About
I’m Zac, currently working on building machine learning (ML) system to detect supply chain disruption at BrainChain AI. I have also been selected as a VC fellow with Laconia Capital.
I previously led R&D and product development, as Head of AI, for an AI delivery and MLOps platform (A360 AI) that abstracts the infrastructure complexity from data scientists, as well as ML team lead for an open source ML platform framework at SylphAI.
I have built, led, managed, and mentored multiple cross-functional teams in data science (DS), ML/ software engineering, and product development, providing technical leadership in generative AI, LLM (Large Language Model), MLOps, data-centric AI, innovative use of DS/ ML from ideation to production at scale, mostly with open-ended projects.
I'm a (hybrid) data scientist + ML engineer; while managing DS/ ML teams, I also do hands-on ML coding, and actively conduct and publish ML research, so I can lead my team with state-of-the-art ML and newest AI trend.
I began my DS career building ML applications to help business become data-driven but my recent work has been focusing on generative AI/ LLM, production ML, and data-centric AI.
I ran an open source project AutoDC (automated data-centric processing) that is complimentary to AutoML (see 2021 NeurIPS DCAI).
I have been serving as an advisor for multiple early stage startups (mostly pre-seed and seed stage), guiding their AI/ ML practices.
I am also holding a research associate position at Stanford University, managing and mentoring for applied ML projects in marina biology, disease ecology, deforestation/ climate change, and remote sensing.
Previously, I built custom ML solutions at Hypergiant, QuantumScape, Driscoll’s, and Monterey Bay Aquarium, in multiple industries, such as commercial space, real-time CRM, solid-state battery manufacturing, supply chain operation, public health, and marine conservation. I also built Kubeflow pipelines on computer vision and NLP components for Google’s AI Hub (now under Vertex AI umbrella).
I have 5+ peer-reviewed applied ML publications and regularly present at top ML conferences (NeurIPS, ICLR, TensorFlow World etc).
My previous research life was in geophysics, earthquake dynamics, and planetary sciences. I built numerical models to simulate ice mountain formation on Saturn's moon Titan and climate patterns inferred from sand dunes on Mars, as well as tsunami propagation caused by mega-earthquakes. My image processing tooling was used to support NASA Cassini Mission, and an improved tsunami simulation code I built has been used to model more tsunami events.
Full CV here; LinkedIn profile.
Timeline
03/2023-now: Founding ML Engineer @ BrainChain AI
05/2023-now: Venture Fellow @ Laconia Capital
11/2017-now: Research Associate (ML) @ Stanford University
01/2023-05/2023: Open Source- ML Lead @ SylphAI
06/2021-03/2023: Head of AI/ Chief Data Scientist @ Andromeda 360 AI | Hypergiant
01/2021-06/2021: Senior ML Engineer/ Senior Software Engineer @ QuantumScape
05/2020-01/2021: Senior Data Scientist @ Hypergiant
06/2019-05/2020: Data Scientist @ Driscoll's
10/2018-06/2019: ML Engineer @ Google (contract via iWorkGlobal)
05/2017-02/2019: Data Scientist @ Monterey Bay Aquarium
08/2013-06/2016: Research Scientist (Data) @ NASA | Smithsonian- joint contractor
08/2010-08/2014: Researcher @ NASA Cassini RADAR Team
ML Research
Github and Google Scholar.
My recent works focus on generative AI, LLM, MLOps and data-centric AI.
I've built an open source data-centric AI tooling, AutoDC (Automated data-centric processing) that aims to automate dataset improvement process. We tested the framework on image data and AutoDC is estimated to reduce roughly 80% of the manual time for data improvement tasks, at the same time, improve the model accuracy by 10-15% with the fixed ML code. We want to expand to NLP and tabular data, open for contributors; check our NeurIPS paper.
Enterprise product development
- A360 AI (team effort): end-to-end ML delivery platform, declarative ML serving (Terraform for ML deployment), SDK
- Active Learning Studio (lead): human-in-the-loop AI, programmatic labeling/ auto-labeling, no-code/ low code
- Efficient AutoML (co-lead): single neural network architecture, based on regularization cocktail (paper), for tabular data
ML pipeline/ Kubeflow component contributions
- BERT preprocessing (Colab)
- TPU ResNet training (Google AI Hub asset)
- ImageNet benchmark: applied data augmentation, TPU, distributed training, 20% more efficient
Applied ML | Data science
I led DS teams to build custom ML solutions and data science use cases to support industry partners and clients as well as academic institutions.
Industry
Domain supported: commercial space, CRM, manufacturing sensory and imagery, supply chain operation, oil and gas, sustainable agriculture.
