Margarita Chli, vice director at the Institute of Robotics and Intelligent Systems at ETH Zurich, is seen standing in front of a room giving a talk.
Margarita Chli, an Amazon Research Award recipient, is vice director at the Institute of Robotics and Intelligent Systems at ETH Zurich, where she heads up the Vision for Robotics Lab.
Lukas Bigler/wavelighthouse

How Margarita Chli is using drones to go where people can’t

When it comes to assisting search-and-rescue missions, dogs are second to none, but an Amazon Research Award recipient says they might have some competition from drones.

Today, using drones in responding to natural or man-made disasters is limited by the fact that they need to be both individually piloted and have their observations interpreted by a human. But what if drones could “see” on their own? What if they could not only make decisions about navigation, but also where to look more closely — or even collaborate with other drones and robots to observe a specific location?

That suite of skills is exactly what Margarita Chli, an Amazon Research Award recipient and vice director at the Institute of Robotics and Intelligent Systems at ETH Zurich (the Swiss Federal Institute of Technology), is exploring. Chli heads up the Vision for Robotics Lab there (V4RL), and she’s been using her 2019 Amazon Research Award (she was awarded one in 2020 as well) to advance robotic vision for small aircraft, including drones.

Chli grew up in Greece and Cyprus with math teachers as parents, so while she was “heavily trained” in the language of mathematics, she didn’t always know robotics would be her professional focus.

Chli says it was really a series of lucky events that led to her introduction to “influential and brilliant scientists who planted the seed of intellectual curiosity in this area.”

After studying computer science and engineering at the University of Cambridge, where she earned her bachelor’s and master’s degrees, she considered her options.

“The coolest thing at the time seemed to be this PhD position at Imperial College in London, where my advisor, Andrew Davison, brought me into the area of robotic vision. That’s how it all started,” says Chli.

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Davison’s expertise was pioneering monocular SLAM (simultaneous localization and mapping), which is about “understanding how a camera moves in space,” says Chli. In pursuing her PhD, Chli did a lot of coding on her laptop, connecting that computer to a single camera and testing algorithms.

During her postdoc at ETH Zurich, which began in 2010, she applied her computer-vision algorithms to small drones. Chli says it was exciting to translate what she was doing on her laptop to a robot that was actually moving. That’s when she envisioned the potential impact for this technology.

“It's one thing to write some code and look at beautiful images, and another thing to get a robot moving – you get a feeling that you're creating something. And even going beyond that, to create something that can help people,” says Chli.

Drones in disaster zones

Her time at ETH Zurich also marked an era where drones, which had once been prohibitively expensive, were becoming more popular and accessible. “The technological hardware side of things was blooming, which meant we could run then-expensive image-processing algorithms onboard smaller and smaller platforms.” Those drones were more expensive, bulkier, less flexible, and lacked the processing power compared to today’s drones, “but nevertheless, the applications and imagination were there already,” she says.

As she wrote her research proposals, Chli expanded her thinking about the power of this technology. “What can we do with this? How we can use drones and robots and robotic vision to have robots in our everyday lives, that that can help us with tasks that we don't want to do?”

Those questions have propelled her research ever since.

Margarita Chli is seen speaking behind a lectern that says ETH Zürich on it, there are two large flower vases just behind her
One of the first projects for Margarita Chli at ETH Zurich: using drones for search-and-rescue missions.
Oliver Bartenschlager

One of the first projects Chli got to work on at ETH Zurich — where she was appointed as a deputy director of the lab she was working as a postdoc — was using drones for search-and-rescue missions. That work involved drones accessing areas that would be too dangerous or time-consuming for rescuers on foot, allowing rescuers to search for missing people with less risk.

Working backwards from the end-user, Chli spoke with rescuers at Club Alpino Italiano and learned that they didn’t want anything in the field that wasn’t directly useful — drones that worked independently made more sense than dedicating human resources to flying and monitoring drones.

These rescuers had lost colleagues to this very risky work, which takes place in harsh weather conditions, and so they were understandably demanding — and skeptical. “They had no time for delays or mistakes from fussy hardware or software,” she says.

The requirement for simplicity and a just-works solution has “been a great drive for my research ever since, to be honest: to develop plug-and-play, no-fuss systems, such that mission experts do not need to also be robotics experts or pilots.”

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While supporting the work of search-and-rescue teams is still an important component of her work, Chli and team have expanded the scope of their research.

