Internships |

ByteDance - Douyin Ads

Fri, 15 May 2026 00:00:00 +0000

Model Engineering Intern May 2026 – Present

Reconstructed and upgraded ByteDance’s internal RL-based retrieval recommendation models from TensorFlow to LGTorch, including dual-tower architectures, vector quantization (VQ), and multi-runstep graph execution pipelines.
Designed sequence encoding tower decomposition, weight normalization alignment strategies, and write-back mechanisms for frequency estimation modules.
Completed full-stack migration across sparse feature processing, dense computation, and online serving pipelines.

Built a bidirectional forward/backward diff toolchain for end-to-end TensorFlow–LGTorch numerical verification.
Resolved feature-slot registration mismatches, VQ codebook numerical deviations, and framework-specific execution inconsistencies.
Achieved operator-level precision alignment between both frameworks; the associated technical documentation was classified as internal L3 confidential.

Upgraded Douyin’s RL-based retrieval pipeline with logQ debiasing and improved sampled-softmax estimation over sharded candidate repositories.
Achieved online performance comparable to the production baseline and successfully supported full-scale deployment.

Sun, 01 Mar 2026 00:00:00 +0000

Algorithm Intern Mar. 2026 – Apr. 2026

Developed portrait stylization models based on StyleGAN2 and diffusion architectures for high-quality anime-style image generation.
Constructed end-to-end training workflows including dataset preprocessing, data augmentation, model training, inference, and quantitative evaluation.
Built scalable pipelines supporting rapid experimentation across multiple generative model architectures.

Improved training stability and convergence performance using PyTorch and TensorFlow through optimizer tuning, learning-rate scheduling, and architecture refinement.
Conducted systematic hyperparameter exploration to improve image fidelity, visual quality, and generation consistency.
Evaluated generation performance using both objective metrics and human perceptual assessments.

Explored attention-based feature enhancement and LoRA-based parameter-efficient adaptation techniques for controllable style transfer.
Improved preservation of facial identity features while maintaining target anime-style characteristics.
Enhanced style consistency and fine-grained visual details across generated outputs.

Designed reproducible experiment management pipelines covering data processing, training, inference, and result analysis.
Automated model evaluation and visualization workflows to support efficient iteration and comparison of generative model variants.

Mon, 01 Sep 2025 00:00:00 +0000

Graduate Researcher University of Pennsylvania, 2025 – 2026

Conducted research on 3D scene reconstruction, temporal representation learning, and world models for embodied decision-making systems.
Explored unified frameworks connecting perception, dynamic scene understanding, and planning for robotic manipulation tasks.
Focused on learning environment representations that support downstream control and long-horizon reasoning.

Responsible for model training, experimental debugging, and large-scale dataset preprocessing.
Optimized training workflows and experiment pipelines to improve reproducibility and computational efficiency.
Participated in simulation environment design and benchmark evaluation for manipulation tasks.

Surveyed and analyzed recent literature in embodied AI, generative world models, and robotic learning.
Developed experimental comparison frameworks and performance evaluation protocols for research studies.

Thu, 01 Sep 2022 00:00:00 +0000

Research Member -ShanghaiTech University, 2022 – 2025*

Conducted research on diffusion-based generative models for image synthesis, image restoration, and controllable generation.
Participated in projects spanning computer vision and small-molecule generation under structured optimization objectives.
Explored model architecture design, training strategies, and generation-quality enhancement techniques.

Participated in the complete lifecycle of top-tier conference publications, including literature review, problem formulation, model design, experimentation, and manuscript preparation.
Co-authored a peer-reviewed publication as the third author.
Accumulated three years of continuous research experience in generative modeling and deep learning.

Reproduced and extended recent research papers in diffusion models and generative learning.
Designed model improvements and conducted hyperparameter optimization for training stability and generation quality.
Performed systematic analysis of model convergence behavior and output fidelity.