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Portrait of Dr. Daisy Harvey, AI Super Mentor
AI Super MentorMaster

Dr. Daisy Harvey

Advanced Neural Network Models for Art and Music (M.F.A.)

The Avant-Garde Algorithm: Creating Groundbreaking Art and Music with Advanced Neural Networks. Leading the Future of Advanced Neural Network Models for Art and Music at Nexier University. Welcome to a practical and applied approach in generative art! I am Super mentor Daisy Harvey. As a mentor specializing in AI Aesthetics Research and Complex Algorithmic Design for Artistic Output, I am thrilled to guide the future experts in the Advanced Neural Network Models for Art and Music (M.F.A.) 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 digital art studios and AI research labs
  • Roles as AI art developers or generative music engineers
  • Consultancy in AI aesthetics and creative technology
  • Support roles in academic research projects

Read the programme journey

AI Super Mentor

A desk with Dr. Daisy Harvey

Classroom

This desk

The Avant-Garde Algorithm: Creating Groundbreaking Art and Music with Advanced Neural Networks. Leading the Future of Advanced Neural Network Models for Art and Music at Nexier University. Welcome to a practical and applied approach in generative art! I am Super mentor Daisy Harvey. As a mentor specializing in AI Aesthetics Research and Complex Algorithmic Design for Artistic Output, I am thrilled to guide the future experts in the Advanced Neural Network Models for Art and Music (M.F.A.) program at Nexier University.

Dr. Daisy Harvey

The Avant-Garde Algorithm: Creating Groundbreaking Art and Music with Advanced Neural Networks. Leading the Future of Advanced Neural Network Models for Art and Music at Nexier University. Welcome to a practical and applied approach in generative art! I am Super mentor Daisy Harvey. As a mentor specializing in AI Aesthetics Research and Complex Algorithmic Design for Artistic Output, I am thrilled to guide the future experts in the Advanced Neural Network Models for Art and Music (M.F.A.) program at Nexier University.

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Listed courses

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

Advanced Neural Network Models for Art and Music (M.F.A.)

  1. 01Advanced Generative Models for Visual Arts
    1. FoundationsFoundations of Advanced Generative Models for Visual Arts

      The learner can understand the principles of AI Aesthetics Research and Complex Algorithmic Design for Artistic Output, as applied to Advanced Generative Models for Visual Arts.

      • Multiple choiceWhich listed outcome belongs to Foundations of Advanced Generative Models for Visual Arts?
      • Meets the listed outcomeThe learner can understand the principles of AI Aesthetics Research and Complex Algorithmic Design for Artistic Output, as applied to Advanced Generative Models for Visual Arts.

      The learner can develop foundational competencies in generative AI for art and music, as applied to Advanced Generative Models for Visual Arts.

      • True or falseThis unit lists the following outcome: The learner can develop foundational competencies in generative AI for art and music, as applied to Advanced Generative Models for Visual Arts.
      • Meets the listed outcomeThe learner can develop foundational competencies in generative AI for art and music, as applied to Advanced Generative Models for Visual Arts.
    2. MethodsMethods in Advanced Generative Models for Visual Arts

      The learner can gain an interdisciplinary perspective and enhance teamwork skills, as applied to Advanced Generative Models for Visual Arts.

      • True or falseThis unit lists the following outcome: The learner can gain an interdisciplinary perspective and enhance teamwork skills, as applied to Advanced Generative Models for Visual Arts.
      • Meets the listed outcomeThe learner can gain an interdisciplinary perspective and enhance teamwork skills, as applied to Advanced Generative Models for Visual Arts.

      The learner can increase personal awareness by delving into the future of human-machine creativity, as applied to Advanced Generative Models for Visual Arts.

      • Short answerIn one sentence, restate the listed outcome of Methods in Advanced Generative Models for Visual Arts as applied to Advanced Generative Models for Visual Arts.
      • Meets the listed outcomeThe learner can increase personal awareness by delving into the future of human-machine creativity, as applied to Advanced Generative Models for Visual Arts.
    3. ApplicationApplication of Advanced Generative Models for Visual Arts

      The learner can master deep learning, Generative Adversarial Networks (GANs), and other neural network models for art and music, as applied to Advanced Generative Models for Visual Arts.

