A city crew truck is seen driving down a flooded street in a downpour
Lise St. Denis, a research scientist at the University of Colorado’s Earth Lab, has spent the past half-decade figuring out how to find useful information on social media in the wake of natural disasters like the flooding seen here.
Mario Beauregard/Adobe

Finding critical information during disasters

Lise St. Denis, a research scientist at the University of Colorado, says social media can be useful for responders. Now she's helping them separate truly useful info from the noise.

Twitter, apart from being a place to catch up on niche topics and post personal takes on the latest news, can be a useful source of vital information during disasters.

Lise St. Denis, a research scientist at the University of Colorado’s Earth Lab, notes social media sites of all stripes can be useful in storms, but also in wildfires, floods, hurricanes, and other natural disasters — because fast, local information is essential in these situations. However, separating truly useful info from the noise is key, which is what St. Denis has been working on for the past half-decade.

“My big vision is that emergency response teams and communities impacted by disasters could get the best possible information out in real time so communities can be optimally informed about what's happening,” she says.

This kind of work requires a marriage of creative thinking and technology, something St. Denis, a 2019 AWS Machine Learning Research Award recipient, has pursued since the beginning of her career.

Lise St. Denis is seen wearing a mask and standing, on the left, while teaching a recent graduate seminar. There is a display screen behind her and two students, also masked, are seen sitting.
Lise St. Denis is seen standing while teaching a recent graduate seminar. After earning her PhD at the University of Colorado in 2016, St. Denis stayed on and is now a research scientist at Earth Lab
Courtesy of Lise St. Denis

At least as far back as college, St. Denis has had a variety of interests she took seriously, despite their seeming disparity. Her undergrad degrees from Colorado State University are in fine arts and computer science. That brought her to illustration and software engineering in her early working life, first at Hewlett Packard. HP supported her graduate work in human factors engineering at the University of Idaho.

She took a break when she had children in the early 2000s, and when she was ready to return to the workforce, she realized she wanted to refine her skills. “I still had a lot of the same interests, but with a different life perspective — I was older. I wanted to do something that I felt like I was making a difference,” says St. Denis. So she went back to graduate school in 2011 initially for a masters in computer science, which led her to the University of Colorado where she discovered Project EPIC (Empowering the Public with Information in Crisis) where she decided to pursue an interdisciplinary doctorate in crisis informatics.

As part of the work for her degree, she met a group of emergency responders, became fascinated by their work, and set out to learn more. She realized that one big challenge they faced was getting the word out to the public. Could, she wondered, social media sites help gather and distribute information?

So when she heard about a plan in New Mexico to adapt the idea of digital volunteerism to emergency risk response — the volunteers in this case would be emergency responders — she went to learn from them.

At the time, social media wasn’t widely embraced within the emergency response field; St. Denis even knew government officials who risked their jobs using social media at work. “A lot of emergency response organizations just saw social media, not as useful, but as more of a hotbed for misinformation and rumor,” says St. Denis.

Even in light of that, some emergency managers remained interested: “As social media gained popularity, they knew this is where they needed to provide updates, engage with a growing audience, and look for breaking information,” recalls St. Denis.

“They formed this network of teams that were called Virtual Operational Support Teams. These teams are known ahead of time and activated through formal emergency protocols and procedures. The first emergency trial of the concept was during the 2011 Shadow Lake Fire in Eastern Oregon. I ended up studying the innovations of this network of teams, and I worked within this community, alongside them, to understand what they were doing,” she explained.

Their work made sense to St. Denis, and so, instead of getting that master’s in computer science, she ended up using what she had learned in New Mexico as a basis for her cross-disciplinary PhD, which included computer science, but also incorporated classes in communication and sociology of disaster.

In 2014, St. Denis was asked to bring her reporting and analysis social media skills to the Carlton Complex fire in Eastern Washington. That fire burned through several communities with a high number of structures lost and very short evacuation windows. Unable to keep up with the speed of the fire’s impact, locals had no way to get their questions answered and there was, understandably, a lot of frustration.

“That convinced me that there had to be a better strategy for filtering and getting to the most relevant information needed during these events,” she says.

She was also wrangling data and doing analysis, and consolidating that information for the teams she was supporting. As part of her research, St. Denis was a part of close to 100 emergency response activations. “I studied the integration of social media into emergency response through virtual teams,” she explains. “And I kept asking myself, ‘What does it mean to integrate them?’”

