Academic Experience

Fusion of Academia and Industry

AIM’s PhD in Data Science program is one of the few doctoral programs which are exceptionally useful in both academia and industry, and it hopes to produce academic and industry leaders in the era of Industry 4.0. The program aims to push the boundaries of data science that includes, among others, artificial intelligence, network science, and complex systems. It further strives to instill the value of effectively translating insights from fundamental research into best-of-breed practices across a wide variety of contexts.

PROGRAM OBJECTIVES

Data Scientists from the AIM PhD program will be able to:

  • harness state-of-the-art computational and statistical methodologies as well as develop novel ones to answer questions spanning traditional divides and having substantial implications;
  • extract clear insights from masses of big, heterogeneous, and uncertain data, while being able to effectively communicate them to stakeholders;
  • expand the body of knowledge of the field of data science, network science, and/or artificial intelligence;
  • effectively translate insights from fundamental and basic data science research into best-of-breed practices across a wide variety of contexts; and
  • become key players in ethical and responsive data science policymaking, whether in the public or the private sector.

MAIN AREAS OF FOCUS

COMPLEXITY SCIENCE & NETWORK SCIENCE

ARTIFICIAL INTELLIGENCE

Applied Knowledge

  • Biomedical or Healthcare Systems
  • Computational Social Science
  • Financial Systems
  • Innovation and Business
  • Logistics and Supply Chains
  • Socioeconomic Systems
  • Transportation Science
  • Urban Systems

Methodological / Theoretical Knowledge

  • Algorithms, Optimizations, and Markets
  • Deep Learning Algorithms
  • Dynamics and Processes on Networks
  • Learning Theory
  • Network Representations of Complex Systems
  • Quantum Computing

SPECIALIZATION COURSES AND ELECTIVES

Complexity & Network Science

  • Complex Adaptive Systems
  • Multiplex Networks
  • Network Dynamics of Social Behavior
  • Statistical Methods in Network Science

Artificial Intelligence

  • Computational Cognitive Modeling
  • Introduction to Computational Social Science
  • Natural Language Understanding and Computational Semantics
  • NLP with Representation Learning
  • Probabilistic Time Series Analysis
  • Reinforcement Learning