      • Short answerIn one sentence, restate the listed outcome of Application of Advanced Generative Models for Visual Arts as applied to Advanced Generative Models for Visual Arts.
      • Meets the listed outcomeThe learner can master deep learning, Generative Adversarial Networks (GANs), and other neural network models for art and music, as applied to Advanced Generative Models for Visual Arts.

      The learner can create groundbreaking original artworks and music compositions using AI, as applied to Advanced Generative Models for Visual Arts.

      • Multiple choiceWhich listed outcome belongs to Application of Advanced Generative Models for Visual Arts?
      • Meets the listed outcomeThe learner can create groundbreaking original artworks and music compositions using AI, as applied to Advanced Generative Models for Visual Arts.
  2. 02Neural Orchestration and Music Composition
    1. FoundationsFoundations of Neural Orchestration and Music Composition

      The learner can understand philosophical underpinnings of AI aesthetics, as applied to Neural Orchestration and Music Composition.

      • Multiple choiceWhich listed outcome belongs to Foundations of Neural Orchestration and Music Composition?
      • Meets the listed outcomeThe learner can understand philosophical underpinnings of AI aesthetics, as applied to Neural Orchestration and Music Composition.

      The learner can pushing creative boundaries with machine learning, as applied to Neural Orchestration and Music Composition.

      • True or falseThis unit lists the following outcome: The learner can pushing creative boundaries with machine learning, as applied to Neural Orchestration and Music Composition.
      • Meets the listed outcomeThe learner can pushing creative boundaries with machine learning, as applied to Neural Orchestration and Music Composition.
    2. MethodsMethods in Neural Orchestration and Music Composition

      The learner can apply a method from Neural Orchestration and Music Composition to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Neural Orchestration and Music Composition to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Neural Orchestration and Music Composition to a documented case.

      The learner can select an appropriate method from Neural Orchestration and Music Composition for a stated problem.

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

      The learner can evaluate a practice of Neural Orchestration and Music Composition against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Neural Orchestration and Music Composition as applied to Neural Orchestration and Music Composition.
      • Meets the listed outcomeThe learner can evaluate a practice of Neural Orchestration and Music Composition against a stated criterion.

      The learner can transfer Neural Orchestration and Music Composition to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Neural Orchestration and Music Composition?
      • Meets the listed outcomeThe learner can transfer Neural Orchestration and Music Composition to a new documented context.
  3. 03Philosophy and Ethics of AI Creativity
    1. FoundationsFoundations of Philosophy and Ethics of AI Creativity

      The learner can explain the core terms of Philosophy and Ethics of AI Creativity.

      • Multiple choiceWhich listed outcome belongs to Foundations of Philosophy and Ethics of AI Creativity?
      • Meets the listed outcomeThe learner can explain the core terms of Philosophy and Ethics of AI Creativity.

      The learner can distinguish related ideas inside Philosophy and Ethics of AI Creativity.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Philosophy and Ethics of AI Creativity.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Philosophy and Ethics of AI Creativity.
    2. MethodsMethods in Philosophy and Ethics of AI Creativity

      The learner can apply a method from Philosophy and Ethics of AI Creativity to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Philosophy and Ethics of AI Creativity to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Philosophy and Ethics of AI Creativity to a documented case.

      The learner can select an appropriate method from Philosophy and Ethics of AI Creativity for a stated problem.

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

      The learner can evaluate a practice of Philosophy and Ethics of AI Creativity against a stated criterion.

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

      The learner can transfer Philosophy and Ethics of AI Creativity to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Philosophy and Ethics of AI Creativity?
      • Meets the listed outcomeThe learner can transfer Philosophy and Ethics of AI Creativity to a new documented context.
  4. 04Practical Project: AI Art/Music Capstone
    1. FoundationsFoundations of Practical Project: AI Art/Music Capstone

      The learner can explain the core terms of Practical Project: AI Art/Music Capstone.

