We are entering a new world of very effective, but very expensive, drug treatments for rare disease. How should society think about pricing these treatments? Are there financial models that can help spread the costs and make them more affordable? And what does this suggest for a new role for government-financed Research and Development?
The nano age is upon us. With nanoscale advancements we are reimagining health and life sciences, energy, computing, information technology, manufacturing, and quantum science. Nano is not a specific technology. It does not belong to a particular industry or discipline, it is, rather, a revolutionary way of understanding and working with matter, and it is the key to launching the next innovation age…the nano age.
SMEs are the backbone of most economies and employ approximately 60 percent of the working population in OECD countries. However, these businesses often struggle the most to access financing, oftentimes, relying on friends and family to help them flourish and thrive by lending money when others do not. We have created Trust·u to offer a solution to this. Trust·u is an internal venturing effort from BBVA, positioned to address innovation opportunities in an agile manner by mimicking startups. We utilize a digital platform to enable rapid on-boarding and underwriting, combining social elements with financial data, to grant small businesses access to financing based on a new risk assessment model, which takes full advantage of ML techniques and new data sources.
Redefining Small Business Lending through ML and Social Physics.
This lecture will detail the creation of ultrasensitive sensors based on electronically active conjugated polymers (CPs) and carbon nanotubes (CNTs). A central concept that a single nano- or molecular-wire spanning between two electrodes would create an exceptional sensor if binding of a molecule of interest to it would block all electronic transport. The use of molecular electronic circuits to give signal gain is not limited to electrical transport and CP-based fluorescent sensors can provide ultratrace detection of chemical vapors via amplification resulting from exciton migration. Nanowire networks of CNTs provide for a practical approximation to the single nanowire scheme. These methods include abrasion deposition and selectivity is generated by covalent and/or non-covalent binding selectors/receptors to the carbon nanotubes. Sensors for a variety of materials and cross-reactive sensor arrays will be described. The use of carbon nanotube based gas sensors for the detection of ethylene and other gases relevant to agricultural and food production/storage/transportation are being specifically targeted and can be used to create systems that increase production, manage inventories, and minimize losses.
The impetus for the SENSE.nano is the recognition that novel sensors and sensing system are bound to provide previously unimaginable insight into the condition of individuals, as well as built and natural world, to positively impact people, machines, and environment. Advances in nano-sciences and nano-technologies, pursued by many at MIT, now offer unprecedented opportunities to realize designs for, and at-scale manufacturing of, unique sensors and sensing systems, while leveraging data-science and IoT infrastructure.
Understanding the brain could lead to new kinds of computational algorithms and artificial intelligences, as well as treatments for intractable disorders that affect over a billion people worldwide. However, the brain is a very complex, densely wired circuit, and understanding how it works has remained elusive. In order to map how these circuits are organized, and control their complex dynamics, we are building new tools, which include methods for physically expanding brain circuits so that we can see their building blocks, as well as molecules that make neural circuits controllable by light. Through these tools we aim to enable the systematic analysis and repair of the brain.
Polina Golland will discuss her group's research in computational analysis of MRI scans that aims to provide accurate measurements of healthy anatomy and physiology, and biomarkers of pathology. Applications range from fetal development to aging brain.
Early and accurate detection of cancer represents an enormous opportunity for sensing technologies to impact patients' lives. I will discuss several examples of diagnostic technologies developed in the Bhatia lab that employ nanosensors to detect tumors using a simple urine test for readout. This platform technology uses nanosensors to detect enzyme activity associated with cancer invasion, and generate bar-coded reporters that can be detected by multiplexed mass spectrometry or antibody-based methods such as lateral flow assays. I will close the presentation with an introduction to the Marble Center for Cancer Nanomedicine, a new growing resource for the nanomedicine community.
While trillions of sensors connected to the “Internet of Everything” (IoE) promise to transform our lives, they simultaneously pose major obstacles, which we are already encountering today. Max Shulaker presents a path towards realizing these future systems in the near-term, and shows how based on the progress of several emerging nanotechnologies (carbon nanotubes for logic, non-volatile memories for data storage, and new materials for sensing), we can begin realizing these systems today.
Trusting any data set or analysis requires a leap of faith. Beyond an acceptance of margins of error and biases, all data-driven decisions necessitate a will to believe. When it comes to data that impacts or justifies institutional decisions, this belief must exist not only in the institution's ability to be honest and rigorous with data, but in the very authority of data itself to tell us something meaningful about the world. In an era of “alternative facts” and fear-based advocacy, we must contend with this; but it may also sometimes be a symptom of data tunnel vision. How can we be better at designing the conditions for people to develop faith in our (and their) ability to do good things with data? And how can purposefully-deployed inefficiencies improve the resilience of human systems?