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.

Did you know…

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

Hua is also hosting podcasts (Apple Podcast, Spotify, 小宇宙). If you would like to chat on his podcasts, feel free to let him know with this google form!

Several fully-funded PhD positions are available in Fall/Spring 2027.

•  I’m looking for highly motivated PhD/intern students to join my group. The official PhD application deadline for the Fall 2026 application cycle is Dec 31, 2025 (details). If you are interested in working with me, please see the instructions and fill out this form.

• 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, AAAI, IJCAI, ICML, NeurIPS, ICLR, WWW, CIKM, ECML-PKDD), which are pluses
  • Recently, we have been looking for students with backgrounds in embedded systems, robotics, and cyber-physical systems.

One PostDoc/Research Scientist position is available in Fall 2026/Spring 2027.

• I’m actively looking for post-docs or research scientists. Please complete this form if you are interested in this position.

• You are expected to have:
  • Prior publication record in top-tier conferences/journals in machine learning, data mining, control, transportation and/or other high-impact journals.
  • Previous experience in building open-source systems.
  • Strong communication, leadership, and community-building skills.

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.