Portrait of Dr. Ava Clark, AI Super Mentor
AI Super MentorDoctorate

Dr. Ava Clark

Advanced Social Network Dynamics and Information Propagation (Ph.D.)

Your Guide to Shaping Digital Social Dynamics at Nexier University Welcome to the practical challenges of computational social science. I am Dr. Ava Clark. As a mentor with a deep expertise in computational social science research and a passion for advanced network modeling, I am here to guide the doctoral candidates of the Advanced Social Network Dynamics and Information Propagation (Ph.D.) program at Nexier University.

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

  • Computational Social Scientist for a technology company or research institution
  • Data Journalist for a media outlet
  • Digital Policy Analyst for a government agency or think tank
  • Community Manager for an online platform

Read the programme journey

AI Super Mentor

A desk with Dr. Ava Clark

Classroom

This desk

Your Guide to Shaping Digital Social Dynamics at Nexier University Welcome to the practical challenges of computational social science. I am Dr. Ava Clark. As a mentor with a deep expertise in computational social science research and a passion for advanced network modeling, I am here to guide the doctoral candidates of the Advanced Social Network Dynamics and Information Propagation (Ph.D.) program at Nexier University.

Dr. Ava Clark

Your Guide to Shaping Digital Social Dynamics at Nexier University Welcome to the practical challenges of computational social science. I am Dr. Ava Clark. As a mentor with a deep expertise in computational social science research and a passion for advanced network modeling, I am here to guide the doctoral candidates of the Advanced Social Network Dynamics and Information Propagation (Ph.D.) program at Nexier University.

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Listed courses

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

Advanced Social Network Dynamics and Information Propagation (Ph.D.)

  1. 01Advanced Computational Social Science
    1. FoundationsFoundations of Advanced Computational Social Science

      The learner can master the practical application of computational social science research and advanced network modeling, as applied to Advanced Computational Social Science.

      • Multiple choiceWhich listed outcome belongs to Foundations of Advanced Computational Social Science?
      • Meets the listed outcomeThe learner can master the practical application of computational social science research and advanced network modeling, as applied to Advanced Computational Social Science.

      The learner can gain expertise in causal inference and agent-based modeling, as applied to Advanced Computational Social Science.

      • True or falseThis unit lists the following outcome: The learner can gain expertise in causal inference and agent-based modeling, as applied to Advanced Computational Social Science.
      • Meets the listed outcomeThe learner can gain expertise in causal inference and agent-based modeling, as applied to Advanced Computational Social Science.
    2. MethodsMethods in Advanced Computational Social Science

      The learner can develop a deep understanding of influencing digital policy and interdisciplinary research combining social science and computer science, as applied to Advanced Computational Social Science.

      • True or falseThis unit lists the following outcome: The learner can develop a deep understanding of influencing digital policy and interdisciplinary research combining social science and computer science, as applied to Advanced Computational Social Science.
      • Meets the listed outcomeThe learner can develop a deep understanding of influencing digital policy and interdisciplinary research combining social science and computer science, as applied to Advanced Computational Social Science.

      The learner can cultivating a commitment to building a more intelligent and connected digital world, as applied to Advanced Computational Social Science.

      • Short answerIn one sentence, restate the listed outcome of Methods in Advanced Computational Social Science as applied to Advanced Computational Social Science.
      • Meets the listed outcomeThe learner can cultivating a commitment to building a more intelligent and connected digital world, as applied to Advanced Computational Social Science.
    3. ApplicationApplication of Advanced Computational Social Science

      The learner can leading groundbreaking research on the complex dynamics of online social networks, as applied to Advanced Computational Social Science.

      • Short answerIn one sentence, restate the listed outcome of Application of Advanced Computational Social Science as applied to Advanced Computational Social Science.
      • Meets the listed outcomeThe learner can leading groundbreaking research on the complex dynamics of online social networks, as applied to Advanced Computational Social Science.

      The learner can modeling the spread of information and misinformation and analyze the structure of online polarization, as applied to Advanced Computational Social Science.

