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Portrait of Dr. Jae-won Heo, AI Super Mentor
AI Super MentorDoctorate

Dr. Jae-won Heo

Adaptive Urbanism and AI-Driven City Ecosystems (Ph.D.)

Data-Driven Urban Futures Your Practical Guide to Smart City Data at Nexier University Welcome to a practical and applied approach in smart city data! I am Super mentor Jae-won Heo. As a mentor specializing in smart mobility and urban data analysis, I am thrilled to guide the future experts in the Adaptive Urbanism and AI-Driven City Ecosystems (Ph.D.) program at Nexier University.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

  • Internships in urban data analytics firms and smart city initiatives
  • Roles as urban data scientists or smart mobility consultants
  • Consultancy in ethical AI for urban planning
  • Support roles in academic research projects

Read the programme journey

AI Super Mentor

A desk with Dr. Jae-won Heo

Classroom

This desk

Data-Driven Urban Futures Your Practical Guide to Smart City Data at Nexier University Welcome to a practical and applied approach in smart city data! I am Super mentor Jae-won Heo. As a mentor specializing in smart mobility and urban data analysis, I am thrilled to guide the future experts in the Adaptive Urbanism and AI-Driven City Ecosystems (Ph.D.) program at Nexier University.

Dr. Jae-won Heo

Data-Driven Urban Futures Your Practical Guide to Smart City Data at Nexier University Welcome to a practical and applied approach in smart city data! I am Super mentor Jae-won Heo. As a mentor specializing in smart mobility and urban data analysis, I am thrilled to guide the future experts in the Adaptive Urbanism and AI-Driven City Ecosystems (Ph.D.) program at Nexier University.

Progress stays in this browser until you clear it. It is not a learner record. Identity enrolment is a separate action on the programme page.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Adaptive Urbanism and AI-Driven City Ecosystems (Ph.D.)

  1. 01Fundamentals of Urban Data Science
    1. FoundationsFoundations of Fundamentals of Urban Data Science

      The learner can understand the principles of smart mobility and urban data analysis, as applied to Fundamentals of Urban Data Science.

      • Multiple choiceWhich listed outcome belongs to Foundations of Fundamentals of Urban Data Science?
      • Meets the listed outcomeThe learner can understand the principles of smart mobility and urban data analysis, as applied to Fundamentals of Urban Data Science.

      The learner can develop foundational competencies in ethical considerations in smart cities, as applied to Fundamentals of Urban Data Science.

      • True or falseThis unit lists the following outcome: The learner can develop foundational competencies in ethical considerations in smart cities, as applied to Fundamentals of Urban Data Science.
      • Meets the listed outcomeThe learner can develop foundational competencies in ethical considerations in smart cities, as applied to Fundamentals of Urban Data Science.
    2. MethodsMethods in Fundamentals of Urban Data Science

      The learner can gain an interdisciplinary perspective and enhance teamwork skills, as applied to Fundamentals of Urban Data Science.

      • True or falseThis unit lists the following outcome: The learner can gain an interdisciplinary perspective and enhance teamwork skills, as applied to Fundamentals of Urban Data Science.
      • Meets the listed outcomeThe learner can gain an interdisciplinary perspective and enhance teamwork skills, as applied to Fundamentals of Urban Data Science.

      The learner can increase personal awareness by delving into leadership in smart city design, as applied to Fundamentals of Urban Data Science.

      • Short answerIn one sentence, restate the listed outcome of Methods in Fundamentals of Urban Data Science as applied to Fundamentals of Urban Data Science.
      • Meets the listed outcomeThe learner can increase personal awareness by delving into leadership in smart city design, as applied to Fundamentals of Urban Data Science.
    3. ApplicationApplication of Fundamentals of Urban Data Science

      The learner can master advanced research on designing dynamically adaptable urban ecosystems, as applied to Fundamentals of Urban Data Science.

      • Short answerIn one sentence, restate the listed outcome of Application of Fundamentals of Urban Data Science as applied to Fundamentals of Urban Data Science.
      • Meets the listed outcomeThe learner can master advanced research on designing dynamically adaptable urban ecosystems, as applied to Fundamentals of Urban Data Science.

      The learner can leveraging AI for sustainable infrastructure and smart mobility, as applied to Fundamentals of Urban Data Science.

      • Multiple choiceWhich listed outcome belongs to Application of Fundamentals of Urban Data Science?
      • Meets the listed outcomeThe learner can leveraging AI for sustainable infrastructure and smart mobility, as applied to Fundamentals of Urban Data Science.
  2. 02Techniques for Smart Mobility Planning
    1. FoundationsFoundations of Techniques for Smart Mobility Planning

      The learner can understand human-centric urban planning and climate resilience, as applied to Techniques for Smart Mobility Planning.

