Petri dishes of engineered and native Proteus mirabilis patterns, here stained with colored dyes used for the lab's bacterial art. Researchers in Biomedical Engineering Professor Tal Danino's lab were brainstorming several years ago about how they could engineer and apply naturally-pattern-forming bacteria. There are many bacteria species, such as Proteus mirabilis (P. These bacteria can sense several stimuli in nature and respond to these cues by "swarming"--a highly coordinated and rapid movement of bacteria powered by their flagella, a long, tail-like structure that causes a whip-like motion to help propel them. For inspiration, Danino's team at Columbia Engineering, which has a good deal of experience using synthetic biology methods to manipulate bacteria, discussed where else they might find similar patterns in nature and what their functions might be.

For the past 40 years, our physicists have run only into dead ends. This great stagnation in the field of physics has seen almost no new discoveries in recent times. If you take a closer look, you'll find no real progress being made after the standard model of particle physics was completed in the 1970s. We have only been able to confirm pre-existing theories but have not found anything beyond them. Concepts that have been known for more than 80 years, like Quantum Gravity, Dark Matter, and Quantum Measurement problems, still remain unsolved. This might not sound alarming, but if we fail to consistently progress in scientific fields, the development of the human race will reach a standstill.

The ability of robots to model themselves without being assisted by engineers is important for many reasons: Not only does it save labor, but it also allows the robot to keep up with its own wear-and-tear, and even detect and compensate for damage. The authors argue that this ability is important as we need autonomous systems to be more self-reliant. A factory robot, for instance, could detect that something isn't moving right, and compensate or call for assistance. "We humans clearly have a notion of self," explained the study's first author Boyuan Chen, who led the work and is now an assistant professor at Duke University. "Close your eyes and try to imagine how your own body would move if you were to take some action, such as stretch your arms forward or take a step backward. Somewhere inside our brain we have a notion of self, a self-model that informs us what volume of our immediate surroundings we occupy, and how that volume changes as we move."

Latent embeddings from our framework colored by physical state variables. A new Columbia University AI program observed physical phenomena and uncovered relevant variables--a necessary precursor to any physics theory. But the variables it discovered were unexpected. These three variables make up Einstein's iconic equation E MC2. But how did Albert Einstein know about these concepts in the first place?

When it comes to imitating human emotion, robots have a long way to go, but researchers at Columbia Engineering's Creative Machines Lab have taken an initial step toward this goal. They designed a robot capable of predicting another robot's intent, essentially placing itself in the shoes (or the little plastic wheels) of the second machine. Here's how engineers designed the experiment: They placed one robot in a 3-foot by 2-foot playpen and programmed it to travel toward any green circle it could see projected on the floor. A large red box obscured the robot's view of some circles, meaning it didn't travel to these spots, even if they were physically closer than others. From above, the observer robot watched its buddy navigate the space for two hours and then began predicting its path.

Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease of prematurity. Characterized by sudden and progressive intestinal inflammation and tissue death, it affects up to 11,000 premature infants in the United States annually, and 15-30% of affected babies die from NEC. Survivors often face long-term intestinal and neurodevelopmental complications. Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting NEC in premature infants before the disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. The pilot study was presented virtually on July 23 at ACM CHIL 2020.

Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease of prematurity. Characterized by sudden and progressive intestinal inflammation and tissue death, it affects up to 11,000 premature infants in the United States annually, and 15-30 percent of affected babies die from NEC. Survivors often face long-term intestinal and neurodevelopmental complications. Researchers from Columbia Engineering and the University of Pittsburgh have developed a sensitive and specific early warning system for predicting NEC in premature infants before the disease occurs. The prototype predicts NEC accurately and early, using stool microbiome features combined with clinical and demographic information. The pilot study was presented virtually on July 23 at ACM CHIL 2020.

The conference will host keynote presentations from leading voices in data-driven innovation, lightning talks from Columbia University researchers, & interactive poster & technology demonstrations. Data Science Day provides a forum for innovators in academia, industry, & government to connect. Keynote Speakers Pat Bajari, Chief Economist, Vice President of Artificial Intelligence, Amazon Eric Schmidt, Technical Advisor to the Board, Alphabet Columbia University & Columbia University Data ScienceInstitute Affiliated Faculty Talks Lightning Talk:Cause, Learn, Optimize & Reason Melanie Wall, Professor, Department of Biostatistics, Mailman School of Public Health; & Director of Mental Health Data Science in the Department of Psychiatry, Columbia University Irving Medical Center & the New York State Psychiatric Institute Samory Kpotufe, Associate Professor, Department of Statistics, Faculty of Arts & Sciences Elias Bareinboim, Associate Professor, Department of Computer Science, Columbia Engineering; & Director of the Causal Artificial Intelligence (CausalAI) Laboratory, Columbia University Clifford Stein, Professor of Industrial Engineering & Operations Research, Department of Computer Science, Columbia Engineering; & Associate Director for Research, Data Science Institute, Columbia University Lightning Talk: Human Machine: A New Hybrid World Oded Netzer, Professor of Business, Marketing Division, Columbia Business School Lydia Chilton, Assistant Professor, Department of Computer Science, Columbia Engineering Sarah Rossetti, Assistant Professor, Biomedical Informatics, Department of Biomedical Informatics; Assistant Professor, School of Nursing, Columbia University Irving Medical Center Lightning Talk: Ethics & Privacy: Terms of Usage Roxana Geambasu, Associate Professor, Department of Computer Science, Columbia Engineering Rafael Yuste, Professor, Department of Biological Sciences, Faculty of Arts & Sciences Jeff Goldsmith, Associate Professor, Department of Biostatistics, Columbia University Mailman School of Public Health What are my transportation/parking options for getting to & from the event? Please visit the following link for directions & parking information: http://transportation.columbia.edu/For How can I contact the organizer with any questions?

Taking a cue from biological cells, researchers from MIT, Columbia University, and elsewhere have developed computationally simple robots that connect in large groups to move around, transport objects, and complete other tasks. This so-called "particle robotics" system -- based on a project by MIT, Columbia Engineering, Cornell University, and Harvard University researchers -- comprises many individual disc-shaped units, which the researchers call "particles." The particles are loosely connected by magnets around their perimeters, and each unit can only do two things: expand and contract. That motion, when carefully timed, allows the individual particles to push and pull one another in coordinated movement. On-board sensors enable the cluster to gravitate toward light sources.

Researchers at Columbia Engineering and MIT Computer Science & Artificial Intelligence Lab (CSAIL) have engineered for the first time a particle robotic swarm with individual components that function as a whole. The novel kind of robot has never been seen before. "You can think of our new robot as the proverbial "Gray Goo," said Hod Lipson, professor of mechanical engineering at Columbia Engineering. "Our robot has no single point of failure and no centralized control. It's still fairly primitive, but now we know that this fundamental robot paradigm is actually possible.