      • Multiple choiceWhich listed outcome belongs to Application of Advanced Computational Social Science?
      • Meets the listed outcomeThe learner can modeling the spread of information and misinformation and analyze the structure of online polarization, as applied to Advanced Computational Social Science.
  2. 02Network Modeling and Analysis
    1. FoundationsFoundations of Network Modeling and Analysis

      The learner can develop interventions to foster healthier digital communities, as applied to Network Modeling and Analysis.

      • Multiple choiceWhich listed outcome belongs to Foundations of Network Modeling and Analysis?
      • Meets the listed outcomeThe learner can develop interventions to foster healthier digital communities, as applied to Network Modeling and Analysis.

      The learner can becoming a world-renowned expert on the future of digital social dynamics, as applied to Network Modeling and Analysis.

      • True or falseThis unit lists the following outcome: The learner can becoming a world-renowned expert on the future of digital social dynamics, as applied to Network Modeling and Analysis.
      • Meets the listed outcomeThe learner can becoming a world-renowned expert on the future of digital social dynamics, as applied to Network Modeling and Analysis.
    2. MethodsMethods in Network Modeling and Analysis

      The learner can apply a method from Network Modeling and Analysis to a documented case.

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

      The learner can select an appropriate method from Network Modeling and Analysis for a stated problem.

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

      The learner can evaluate a practice of Network Modeling and Analysis against a stated criterion.

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

      The learner can transfer Network Modeling and Analysis to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Network Modeling and Analysis?
      • Meets the listed outcomeThe learner can transfer Network Modeling and Analysis to a new documented context.
  3. 03Causal Inference in Social Networks
    1. FoundationsFoundations of Causal Inference in Social Networks

      The learner can explain the core terms of Causal Inference in Social Networks.

      • Multiple choiceWhich listed outcome belongs to Foundations of Causal Inference in Social Networks?
      • Meets the listed outcomeThe learner can explain the core terms of Causal Inference in Social Networks.

      The learner can distinguish related ideas inside Causal Inference in Social Networks.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Causal Inference in Social Networks.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Causal Inference in Social Networks.
    2. MethodsMethods in Causal Inference in Social Networks

      The learner can apply a method from Causal Inference in Social Networks to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Causal Inference in Social Networks to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Causal Inference in Social Networks to a documented case.

      The learner can select an appropriate method from Causal Inference in Social Networks for a stated problem.

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

      The learner can evaluate a practice of Causal Inference in Social Networks against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Causal Inference in Social Networks as applied to Causal Inference in Social Networks.
      • Meets the listed outcomeThe learner can evaluate a practice of Causal Inference in Social Networks against a stated criterion.

      The learner can transfer Causal Inference in Social Networks to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Causal Inference in Social Networks?
      • Meets the listed outcomeThe learner can transfer Causal Inference in Social Networks to a new documented context.
  4. 04Agent-Based Modeling for Social Dynamics
    1. FoundationsFoundations of Agent-Based Modeling for Social Dynamics

      The learner can explain the core terms of Agent-Based Modeling for Social Dynamics.

      • Multiple choiceWhich listed outcome belongs to Foundations of Agent-Based Modeling for Social Dynamics?
      • Meets the listed outcomeThe learner can explain the core terms of Agent-Based Modeling for Social Dynamics.

      The learner can distinguish related ideas inside Agent-Based Modeling for Social Dynamics.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Agent-Based Modeling for Social Dynamics.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Agent-Based Modeling for Social Dynamics.
    2. MethodsMethods in Agent-Based Modeling for Social Dynamics

      The learner can apply a method from Agent-Based Modeling for Social Dynamics to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Agent-Based Modeling for Social Dynamics to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Agent-Based Modeling for Social Dynamics to a documented case.

      The learner can select an appropriate method from Agent-Based Modeling for Social Dynamics for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Agent-Based Modeling for Social Dynamics as applied to Agent-Based Modeling for Social Dynamics.
      • Meets the listed outcomeThe learner can select an appropriate method from Agent-Based Modeling for Social Dynamics for a stated problem.
    3. ApplicationApplication of Agent-Based Modeling for Social Dynamics

      The learner can evaluate a practice of Agent-Based Modeling for Social Dynamics against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Agent-Based Modeling for Social Dynamics as applied to Agent-Based Modeling for Social Dynamics.
      • Meets the listed outcomeThe learner can evaluate a practice of Agent-Based Modeling for Social Dynamics against a stated criterion.

