Yingtao Tian
Research Scientist  Google DeepMind / alan.yt.tian@gmail.com / alantian.net

ResearchInterests

I am excited at combining computational approach, with artists, culture, humanities and designer’s
consideration. I propose techniques and tools that have both addressed the needs in creative
settings and improved the core machine learning. Leading collaborations with internal andexternal stake-holders. More details in my research statement.

Research /Industry
Experience

Google DeepMind (formerly Google Brain) Research Scientist
2019 - Present• Generating artifacts with artistical discretion.
– Proposed novel models for abstract art, multiple text prompts and collective intelligence.
• Machine learning-boosted tools for historical and cultural works.
– Pioneered in making ML-ready dateset for pre-modern japanese artworks / longest consecutive typhoon satellite image.
– Proposed ML-based historical figure analysis / calligraphy generation for CJK characters.
• Advancing Machine Learning Techniques in Black-Box Optimization.
– Pioneered in fitting evolution strategy, an black-box optimization, to modern settings.
– Proposing fundation models (LLMs and transformers) based approaches to black-box optimizations.
Internships Google Brain, Facebook, Google, Microsoft Research Asia
• Proposed latent space transfer for multi-modality tasks.
• Implemented natural language interface to database system.

MajorPublications

Periodically 2013 - 2018

Generating Artifacts with Artistical Discretion
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts. Yingtao Tian,David Ha. Proceedings of EvoMUSART 2022
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport. Yingtao Tian,David Ha, Marco Cuturi. Proceeding of ICCC 2022
Evolving Collective AI: Simulation of Ants Communicating via Chemicals. Ryosuke Takata, Yujin Tang, Yingtao Tian, Norihiro Maruyama, Hiroki Kojima, Takashi Ikegami. The 2023 Conference on Artificial Life

Machine learning-Boosted Tools for Historical and Cultural works
KaoKore: A Pre-modern Japanese Art Facial Expression Dataset. Yingtao Tian, Chikahiko Suzuki, TarinClanuwat, Mikel Bober-Irizar, Alex Lamb, Asanobu Kitamoto. Proceeding of ICCC 2020
Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation. Yingtao Tian, Tarin Clanuwat,Chikahiko Suzuki, Asanobu Kitamoto. Proceeding of ICCC 2021
MingOfficial: A Ming Official Career Dataset and a Historical Context-Aware Representation Learning Framework You-Jun Chen, Hsin-Yi Hsieh, Yu Tung Lin, Yingtao Tian, Bert Chan, Yu-Sin Liu, Yi-Hsuan Lin,
Richard Tzong-Han Tsai. Proceeding of EMNLP 2023
Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones.
Asanobu Kitamoto, Jared Hwang, Bastien Vuillod, Lucas Gautier, Yingtao Tian, Tarin Clanuwat. Proceedingof NeurIPS 2023 Systems Datasets and Benchmarks Track
DiffCJK: Conditional Diffusion Model for High-Quality and Wide-coverage CJK Character Generation. Yingtao Tian Proceeding of ICCC 2024

Advancing Machine Learning Techniques in Black-Box Optimization

EvoJAX: Hardware-Accelerated Neuroevolution Yujin Tang, Yingtao Tian, David Ha. Proceeding of GECCO2022
NeuroEvoBench: Benchmarking Neuroevolution for Large-Scale Machine Learning Applications. Robert Tjarko
Lange, Yujin Tang, Yingtao Tian. Proceeding of NeurIPS 2023 Systems Datasets and Benchmarks Track
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards. Shanchuan
Wan, Yujin Tang, Yingtao Tian, Tomoyuki Kaneko. IProceeding of IJCAI 2023
Large Language Models As Evolution Strategies. Robert Tjarko Lange, Yingtao Tian, Yujin Tang. Proceedingof GECCO 2024
Evolution Transformer: In-Context Evolutionary Optimization. Robert Tjarko Lange, Yingtao Tian, Yujin
Tang. Proceeding of GECCO 2024
Position Paper: Leveraging Fuoundational Models for Black-Box Optimization: Benefits, Challenges, and Future
Directions. Xingyou Song, Yingtao Tian, Robert Tjarko Lange, Chansoo Lee, Yujin Tang, Yutian Chen
Proceeding of ICML 2024

Representation Learning for Data in Multiple Modalities
Learning to Represent Bilingual Dictionaries. Muhao Chen*, Yingtao Tian*, Haochen Chen, Kai-Wei Chang,Steven Skiena, Carlo Zaniolo. In the Proceedings of the SIGNLL Conference on Computational Natural Language
Learning (CoNLL) 2019Social Relation Inference via Label Propagation. Yingtao Tian*, Haochen Chen, Bryan Perozzi, Muhao Chen,
Xiaofei Sun, Steven Skiena. In the proceeding of the 41st European Conference on Information Retrieval (ECIR2019)
Syntax-Directed Variational Autoencoder for Structured Data. Hanjun Dai*, Yingtao Tian*, Bo Dai, StevenSkiena, Le Song. In Proceedings of the International Conference on Learning Representations (ICLR) 2018

Education

State University of New York at Stony Brook, New York, U.S.
2014 - 2019
Ph.D, Computer Science. Advisor: Prof. Steven Skiena
Thesis: Representation Learning-based Approaches for Modeling Data in Multiple Modalities
Fudan University, Shanghai, China.
B.Sc., Computer Science and Technology

Awards27th place, 35th Annual World Final of the ACM-ICPC , 2011
Gold Medal, ACM-ICPC Asia Chengdu Regional Contest, 2011
Championship and Gold medal, ACM-ICPC Asia Amritapri Regional Contest, 20102010 - 2014