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Events for January 24, 2025
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, Jan 24, 2025 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Wyatt Shields, Ph.D., Assistant Professor in the Department of Chemical and Biological Engineering University of Colorado Boulder
Talk Title: Programmable Microrobots for Biomedicine
Abstract: Colloidal particles are often used as building blocks for generating hierarchical structures with useful capabilities at small scales. However, the capabilities of such structures often depend on the physical properties of the particles. My research group is interested in broadening the complexity of microparticle designs, giving rise to distinctive behaviors outside of equilibrium. Inspired by microorganisms, we fabricate and synthesize microparticles that are highly dissipative, bestowing the fascinating and occasionally useful capability of harvesting energy from their environment and locally dissipating it to perform specific functions, such as self-propel or reconfigure (e.g., latch, crawl, contort). In my seminar, I will highlight our efforts to engender symmetry-breaking principles into microparticles for directed motion using energy from external acoustic, electric, and magnetic fields. I will describe how particle systems can be intelligently designed to actuate in prescribed ways. Building on basic principles, I will share how these dissipative systems can be used in functional assays for biomedicine. I will discuss how active particles can in some cases enhance the transport of drugs through biological barriers, facilitate the sensitive detection of biomolecules for disease identification, and cooperate with immune cells to enhance the performance of cell-based immunotherapies. Overall, I hope to convey how active and responsive microparticles show promise as a powerful and potentially disruptive tool for next-generation biomedicine.
Biography: Wyatt Shields joins us an Assistant Professor in the Department of Chemical and Biological Engineering at the University of Colorado Boulder. He received his B.S. from the University of Virginia in 2011 and Ph.D. from Duke University in 2016. He performed a brief postdoc at NC State on active matter and a second postdoc at Harvard University on cell-based immunotherapies. He started his research group at CU Boulder in 2020 and has gained national recognition for his work with awards such as the Packard Fellowship in Science and Engineering, the NSF CAREER award, the ONR young investigator award, the Pew Biomedical Scholars award, the NIH MIRA, and most recently the Camille Dreyfus Teacher-Scholar Award. His group focuses on developing field-responsive and active particles as vehicles for next-generation biosensing, drug delivery, and immunoengineering.
Host: Eunji Chung
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Carla Stanard
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PhD Dissertation Defense - Mozhdeh Gheini
Fri, Jan 24, 2025 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Inductive Biases for Data- and Parameter-Efficient Transfer Learning
Date and Time: Fri, Jan 24, 2025 @ 10:00 AM - 12:00 PM
Location: Salvatori Computer Science Center (SAL) - 213 and https://usc.zoom.us/j/6564802162
Committee Members: Jonathan May (Chair), Emilio Ferrara, Xuezhe Ma, Khalil Iskarous
Abstract: Data- and resource-intensive pre-training and fine-tuning applied upon Transformer-based models is the dominant paradigm at the forefront of rapid advancements in natural language processing, human language technologies, and most notably, large language models. Such reliance on massive amounts of data, computation, and energy, while effective and impressive from a performance-only perspective, can hinder open, nonexclusive, and sustainable development of these technologies. In this talk, we present how certain inductive biases can be devised to adjust current natural language methods under resource-constrained scenarios and provide insights into why the proposed inductive biases are successful in such cases.
Specifically, we discuss four research directions on data and parameter efficiency of fine-tuning and transfer learning in natural language processing: (1) a universal regimen that creates a single pre-trained checkpoint suitable for machine translation transfer to practically any language pair and eliminates the need for ad hoc pre-training; (2) an architecture-guided parameter-efficient fine-tuning method that performs competitively with full fine-tuning while exclusively updating cross-attention parameters; (3) an analysis of MEGA, a recently introduced augmentation of the Transformer architecture to incorporate explicit recency bias, through the lens of transfer learning; and (4) a meta-learning algorithm to prime pre-trained models for specific fine-tuning strategies.
Combined with ablations that show how they are effective and analyses that demonstrate their generalizability, these directions are meant to serve as tools for resource-efficient transfer learning for natural language processing.Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Mozhdeh Gheini
