FEATURED STORY  |  Engineering for Humanity

Columbia Engineering has a long tradition of pushing the frontiers of the discipline to address key needs of humanity. Today, we remain true to that history, devising creative solutions for the grand challenges of our times while setting a bold path forward for the future.

From building better models for predicting extreme weather to uncovering the mechanical forces that drive tissue development, we are creating the tools needed to understand our changing climate and harness potent forces to heal the human body. We are pioneering new paradigms for human-computer interaction that will streamline problem-solving, developing methods for ensuring the safety and reliability of driverless cars, and inventing technologies to support revolutionary materials and systems capable of ushering in a new age of nanoscale machinery.

This work embodies our aspirations to drive innovative research that makes a positive impact—helping to create a sustainable, healthy, secure, connected, and creative humanity. Our vision, Columbia Engineering for Humanity, is exemplified today by the diverse and pioneering work that our faculty and students are pursuing across departments and disciplines and in partnership with sister schools, institutes, government, and industry.

We have never been more optimistic about the role engineering can play in the service of society and in bringing those advances to populations across the country and around the world.

Read on to see why.

Imagine a world where the hidden work of evaporation could help predict weather extremes.

From droughts to floods, climate affects extreme weather in ways that scientists need to understand and predict. But the climate system represents a complex and interconnected web of factors, which makes creating accurate models and long-term projections difficult.

Pierre Gentine, professor of earth and environmental engineering, provides insights into this system by focusing on water—in particular, evaporation—and its role in land/ atmosphere interaction, with the ultimate goal of linking this knowledge back to climate response.

Through a combination of multiscale simulations, machine learning, and remote sensing, he builds models to help characterize climate and has steered a number of groundbreaking studies detailing its impact on water resources and agriculture.

One recent study from his group found that almost a third of the factors contributing to subseasonal climate variability can be attributed to vegetation (through the release of water vapor during photosynthesis). Another study revealed that plants, which can regulate evaporation through stomata (small pores at the leaf surface), will have a greater impact on water resources than precipitation or temperature in an enriched CO2 future. Plants that can access carbon more easily will show higher rates of photosynthesis and less water loss, which affects the water cycle.

With colleague Adam Sobel, professor of applied physics and applied mathematics, Gentine has worked on a modeling approach to more accurately simulate the continental tropical climate, especially in the Amazon. Simultaneously, his lab uses machine learning to better simulate clouds and precipitation in climate models, a long-standing challenge for scientists.

Gentine is also engaged in promising work on evaporation as a potential energy source. He contributed to a recent study that showed evaporation from lakes and reservoirs in the U.S. could potentially provide up to 325 gigawatts of power—equal to much of the country’s power needs.


Imagine a world where cells could teach us how to heal the human body and prevent disease.

Much research has been conducted on how biochemical factors and genetics affect embryonic development—far less is known about the role mechanical forces play in how tissues first take shape.

Columbia Engineering is helping lead the way in this area of inquiry. With her interdisciplinary Living Materials Lab, Karen Kasza, Clare Boothe Luce Assistant Professor of Mechanical Engineering, brings together engineers, biologists, and physicists to investigate the mechanics of cell and tissue behavior.

“Force is a way to communicate,” says Kasza. “We’d like to understand how cells communicate through force and how other cells elsewhere in the tissue respond to that signal.”

Kasza uses fruit flies as model organisms for her research because of similarities with human genes and development and the fact that researchers can observe how cells and tissues move inside fly embryos. In this way, these models offer insights into how human cells move, change shape, grow, and self-organize into tissues with the properties necessary to function normally.

Recently, she has studied the role of mechanical forces in convergent extension, a process whereby the fruit fly embryo elongates, an important event in human development as well. She also contributed to a study on how cells respond to their environment by altering their mechanical properties and how this process can determine the function, and even fate, of the cell.

By identifying problems that arise in tissue development, Kasza hopes to uncover mechanical factors involved in human birth defects, and even in cancer. Her findings will also result in new pathways for building functional tissues in the lab.

Increased understanding of cell behavior, Kasza believes, could benefit many fields, from tissue engineering and materials science to robotics. “Insights into the rules of how cells communicate and coordinate their activities to build tissues and organs could translate into other areas that involve self-organization and group coordination,” says Kasza.


Imagine a world where technology can help ensure public trust in innovation.

From self-driving cars to speech recognition, artificial intelligence (AI) applications make use of deep learning to replicate the decision-making process of the human brain. These advances are bringing about rapid changes in society that can pose tough questions in areas like cybersecurity, privacy, and safety. Suman Jana, assistant professor of computer science, sees these issues as catalysts for innovation.

