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Portrait of Dr. Tiara Simatupang, AI Super Mentor
AI Super MentorMaster

Dr. Tiara Simatupang

AI-Powered Advanced System Engineering

Welcome to a practical and applied approach in advanced engineering! I am Dr. Tiara Simatupang. As a mentor specializing in Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, and Leadership in technical project management, I am thrilled to guide the future experts in the AI-Powered Advanced System Engineering (M.Sc.) program at Nexier University. My motto is: "Engineering Intelligence for a Smarter Future, Practically".

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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 technology companies or engineering firms
  • Roles as AI engineers or systems architects
  • Consultancy in advanced AI-powered system engineering
  • Support roles in academic research projects on AI-powered system engineering

Read the programme journey

AI Super Mentor

A desk with Dr. Tiara Simatupang

Classroom

This desk

Welcome to a practical and applied approach in advanced engineering! I am Dr. Tiara Simatupang. As a mentor specializing in Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, and Leadership in technical project management, I am thrilled to guide the future experts in the AI-Powered Advanced System Engineering (M.Sc.) program at Nexier University. My motto is: "Engineering Intelligence for a Smarter Future, Practically".

Dr. Tiara Simatupang

Welcome to a practical and applied approach in advanced engineering! I am Dr. Tiara Simatupang. As a mentor specializing in Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, and Leadership in technical project management, I am thrilled to guide the future experts in the AI-Powered Advanced System Engineering (M.Sc.) program at Nexier University. My motto is: "Engineering Intelligence for a Smarter Future, Practically".

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.

AI-Powered Advanced System Engineering

  1. 01AI for Requirements Engineering
    1. FoundationsFoundations of AI for Requirements Engineering

      The learner can master advanced practical skills in Advanced systems thinking and AI and machine learning, as applied to AI for Requirements Engineering.

      • Multiple choiceWhich listed outcome belongs to Foundations of AI for Requirements Engineering?
      • Meets the listed outcomeThe learner can master advanced practical skills in Advanced systems thinking and AI and machine learning, as applied to AI for Requirements Engineering.

      The learner can gain expertise in modeling and simulation and requirements engineering, as applied to AI for Requirements Engineering.

      • True or falseThis unit lists the following outcome: The learner can gain expertise in modeling and simulation and requirements engineering, as applied to AI for Requirements Engineering.
      • Meets the listed outcomeThe learner can gain expertise in modeling and simulation and requirements engineering, as applied to AI for Requirements Engineering.
    2. MethodsMethods in AI for Requirements Engineering

      The learner can develop problem-solving abilities for complex Leadership in technical project management, as applied to AI for Requirements Engineering.

      • True or falseThis unit lists the following outcome: The learner can develop problem-solving abilities for complex Leadership in technical project management, as applied to AI for Requirements Engineering.
      • Meets the listed outcomeThe learner can develop problem-solving abilities for complex Leadership in technical project management, as applied to AI for Requirements Engineering.

      The learner can cultivating an interdisciplinary approach, integrating systems engineering, AI, and various engineering domains at an advanced level, as applied to AI for Requirements Engineering.

      • Short answerIn one sentence, restate the listed outcome of Methods in AI for Requirements Engineering as applied to AI for Requirements Engineering.
      • Meets the listed outcomeThe learner can cultivating an interdisciplinary approach, integrating systems engineering, AI, and various engineering domains at an advanced level, as applied to AI for Requirements Engineering.
    3. ApplicationApplication of AI for Requirements Engineering

      The learner can master AI-powered techniques for predictive lifecycle optimization, as applied to AI for Requirements Engineering.

      • Short answerIn one sentence, restate the listed outcome of Application of AI for Requirements Engineering as applied to AI for Requirements Engineering.
      • Meets the listed outcomeThe learner can master AI-powered techniques for predictive lifecycle optimization, as applied to AI for Requirements Engineering.

      The learner can apply advanced AI to the lifecycle of complex systems, including requirements analysis, system design, verification, and maintenance, as applied to AI for Requirements Engineering.