Chli also envisions drones being used for inspecting hard-to-reach areas like wind-turbine blades, or power plants. “In 2012, there was a big explosion in the power plant on the island where I come from in Cyprus. We needed drones to be able to inspect the boilers for cracks to figure out how safe it was for humans to go closer,” she says.

Truly useful robots

This incident inspired Chli to focus on designing robots with real utility.

“I found it quite astonishing that we would see in the news robots that could do all sorts of gimmicky things, but we didn’t have reliable enough robots that could really help humans in a time of dire need.” She wanted to change that, and with her background in robotic vision and interest in drones, creating an unmanned aerial vehicle (UAV) that could “see” was the next challenge. In 2013, she was part of the team that ran the first vision-based autonomous flights of a small helicopter.

Margarita Chli is seen standing on a garden terrace, a drone is hovering over her shoulder in the background.
Margarita Chli is tackling drone challenges such as how a drone can maintain estimating its motion as accurately as possible.
Daniel Winkler

That same year, Chli took a post as a professor at the University of Edinburgh as a Chancellor's fellow. There, she started Vision for Robotics Lab (V4RL), which focuses on vision for robots, especially UAVs. In 2015, she returned to ETH Zurich, where she’s now professor and continues to lead V4RL.

Her research has been accelerated thanks to the resources made available to her as an Amazon Research Award recipient; resources that include access to AWS EC2 and S3.

“I think that what Amazon is doing is a great thing, because it's helping us actually see what researchers can do with its tools and it is democratizing where research is going,” she says.

She’s using those tools to tackle some of the most important problems in her work at ETH Zurich, like “how to figure out where a good spot to land is for our drones, and how we can keep a drone estimating its motion as accurately as possible, without being affected by water, trees, pedestrians, cars, and other dynamic, moving parts of the scene.” While flight-critical tasks must be processed on the drones themselves, transferring other processing tasks to the cloud, like semantic segmentation and high-level path planning, makes sense, says Chli.

Drones helping humanity

Chli thinks drones that can see and make decisions on their own will serve humanity outside search-and-rescue operations.

Researchers tracking wildlife migrations or large, dispersed herds could use drones to keep tabs on individual animals in ways humans on foot can’t, while at the same time understanding group movements.

Robots are going to help us in many ways that today we cannot really imagine, in ways we never thought possible.
Margarita Chli

“Archaeologists have come to us and said, ‘We have about 250 archaeological sites in Greece, we have a few tools around like a tripod, and I can put it in different places and take laser scans, but it's heavy, it's bulky. I don't want to find holes in my model, because I don't have time to go back to every one of these sites to capture new data.’ That’s where drones could be ideal, because they can map an area,” says Chli.

Chli says she’s become a bit of a drone evangelist because often when people hear her speak about autonomous drones, they think of military applications — whereas her focus is on what robots can do to improve the human condition.

Chli said she understands how that distrust emerged. “This technology has been growing very quickly, particularly comparing the progress today to a few years back,” she said. “And the less we know about how this technology works, the more scared we are of it.”

That’s why, she says, it’s important to raise questions and have open dialogues to address concerns because, as she sees it, robots are going to be part of our everyday lives.

“Robots are going to help us in many ways that today we cannot really imagine,” Chli says, “in ways we never thought possible.”

