About
I'm Kun Qian (钱坤), an engineering and applied science leader with over a decade of experience turning research ideas into production AI systems at scale. My work sits at the intersection of enterprise AI, human-in-the-loop machine learning, NLP, and data intelligence — always with a focus on shipping products that make a measurable difference.
At Adobe, I lead a cross-geo team of applied scientists, ML engineers, and software engineers in the Agent Orchestration group. We build the core Data Agents behind Adobe AI Assistant — the 2nd highest-traffic agent on the platform. I also drive key cross-org initiatives including the Configurable Entity Linking Service (one of the most widely adopted services across Adobe Experience Platform) and the Field Discovery Agent (AI-powered XDM field search via natural language). My team's current focus is intelligent structured data query and visualization skills, along with platform infrastructure such as Federated Knowledge Graph, Entity Linking, Semantic Graph, NL2SQL, and NL2APIs.
Prior to Adobe, I was part of the Apple Knowledge Platform team, working on a large-scale knowledge graph to power question answering at Apple, improved search ranking at hundred-million-dollar revenue scale at Amazon, and published 20+ papers on human-in-the-loop ML, entity resolution, and explainable AI during four years at IBM Research. I hold a PhD from UC Santa Cruz, where I worked with Balder ten Cate, Phokion Kolaitis, and Wang-Chiew Tan on approximation algorithms for data integration.
My research has been recognized with a DSRI Best Practices Paper Award (IAAI 2025) and a Best Demo Award (ISWC 2020). I serve as Area Chair for ACL 2025 & 2026 and co-organize the DaSH workshop series at VLDB.
Originally from Chongqing, China. I enjoy cooking Szechuan food, reading, and traveling with my family. I live in the Seattle area with my wife and son.
Experience
| 2024.02 – Present | Sr. Machine Learning Manager (promoted 2024.09) | Adobe |
| 2022.03 – 2024.01 | Senior ML Researcher and Engineer | Apple |
| 2021.03 – 2022.06 | Applied Scientist | Amazon |
| 2017.02 – 2021.03 | Research Scientist | IBM Research |
| 2010.10 – 2011.08 | Project Officer | Nanyang Technological University |
Education
| Ph.D. | University of California, Santa Cruz |
| M.S. | Beihang University (exchange at Kyushu University) |
| B.S. | Chongqing University |
Professional Services
Workshop Co-Chair
- DaSH — Data Science with Human-in-the-loop @ VLDB 2026
- DaSH — Data Science with Human-in-the-loop @ VLDB 2025
- Data Agent Competition (co-host)
Conference Program Committee
- ACL 2026 (Area Chair)
- ACL 2025 (Area Chair)
- PVLDB (2022, 2023, 2024)
- EMNLP (2021, 2022)
- NAACL 2021
- DaSH@KDD 2021, DaSH@NAACL 2021
- IUI 2021 (demo)
- ACL 2020, IJCAI 2020, AAAI (2020, 2021)
- ICDE 2020 (industry track)
- IEEE BigData 2019, WebDB@SIGMOD 2018
Journal Referee
- ACM TODS (2018, 2019)
- IEEE TKDE (2019)
Publications
Research Interests: information extraction, large language models, human-in-the-loop machine learning, active learning, weak supervision, deep learning, explainable AI, data integration and data exchange, multilingual search.
My work has been published in various notable AI/NLP/DB/HCI conferences and journals including: AAAI, ACL, EMNLP, COLING, VLDB, PODS, ICDE, TODS, CIKM, IUI, ACM DIS, ISWC, etc. with a Best Demo Award at ISWC 2020 and a DSRI AI Incidents and Best Practices Paper Award at IAAI 2025.