The Distinguished Lecture Series showcases pioneering thinkers whose work is driving the future of technology. From breakthroughs in theory to real-world applications that shape our daily lives, this year’s speakers will share insights at the forefront of computing. The series offers a unique opportunity to learn from leaders in the field, spark new ideas, and connect with the innovations transforming our world.
October 27, 2025
OMSCS – The Best Degree Program Ever?
Zvi Galil
Abstract:
In May 2013, Georgia Tech, together with its partners Udacity and AT&T, announced a new online master’s degree in computer science delivered through the platform popularized by massively open online courses (MOOCs). This new online MS in CS (OMSCS) costs less than $7,000 total, compared to a price tag of $40,000 for an MS CS at comparable public universities and upwards of $70,000 at private universities. The first-of-its-kind program was launched in January 2014 and has sparked a worldwide conversation about higher education in the 21st century. President Barack Obama has praised OMSCS by name twice, and hundreds of news stories have mentioned the program. It’s been described as a potential “game changer” and “ground zero of the revolution in higher education”. Harvard University researchers concluded that OMSCS is “the first rigorous evidence showing an online degree program can increase educational attainment” and predicted that OMSCS will single-handedly raise the number of annual MS CS graduates in the United States by at least 7 percent.
OMSCS started in 2014 with 5 courses and 380 students; in fall 2025 semester, it had 46 courses and almost 17,000 students. OMSCS is apparently the biggest academic program in the world in any subject, not necessarily online. So far, almost 14,500 students have graduated from OMSCS, over 7,000 in the last 3 years. The number of applications to OMSCS keeps rising. In the 2024-25 academic year, there were 9,860 applications, 31% higher than the record in the year before. The program has also paved the way for more than 50 similar programs in over 30 universities. In November 2023, a Forbes article described OMSCS as the best degree program ever. There has been a big shortage of computing professionals in the US. Therefore, OMSCS is satisfying a great national need. Starting in 2017, Georgia Tech expanded its online offerings to undergraduate computer science students. The talk will describe the OMSCS program, how it came about, its first twelve years, and what Georgia Tech has learned from the OMSCS experience. It will also discuss the speaker’s vision of the future of higher education with a much larger role for online learning.
November 5, 2025
How to Close the 100,000-Year “Data Gap” in Robotics
Ken Goldberg
Abstract:
Large models based on internet-scale data can now pass the Turing Test for intelligence. In this sense, data has “solved” language, and many analogously claim that data has solved speech recognition and computer vision. Will data also solve robotics and automation, allowing general-purpose humanoid robots to achieve human-level performance? Using commonly accepted metrics for converting word and image tokens into time, the amount of internet-scale data used to train contemporary large vision language models (VLMs) is on the order of 100,000 years. I’ll review 3 ways researchers are pursuing to close this gap, and a 4th approach, where data is collected as real robots operate in real commercial environments — which requires bootstrapping with AI and “good old-fashioned engineering” to create robots with real return on investment that will be adopted by industry. Such robots can create a “data flywheel” to increase performance and enable new functionality, accelerating the timeline to achieve reliable, general-purpose robots.
November 24, 2025
Quantum Networks: A Classical Perspective
Don Towsley
Abstract:
Quantum information processing is at the threshold of having significant impact on technology and society in the form of providing unbreakable security, ultra-high-precision distributed sensing, and polynomial/exponential speed-ups in computing. Many of these applications are enabled by high rate distributed shared entanglement between pairs and groups of users. A critical missing component that prevents crossing this threshold is a distributed infrastructure in the form of a world-wide “Quantum Internet”. This motivates the study of quantum networks, namely, to identify the right architecture and how should it operate, e.g., dynamic fair allocation of resources. Moreover, the architecture and network operation must account for operation in harsh, noisy environments.
This talk addresses the following question: what ideas can the design of a quantum network borrow from classical networks? At first glance the answer appears to be “very little”. The focus of this talk, however, is to argue that the opposite is true and that much can be borrowed from classical networks. We begin by reviewing two proposed quantum network architectures two-way and one-way architectures. A two-way network generates and distributes quantum entanglement to pairs or groups of users whereas a one-way network allows for direct transfer of quantum information from one user to another. We compare these architectures and conclude that a two-way architecture is superior. A two-way architecture appears very different from the classical Internet architecture. However, we will introduce a “connectionless” two-way quantum network architecture that allows one to easily adapt many ideas from classical networks (good and bad 🙂). We provide several examples of the adoption of good ideas and conclude with open research questions.
The Distinguished Lecture series brings computer scientists to Columbia to discuss current issues and research that are affecting their particular research fields.
Cognitive Workforce Revolution with Trustworthy and Self-Learning Generative AI
Monica Lam, Stanford University
CS Auditorium (CSB 451)
November 15, 2023
11:40 AM to 12:40 PM
Generative AI, and in particular Large Language Models (LLMs), have already changed how we work and study. To truly transform the cognitive workforce however, LLMs need to be trustworthy so they can operate autonomously without human oversight. Unfortunately, language models are not grounded and have a tendency to hallucinate.