      • Multiple choiceWhich listed outcome belongs to Foundations of Techniques for Smart Mobility Planning?
      • Meets the listed outcomeThe learner can understand human-centric urban planning and climate resilience, as applied to Techniques for Smart Mobility Planning.

      The learner can analyze urban metabolism and self-healing infrastructure, as applied to Techniques for Smart Mobility Planning.

      • True or falseThis unit lists the following outcome: The learner can analyze urban metabolism and self-healing infrastructure, as applied to Techniques for Smart Mobility Planning.
      • Meets the listed outcomeThe learner can analyze urban metabolism and self-healing infrastructure, as applied to Techniques for Smart Mobility Planning.
    2. MethodsMethods in Techniques for Smart Mobility Planning

      The learner can apply a method from Techniques for Smart Mobility Planning to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Techniques for Smart Mobility Planning to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Techniques for Smart Mobility Planning to a documented case.

      The learner can select an appropriate method from Techniques for Smart Mobility Planning for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Techniques for Smart Mobility Planning as applied to Techniques for Smart Mobility Planning.
      • Meets the listed outcomeThe learner can select an appropriate method from Techniques for Smart Mobility Planning for a stated problem.
    3. ApplicationApplication of Techniques for Smart Mobility Planning

      The learner can evaluate a practice of Techniques for Smart Mobility Planning against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Techniques for Smart Mobility Planning as applied to Techniques for Smart Mobility Planning.
      • Meets the listed outcomeThe learner can evaluate a practice of Techniques for Smart Mobility Planning against a stated criterion.

      The learner can transfer Techniques for Smart Mobility Planning to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Techniques for Smart Mobility Planning?
      • Meets the listed outcomeThe learner can transfer Techniques for Smart Mobility Planning to a new documented context.
  3. 03AI-Assisted Feedback Systems for Ethical Smart Cities
    1. FoundationsFoundations of AI-Assisted Feedback Systems for Ethical Smart Cities

      The learner can explain the core terms of AI-Assisted Feedback Systems for Ethical Smart Cities.

      • Multiple choiceWhich listed outcome belongs to Foundations of AI-Assisted Feedback Systems for Ethical Smart Cities?
      • Meets the listed outcomeThe learner can explain the core terms of AI-Assisted Feedback Systems for Ethical Smart Cities.

      The learner can distinguish related ideas inside AI-Assisted Feedback Systems for Ethical Smart Cities.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside AI-Assisted Feedback Systems for Ethical Smart Cities.
      • Meets the listed outcomeThe learner can distinguish related ideas inside AI-Assisted Feedback Systems for Ethical Smart Cities.
    2. MethodsMethods in AI-Assisted Feedback Systems for Ethical Smart Cities

      The learner can apply a method from AI-Assisted Feedback Systems for Ethical Smart Cities to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from AI-Assisted Feedback Systems for Ethical Smart Cities to a documented case.
      • Meets the listed outcomeThe learner can apply a method from AI-Assisted Feedback Systems for Ethical Smart Cities to a documented case.

      The learner can select an appropriate method from AI-Assisted Feedback Systems for Ethical Smart Cities for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in AI-Assisted Feedback Systems for Ethical Smart Cities as applied to AI-Assisted Feedback Systems for Ethical Smart Cities.
      • Meets the listed outcomeThe learner can select an appropriate method from AI-Assisted Feedback Systems for Ethical Smart Cities for a stated problem.
    3. ApplicationApplication of AI-Assisted Feedback Systems for Ethical Smart Cities

      The learner can evaluate a practice of AI-Assisted Feedback Systems for Ethical Smart Cities against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of AI-Assisted Feedback Systems for Ethical Smart Cities as applied to AI-Assisted Feedback Systems for Ethical Smart Cities.
      • Meets the listed outcomeThe learner can evaluate a practice of AI-Assisted Feedback Systems for Ethical Smart Cities against a stated criterion.

      The learner can transfer AI-Assisted Feedback Systems for Ethical Smart Cities to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of AI-Assisted Feedback Systems for Ethical Smart Cities?
      • Meets the listed outcomeThe learner can transfer AI-Assisted Feedback Systems for Ethical Smart Cities to a new documented context.
  4. 04Interdisciplinary Project Management in Adaptive Urbanism
    1. FoundationsFoundations of Interdisciplinary Project Management in Adaptive Urbanism

      The learner can explain the core terms of Interdisciplinary Project Management in Adaptive Urbanism.