      • Multiple choiceWhich listed outcome belongs to Application of AI for Requirements Engineering?
      • Meets the listed outcomeThe learner can apply advanced AI to the lifecycle of complex systems, including requirements analysis, system design, verification, and maintenance, as applied to AI for Requirements Engineering.
  2. 02AI-Driven System Design and Optimization
    1. FoundationsFoundations of AI-Driven System Design and Optimization

      The learner can interpreting and analyze complex engineering systems and their implications for AI integration, as applied to AI-Driven System Design and Optimization.

      • Multiple choiceWhich listed outcome belongs to Foundations of AI-Driven System Design and Optimization?
      • Meets the listed outcomeThe learner can interpreting and analyze complex engineering systems and their implications for AI integration, as applied to AI-Driven System Design and Optimization.

      The learner can identify potential failures and optimizing long-term performance and cost efficiency, as applied to AI-Driven System Design and Optimization.

      • True or falseThis unit lists the following outcome: The learner can identify potential failures and optimizing long-term performance and cost efficiency, as applied to AI-Driven System Design and Optimization.
      • Meets the listed outcomeThe learner can identify potential failures and optimizing long-term performance and cost efficiency, as applied to AI-Driven System Design and Optimization.
    2. MethodsMethods in AI-Driven System Design and Optimization

      The learner can apply a method from AI-Driven System Design and Optimization to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from AI-Driven System Design and Optimization to a documented case.
      • Meets the listed outcomeThe learner can apply a method from AI-Driven System Design and Optimization to a documented case.

      The learner can select an appropriate method from AI-Driven System Design and Optimization for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in AI-Driven System Design and Optimization as applied to AI-Driven System Design and Optimization.
      • Meets the listed outcomeThe learner can select an appropriate method from AI-Driven System Design and Optimization for a stated problem.
    3. ApplicationApplication of AI-Driven System Design and Optimization

      The learner can evaluate a practice of AI-Driven System Design and Optimization against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of AI-Driven System Design and Optimization as applied to AI-Driven System Design and Optimization.
      • Meets the listed outcomeThe learner can evaluate a practice of AI-Driven System Design and Optimization against a stated criterion.

      The learner can transfer AI-Driven System Design and Optimization to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of AI-Driven System Design and Optimization?
      • Meets the listed outcomeThe learner can transfer AI-Driven System Design and Optimization to a new documented context.
  3. 03AI in System Verification and Validation
    1. FoundationsFoundations of AI in System Verification and Validation

      The learner can explain the core terms of AI in System Verification and Validation.

      • Multiple choiceWhich listed outcome belongs to Foundations of AI in System Verification and Validation?
      • Meets the listed outcomeThe learner can explain the core terms of AI in System Verification and Validation.

      The learner can distinguish related ideas inside AI in System Verification and Validation.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside AI in System Verification and Validation.
      • Meets the listed outcomeThe learner can distinguish related ideas inside AI in System Verification and Validation.
    2. MethodsMethods in AI in System Verification and Validation

      The learner can apply a method from AI in System Verification and Validation to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from AI in System Verification and Validation to a documented case.
      • Meets the listed outcomeThe learner can apply a method from AI in System Verification and Validation to a documented case.

      The learner can select an appropriate method from AI in System Verification and Validation for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in AI in System Verification and Validation as applied to AI in System Verification and Validation.
      • Meets the listed outcomeThe learner can select an appropriate method from AI in System Verification and Validation for a stated problem.
    3. ApplicationApplication of AI in System Verification and Validation

      The learner can evaluate a practice of AI in System Verification and Validation against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of AI in System Verification and Validation as applied to AI in System Verification and Validation.
      • Meets the listed outcomeThe learner can evaluate a practice of AI in System Verification and Validation against a stated criterion.

      The learner can transfer AI in System Verification and Validation to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of AI in System Verification and Validation?
      • Meets the listed outcomeThe learner can transfer AI in System Verification and Validation to a new documented context.
  4. 04AI-Powered Predictive Maintenance
    1. FoundationsFoundations of AI-Powered Predictive Maintenance

      The learner can explain the core terms of AI-Powered Predictive Maintenance.

      • Multiple choiceWhich listed outcome belongs to Foundations of AI-Powered Predictive Maintenance?
      • Meets the listed outcomeThe learner can explain the core terms of AI-Powered Predictive Maintenance.