Fast forward to today and she’s still researching that basic question. After earning her PhD at the University of Colorado in 2016, St. Denis stayed on at the university and is now a research scientist at Earth Lab. “We have all this existing information from all these different sources, and we want to do a better job of making it available so scientists can leverage it and make use of it for hazards analysis.”

Thus far, Twitter has shown the most promise for what St. Denis hopes to implement. The idea is that an emergency manager would receive a live stream of truly useful content, including selected tweets from reliable sources. “The managers could keep an eye on that as part of their emergency management response,” says St. Denis.

This is extremely practical, real-world information, that can help save lives because it is personalized, says St. Denis. The information is coming from community members who are directly impacted by these disasters. “It's not the media coverage or the broad outside information,” says St. Denis. “It contains new information such as what roads are passable or where fuel outages exist” or where information gaps exist such as, ‘I don't know where to evacuate my livestock,’ or ‘I need to know who has gas,’ or ‘Is my water supply safe to drink?’”

And while her research hasn’t yet translated into an actual tool for emergencies, St. Denis sees the light around the corner. She recently became part of the Pandemic Hyper-Accelerator for Science and Technology (PHAST). “As part of the PHAST program I have been paired with skilled entrepreneurs who are helping me to look at my problem from a systematic, opportunity-driven perspective,” she explains. “We’ve been interviewing emergency response and crisis response professionals across different contexts to understand specifics about the tools they are using, as well as the specific values of or consequences for information when it is found or not found.”

Utilizing machine learning

St. Denis first realized she would need to utilize machine learning when studying data from the Carlton Complex fire. “I realized that I had some intuition for how I could take the noise off the top to get to the information that I wanted. But the only way that was going to matter is if I could do that in near real time — which would require machine learning,” she says. So she applied for an AWS Machine Learning Research Award and received it in 2019.

She and her team used AWS Lambda and AWS Fargate to query the Twitter API for relevant tweets, and stored the raw data in Amazon S3. St. Denis also used standard machine learning libraries to build her prototype because she wanted everything to be open source. “We're hoping, as we move forward, to move into more sophisticated data collection and AWS tools,” she says.

St. Denis and her team have published two papers on the design of the work done so far, and proved that the prototype they’ve built works equally well across multiple types of hazards. They’ve even used it for work they did examining US-based public response to stay-at-home orders at the onset of the COVID-19 pandemic.

“I have spent over a decade working with some of the most innovative responders in the field, but fundamentally nothing has changed in terms of tools,” she says. “I think that this social media-based tool has a lot of potential, and so it's been really exciting. Now that I have this starter funding, it could go pretty quickly.”

