This course introduces learners to the use of data science, digital technologies, and AI across public health and clinical care. The course covers public health informatics, AI applications across medical specialties, digital diagnostics, large language models in clinical practice, AI integration into clinical workflows, responsible and explainable AI, bias and governance, regulatory considerations, and precision medicine using genomic and clinical data. Emphasis is placed on evaluating the accuracy, safety, effectiveness, and implementation feasibility of digital and AI-enabled tools, particularly in resource-constrained settings.

Digital health is fundamentally reshaping the delivery, measurement, and improvement of care. We are currently witnessing a “perfect storm” of transformation in healthcare, driven by maturing technologies like AI and ubiquitous mobile connectivity, a global workforce crisis, and the lasting catalytic effects of the COVID-19 pandemic. Yet, a significant gap remains between potential and impact. This course exists to bridge that gap. It challenges students to go beyond merely using tools to critically evaluate them, mastering the “human factor” of implementation, and designing sustainable interventions that directly address real-world inequities in access and quality.

Transformer-based neural architectures have fundamentally transformed AI, enabling the development of LLM model families, such as ChatGPT, Gemini, and Claude. This graduate-level course delves into the design and deployment of LLM-based systems, with a focus on both technical foundations and societal impact. Topics include optimization algorithms, scalability, real-world applications, and use in domains like healthcare, education, and productivity. Ethical considerations are addressed, along with advanced topics such as robustness, domain adaptation, and reducing bias and hallucinations in model outputs. [Course Outline (2024)].

Why do some children escape poverty while equally talented others do not? Why do people steal electricity, spread misinformation, or believe conspiracy theories? And what happens when machines begin to read, write, and make decisions? CS233 examines these questions using the modern toolkit of computational social science: large-scale behavioral data, experimental and quasi-experimental methods for identifying causation, machine learning for prediction, and LLMs as both research tools and objects of study. Examples include misinformation, electricity theft, online markets, and health care in low-resource settings.

This course introduces the key principles and techniques of data science to prepare students for scientific work and advanced courses related to data analysis, machine learning and computational social science. It is Python-based hands-on course covering topics that span the entire data science life cycle including data collection, data exploration, visualization, statistical inference, causal inference, foundations of machine learning, and data ethics

Data structures are the key abstractions for organizing data in computer systems and thus form an essential building block for efficient algorithms. They are used in the design of a wide variety of applications today (e.g., cryptocurrencies, social networking, and big data processing frameworks). This course introduces the fundamentals of data structures and aims to provide a deep understanding of how different ways of structuring information in computer systems lead to different tradeoffs in terms of time and memory.

This is a graduate-level course on computer networking research. It involves lectures, paper readings, discussions, and a semester-long research project. Each year the course focuses on 4-5 hot topics in networking research. In the last edition, we covered the modules on (i) Edge Computing and Systems for Machine Learning, (ii) Cellular Networks, (iii) Web, Video Streaming, and ICT for Development, (iv) Internet Censorship & Privacy

A course that presents a critical view of the role of privacy in the modern age. It aims to provide a broad and varied understanding of economic (e.g., new business models), policy (e.g., balancing societal needs and individual rights), ethical, legal (e.g., data privacy regulations) and technical (e.g. privacy-enhancing technologies) perspectives about the role of privacy in the era of big data, cloud computing and AI.

This course deals with the nature, formulation, and analysis of probabilistic situations and introduces the fundamentals of probability with special emphasis on applications. It provides a rigorous understanding of probability concepts including random variables, expectations, joint distributions, limit theorems, and stochastic processes (including Markov Chains). In addition, it covers two applications areas where probability is extensively used: (a) queuing theory and (b) machine learning.

An introductory course on the foundations of large-scale networked systems. The first half of the course covers networking fundamentals, with the Internet as the case study. The second covers the fundamental concepts involved in designing scalable and fault tolerant distributed applications. It provides students with hands-on experience with applying the learnt concepts through multiple programming assignments.