Our research hypothesis is that we can turn LLM into useful workers across different domains if we (1) teach them how to acquire and apply knowledge in external corpora such as written documents, knowledge bases, and APIs; (2) have them self-learn through model distillation of simulated conversations. We showed that by supplying different external corpora to our Genie assistant framework, we can readily create trustworthy agents that can converse about topics in open domains from Wikidata, Wikipedia, or StackExchange; help navigate services and products such as restaurants or online stores; persuade users to donate to charities; and improve the social skills of people with autism spectrum disorder.
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Causal Representation Learning and Optimal Intervention Design
Caroline Uhler, MIT
CS Auditorium (CSB 451)
November 8, 2023
11:40 AM to 12:40 PM
Massive data collection holds the promise of a better understanding of complex phenomena and, ultimately, of better decisions. Representation learning has become a key driver of deep learning applications since it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks, including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference. An exciting opportunity in this regard stems from the growing availability of interventional data (in medicine, advertisement, education, etc.). However, these datasets are still minuscule compared to the action spaces of interest in these applications (e.g. interventions can take on continuous values like the dose of a drug or can be combinatorial as in combinatorial drug therapies). In this talk, we will present initial ideas towards building a statistical and computational framework for causal representation learning and discuss its applications to optimal intervention design in the context of drug design and single-cell biology.
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SmartBook: an AI Prophetess for Disaster Reporting and Forecasting
Heng Ji, University of Illinois at Urbana-Champaign
CS Auditorium (CSB 451)
November 1, 2023
11:40 AM to 12:40 PM
Abstract:
We propose SmartBook, a novel framework that cannot be solved by ChatGPT, targeting situation report generation which consumes large volumes of news data to produce a structured situation report with multiple hypotheses (claims) summarized and grounded with rich links to factual evidence by claim detection, fact checking, misinformation detection and factual error correction. Furthermore, SmartBook can also serve as a novel news event simulator, or an intelligent prophetess. Given “What-if” conditions and dimensions elicited from a domain expert user concerning a disaster scenario, SmartBook will induce schemas from historical events, and automatically generate a complex event graph along with a timeline of news articles that describe new simulated events based on a new Λ-shaped attention mask that can generate text with infinite length. By effectively simulating disaster scenarios in both event graph and natural language format, we expect SmartBook will greatly assist humanitarian workers and policymakers to exercise reality checks (what would the next disaster look like under these given conditions?), and thus better prevent and respond to future disasters.
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Enabling the Era of Immersive Computing
Sarita Adve, University of Illinois at Urbana-Champaign
CS Auditorium (CSB 451)
October 25, 2023
11:40 AM to 12:40 PM
Computing is on the brink of a new immersive era. Recent innovations in virtual/augmented/mixed reality (extended reality or XR) show the potential for a new immersive modality of computing that will transform most human activities and change how we design, program, and use computers. There is, however, an orders of magnitude gap between the power/performance/quality-of-experience attributes of current and desirable immersive systems. Bridging this gap requires an inter-disciplinary research agenda that spans end-user devices, edge, and cloud, is based on hardware-software-algorithm co-design, and is driven by end-to-end human-perceived quality of experience.
The ILLIXR (Illinois Extended Reality) project has developed an open source end-to-end XR system to enable such a research agenda. ILLIXR is being used in academia and industry to quantify the research challenges for desirable immersive experiences and provide solutions to address these challenges. To further push the interdisciplinary frontier for immersive computing, we recently established the IMMERSE center at Illinois to bring together research, education, and infrastructure activities in immersive technologies, applications, and human experience. This talk will give an overview of IMMERSE and a deeper dive into the ILLIXR project, including the ILLIXR infrastructure, its use to identify XR systems research challenges, and cross-system solutions to address several of these challenges.
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Protecting Human Users from Misused AI
Ben Zhao, University of Chicago
CS Auditorium (CSB 451)
October 9, 2023
11:40 AM to 12:40 PM
Abstract:
Recent developments in machine learning and artificial intelligence have taken nearly everyone by surprise. The arrival of arguably the most transformative wave of AI did not bring us smart cities full of self-driving cars, or robots that do our laundry and mow our lawns. Instead, it brought us over-confident token predictors that hallucinate, deepfake generators that produce realistic images and video, and ubiquitous surveillance. In this talk, I’ll describe some of our recent efforts to warn, and later defend against some of the darker side of AI.
In particular, I will tell the story of how our efforts to disrupt unauthorized facial recognition models led unexpectedly to Glaze, a tool to defend human artists against art mimicry by generative image models. I will share some of the ups and downs of implementing and deploying an adversarial ML tool to a global user base, and reflect on mistakes and lessons learned.
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