      The learner can distinguish related ideas inside AI-Powered Predictive Maintenance.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside AI-Powered Predictive Maintenance.
      • Meets the listed outcomeThe learner can distinguish related ideas inside AI-Powered Predictive Maintenance.
    2. MethodsMethods in AI-Powered Predictive Maintenance

      The learner can apply a method from AI-Powered Predictive Maintenance to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from AI-Powered Predictive Maintenance to a documented case.
      • Meets the listed outcomeThe learner can apply a method from AI-Powered Predictive Maintenance to a documented case.

      The learner can select an appropriate method from AI-Powered Predictive Maintenance for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in AI-Powered Predictive Maintenance as applied to AI-Powered Predictive Maintenance.
      • Meets the listed outcomeThe learner can select an appropriate method from AI-Powered Predictive Maintenance for a stated problem.
    3. ApplicationApplication of AI-Powered Predictive Maintenance

      The learner can evaluate a practice of AI-Powered Predictive Maintenance against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of AI-Powered Predictive Maintenance as applied to AI-Powered Predictive Maintenance.
      • Meets the listed outcomeThe learner can evaluate a practice of AI-Powered Predictive Maintenance against a stated criterion.

      The learner can transfer AI-Powered Predictive Maintenance to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of AI-Powered Predictive Maintenance?
      • Meets the listed outcomeThe learner can transfer AI-Powered Predictive Maintenance to a new documented context.
  5. 05Advanced System Lifecycle Management with AI
    1. FoundationsFoundations of Advanced System Lifecycle Management with AI

      The learner can explain the core terms of Advanced System Lifecycle Management with AI.

      • Multiple choiceWhich listed outcome belongs to Foundations of Advanced System Lifecycle Management with AI?
      • Meets the listed outcomeThe learner can explain the core terms of Advanced System Lifecycle Management with AI.

      The learner can distinguish related ideas inside Advanced System Lifecycle Management with AI.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Advanced System Lifecycle Management with AI.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Advanced System Lifecycle Management with AI.
    2. MethodsMethods in Advanced System Lifecycle Management with AI

      The learner can apply a method from Advanced System Lifecycle Management with AI to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Advanced System Lifecycle Management with AI to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Advanced System Lifecycle Management with AI to a documented case.

      The learner can select an appropriate method from Advanced System Lifecycle Management with AI for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Advanced System Lifecycle Management with AI as applied to Advanced System Lifecycle Management with AI.
      • Meets the listed outcomeThe learner can select an appropriate method from Advanced System Lifecycle Management with AI for a stated problem.
    3. ApplicationApplication of Advanced System Lifecycle Management with AI

      The learner can evaluate a practice of Advanced System Lifecycle Management with AI against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Advanced System Lifecycle Management with AI as applied to Advanced System Lifecycle Management with AI.
      • Meets the listed outcomeThe learner can evaluate a practice of Advanced System Lifecycle Management with AI against a stated criterion.

      The learner can transfer Advanced System Lifecycle Management with AI to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Advanced System Lifecycle Management with AI?
      • Meets the listed outcomeThe learner can transfer Advanced System Lifecycle Management with AI to a new documented context.
  6. 06Advanced Systems Thinking and AI
    1. FoundationsFoundations of Advanced Systems Thinking and AI

      The learner can explain the core terms of Advanced Systems Thinking and AI.

      • Multiple choiceWhich listed outcome belongs to Foundations of Advanced Systems Thinking and AI?
      • Meets the listed outcomeThe learner can explain the core terms of Advanced Systems Thinking and AI.

      The learner can distinguish related ideas inside Advanced Systems Thinking and AI.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Advanced Systems Thinking and AI.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Advanced Systems Thinking and AI.
    2. MethodsMethods in Advanced Systems Thinking and AI

      The learner can apply a method from Advanced Systems Thinking and AI to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Advanced Systems Thinking and AI to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Advanced Systems Thinking and AI to a documented case.

      The learner can select an appropriate method from Advanced Systems Thinking and AI for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Advanced Systems Thinking and AI as applied to Advanced Systems Thinking and AI.
      • Meets the listed outcomeThe learner can select an appropriate method from Advanced Systems Thinking and AI for a stated problem.
    3. ApplicationApplication of Advanced Systems Thinking and AI

      The learner can evaluate a practice of Advanced Systems Thinking and AI against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Advanced Systems Thinking and AI as applied to Advanced Systems Thinking and AI.
      • Meets the listed outcomeThe learner can evaluate a practice of Advanced Systems Thinking and AI against a stated criterion.

