UROP Research Mentor Project Submission Portal: Submission #1030

serial: '1030'
sid: '19746'
uuid: 0d054061-fb7c-4c8f-ad16-9f4e514354a5
uri: /urop-research-mentor-project-submission-portal
created: '1751292163'
completed: '1751292163'
changed: '1761835647'
in_draft: '0'
current_page: ''
remote_addr: 69.254.218.229
uid: '0'
langcode: en
webform_id: urop_project_proposal_portal
entity_type: node
entity_id: '1116'
locked: '0'
sticky: '0'
notes: ''
metatag: meta
data:
  roundtable_info: {  }
  approximately_how_many_hours_a_week_would_the_research_assistant: '10'
  are_you_currently_looking_for_students_: 'No'
  confirmation_1: '1'
  contact_email_fsu_email: ''
  contact_email_fsu_email2: ''
  contact_email_fsu_email_if_affiliated_: guang.wang@fsu.edu
  faculty_advisor_confirmation: ''
  faculty_advisor_name: ''
  faculty_advisor_s_fsu_email: ''
  fsu_college: 'Arts and Sciences'
  fsu_department_if_applicable_: 'Computer Science'
  headshot_optional_: ''
  if_the_project_location_is_off_campus_does_the_student_need_to_p: 'No'
  mentoring_philosophy: 'My mentoring goal is to encourage every student to learn something. Based on my previous mentoring experiences, I think all students are talented and my role as a teacher is to guide them to knock on the correct door. To this end, my mentoring philosophy concentrates on encouraging students to ask questions. I treat all students with respect and maintain academic fairness. In addition, I strive to create a friendly learning environment and make students feel comfortable and supported. I think students can improve their performance after they know what they do not know, and a very effective way is by asking questions, so I usually encourage students to ask questions.'
  mentor_handbook_and_faqs: '1'
  name_of_other_faculty_collaborator_if_applicable_: ''
  number_of_assistants_needed_faculty_postdoc_max_6_graduate_stude: '2'
  other_faculty_collaborator_s_preferred_pronouns: ''
  overall_research_project_description: |-
    This project aims to harness the power of large-scale, diverse, and real-time urban data to address critical challenges in city planning, sustainability, mobility, and public services. As cities become increasingly digitized, massive volumes of data, ranging from traffic flows, environmental sensors, social media, utility usage, and public transit records, offer unprecedented opportunities to understand and improve urban systems. However, effectively analyzing and visualizing these heterogeneous data sources to support timely and effective decision-making remains a significant challenge.

    This research project will develop a comprehensive urban big data analytics and visualization framework that integrates spatiotemporal data mining, machine learning, and interactive visualization techniques to support smart city applications. Specifically, the project will:

    Design scalable data pipelines to collect, clean, and integrate multimodal urban data streams;

    Develop advanced analytics models to detect patterns, predict urban dynamics, and identify anomalies in domains such as mobility, energy consumption, housing, and public health;

    Create intuitive, user-centered visualization tools that enable policymakers, city planners, and community stakeholders to explore insights, simulate scenarios, and support evidence-based decision-making.
  please_add_any_additional_information_here: ''
  please_provide_a_link_to_your_publications_a_video_clip_or_a_web: guangwang.me
  please_select_the_choice_that_most_accurately_describes_your_exp: 'Partially Remote'
  please_select_the_location_of_your_project_: 'On FSU Main Campus'
  position_availability_for_student_research: 'Flexible schedule'
  position_title: Faculty
  primary_research_mentor_name: 'Guang Wang'
  project_keywords: 'Big Data, AI, Visualization, Smart Cities'
  relevant_student_major_s_: |
    Open to all majors
  research_mentor_preferred_pronoun2: ''
  research_mentor_pronouns: ''
  research_mentor_supervisor_if_different_from_above_: ''
  research_tasks_for_student_research_assistant_s_: |-
    Literature Review: Conduct a comprehensive review of recent advancements in urban informatics, smart city initiatives, big data analytics, and interactive visualization tools.

    Data Collection: Gather and curate diverse urban datasets, including transportation logs, air quality data, social media feeds, utility usage records, census information, and IoT sensor data.

    Data Analysis: Apply statistical and machine learning methods to discover patterns, trends, and anomalies in urban dynamics (e.g., traffic congestion, pollution hotspots, energy usage patterns).

    Visualization Design and Implementation: Design user-friendly, interactive dashboards and visual analytics tools tailored to different user groups (e.g., city officials, community leaders, researchers).
  roundtable_times_and_zoom_links: ''
  skills_that_research_assistants_may_need_: |
    Data collection and analysis skills are required.
    Experience in Python is required.
    Familiar with ArcGIS (recommended).
  title_of_the_project: 'Urban Big Data Analytics and Visualization for Smart Cities'
  update_url: 'https://cre.fsu.edu/urop-research-mentor-project-submission-portal?token=WTx0cdmYwKU4TDMyH3fM_EPfKGtWDMcEG9-XJdm7nK8'
  urop_performance_evaluation: '1'
  urop_poster_presentation: '1'
  when_potential_research_assistants_are_reaching_out_via_email_2: ''
  when_potential_research_assistants_are_reaching_out_via_email_wh: ''
  when_students_are_reaching_out_via_email_what_is_your_preferreda: ''
  would_you_like_to_participate_in_the_urop_research_mentor_round2: 'No'
  would_you_like_to_participate_in_the_urop_research_mentor_roundt: ''
  year: '2025'