Danqing Zhang - Applied Science

My Ph.D. training is on understanding complex large-scale systems and developing tools for their design and operation. The understanding of how such systems work requires knowledge about the constitutive laws that govern them, and it also requires an understanding of the theoretical paradigms that are used to model, control and optimize such systems. I have been working on urban cyber-physical systems, societal systems and natural systems.
After academia, I have been working on large scale product search system. When designing NLP features, I also have a systematic view to consider the impact of my features on downstream applications and the whole product search system.

Past Projects

(2023)auction allocation, pricing, auction simulators
(2023)bidding: autobidding bidding, bid optimization, budget pacing
(2023)Utilizing Generative AI in E-commerce: conversational recommendation system, explainable AI
(2021-2022)E-commerce Guided Reformulation, Query Rewrite [KDD'2023, ICDE'2024]
(2021)Semantic Parsing [NAACL'2022(Findings)]
(2021 Summer)Sequential Recommendation: product recommendation & query suggestion [WWW'2022]
(2021)Knowledge Graph: Entity Alignment & Commonsense Knowledge Graph Construction
(2019-2020)Hierarchical text classification [NAACL'2021]
(2020-2021)Named-entity Recognition (NER) & Entity Linking [ACL'2021], [CIKM'2021], [EMNLP'2021]
(2018-2020)Extreme multi-label text classification (XMC)[SIGIR eCom'20 best paper], [arXiv'20]
(2017-2018)Driver attention, Autonomous cars, Self-driving vehicles [ACCV'2018]
(2014-2018)Agent-based modeling [TRC'2019][KDD-LSTS'2016]
(2017)Air traffic management: ground delay program [TRE'2019], [ATM R&D'2017 best paper]
(2016-2017)computational social science, discrete choice models [KDD-MLG'18],[arXiv'18]
(2013-2014)location-based service (LBS), geodatabase, geoanalysis, remote sensing

Mentored & Co-mentored Interns

2023:
Kelong Mao (2023 Summer), Gaoling School of Artificial Intelligence, Renmin University of China
Yunzhe Li (2023 Summer), University of Illinois Urbana-Champaign

2022:
Yu Wang (2022 Summer), University of Illinois at Chicago, co-mentored with primary mentor Zhengyang Wang
Haoyu Wang (2022 Summer), Purdue University, co-mentored with primary mentor Ruirui Li

2021:
Yujia Xie (2021 Spring), Georgia Tech: XMC, Contrastive Learning, co-mentored with primary mentor Zheng Li
Ruijie Wang (2021 Summer), University of Illinois Urbana-Champaign: Dynamic Graph Mining, co-mentored with primary mentor Zheng Li
Sheikh Muhammad Sarwar (2021 Summer), University of Massachusetts, Amherst: Attribute Value Extraction, co-mentored with primary mentor Sreyashi Nag
Tong Zhao (2021 Summer & Fall), University of Notre Dame: Graph Data Augmentation, co-mentored with primary mentor Xianfeng Tang
Jingfeng Yang (2021 Fall), Georgia Tech: semantic parsing, text2sql, co-mentored with primary mentor Haoming Jiang
Wei Jin (2021 Fall), Michigan State University: dataset condensation, co-mentored with primary mentor Xianfeng Tang

2020:
Hui Liu (2020 Summer & Fall), Queen's University: Hierarchical text classification, Zero shot learning
Haoming Jiang (2020 Fall), Georgia Tech: NER, Weak supervision

2019:
Tao Li (2019 Summer), University of Utah: XMC, Data augmentation

Supervisors/ Mentors/ Collaborators

Supervisors

Alexei Pozdnoukhov (2014-2019), Civil & Environmental Engineering, UC Berkeley
Jian Peng (2013-2014), College of Urban and Environmental Science, Peking University (PKU)
Pingchou Han (2013-2014), College of Engineering, Peking University (PKU)