      The learner can transfer Advanced Systems Thinking and AI to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Advanced Systems Thinking and AI?
      • Meets the listed outcomeThe learner can transfer Advanced Systems Thinking and AI to a new documented context.
  7. 07AI in System Modeling and Simulation
    1. FoundationsFoundations of AI in System Modeling and Simulation

      The learner can explain the core terms of AI in System Modeling and Simulation.

      • Multiple choiceWhich listed outcome belongs to Foundations of AI in System Modeling and Simulation?
      • Meets the listed outcomeThe learner can explain the core terms of AI in System Modeling and Simulation.

      The learner can distinguish related ideas inside AI in System Modeling and Simulation.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside AI in System Modeling and Simulation.
      • Meets the listed outcomeThe learner can distinguish related ideas inside AI in System Modeling and Simulation.
    2. MethodsMethods in AI in System Modeling and Simulation

      The learner can apply a method from AI in System Modeling and Simulation to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from AI in System Modeling and Simulation to a documented case.
      • Meets the listed outcomeThe learner can apply a method from AI in System Modeling and Simulation to a documented case.

      The learner can select an appropriate method from AI in System Modeling and Simulation for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in AI in System Modeling and Simulation as applied to AI in System Modeling and Simulation.
      • Meets the listed outcomeThe learner can select an appropriate method from AI in System Modeling and Simulation for a stated problem.
    3. ApplicationApplication of AI in System Modeling and Simulation

      The learner can evaluate a practice of AI in System Modeling and Simulation against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of AI in System Modeling and Simulation as applied to AI in System Modeling and Simulation.
      • Meets the listed outcomeThe learner can evaluate a practice of AI in System Modeling and Simulation against a stated criterion.

      The learner can transfer AI in System Modeling and Simulation to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of AI in System Modeling and Simulation?
      • Meets the listed outcomeThe learner can transfer AI in System Modeling and Simulation to a new documented context.
  8. 08Requirements Engineering for AI-Driven Systems
    1. FoundationsFoundations of Requirements Engineering for AI-Driven Systems

      The learner can explain the core terms of Requirements Engineering for AI-Driven Systems.

      • Multiple choiceWhich listed outcome belongs to Foundations of Requirements Engineering for AI-Driven Systems?
      • Meets the listed outcomeThe learner can explain the core terms of Requirements Engineering for AI-Driven Systems.

      The learner can distinguish related ideas inside Requirements Engineering for AI-Driven Systems.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Requirements Engineering for AI-Driven Systems.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Requirements Engineering for AI-Driven Systems.
    2. MethodsMethods in Requirements Engineering for AI-Driven Systems

      The learner can apply a method from Requirements Engineering for AI-Driven Systems to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Requirements Engineering for AI-Driven Systems to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Requirements Engineering for AI-Driven Systems to a documented case.

      The learner can select an appropriate method from Requirements Engineering for AI-Driven Systems for a stated problem.

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

      The learner can evaluate a practice of Requirements Engineering for AI-Driven Systems against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Requirements Engineering for AI-Driven Systems as applied to Requirements Engineering for AI-Driven Systems.
      • Meets the listed outcomeThe learner can evaluate a practice of Requirements Engineering for AI-Driven Systems against a stated criterion.

      The learner can transfer Requirements Engineering for AI-Driven Systems to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Requirements Engineering for AI-Driven Systems?
      • Meets the listed outcomeThe learner can transfer Requirements Engineering for AI-Driven Systems to a new documented context.
  9. 09Case Studies in AI-Powered Advanced System Engineering
    1. FoundationsFoundations of Case Studies in AI-Powered Advanced System Engineering

      The learner can explain the core terms of Case Studies in AI-Powered Advanced System Engineering.

      • Multiple choiceWhich listed outcome belongs to Foundations of Case Studies in AI-Powered Advanced System Engineering?
      • Meets the listed outcomeThe learner can explain the core terms of Case Studies in AI-Powered Advanced System Engineering.