Research areas

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Sponsored Products and Brands at Amazon Ads is reimagining the advertising landscape through industry-leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of reinventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle, from ad creation and optimization to performance analysis and customer insights. We deliver billions of ad impressions and millions of clicks daily, and are breaking fresh ground to improve both the shopper and advertiser experience. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. The General Shopping Intelligence (GSI) team is a highly motivated, collaborative, and fun-loving group with a strong entrepreneurial spirit and bias for action. We provide advanced real-time machine learning services that connect shoppers with the right ads across all platforms and surfaces worldwide. Through deep understanding of both shoppers and products, we help shoppers discover new products they love, enable advertisers to reach their customers most efficiently, and help Amazon continuously innovate on behalf of all customers. We are seeking a motivated Applied Scientist who loves to innovate at the intersection of customer experience, deep learning, generative AI and high-scale machine learning systems. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities As an Applied Scientist, you will: * Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities * Develop scalable and effective machine-learning models and Generative AI solutions to solve business problems * Run regular A/B experiments, gather data, and perform statistical analysis * Work closely with software engineers to deliver end-to-end solutions into production * Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving * Conduct research on new generative AI modeling to optimize all aspects of Sponsored Products and Brands business About the team We are pioneers in applying advanced machine learning and generative AI algorithms in Sponsored Products and Brands business. We empower every customer with a customized discovery experiences from back-end optimization (such as customized response prediction models) to front-end CX innovation (such as widgets), to help shoppers feel understood and shop efficiently on and off Amazon.
US, WA, Seattle
Amazon Leo is a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband network connectivity to unserved and underserved communities around the world. We are looking for an Applied Scientist to be a founding scientist on the Engineering and R\&D team within Leo Infrastructure and IP Security. The team defends the manufacturing lines, launch sites, and global ground infrastructure behind the constellation from the most sophisticated threat actors on the planet. These requirements create open scientific problems at the intersection of agentic AI, real-time stream processing, graph-based reasoning, and behavioral analytics. You will build the science behind a neurosymbolic reasoning platform and the models that detect the behavior of sophisticated threat actors. This is an R\&D role with a production mandate, where you define the problem rather than solve a pre-scoped one, and every model, detection, and agent workflow you build becomes the system Leo's security teams use to protect the constellation. #### Export Control Requirement Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities - Design and implement scalable, production-grade neurosymbolic systems that integrate symbolic reasoning over graph-based knowledge representations with LLM agents to deliver reliable, verifiable security outcomes. - Design and run reinforcement learning and fine-tuning pipelines (GRPO, PPO, DPO) to optimize language models for security reasoning, triage, and detection-authoring tasks. - Build behavioral and statistical models that detect threat actor behavior, and design the evaluation frameworks that measure model performance against that behavior before trusting a model in production. - Design and build multi-agent systems that autonomously triage, enrich, and contain security events, including the constrained reasoning, safety guardrails, and validation mechanisms that make automated decisions trustworthy at scale. - Own the end-to-end science lifecycle, from research and experimentation through production deployment, defining the metrics that measure system performance and real-world security impact. - Advance the state of the art through publications at top-tier venues, patents, or open-source contributions, and shape the scientific agenda and research culture from day one. A day in the life You will move between research and production in the same week: framing an ambiguous security problem as a scientific question, prototyping an approach, and partnering with software engineers to ship it as a capability the platform runs continuously. Security engineers on your team translate threat intelligence into the adversary behaviors that matter; you build the models that detect those behaviors and evaluate model performance against them. You will obsess over the two latencies that define the platforms, the time from event to detection and the time from detection to containment action, and design agents and detections that drive both down. You will backtest candidate detections against retained telemetry, review evaluation results before a model or agent capability graduates to automated execution, and deliver scientific artifacts that ship. About the team Leo Infrastructure and IP Security protects the people, facilities, hardware, and supply chain behind a global satellite constellation. The Engineering and R\&D team within this organization builds the platforms and tooling the security pillar teams operate on, moving security operations from manual triage to correlation-based detection, automated response, and agentic AI. The team is composed of applied scientists, software engineers, and security engineers working across physical and digital security domains. #### Inclusive Team Culture In Amazon Security, it's in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. #### Training & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. #### Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
US, WA, Seattle
Ever wonder how you can keep the world’s largest selection also the world’s safest and legally compliant selection? Then come join a team with the charter to monitor and classify the billions of items in the Amazon catalog to ensure compliance with various legal regulations. The Classification and Policy Platform team is looking for Sr. Applied Scientists to build technology to automatically monitor the billions of products on the Amazon platform. The software and processes built by this team are a critical component of building a catalog that our customers trust. You will have an opportunity to work with cutting edge machine learning algorithms on large datasets. You will need to build Amazon scale applications running on Amazon Cloud that both leverage and create new technologies to process large volumes of data that derive patterns and conclusions from the data. We are looking for highly motivated applied scientists and engineers interested in delivering the next level of innovation to product search for Amazon. As an Applied Scientist on the CPP team, you will be responsible for working across backend, client, business development, and data engineering teams to coordinate deep-dives, inform roadmaps, visualize metrics, and create predictive models to determine how we can best serve our customers. Responsibilities include: - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching and ranking problems, including filtering, new content indexing, and apply document understanding - Conducting and coordinating process development leading to improved and streamlined processes for model development. Strong customer focus is essential - Working closely with Product Managers to expand depth of our product insights with data, create a variety of experiments, and determine the highest-impact projects to include in planning roadmaps - Providing technical and scientific guidance to your team members - Communicating effectively with senior management as well as with colleagues from science, engineering, and business backgrounds - Being a cultural leader that ensures teams are collecting, understanding, and using data to inform every decision that impacts our customers The successful candidate will have an established background in developing customer-facing experiences, a strong technical ability, a start-up mentality, excellent project management skills, and great communication skills. Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. Please visit https://www.amazon.science for more information.