This talk introduces Emerald, a novel MIT technology for in-home non-intrusive patient monitoring. The Emerald device is a WiFi-like box that runs customized machine learning algorithms to learn digital biomarkers from the wireless signals in the patient's home. It can remotely monitor the patient’s gait speed, falls, respiratory signal, heart rate, and sleep quality and stages. The sensing is completely passive – i.e., the patient can go about her normal life without having to wear any sensors on her body, write a diary, or actively measure herself. This talk will discuss the technology and the results from pilot studies in various therapeutic areas.
Principal Investigator Themistoklis Sapsis
The biopharma industry has been continuously growing and progressing with digital transformation. The Symposium will highlight the Current Status and Promise of mRNA Biotherapeutics and introduce Innovation in mRNA Biotherapeutics Manufacturing at MIT.
Join us at the 2024 MIT Seoul Life Science Symposium, organized by KoreaBio (Korea Biotechnology Industry Organization) and MIT ILP. The symposium will also bring you closer to the MIT Life Science Innovation Ecosystem and allow you to explore global trends in MIT’s innovative Drug Discovery and Manufacturing research and experiments from leading MIT faculty and MIT-connected startups.
Architected materials—i.e., materials whose three-dimensional (3D) micro- or nanostructure has been engineered to attain a specific purpose—are ubiquitous in nature and have enabled properties that are unachievable by all other existing materials. Their concept relies on maximizing performance while requiring a minimal amount of material. Several human-made 3D architected materials have been reported to enable novel mechanical properties such as high stiffness-to-weight ratios or extreme resilience, especially when nanoscale features present. However, most architected materials have relied on advanced additive manufacturing techniques that are not yet scalable and yield small sample sizes. Additionally, most of these nano- and micro-architected materials have only been studied in controlled laboratory conditions, while our understanding of their performance in real-world applications requires attention.
In this talk, we will explain the concept of architected materials, providing various examples that we routinely fabricate and test in our laboratory at MIT, and we will discuss how nanoscale features significantly enhance their performance. We will also discuss ongoing research directions that will not only allow us to scale-up their fabrication, but also understand how they perform in realistic conditions outside the laboratory—towards contributing to more efficient material solutions in industry and beyond.
Principal Investigator Zachary Hartwig
Principal Investigator Richard Temkin
This talk introduces a new generation of machine learning methods that provide state of the art performance and are very interpretable, introducing optimal classification (OCT) and regression (ORT) trees for prediction and prescription with and without hyperplanes. This talk shows that (a) Trees are very interpretable, (b) They can be calculated in large scale in practical times, and (c) In a large collection of real world data sets, they give comparable or better performance than random forests or boosted trees. Their prescriptive counterparts have a significant edge on interpretability and comparable or better performance than causal forests. Finally, we show that optimal trees with hyperplanes have at least as much modeling power as (feedforward, convolutional, and recurrent) neural networks and comparable performance in a variety of real world data sets. These results suggest that optimal trees are interpretable, practical to compute in large scale, and provide state of the art performance compared to black box methods.
Principal Investigator Nicholas Fang
Principal Investigator Leonard Guarente
Principal Investigator Anne White