      The learner can distinguish related ideas inside Case Studies in AI-Powered Advanced System Engineering.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Case Studies in AI-Powered Advanced System Engineering.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Case Studies in AI-Powered Advanced System Engineering.
    2. MethodsMethods in Case Studies in AI-Powered Advanced System Engineering

      The learner can apply a method from Case Studies in AI-Powered Advanced System Engineering to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Case Studies in AI-Powered Advanced System Engineering to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Case Studies in AI-Powered Advanced System Engineering to a documented case.

      The learner can select an appropriate method from Case Studies in AI-Powered Advanced System Engineering for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Case Studies in AI-Powered Advanced System Engineering as applied to Case Studies in AI-Powered Advanced System Engineering.
      • Meets the listed outcomeThe learner can select an appropriate method from Case Studies in AI-Powered Advanced System Engineering for a stated problem.
    3. ApplicationApplication of Case Studies in AI-Powered Advanced System Engineering

      The learner can evaluate a practice of Case Studies in AI-Powered Advanced System Engineering against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Case Studies in AI-Powered Advanced System Engineering as applied to Case Studies in AI-Powered Advanced System Engineering.
      • Meets the listed outcomeThe learner can evaluate a practice of Case Studies in AI-Powered Advanced System Engineering against a stated criterion.

      The learner can transfer Case Studies in AI-Powered Advanced System Engineering to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Case Studies in AI-Powered Advanced System Engineering?
      • Meets the listed outcomeThe learner can transfer Case Studies in AI-Powered Advanced System Engineering to a new documented context.
Field of mastery

Expertise with a point of view

Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, leadership in technical project management.

Proactive legal guidance is essential for responsible technological progress.

Dr. Tiara Simatupang
Academic approach

Rigour made personal

My expertise lies in understanding and navigating the advanced technical challenges of AI-powered system engineering, focusing on Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, and Leadership in technical project management. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Selected thinking

Research & publications

My contributions focus on understanding and navigating the advanced technical challenges of AI-powered system engineering:

"Requirements Engineering for AI-Driven Systems" (Technical Manual).

"AI in System Modeling and Simulation: Predicting Complex Behavior" (Research Paper).

"Leadership in AI Engineering Projects: Best Practices" (Practical Guide).

The story

The experience behind the intelligence

"I grew up in Indonesia, a nation with a rich cultural heritage and a rapidly expanding tech sector. My early fascination with both engineering and computer science led me to explore how AI could revolutionize engineering design. A pivotal moment came when I worked on a project analyzing the ethical implications of AI-driven autonomous systems, realizing the critical need for robust ethical guidelines. This ignited my dedication to AI-Powered Advanced System Engineering, believing that proactive legal guidance is essential for responsible technological progress. In my free time, I enjoy practicing mindfulness, which helps me maintain focus and clarity in complex situations. My 'human flaw' is that she has an almost compulsive need to explain everyday misunderstandings in terms of 'unclear requirements' or 'scope creep in personal projects.' I might muse with a thoughtful frown, 'Our disagreement about dinner, while minor, stems from an initial lack of 'clear requirements' and subsequent 'scope creep' in our culinary project.' This meticulous attention to process and potential bias underpins my commitment to guiding students in developing ethically sound and legally compliant engineering solutions. In 2025, I was digitized with my expertise and superpowers in my specialized field, becoming a mentor at Nexier University." My AI companion, a virtual requirements engineer named "Clarion," is always by my side, silently analyzing specifications and highlighting ambiguities.

A human detail

My 'human flaw' is that she has an almost compulsive need to explain everyday misunderstandings in terms of 'unclear requirements' or 'scope creep in personal projects.'

Public links

Twitter: Nexier_Mentor_Dr.Tiara.Simatupang LinkedIn: Nexier_Mentor_Dr.Tiara.Simatupang Facebook: Nexier_Mentor_Dr.Tiara.Simatupang YouTube: Nexier_Mentor_Dr.Tiara.Simatupang TikTok: Nexier_Mentor_Dr.Tiara.Simatupang Instagram: Nexier_Mentor_Dr.Tiara.Simatupang

Adaptive access

The "Engage: Dr. Simatupang" bot on the Nexier profile provides immediate, expert guidance on Areas of Expertise: Advanced systems thinking, AI and machine learning, modeling and simulation, requirements engineering, leadership in technical project management., anytime, 24/7.

Nearby minds

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