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Data Science and Biology – Speaker Series

Data Science and Biology - Speaker Series Hosted by Diversity in Data Science & CELLebrate   Event Description Data Science and Biology - Speaker Series provides an exciting opportunity to hear from experienced professionals about various data science techniques with applications to the biological sciences. All undergraduate/graduate students, faculty, and staff interested in STEM are […]

Event Series Special Seminar Series

Towards the Statistically Principled Design of ML Algorithms | Frederic Koehler

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

What are the optimal algorithms for learning from data? Have we found them already, or are better ones out there to be discovered? Making these questions precise, and answering them, requires taking on the mathematically deep interplay between statistical and computational constraints. It also requires reconciling our theoretical toolbox with surprising new phenomena arising from practice, which seem to violate conventional rules of thumb regarding algorithm and model design. I will discuss progress along these lines: in terms of designing new algorithms for basic learning problems, controlling generalization in large statistical models, and understanding key statistical questions for generative modeling.

Event Series Special Seminar Series

Intelligent mobile systems for equitable healthcare

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Access to even basic medical resources is greatly influenced by factors like an individual's birth country and zip code. In this talk, I will present my work on designing AI-based mobile systems for equitable healthcare. I will showcase three systems that are not only interesting from an AI standpoint but are also having real-world medical impact. The first system can detect ear infections using only a smartphone and a paper cone. The second system enables low-cost newborn hearing screening using inexpensive earphones. Lastly, I will present an ambient sensing system that employs smart devices to detect emergent and life-threatening medical events such as cardiac arrest. Through these examples, I will demonstrate how new applied machine learning and sensing approaches that generalize across hardware and work in real-world environments can help to address pressing societal problems.

Event Series IPE Data Lunchtime Series

The Interplay of Technology, Ethics, and Policy

Public Engagement Building (PEB) 721 9625 Scholars Drive North MC 0305, La Jolla, CA, United States

Abstract: Technology is often designed and deployed without critical reflection of the values that it embodies. Value trade-offs—between security and privacy, free speech and dignity, autonomy and human agency, and different conceptions of fairness—abound in many technologies that are now achieving great scale in commonly used tech platforms. The decisions made by the people inside […]

Event Series IPE Data Lunchtime Series

Just Opt Out? Lessons Learned From a Decade of Evasion

Public Engagement Building (PEB) 721 9625 Scholars Drive North MC 0305, La Jolla, CA, United States

Abstract: With the rise of techlash, an increasing number of users wish they could just say no to data tracking, surveillance capitalism, and the socially divisive effects of creepy technologies in our daily lives. But can we truly walk away from these systems? And what do we learn when we do? In this talk, Vertesi tells […]

Scaling and Generalizing Approximate Bayesian Inference | David Blei

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this talk I review and discuss innovations in variational inference (VI), a method that approximates probability distributions through optimization. VI has been used in myriad applications in machine learning and Bayesian statistics.

Event Series IPE Data Lunchtime Series

Beyond ‘The Algorithm’: Fields, Drama, and Extreme Content Among Vegan Influencers

Public Engagement Building (PEB) 721 9625 Scholars Drive North MC 0305, La Jolla, CA, United States

Abstract: Existing research on polarization on social media platforms emphasizes the role of algorithmic "filter bubbles" and platform failure in amplifying extreme attitudes among online audiences. This article provides a different approach by focusing on online creators rather than audiences. Christin adapts field theory to examine the dynamics structuring exchanges between social media influencers, which […]

The Emergence of General AI for Medicine | Dr. Peter Lee

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Dr. Peter Lee is Corporate Vice President of Research and Incubations at Microsoft where he leads Microsoft Research and incubates new research-powered products and lines of business in areas such as artificial intelligence, computing foundations, health, and life sciences. He speaks and writes widely on science and technology trends, including the attached NEJM article “Benefits, Limits, […]

Event Series Distinguished Lecturer Series

Steampunk Data Science

How did scientists make sense of data before statistics and computing? This talk will explore this question by focusing on the discovery of vitamins, which occurred in the early 20th century just before the advent of modern statistical methodology. I will describe the varied practices in experimentation and reporting and highlight the sorts of insights required to uncover what "works." Through this discussion, I will draw connections to contemporary data science tools to illustrate their pros and cons in facilitating discovery.

Event Series Distinguished Lecturer Series

The Synergy between Machine Learning and the Natural Sciences | Max Welling

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Abstract: Traditionally machine learning has been heavily influenced by neuroscience (hence the name artificial neural networks) and physics (e.g. MCMC, Belief Propagation, and Diffusion based Generative AI). We have recently witnessed that the flow of information has also reversed, with new tools developed in the ML community impacting physics, chemistry and biology. Examples include faster DFT, Force-Field accelerated MD simulations, PDE Neural Surrogate models, generating druglike molecules, and many more. In this talk I will review the exciting opportunities for further cross fertilization between these fields, ranging from faster (classical) DFT calculations and enhanced transition path sampling to traveling waves in artificial neural networks.