“How do I create a tool that allows a policy maker to make good policy?” he asks.

Jana, working with colleagues at Columbia Engineering and other universities, seeks to create automated tools that search out and fix vulnerabilities in computer systems that could lead to breaches in security and privacy, while ensuring these exceptionally complex programs are safely deployed in platforms such as autonomous vehicles.

Recently, he worked with a group of researchers to create DeepXplore, a debugging tool that directly addresses the “black box” issue in deep learning, in which the intricate nature of a system often obscures how it “learned” an error.

“How do we figure out what it’s figuring out?” asks Jana. With DeepXplore, the team essentially reverse-engineered the learning process to understand the mistakes made by the AI algorithm used in self-driving cars and in other applications. They fed the system real-world inputs that were difficult or confusing in order to determine how and where the algorithm erred in the decision-making process. Their DeepXplore tool then retrained the system to fix the bug.

DeepXplore was able to detect thousands of errors in deep learning applications that had not been found before. In the future, Jana envisions agencies using this technology to measure the reliability of self-driving cars and other deep learning systems, just as the Department of Motor Vehicles currently licenses human drivers.

“This is the moment to get the right policies in place,” says Jana. “Our goal is to provide tools that regulators, developers, and manufacturers can use.”


Imagine a world where humans help computers connect the dots to find better solutions.

Lydia Chilton, assistant professor of computer science, sees enormous opportunities for humans and computers to work together to solve problems faster and more efficiently. And by humans, Chilton, who joined the faculty in fall 2017, means lots of humans. She is part of a growing number of researchers across various disciplines interested in crowds and computers as joint problem-solvers.

With a focus on human-computer interaction, Chilton’s current research involves teaching computers to mimic the design process that humans follow when approaching a creative task. Rather than a light bulb moment of inspiration or insight, Chilton believes design to be a process that can be deconstructed—a series of steps, or “microtasks,” that can be translated for the computer.

Crowdsourcing is increasingly seen as a way to find better solutions through filling in knowledge gaps. In the case of computers, crowdsourcing as a part of the computation process can address subjective areas—such as what humans find funny or iconic—that computers cannot grasp.

Currently, Chilton is using this process to computationally create visual metaphors and satirical headlines. The design process typically begins with brainstorming, which is where crowdsourcing plays an invaluable role. Chilton also incorporates crowdsourcing at points along the way, fostering collaboration between humans and computers by giving them different, clearly defined microtasks in the process.

This collective method can also be put toward crafting messages to inspire positive social changes. A motivating example Chilton uses is an ad to discourage smoking. It portrays a gun with cigarettes in the chamber instead of bullets as a visual metaphor to convey the idea that “smoking kills.” To create such ads, humans direct and annotate the symbols, and computers search across the symbols to find the ones that can be combined, proving that humans and computers are the ultimate team.


Imagine a world where materials with tailor-made properties could self-assemble into systems that improve almost every area of life.

Creative thinking can inspire astonishing breakthroughs in engineering—including entirely new materials and systems. Oleg Gang, professor of chemical engineering and of applied physics and material science, is among the world’s top engineers spearheading the invention of a new class of custom-made nanoscale materials and devices that promise innovative ways to address problems while transforming industries such as manufacturing, medicine, energy, electronics, and telecommunications.

In nature, structures self-assemble through internal instructions and are able to adapt and respond to their environments. This is true on many scales and for many systems, from crystal formation to the biological world. In Gang’s lab, researchers work to construct new self-assembling structures based on nanoscale objects—such as man-made nanoparticles or biomolecules borrowed from nature—by both programming the assembly and controlling the properties of the resulting structure. The goal is to create fully designed materials with properties that mimic or even go beyond those found in nature, with the versatile architectures and ability to function as nanoscale machinery.

DNA holds much promise for self-assembly research, as it possesses great flexibility of form as well as the ability to establish a “language” for particle interaction. Yet its inherently fragile nature makes it challenging for use in systems that need to function across a broad range of conditions. Recently, Gang’s group was able to mineralize architectures formed by DNA scaffolds and nanoparticles, rendering them stable in extreme temperatures and pressures, a feat that will open up numerous applications.

Gang hopes these advances could one day lead to implants that marry organic and inorganic components and to “smart” materials that can change their shape, appearance, or function according to context.

“There are systems that exist only in our imagination,” says Gang. “I want to figure out the principles that can bring them to reality.”

By Allison Elliott