Prospective Students

I'm actively recruiting students who are excited about doing fun research with me. My group has opennings for both graduate and undergraduate research assistants. 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 put 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 Undergrad Students at USC: I only admit master students who have relevant research experiences on areas of mine (or at least good grades on relevant courses) and could devote enough time on research. Please highlight these in your email including a time plan. For undergraduate students, please include your time plan in the email.

  • Visitors & Interns: I only take very few visitors/interns per year, mostly recommended by my collaborators. They usually have external funding support.

  • 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.