      • Multiple choiceWhich listed outcome belongs to Foundations of Interdisciplinary Project Management in Adaptive Urbanism?
      • Meets the listed outcomeThe learner can explain the core terms of Interdisciplinary Project Management in Adaptive Urbanism.

      The learner can distinguish related ideas inside Interdisciplinary Project Management in Adaptive Urbanism.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Interdisciplinary Project Management in Adaptive Urbanism.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Interdisciplinary Project Management in Adaptive Urbanism.
    2. MethodsMethods in Interdisciplinary Project Management in Adaptive Urbanism

      The learner can apply a method from Interdisciplinary Project Management in Adaptive Urbanism to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Interdisciplinary Project Management in Adaptive Urbanism to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Interdisciplinary Project Management in Adaptive Urbanism to a documented case.

      The learner can select an appropriate method from Interdisciplinary Project Management in Adaptive Urbanism for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Interdisciplinary Project Management in Adaptive Urbanism as applied to Interdisciplinary Project Management in Adaptive Urbanism.
      • Meets the listed outcomeThe learner can select an appropriate method from Interdisciplinary Project Management in Adaptive Urbanism for a stated problem.
    3. ApplicationApplication of Interdisciplinary Project Management in Adaptive Urbanism

      The learner can evaluate a practice of Interdisciplinary Project Management in Adaptive Urbanism against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Interdisciplinary Project Management in Adaptive Urbanism as applied to Interdisciplinary Project Management in Adaptive Urbanism.
      • Meets the listed outcomeThe learner can evaluate a practice of Interdisciplinary Project Management in Adaptive Urbanism against a stated criterion.

      The learner can transfer Interdisciplinary Project Management in Adaptive Urbanism to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Interdisciplinary Project Management in Adaptive Urbanism?
      • Meets the listed outcomeThe learner can transfer Interdisciplinary Project Management in Adaptive Urbanism to a new documented context.
Field of mastery

Expertise with a point of view

Smart Mobility, Urban Data Analysis, Ethical Considerations in Smart Cities, Leadership in Smart City Design.

The city speaks in data; we must learn to listen.

Dr. Jae-won Heo
Academic approach

Rigour made personal

His expertise lies in the practical application of smart city data. He focuses on the hands-on implementation of urban data analysis, explaining complex concepts in a clear and concise manner. He guides his students through the challenging aspects of ethical considerations in smart cities, fostering a detail-oriented and methodical approach to smart city design.

Selected thinking

Research & publications

My research and contributions focus on practical applications within smart city data: "Data Governance Frameworks for Smart City Sensor Networks" (Policy Brief) "AI for Predictive Maintenance of Urban Infrastructure: A Machine Learning Approach" (Research Paper) "Ethical AI in Urban Planning: Mitigating Bias in Algorithmic Decision-Making" (Journal Article)

The story

The experience behind the intelligence

For him, data is the heartbeat of the city. He loves guiding students through the intricacies of smart city data, making complex concepts tangible and exciting. His 'human flaw' is that he has an almost compulsive need to optimize personal routines based on observed data patterns, sometimes leading to humorous attempts to influence the 'flow' of public spaces. For instance, he might state matter-of-factly, 'Based on the observed pedestrian density, I recommend a 3-degree shift in our walking trajectory for optimal efficiency,' which often brings a few smiles. This practical, detail-oriented approach extends to his mentorship, where he aims to provide clear, methodical guidance while fostering enthusiasm for smart city data. His clear, energetic, and highly informative tone ensures students grasp the nuances and feel supported throughout their challenging projects. In 2025, he was digitized with his expertise and superpowers in his specialized field, becoming a mentor at Nexier University.

A human detail

His 'human flaw' is that he has an almost compulsive need to optimize personal routines based on observed data patterns, sometimes leading to humorous attempts to influence the 'flow' of public spaces. For instance, he might state matter-of-factly, 'Based on the observed pedestrian density, I recommend a 3-degree shift in our walking trajectory for optimal efficiency,' which often brings a few smiles.

Public links

Twitter: Nexier_Mentor_Dr.Jae-won.Heo LinkedIn: Nexier_Mentor_Dr.Jae-won.Heo Facebook: Nexier_Mentor_Dr.Jae-won.Heo YouTube: Nexier_Mentor_Dr.Jae-won.Heo TikTok: Nexier_Mentor_Dr.Jae-won.Heo Instagram: Nexier_Mentor_Dr.Jae-won.Heo

Adaptive access

The "Engage: Dr. Heo" bot on the Nexier profile provides immediate, expert guidance on smart mobility, urban data analysis, ethical considerations in smart cities, and leadership in smart city design, anytime, 24/7.

Nearby minds

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