Qingsong Wen's Homepage
Qingsong Wen, Head of AI Research & Chief Scientist @ Squirrel AI, SMIEEE, Ph.D.
AI for Time Series (AI4TS) & AI for Education (AI4EDU)
Email: qingsongedu AT gmail DOT com
Short Biography:
Qingsong Wen is the Head of AI Research & Chief Scientist at Squirrel Ai Learning Inc., leading a team (in both Seattle and Shanghai) working in EdTech area via AI technologies (like LLM, AI Agent, GenAI, Transformer, SSL, GNN, XAI, AutoML, etc.). Before that, he worked at Alibaba, Qualcomm, Marvell, etc., and received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology, USA. His research interests include machine learning, decision intelligence, and signal processing, especially AI for Time Series (AI4TS) & AI for Education (AI4EDU). He has published over 100 top-ranked AI conference and journal papers, had multiple Oral/Spotlight Papers at NeurIPS/ICLR, had multiple Most Influential Papers at IJCAI, received multiple IAAI Deployed Application Awards at AAAI, and won First Place of SP Grand Challenge at ICASSP. Currently, he serves as Organizer/Co-Chair of Workshop on AI for Time Series (AI4TS @ KDD, ICDM, SDM, AAAI, IJCAI) and Workshop on AI for Education (AI4EDU @ KDD, CAI). He also serves as Associate Editor for Neurocomputing, Associate Editor for IEEE Signal Processing Letters, Guest Editor for Applied Energy, and Guest Editor for IEEE Internet of Things Journal. In addition, he has regularly served as Area Chair/(S)PC of the AI conferences including KDD, AAAI, IJCAI, ICDM, ICASSP, etc.
Recent News:
Hiring Applied/Research Scientists and Research Interns (Seattle & Shanghai)! (previous interns from MIT, CMU, Oxford, Harvard, Gatech, USC, HKU, Tsinghua, etc.)
I am open to collaborating with highly motivated researchers!
04/2024: One paper on Self-Supervised Learning for Time Series was accepted by IEEE TPAMI (IF=23.6)!
04/2024: One paper on Foundation Models for Education was accepted by IEEE Intelligent Systems (IF=6.4)!
04/2024: Served as Associate Editor for IEEE Signal Processing Letters!
03/2024: Glad that we will organize KDD 2024 Workshop on AI for Education (KDD'24-AI4EDU)!
03/2024: Glad that we will organize KDD 2024 Workshop on Mining and Learning from Time Series (KDD'24-MiLeTS)!
03/2024: Glad that we will organize IJCAI 2024 Workshop on AI for Time Series (IJCAI'24-AI4TS)!
03/2024: One paper was accepted by ICDE 2024!
01/2024: Two papers were accepted by WWW 2024!
01/2024: Seven papers were accepted by ICLR 2024 (related to Time Series, LLM, Transformer, GNN, XAI, TPP, and all are Open-Sourced)!
01/2024: One paper was selected as a "Spotlight" at ICLR 2024 (Top 5%)!
01/2024: Glad that we will organize IEEE CAI 2024 Workshop on AI for Education (CAI'24-AI4EDU)!
11/2023: One paper titled "Weakly Guided Adaptation for Robust Time Series Forecasting" was accepted by VLDB 2024!
11/2023: Glad that we will organize SDM 2024 Workshop on AI for Time Series (SDM'24-AI4TS)!
09/2023: Glad that we will organize AAAI 2024 Workshop on AI for Time Series (AAAI'24-AI4TS)!
09/2023: Two papers on Time Series Forecasting and Anomaly Detection were accepted by NeurIPS 2023!
09/2023: One IJCAI'23 paper titled "Transformers in Time Series: A Survey" was selected as Most Influential IJCAI Papers, Rank 1st (1/700+)!
09/2023: One tutorial titled "Robust Time Series Analysis and Applications: An Interdisciplinary Approach" was accepted by ICDM 2023 Tutorials!
09/2023: Served as an Area Chair for ICASSP 2024!
