Georgia Tech in Summer 2026: three sections (one in person, two online), 58 students in 28 project teams. Participant in Georgia Tech's Tech to Teaching program; completed CETL 8717 Course Design for Higher Education.
NeurIPS 2024-2026, ICLR 2025-2026, ICML 2025-2026, and IEEE Internet of Things Journal.
Selected talks
All talksInvited Talk, Knowledge Engineering Group, Tsinghua University (hosted by Prof. Jie Tang, founder of Z.ai)
PhD Seminar, Georgia Tech Machine Learning Student Seminar
PhD Seminar, Georgia Tech ISyE PhD Student Seminar
Honors and awards
Computing grants
Subquadratic HyperMLP via Convolution-Based Sequence Mixing
Scaling Zero-Sum Linear Attention for Cross-Modal Foundation Models
Advanced Transformer-based Models for Long-term Time Series Forecasting
Professional experience
Led development of deep learning and statistical models for medical insurance forecasting and epidemic monitoring; co-authored 2 medical time-series papers, filed 6 patents as first inventor, and delivered technical solutions for medical facilities across 4 major cities handling up to 10 million data points daily.
Applied machine learning and causal inference to cost analysis and sourcing decisions; built AWS-based dashboard web apps that informed decision-making and reported savings of over USD 100,000 monthly.