Seven Ground-Breaking Disruptive Technologies at Stanford

Structures and Composites Laboratory:


The Structures and Composites Laboratory at Stanford’s School of Engineering is led by Prof. Fu-Kuo Chang and has a main focus on intelligent and light-weight structures, with emphasis on structural health monitoring. Among a wide span of applications, smart materials enable structures with multi-functional capabilities. This has applications spanning from electric vehicles to building beams with in-built batteries that effectively would make a building self-sustainable if coupled with solar panels.  Batteries: Some of the work promises greater battery efficiency that can substantially reduce the weight of a battery while delivering the same or greater among of power. 

 

Dramatically Lower Cost IoT with Network Enablement. Another highly interesting project is on what Dr. Chang refers to as stretchable materials with in-built IoT
 

 

sensors and connectivity that can be built into a wide variety of physical materials ranging from plastic, building materials, cloth, and metal. Applications can span from robotic skin (Robotik Startup) to aerospace to medical applications like wearable-sensor clothing that can monitor vital signs.

 

Brain in Silicon Laboratory:

 The Brain in Silicon Laboratory at Stanford’s School of Engineering has several projects aimed at creating computer chips that emulate human behavior. Under the leadership of Dr. Kwabena Boahen, the goal is to design a computer that works like a brain. This type of neuromorphic computer has the potential to transform various industries and lead to an affordable super-computer. A primary application area is Edge Computing – a mini-brain chip can be embedded into IoT material to decide how to collect, process, and store data efficiently reducing Cloud cost. http://web.stanford.edu/group/brainsinsilicon/ 

 

Persuasive Technology Laboratory:
Stanford’s Persuasive Technology Laboratory is led by Dr. D.J.Fogg, and does research that creates insight on the question of how computers can change how we think and behave. Current projects look at how smart phones can be used as platforms for persuasion, and the creation of a platform that matches desired behaviors with solutions to reach that desired outcome. https://captology.stanford.edu/ 

 

 

Visual Modeling Platform:

Dr. Ram Rajagopal and the team are working on The Visual Modeling Platform will visualize social and commercial activity within a city, in real-time. This will be done by crossing of various big data layers on movement, logistics, infrastructure, utility usage, transactions and real estate markets. The aim is to provide a tool for “what if analysis” based on various urban scenarios, such as how a new road or office building will impact surrounding transactions and real estate values, how a lack of parking impacts retail sales, or how travel-time to a shopping center impacts local on-line purchasing. It is possible to attach physical data to the model and visualize impact. For example, one could “drop real earthquake data” in a city and see visually the resulting damage. Other physical data could include financial transactions in vehicles. It will be possible to build “vertical applications” off of the core model in areas like real estate, financial transactions, transportation, healthcare, etc. https://gpc.stanford.edu/visual-moedling-project.

 

Predictive Analytics:

The core innovation will focus on predictive analytics – can we successfully predict outcomes in the future with a high degree of certainty. 

Commercial Real Estate & Construction: The initial application is commercial real estate, so that value is related to physical infrastructure, movement and activity taking place within and around buildings. Identification of demand and understanding of how types of real estate interact can tell us the value of a new office building and how it will impact surrounding real estate values, and if this is a better investment than a residential or mixed-use building. Identification of usage will allow real estate owners to understand tenant needs, so that they can adjust their offering and make better predictions of occupancy and rents. 

 

Financial Transactions: For financial services, valuable insights can be gained by identifying consumer groups by area and flow, so that geo-temporal flows of different segments of consumers can be visualized - such as when affluent consumers move between neighborhoods during the day. This will allow for identification of untapped market potential within cities, and more notably for differentiation of pricing and marketing of financial services. 

 

Transportation: The relationship between movement, transportation, physical transactions and online purchases has the potential to provide valuable insights - an example is that identification of how a lack of efficient travel options to retail locations impacts physical transactions and online purchases. This can provide insight into the choice of payment method, and the elasticity of spending in relation to travel-time for various products. 

 

Another source of value comes from the ability to illustrate spillover effects and how movement, congestions, transactions and real estate values interact. This provides a platform for understanding where and when to offer services, relating pricing to the total value that is created. An example is that if a certain demographic is attracted by one type of service, and in turn also has a positive impact on other commercial activity or real estate value - services aimed at that specific demographic should be priced higher given the positive spillover effects.

 

There is also potential for prediction on a larger scale through identification of urban trends - such as seeing when and where millennial with university degrees live, work, play, and consume. Prediction of change will allow for better planning on pricing, and prediction of transaction volume.

 

Stanford Robotics Laboratory:

Oussama Khatib, Professor, Department of Computer Science - manages the Robotics Lab.

The lab is interested in methodologies and technologies of autonomous robots, cooperative robots, human-centered robotics, haptic interaction, dynamic simulation, virtual environments, augmented teleoperation, and human-friendly robot design. 

 

 

Stanford Imaging Laboratory:

The Computational Vision and Geometry Lab (CVGL) at Stanford is directed by Prof. Silvio Savarese. Lab research addresses the theoretical foundations and practical applications of computational vision. Their interest lies in discovering and proposing the fundamental principles, algorithms and implementations for solving high level visual recognition and reconstruction problems such as object and scene understanding as well as human behavior recognition in the complex 3D world.

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