Professors/ Postdocs who are/were my research mentors

Zuo-Jun (Max) Shen (2017-2019), Professor of Department of Industrial Engineering and Operations Research at the University of California, Berkeley: urban simulation, qualify exam
Joan Walker (2016-2018), Professor of Civil and Environmental Engineering at the University of California, Berkeley: prelim exam, qualify exam, Ph.D. dissertation
Greg Biging (2015-2018), Professor Emeritus of Department of Environmental Science, Policy, & Management at the University of California, Berkeley: qualify exam, Ph.D. dissertation
Karl Zipser (2017-2018), Assistant Researcher at Helen Wills Neuroscience Institute at the U.C. Berkeley: autonomous driving, visual perception
Sascha Hornauer (2017-2018), Assistant Professor at Mines ParisTech: autonomous driving, visual perception
Michael Mahoney (2017-2018), ICSI and the Department of Statistics at UC Berkeley: statistical learning models, computational social science, qualify exam
Kimon Fountoulakis (2017-2018), Assistant Professor in Computer Science University of Waterloo: statistical learning models, computational social science
Paul Grigas (2017-2018), Assistant Professor, Industrial Engineering and Operations Research at the University of California, Berkeley: optimization for statistical learning, qualify exam
Mark Hansen (2015-2017), Professor of Civil and Environmental Engineering at the University of California, Berkeley: aviation data mining
Joshua Blumenstock (2017), Associate Professor at the U.C. Berkeley School of Information and the Goldman School of Public Policy: smart cities, call detail records, computational social science
Bruno Olshausen (2017), Professor, Helen Wills Neuroscience Institute at the U.C. Berkeley: visual perception
Scott Moura (2016-2017), Clare & Hsieh Wen Shen Distinguished Professor in Energy, Civil Infrastructure and Climate, Systems at the University of California, Berkeley: prelim exam
Paul Waddell (2016), Professor Emeritus of City & Regional Planning at the University of California, Berkeley: urban simulation
David O’Sullivan (2015-2016), Professor of Professor of Geography and Geospatial Science, School of Geography, Environment and Earth Sciences at Victoria University of Wellington: From the geography of social interactions toward data-driven simulations of urban neighborhoods

Collaborators

Junyu Cao (2016-2019), Assistant Professor at McCombs School of Business at The University of Texas at Austin: statistical learning models, computational social science, smart cities
Sid Feygin (2016-2019), Head of Simulation at Marain: agent-based modeling, smart city
Dounan Tang (2016-2019), Lime: urban simulation
Yi Liu (2015-2019), Amazon: aviation data mining
Yulin Liu (2015-2019), Amazon: aviation data mining
Ye Xia (2017-2018), Google: Driver attention, Autonomous cars, Self-driving vehicles
Jinkyu Kim (2017-2018), professor at Korea University Computer Science: Driver attention, Autonomous cars, Self-driving vehicles
Jun Ma (2017-2018), Amazon: graph fraud detection
Yan Zhang (2017-2018), Amazon: graph fraud detection
Yun Wang (2017-2018), Amazon: graph fraud detection
Mogeng Yin (2014-2016), Meta: generative model for trajectory prediction

Professional Activities

Conference Reviewer

Conference on Language Modeling (https://colmweb.org/)Conference on Language Modeling (https://colmweb.org/)' 2024
EACL' 2024, The 18th Conference of the European Chapter of the Association for Computational Linguistics
TheWebConf' 2024
ECML-PKDD' 2021, 2022, 2023
ICML' 2023, 2024
AAAI' 2023, 2024
NeurIPS' 2022, 2023
ACL Rolling Review (ACL 2022, NAACL 2022, ACL 2023, EMNLP 2023)
ICLR'2021
NLPCC'2021
CIKM'2021 Short Paper
SIGIR'2021

Program Committee

2023 IEEE BigData
CIKM'2022 Applied Research Track, CIKM'2023 Long/Short Research Paper
2023 TheWebConf2023-Companion
2022 WSDM workshop: Decision Making for Modern Information Retrieval System
2021 SIGIR workshop on eCommerce

Journal Reviewer

Transportation Research Part A: Policy and Practice, h-index: 142
Transportation Research Part C: Emerging Technologies, h-index: 133
Transportation Research Part E: Logistics and Transportation Review, h-index: 122
International Transactions on Electrical Energy Systems, h-index: 42
International Journal of Computational Intelligence Systems, h-index: 41
International Journal of Information Technology & Decision Making, h-index: 42
Complex & Intelligent Systems
Applied Computing and Intelligence, h-index: 66
INFORMS Journal on Computing, h-index: 80

Others

Organizer: A9 ML research talk
Session Chair: ECML-PKDD Applied Research Track E-commerce and Finance Session
Mentorship: I have mentored 3 full-time applied scientists at Amazon for their career growth

Side Projects

Deep Learning Research Project Template, Done
Latent Variable & Graph Neural Networks (GNN) & Probabilistic Graphical Models (PGM), In Progress
Research on Interaction-aware Trajectory Prediction using Lyft L5 prediction dataset, In Progress
Research on Multi-agent RL applications for Macro Traffic Control, In Progress