Changqing Fu

I'm an AI research scientist working across foundation models, model evaluation, neural architecture design, and large-scale training and inference systems. I completed my PhD in Applied Mathematics at CEREMADE, PSL Research University in 2025, advised by Laurent D. Cohen, and previously worked with PRAIRIE and Google TPU Research Cloud. I now work at Mercor on evaluation and benchmarking for advanced AI systems. Open to work: I'm open to research scientist, research engineer, and applied machine learning roles. Email: changqingfu@changqingfu.com Links: CV | Google Scholar | GitHub | LinkedIn Research themes: Foundation-model architectures | Training and inference efficiency | Model evaluation

Highlights

• 5 peer-reviewed papers and public research reports on foundation-model architecture, generative modeling, and training and inference efficiency.

• Built and maintained distributed training pipelines on Jean-Zay and Google TPUv3, including PyTorch to JAX/XLA migration and experiments at 10k+ GPU-hour scale.

• DeepPrism achieved up to 1000x parameter reduction for generative models while preserving generation quality.

• At Mercor, designed benchmark tasks and grading workflows for model evaluation.

Research Agenda

I aim to build reusable foundations for AI progress through benchmark design, efficient foundation-model architectures, and mathematically grounded views of neural computation.

• Benchmarks and evaluation for advanced reasoning and model reliability.

• Efficient foundation-model architectures with better training and inference tradeoffs.

• Mathematical frameworks that explain and guide scalable neural architectures.

Experience

• Mercor (2025-present), Machine Learning Researcher, AI Evaluation. Worked on evaluation datasets, benchmarks, and quality-control pipelines for advanced models.

• Google TPU Research Cloud (2023-2025), Research Fellow. Migrated large-scale training pipelines from PyTorch to JAX, improved TPUv3 throughput via XLA compilation and sharded training, and reduced image-generation pretraining from days to hours.

• PRAIRIE and CEREMADE, PSL (2019-2025), PhD Researcher. Developed geometric and axiomatic views of neural architectures, ran reproducible large-scale diffusion experiments, and published work showing up to 10 percent lower generalization error and 1000x parameter reduction.

• Earlier industry experience (2016-2018). Built ranking, recommendation, and forecasting systems in finance and energy, including Spark pipelines over TB-scale data for 30M+ users and double-digit business or accuracy gains.

Selected Research & Evaluation Work

• Transformers Are Optimal Effective Fields (NeurIPS 2025 workshop). Axiomatic derivation of Transformer-like architectures and a canonical viewpoint on neural-network design. talk poster

• Evaluation and benchmarking work (Mercor, 2025-present). Contributed to benchmark design and quality-control workflows for advanced AI systems.

• DeepPrism: Channel Convolution for Lightweight Generative Models (ACM VSIP 2023, Best Presentation Award). Architectural constraints for smaller and faster generative models, demonstrating up to 1000x parameter reduction while preserving competitive quality. slides

• Conic Activation Functions (PMLR 2024) and Conic Linear Units (VISAPP 2024). Geometric activation design for improved generative-model generalization, model fusion, and rotational symmetry. poster

• Geometric Deformation on Objects: Unsupervised Image Manipulation via Conjugation (SSVM 2021). Contour-guided image manipulation with a robust two-stage pipeline and adaptation to shifted inputs. slides

Recognition & Service

• Reviewer for NeurIPS, ICLR, CVPR, ICCV, ECCV, AAAI, AISTATS, and BMVC.

• Best Presentation Award, ACM VSIP 2023 for DeepPrism.

• Selected invited talks and workshops: Yonsei University Seminar on Tropical Mathematics and Machine Learning (2026), ML Collective Research Jam (2025), NYU Paris Workshop on Optimal Transport, Mean Field Games, and Machine Learning (2025), Fudan Applied Math Seminar (2025), and Stability AI Research Program at CogX (2023).

• AugmentAI Research Prize at ETHCC 2023 for WebGPU for Zero-Knowledge Proofs.