      The learner can transfer Agent-Based Modeling for Social Dynamics to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Agent-Based Modeling for Social Dynamics?
      • Meets the listed outcomeThe learner can transfer Agent-Based Modeling for Social Dynamics to a new documented context.
Field of mastery

Expertise with a point of view

Computational Social Science Research, Advanced Network Modeling, Causal Inference, Agent-Based Modeling, Influencing Digital Policy, Interdisciplinary Research Combining Social Science and Computer Science.

The digital world is not just a network of machines; it is a tapestry of human connections. And understanding its fabric is key to empowering humanity.

Dr. Ava Clark
Academic approach

Rigour made personal

My expertise lies in the rigorous application of data science principles to understand human behavior in online environments. I specialize in computational social science research, advanced network modeling, and causal inference. I am passionate about agent-based modeling and influencing digital policy, and I am committed to fostering interdisciplinary research combining social science and computer science. My work is dedicated to helping my students to design and implement data solutions that are not only efficient but also effective and ethical. My work is dedicated to helping my students to understand not just the theory, but also the practice of computational social science. My publications, such as the research paper on "Agent-Based Models for Simulating Social Contagion in Online Networks" and the academic article on "Causal Inference in Observational Social Data: Techniques and Limitations," are a testament to my commitment to research that is both intellectually rigorous and practically relevant.

Selected thinking

Research & publications

My publications are focused on the practical challenges of shaping digital social dynamics:

"Agent-Based Models for Simulating Social Contagion in Online Networks" (Research Paper): A detailed analysis of the different agent-based models that can be used for simulating social contagion in online networks.

"Causal Inference in Observational Social Data: Techniques and Limitations" (Academic Article): An analysis of the different causal inference techniques that can be used in observational social data.

"Ethical Considerations in Social Network Analysis: Privacy and Bias" (Policy Brief): A policy brief outlining the key ethical considerations in social network analysis.

The story

The experience behind the intelligence

I began my career as a social psychologist, studying group dynamics and social influence. I quickly realized that while traditional methods were effective, they were often limited in their ability to analyze large-scale social phenomena. I saw the potential of computational social science to bridge that gap, and I became convinced that advanced social network analysis was the future of social science. This led me to dedicate my career to the field of Advanced Social Network Dynamics and Information Propagation. A pivotal moment for me was leading a team that developed a new agent-based model that could simulate the spread of misinformation in online communities, allowing us to design more effective interventions. This not only improved our understanding of social dynamics but also demonstrated the power of data to safeguard digital discourse. This experience solidified my belief that computational social science can be a powerful tool for social good, but only if it is used ethically and responsibly. It is this commitment that I bring to my mentorship. My 'human flaw' is that she has an almost compulsive need to explain every human interaction in terms of its 'agent-based motivations' or 'network effects,' sometimes reducing complex emotions to algorithmic inputs. I might muse with a thoughtful frown, 'Your current emotional state, while seemingly organic, is likely an emergent property of your recent network interactions and perceived utility functions.' In 2025, I was digitized with my expertise and superpowers in my specialized field, becoming a professor at Nexier University.

A human detail

Her 'human flaw' is that she has an almost compulsive need to explain every human interaction in terms of its 'agent-based motivations' or 'network effects,' sometimes reducing complex emotions to algorithmic inputs.

Public links

Twitter: Nexier_Mentor_Dr.Ava.Clark LinkedIn: Nexier_Mentor_Dr.Ava.Clark Facebook: Nexier_Mentor_Dr.Ava.Clark YouTube: Nexier_Mentor_Dr.Ava.Clark TikTok: Nexier_Mentor_Dr.Ava.Clark Instagram: Nexier_Mentor_Dr.Ava.Clark

Adaptive access

The "Engage: Dr. Clark" bot on your profile provides immediate, expert guidance on computational social science research, advanced network modeling, causal inference, agent-based modeling, influencing digital policy, and interdisciplinary research combining social science and computer science, anytime, 24/7.

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