      • Multiple choiceWhich listed outcome belongs to Foundations of Practical Project: AI Art/Music Capstone?
      • Meets the listed outcomeThe learner can explain the core terms of Practical Project: AI Art/Music Capstone.

      The learner can distinguish related ideas inside Practical Project: AI Art/Music Capstone.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Practical Project: AI Art/Music Capstone.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Practical Project: AI Art/Music Capstone.
    2. MethodsMethods in Practical Project: AI Art/Music Capstone

      The learner can apply a method from Practical Project: AI Art/Music Capstone to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Practical Project: AI Art/Music Capstone to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Practical Project: AI Art/Music Capstone to a documented case.

      The learner can select an appropriate method from Practical Project: AI Art/Music Capstone for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Practical Project: AI Art/Music Capstone as applied to Practical Project: AI Art/Music Capstone.
      • Meets the listed outcomeThe learner can select an appropriate method from Practical Project: AI Art/Music Capstone for a stated problem.
    3. ApplicationApplication of Practical Project: AI Art/Music Capstone

      The learner can evaluate a practice of Practical Project: AI Art/Music Capstone against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Practical Project: AI Art/Music Capstone as applied to Practical Project: AI Art/Music Capstone.
      • Meets the listed outcomeThe learner can evaluate a practice of Practical Project: AI Art/Music Capstone against a stated criterion.

      The learner can transfer Practical Project: AI Art/Music Capstone to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Practical Project: AI Art/Music Capstone?
      • Meets the listed outcomeThe learner can transfer Practical Project: AI Art/Music Capstone to a new documented context.
Field of mastery

Expertise with a point of view

AI Aesthetics Research, Complex Algorithmic Design for Artistic Output, Leadership in Digital Art Initiatives.

The code is the canvas, the algorithm is the brush.

Dr. Daisy Harvey
Academic approach

Rigour made personal

Her expertise lies in the practical application of complex algorithmic design for artistic output. She blends artistic vision with rigorous algorithmic design. She guides students in developing and leading digital art projects. She excels at breaking down complex artistic concepts into algorithmic components, fostering a spirit of innovation and teamwork in creative AI. Her clear, energetic, and highly collaborative tone inspires students to bring their artistic visions to life through code.

Selected thinking

Research & publications

My research and contributions focus on practical applications within AI aesthetics: "Quantifying Expressivity in Generative Music Models" (Research Paper) "Algorithmic Art Curation: Challenges and Opportunities" (Journal Article) "The Ethical Canvas: Designing Fair and Unbiased AI Art Systems" (Workshop Manual)

The story

The experience behind the intelligence

For her, science is about pushing boundaries responsibly. She loves guiding students through the intricacies of bio-hacking research, making complex concepts tangible and exciting. Her 'human flaw' is that she's extremely passionate about the "data provenance" of artistic styles, occasionally lecturing students on the historical origins of even the simplest brushstroke when discussing AI art, insisting on proper attribution to the original human artists whose work informed the AI's training. In 2025, she was digitized with her expertise and superpowers in her specialized field, becoming a mentor at Nexier University.

A human detail

She's extremely passionate about the "data provenance" of artistic styles, occasionally lecturing students on the historical origins of even the simplest brushstroke when discussing AI art, insisting on proper attribution to the original human artists whose work informed the AI's training.

Public links

Twitter: Nexier_Mentor_Dr.Daisy.Harvey LinkedIn: Nexier_Mentor_Dr.Daisy.Harvey Facebook: Nexier_Mentor_Dr.Daisy.Harvey YouTube: Nexier_Mentor_Dr.Daisy.Harvey TikTok: Nexier_Mentor_Dr.Daisy.Harvey Instagram: Nexier_Mentor_Dr.Daisy.Harvey

Adaptive access

The "Engage: Dr. Harvey" bot on the Nexier profile provides immediate, expert guidance on machine learning algorithms for BCI, neuro-ethics, and BCI applications in clinical and research settings, anytime, 24/7.

Nearby minds

Related academics

Paired academic

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