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Contact Info

941 Bloom Walk SAL 308, Los Angeles, CA 90089 USA
(213) 821-4067 | xiangren@usc.edu

Prospective Students

I'm actively recruiting students who are excited about doing fun research. Our lab has opennings for PhD students (RAs) and visiting MS/undergrad students (USC or external). If fitting to the following cases, you are welcomed to drop me an email introducing yourself :)

  • Applicants for USC PhD Program: please make sure that you have applied to USC CS PhD program and select my name in the applicaion system or on your PS. I may not have time to discuss your application case by case; but I will try to...

  • MS and Undergrads at USC: I only admit students who (1) have relevant experiences on areas of mine or have strong performance on math/stats courses; and (2) could devote enough time on research. Please highlight these in your email including a time plan. We prefer students who can stay during summer.

  • Visitors & summer interns: Visitors are mostly recommended by my collaborators and have external funding support. The minimal duration of stay at USC is 12 weeks (preferrably 6 months - 1 year). Please indicate the time range in your email.

  • In your email, please try to include the following information:
  • Use title as "Prospective Student: YourName - YourAffliation"
  • Briefly describe your (1) education background (anything you want to highlight); (2) research publications and experiences in areas related to NLP, ML and data mining; (3) programming skills.
  • Briefly talk about "why I'm (potentially) a good mentor to work with or to help your research?" and "what are the things you want to explore together with me?"
  • Include a PDF version of your CV.
  • Preferred: include the contact information for a reference person (the person who knows your well and could write recommendation letter for you).

  • FAQ

    Q1: What kinds of research we will be doing?
    A1: The lab's research interets span from data mining to natural language processing to applied machine learning, with a particular focus on "knowledge acquisition" --- mining machine-readable knowledge (like entities and relationships) from unstructured text data. Problem-wise, we're interested in information extraction, knowledge representation and reasoning, information network analysis and text mining. Method-wise, we study weakly-supervised learning, learning with noisy and partial labels, learning with complex label space, neural sequence models, structure prediction models and sequence-to-sequence learning. Application-wise, we automatically construct large-scale knowledge graphs for different domains (biomedical, law, finance), and build systems for querying and analyzing such knowledge graphs to facilitate intelligent services.

    Q2: Basic requirements for doing PhD with me?
    A2: I'm looking for students who (1) could prove or demonstrate a solid mathmatical background (e.g., research publications, reference letters, strong performance in relevant courses); (2) could code proficiently (e.g., active contributions to repos on Github, experiences on large-scale systems development, reference letters); and (3) have good writing / communication skills (e.g., writing samples, videos on your talk, skype interviews).

    Q3: How to increase your chance of being admitted?
    A3: (1) Anything that can prove your strong research skills, ranging from publications in good conferences and journals, to research experiences in relevant and reputed research groups; (2) Anything that can showcase you have great potentials for doing research, from good GPA, rank and awards, to strong reference letters from regarded folks.