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Songgaojun (Amy) Deng

I am a Postdoc Researcher in the AIRLab at the University of Amsterdam, working with Prof. dr. Maarten de Rijke and Dr. Sebastian Schelter since January 2023.

I received my PhD in Computer Science from Stevens Institute of Technology in 2022, supervised by Dr. Yue Ning.

I am on the job market, please reach out to me if you think I am a good fit in your or other departments!

Google Scholar | DBLP | Github | LinkedIn | CV

Research interests

I am broadly interested in machine learning and data mining problems motivated by real-world problems in social, health informatics, e-commerce, etc. My research focuses on enhancing the explanability and robustness of predictive modeling for temporal data.

  • Out-of-Distribution generalization in time series
    • My ongoing work and future perspectives in this direction focus on refining problem formulation, task definition, achieving interpretability in OOD generalization for time series data, and addressing potential ethical and fairness considerations beyond generalization.
  • Explainable and robust human event modeling
    • As Large Language Models (LLMs) impact various fields of study, the future direction involves harnessing them to reshape event sequence modeling and future event reasoning and explanation.

News

  • 01/2024          Paper on Domain Generalization in Time Series Forecasting was accepted by TKDD.
  • 10/2023          Paper on Graph Contrastive Learning was accepted by KAIS.
  • 01/2023          I join AIRLab, University of Amsterdam as a Postdoc Researcher.
  • 08/2022          Paper on causality enhanced societal event forecasting was accepted by ICDM 2022.
  • 07/2022          Passed Ph.D. Dissertation Defense. Thanks to all committee members!
  • 06/2022          Paper on causal event analysis was accepted by KDD 2022.
  • 05/2022          2022 Recipient of the Excellence in Graduate Research at Stevens.
  • 08/2021          Paper on interpretable event prediction was accepted by CIKM 2021.
  • 05/2021          Received Stevens Excellence Doctoral Fellowship.
  • 04/2021          Passed Ph.D. Dissertation Proposal Defense. Many thanks to all committee members!
  • 07/2020          Paper on predicting long-term influenza-like illness cases was accepted by CIKM 2020.
  • 06/2020          I joined Targeting Science team at Yahoo Research as a summer intern!
  • 06/2019          Received KDD 2020 student travel award.
  • 06/2020          Paper on dynamic knowledge graph based multi-event forecasting was accepted by KDD 2020.
  • 09/2019          Received Women in Machine Learning (WiML@NeurIPS 2019) travel grant.
  • 05/2019          Successfully passed my PhD Qualifying Exam (Oral Part). Officially a Ph.D. candidate!
  • 05/2019          Received KDD 2019 student travel award.
  • 04/2019          First paper on modeling dynamic event context graphs was accepted by KDD 2019.
  • 04/2019          Received a travel grant to attend CRA Women in Computing Workshop.
  • 12/2018          Successfully passed my PhD Qualifying Exam (Written Part).
  • 08/2018          I will come to Stevens in Fall as a Ph.D. student.

Services

Teaching and Supervision

Teaching
  • Recommender Systems (June 2023) – MSc Artificial Intelligence
    • University of Amsterdam, Amsterdam, The Netherlands
    • Developed and delivered lectures, guided students in reproducing recommendation methods, and conducted assessments.
  • CS 584: Natural Language Processing (Fall 2019)
    • Department of Computer Science, Stevens Institute of Technology
    • Prepared teaching materials such as lecture slides.
Supervision
  • Daniel Uyterlinde (BSc., March 2023 – June 2023), University of Amsterdam
    • Thesis: Improving generalization ability of Transformer models in time series forecasting
  • Alihan Ince (BSc., March 2023 – June 2023), University of Amsterdam
    • Thesis: Enhancing Air Quality Prediction through Transfer Learning and Model Adaptation: Leveraging LSTM and TCN Models
  • Taiki Lazos (BSc., March 2023 – June 2023), University of Amsterdam
    • Thesis: Integrating Crude Oil Price for Improved Stock Price Forecasting on Oil Companies using LSTM
  • Enes Doğan (BSc., March 2023 – June 2023), University of Amsterdam
    • Thesis: Time Series Forecasting of Biobridge Strain: A Deep Learning Approach with LSTM Models

Contact

Address:
L5.48
Lab42, Science Park 900, Amsterdam, Netherlands
1098 XH, Amsterdam

Email:
s dot lastname at uva.nl