Prospective Students

I am looking for students (PhD, Masters, undergrads, interns) who are passionate about research, interested in data mining, machine learning, and human-computer interaction research, and strong in programming and/or math. You will be mainly working on data mining and machine learning in real-world applications, especially on reinforcement learning, urban computing, and human-in-the-loop machine learning. Master’s and undergraduate students within ASU and self-funded visiting students/scholars are also welcome to apply.

If you are interested in working with me, please see the instructions and fill out this form.

Did you know…

Hua goes live from 8:00 pm to 8:30 pm, Phoenix time, every Monday (Oct. 14, 2025, to Dec. 14, 2024) on Twitch. Chat with him directly with your questions.

Several fully-funded PhD positions are available in Fall 2025/Spring 2026.

•  I’m looking for highly motivated PhD/intern students to join my group. The official PhD application deadline for the Fall 2025 application cycle is Dec 31, 2024 (details). 

You are expected to have:

• Programming experience (preferably with Python and/or C++)
• Background in machine learning and/or data mining, which is a plus
• Mathematical foundation (e.g. probability theory, statistics, linear algebra, and optimization), which is a plus
• Experience with deep learning frameworks such as PyTorch and TensorFlow, which is a plus
• Publications (at venues such as KDD, WWW, CIKM, AAAI, IJCAI, ICML, NeurIPS, ICLR, ECML-PKDD), which are pluses
• Recently, we are looking for students with backgrounds in embedded systems, robotics, and cyber-physical systems.

ASU MS students

I typically recruit ASU Master students from my CSE 572 Data Mining course (please consider taking it first) or MORE.

ASU Undergrad students

I typically recruit ASU undergraduate students from hackathons or FURI and GCSP.

ASU’s FURI/MORE/GCSP Program

ASU offers excellent opportunities for student research, such as FURI and GCSP for undergraduates and MORE for master’s students. You can find additional details about these programs below.

• Students are required to submit a concise research proposal, typically 2-3 pages long. Over time, I’ve reviewed many proposals and developed a set of guidelines to help students craft effective submissions.
  • Choosing a Topic: Some students approach me with a clear topic in mind. However, due to time constraints and the need to align with my lab’s broader research goals, I generally support only those students who are already volunteering in my lab or specific topics closely related to our lab. These students often refine their ideas into topics that I can feasibly support.
  • Writing the proposal: While there are many ways to write an effective proposal, the following way of organizing the idea seems to be effective.
    1. State the Problem (2-3 sentences):
       Clearly articulate the problem you aim to solve without delving into technical details. Focus on what the problem is and why it matters.
    2. Summarize Related Work (6 sentences):
       Discuss three key pieces of related research. Avoid summarizing numerous papers; instead, focus on how these works connect to your proposal. For example:
       • “In prior work, the authors addressed {a specific problem}, but their solution {lacked a certain feature}. Our approach aims to address this gap by {briefly state improvement}.”
       • Alternatively: “The method provides a foundation for {specific task}, which we plan to build upon and extend to {new application}.”
    3. Proposals should demonstrate that you have a clear and realistic plan. Here’s a recommended structure:
       • Phase 1: Develop Theory (2-4 weeks): Conduct literature reviews or design experiments to establish a theoretical foundation. This may involve conceptual work rather than mathematical modeling.
       • Phase 2: Build a Prototype (4-8 weeks): Translate theoretical ideas into a working prototype or system.
       • Phase 3: Experimentation and Evaluation (4-8 weeks): Run experiments using datasets or simulations, define metrics for success, and analyze results to validate hypotheses.
       • Phase 4: Write-Up (2-3 weeks): Summarize findings in a final report or paper.
  • Hints:
    • Review your draft critically as if you were evaluating someone else’s proposal. Ask questions like: Is the problem compelling? Is the scope realistic? Does the student have the skills and resources needed? Would I fund this project?
    • If continuing an existing project, highlight how your new work builds on prior efforts.
    • Mention any relevant experience in research labs, especially if it relates to the proposed work—this demonstrates your preparedness and commitment.
    • Think creatively about your budget. For instance, request funding for conference travel, cloud computing resources (e.g., AWS), technical books, software, or equipment that will support your project.
    • Start early! Though short, proposals require significant thought and effort to craft effectively.

I will read every email, but unfortunately, I cannot afford to reply to all of them.