Ages 21

Causality at the Intersection of Simulation, Inference, Science, and Learning: Post-talk Conversation

The sciences are replete with high-fidelity simulators: computational manifestations of causal, mechanistic models. Ironically, while these simulators provide our highest-fidelity physical models, they are not well suited for inferring properties of the model from data. Professor Kyle Cranmer of New York University will describe the emerging area of simulation-based inference and describe how machine learning is being brought to bear on these challenging problems.

The Neuroscience of Human Decisions: Mapping as Knowing Lecture Series Virtual Talk

Mariano Sigman is one of the most outstanding neuroscientists in the world, with over 150 publications in the most prestigious scientific journals. He is also passionate about experimentation and has worked with magicians, chess masters, musicians, athletes and visual artists to bring his knowledge of neuroscience to different aspects of human culture and apply it in different contexts. He has participated twice (2016 and 2017) in the TED global events in Vancouver, the second with Dan Ariely.

Inference Project Virtual Talk: "Bias in Algorithms"

Artificial algorithms are increasingly being deployed to inform, endorse, and govern various aspects of today’s society. Their reach includes the domains of hiring, lending, medicine, criminal justice, insurance, allocation of public services, social and business interactions, and the dissemination of information and news.

"I Prove a Theorem and the House Expands: Geometry and Poetry," Virtual Talk by Jordan Ellenberg

We are delighted that famed author and mathematician Jordan Ellenberg will be joining us for a virtual talk this semester. In his words, our speaker wrote a book about geometry and found himself, surprisingly, constantly talking about poetry.

Inference Project Talk and Discussion: The Inference of Nature: Cause and Effect in Molecular Biology

Theoretical approaches have always played an important role in biology, dating back to Mendel’s peas. In today’s era of genomics and big data in biology, statistical and computational tools are even more vital for biologists seeking to infer causation in living systems. To illustrate the range of methods, from modelling to machine learning, and how they contribute to understanding biological mechanisms, Dr. Teichmann will pick examples from some of the core problems her lab has been investigating as case studies.

Inference Project Talk: "No Cause for Concern: Indefinite Causal Ordering as a Tool for Understanding Entanglement"

Understanding the sorts of explanations and inferences that causal processes countenance is of course of great interest to philosophers and physicists (among others). But what can be said about physical processes that fail to exhibit classical causal structure? Indefinite causal ordering among events made possible by quantum correlations has become a fruitful arena of study recently, yielding new insights for quantum computing and communication, approaches to quantum gravity, and even for foundational issues in quantum mechanics.

Inference Project Talk: "No Cause for Concern: Indefinite Causal Ordering as a Tool for Understanding Entanglement"

Understanding the sorts of explanations and inferences that causal processes countenance is of course of great interest to philosophers and physicists (among others). But what can be said about physical processes that fail to exhibit classical causal structure? Indefinite causal ordering among events made possible by quantum correlations has become a fruitful arena of study recently, yielding new insights for quantum computing and communication, approaches to quantum gravity, and even for foundational issues in quantum mechanics.

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