06/2023: Served as Guest Editor of the Special Issue on "Energy Internet: A Cyber-Physical-Social Perspective" at IEEE Internet of Things Journal (IF=10.6)!
05/2023: One paper on Time Series Anomaly Detection was accepted by KDD 2023!
05/2023: One paper titled "MagicScaler: Uncertainty-aware, Predictive Autoscaling" was accepted by VLDB 2023!
04/2023: One paper titled "Transformers in Time Series: A Survey" was accepted by IJCAI 2023!
04/2023: Glad that we will organize KDD 2023 Workshop on Mining and Learning from Time Series (KDD'23-MiLeTS)!
03/2023: Glad that we will organize IJCAI 2023 Workshop on AI for Time Series (IJCAI'23-AI4TS)!
03/2023: Glad that we will organize ICDM 2023 Workshop on AI for Time Series (ICDM'23-AI4TS)!
03/2023: Three papers on Load Forecasting were accepted by IEEE TSG (IF=10.275)!
02/2023: Honored to be elevated to IEEE Senior Member!
01/2023: Six Patent Applications on Time Series and AIOps would be filed!
12/2022: Served as an SPC/PC Member for IJCAI & KDD 2023!
11/2022: Honored to be elected as a Member of the IEEE Machine Learning for Signal Processing Technical Committee (MLSP TC)!
11/2022: One paper on large-scale air quality prediction using Transformer was accepted by AAAI 2023!
10/2022: Honored to be invited to give a Talk on Robust and Intelligent Time Series Analysis and Application at CS Dept. of Tsinghua University!
10/2022: Two papers on Cloud Autoscaling and Energy Forecasting Systems were accepted by AAAI/IAAI 2023 with two AAAI/IAAI Deployed Application Awards!
10/2022: Our paper on Time Series Forecasting was selected as an "Oral" at NeurIPS 2022 (Top 2%)!
09/2022: Two papers on Time Series Forecasting and Temporal Event Prediction were accepted by NeurIPS 2022!
10/2022: Our Product "Accurate and Trustworthy Energy Forecasting", powered by Intelligent Time Series and Decision, was launched at Alibaba Cloud!
07/2022: Honored to be invited at 2022 CIKM Workshop on Applied Machine Learning Methods for Time Series Forecasting to give a Keynote Talk!
07/2022: One paper titled "A Cyber-Physical-Social Perspective on Future Smart Distribution Systems" was accepted by Proceedings of the IEEE!
05/2022: One paper titled "FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting" was accepted by ICML 2022!
05/2022: One tutorial titled "Robust Time Series Analysis and Applications: An Industrial Perspective" was accepted by KDD 2022 Tutorials!
05/2022: One paper titled "Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting" was accepted by KDD 2022!
04/2022: One paper titled "Robust Load Forecasting towards Adversarial Attacks via Bayesian Learning" was accepted by IEEE TPWRS (IF=7.326)!
04/2022: One paper titled "Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series" was accepted by IEEE TKDE (IF=9.235)!
03/2022: One paper titled "RobustScaler: QoS-Aware Autoscaling for Complex Workloads" was accepted by ICDE 2022 (see Mo4Tech, Alicloudnative)!
03/2022: Served as Guest Editor of the Special Issue on "Data Openness and Sharing for Low Carbon Energy Systems" at Applied Energy (IF=11.446)!
03/2022: Our Product "Advanced Horizontal Pod Autoscaler" was launched at Alibaba Cloud, powered by our Intelligent Time Series and Decision (Aliyun Developer, InfoQ)!
02/2022: Our team obtained "Root Cause Analysis for Wireless Network Fault Localization"(AIOps Challenge), 2022 ICASSP Grand Challenge, 1st Place (1/382)!
02/2022: One IJCAI'21 paper titled "Time Series Data Augmentation for Deep Learning: A Survey" was selected as Most Influential IJCAI Papers, Rank 1st (1/600+)!
01/2022: One US Patent titled "Change of Variance Detection in Time Series Data" was granted!