Academia
Domain supported: remote sensing, marine biology and conservation, disease ecology, deforestation/ climate change, and geophysics/ planetary science.
Peer-reviewed Publications
Applied ML
- Jerette, J., Liu, Z.Y.-C., Chimote, P., Hastie, T., Fox, E., and Ferretti, F. Shark detection and classification with machine learning. Ecological Informatics (2022), doi: 10.1016/j.ecoinf.2022.101473.
- Liu, Z.Y.-C., Chamberlain, A.J., Tallam, K., Jones, I.J., Lamore, L.L., Bauer, J. et al. Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa. Remote Sensing, SI: Remote Sensing and Infectious Diseases (2022), 10.3390/rs14061345b.
- Liu, Z.Y.-C., Roychowdhury, S., Tarlow, S., Nair, A., Badhe, S., and Shah, T. AutoDC: Automated data-centric processing. NeurIPS (2021), arXiv:2111.12.
- Liu, Z.Y.-C., Tarlow, S., Akbar, M., Donnellan, Q., and Senkow, D. Improved orbital propagator integrated with SGP4 and machine learning. SmallSat 2021, paper link.
- Tallam, K., Liu, Z.Y.-C., Chamberlin, A.J., Jones, I.J., Shome, P., Riveau, G., Ndione, R.A. et al. Identification of Snails and Schistosoma of Medical Importance via Convolutional Neural Networks: A Proof-of-Concept Application for Human Schistosomiasis. Frontiers in Public Health (2021): 900, doi: 10.3389/fpubh.2021.642895.
- Liu, Z.Y.-C., Moxley, J.H., Kanive, P., Gleiss, A.C., Maughan, M., Bird, L., Jewell, O. et al. Deep learning accurately predicts white shark locomotor activity from depth data. Animal Biotelemetry 7, no. 1 (2019): 1-13, doi: 10.1186/s40317-019-0175-5.
Science
- Jones, I.J., MacDonald, A.J., Hopkins, S.R., Lund, A.J., Liu, Z.Y.C., Fawzi, N.I., Purba, M.P., Fankhauser, K., Chamberlin, A.J., Nirmala, M. and Blundell, A.G. Improving rural health care reduces illegal logging and conserves carbon in a tropical forest. Proceedings of the National Academy of Sciences (2022), 117(45), pp.28515-28524, doi.org/10.1073/pnas.2009240117.
- Liu, Z.Y.-C., Radebaugh J., Harris, R., Christiansen, E.H, and Rupper, S. Role of fluids in the tectonic evolution of Titan. Icarus (2016) (SI: Titan's Surface and Atmosphere), 270, 2-13, doi: 10.1016/j.icarus.201_6.02.016.
- Radebaugh, J., Ventra, D., Lorenz, R.D., Farr, T. Kirk, R.L., Hayes, A., Malaska, M., Birch, S., Liu, Z.Y.-C., Lunine, J., Barnes, J., Le Gall, A., Lopes, R.M.C., Stofan, E., Wall, S., Paillou, P., and Wood, C.A. Alluvial and fluvial fans on Saturn’s moon Titan reveal processes, materials and regional geology. In, Ventra, D. & Clarke, L. E. (eds) Geology and Geomorphology of Alluvial and Fluvial Fans: Terrestrial and Planetary Perspectives. Geological Society, London, Special Publications (2016), 440, doi: 10.1144/SP440.6.
- Liu, Z.Y.-C., Radebaugh J., Harris, R., Christiansen, E.H, Kirk, R.L., Neish, C.D.,Lorenz, R.D., and the Cassini Radar Team. The tectonics of Titan: Global structural mapping from Cassini radar. Icarus (2016) (SI: Titan's Surface and Atmosphere), 270, 14-29, doi: 10.1016/j.icarus.2015.11.021.
- Liu, Z.Y.-C. and Zimbelman, J.R. Recent near-surface wind directions inferred from mapping sand ripples on Martian dunes. Icarus (2015), 261, 169-181, doi: 10.1016/j.icarus.2015.08.022.
- Liu, Z.Y.-C. and Hargitai H. Mountain (Titan). (2014) In: A. Kereszturi and H. Hargitai (Eds), Encyclopedia of Planetary Landforms, Springer-Verlag Berlin Heidelberg, Germany, doi: 10.1007/978-1-4614-9213-9_508-1.
- Liu, Z.Y.-C. and Harris, R. Discovery of Possible Mega-Thrust Earthquake along the Seram Trough from Records of 1629 Tsunami in Eastern Indonesian Region. Natural Hazards (2013) (SI: Extreme Geohazards), 72(3), 1311-1328, doi: 10.1007/s11069-013-0597-y.