Related content

IN, HR, Gurugram
Building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. Key job responsibilities 1. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 2. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. 3. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 4 Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 5. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing.
US, WA, Seattle
Estimating the demand response of a pricing decision is genuinely hard. The causal effects are delayed, noisy, and confounded by factors that standard experiment analysis wasn't designed to handle. Most pricing teams default to heuristics not because they don't care about customer responses, but because measuring them rigorously is an unsolved problem. P2OS is building the science to solve it. We're hiring an Economist to own that work — defining how we estimate digital demand response in a pricing context, building the identification strategies that make those estimates credible, and translating outputs into something pricing teams can use to make better decisions. The role sits at the intersection of econometric methodology and production-quality analysis, and requires someone who can operate independently in both. As science lead, you'll own the digital pricing methodology domain, and be the internal authority on causal inference for pricing across P2OS and partner teams. Key job responsibilities * Own the end-to-end digital pricing methodology for pricing — identification strategy, modeling choices, validation approach, and business use cases — and drive adoption across pricing contexts * Deliver high-stakes analyses connecting digital pricing estimates to a concrete pricing decision and strategy change at VP+ level * Apply advanced causal methods to live pricing problems; document approaches so the team can build on and extend them. * Provide causal inference guidance on pricing experiment questions as they arise — being the methodology resource when experiments generate relevant questions * Serve as cross-team economic advisor to Digital Finance, Customer Behavior, and Demand Science on assumptions and causal identification * Actively mentor junior scientists, earn trust of cross-functional tech and product partners. A day in the life In a typical day, you'll move between methodology work and stakeholder-facing analysis. - On the science side, that means reviewing identification assumptions with the Causal AS, validating estimation choices for the LTV framework, and documenting methodology decisions in ways that non-economists can act on. - On the applied side, you'll be in rooms with Finance, Pricing PMs, and other science teams: aligning on LTV definitions, resolving disagreements between competing metrics, and translating causal findings into recommendations that land in strategy reviews. - As tech lead, you need to work to develop the economists and scientists on your scrum: structured reviews, identification strategy feedback, and raising the quality of analyses before they reach stakeholders. The mix shifts, but the through-line is to progress the LTV methodology from open questions to shipped frameworks, and making sure the team's causal work is rigorous enough to hold up when it counts. About the team P2Optimization Science (P2OS) is responsible for the ML models and analytical frameworks that drive pricing decisions at scale. The team spans demand lift modeling, pricing error detection, customer lifetime value, and experimentation. Our small team of specialized applied scientists and economists works closely alongside engineers, and pricing product managers.
US, WA, Seattle
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!
US, MA, Boston
Are you interested in how to build AI reasoning systems that give provably correct answers? Are you excited by science at the interface of classical AI reasoning and Large Language Models (LLMs)? Would you like to apply your technology to serve operations customers better? Amazon Robotics is looking for a talented Applied Scientist in Neurosymbolic AI. You will innovate on combining language models (LMs) with classical AI reasoning. You will work with a team of scientists and engineers to achieve this. You will publish your results in papers at leading venues in AI. You will be part of a larger team and have the opportunity to work on problems such as: using LMs to generate plans, using AI reasoning to verify plan correctness, learning efficient reasoning strategies, self-improving models. You will work on basic science and on business problems in robotics, automation and fulfillment across our operations. Key job responsibilities In this role you will: • Work closely with other scientists and engineers, and be part of Amazon’s diverse global science community. • Publish your research in top-tier academic venues and hone your presentation skills. • Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise. A day in the life You'll meet regularly with your technical lead and your team on your ideas, get guidance and feedback, work together on architectures and algorithms, author papers, build AI systems, all with the aim of delivering results for your operations customers. You'll work closely with other scientists to review your plans and results. You'll meet with engineers to implement your ideas at scale. About the team The Veritas team is a science team working at the boundary between language models and classical AI reasoning. We work across on customer problems in fulfillment, automation and robotics. We focus on high quality research science informed by practical problems.
US, NY, New York
The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
US, NY, New York
The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems Disambiguate problems to propose clear evaluation frameworks and success criteria Work autonomously and write high quality technical documents Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production Partner closely with other scientists to deliver large, multi-faceted technical projects Share and publish works with the broader scientific community through meetings and conferences Communicate clearly to both technical and non-technical audiences Contribute new ideas that shape the direction of the team's work Mentor more junior scientists and participate in the hiring process About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
US, WA, Seattle
Economists in this role partner with business stakeholders to distill complex problems into testable economic questions and generate actionable insights. They collaborate with engineers and scientists to estimate models on large-scale data, design pilots, measure impact, and scale successful prototypes into improved policies and programs. They leverage AI tools to scale economic study for broader business impact. They communicate findings to business leaders, incorporate feedback, and deliver customer-centric solutions at scale.
CA, BC, Vancouver
The Alexa Daily Essentials team delivers experiences critical to how customers interact with Alexa as part of daily life. Alexa users engage with our products across experiences connected to Timers, Alarms, Calendars, Food, and News. Our experiences include critical time saving techniques, ad-supported news audio and video, and in-depth kitchen guidance aimed at serving the needs of the family from sunset to sundown. As a Data Scientist on our team, you'll work with complex data, develop statistical methodologies, and provide critical product insights that shape how we build and optimize our solutions. You will work closely with your Analytics and Applied Science teammates. You will build frameworks and mechanisms to scale data solutions across our organization. If you are passionate about redefining how AI can improves everyone's daily life, we’d love to hear from you. Key job responsibilities Problem-Solving - Analyze complex data to identify patterns, inform product decisions, and understand root causes of anomalies. - Develop analysis and modeling approaches to drive product and engineering actions to identify patterns, insights, and understand root causes of anomalies. Your solutions directly improve the customer experience. - Independently work with product partners to identify problems and opportunities. Apply a range of data science techniques and tools to solve these problems. Use data driven insights to inform product development. Work with cross-disciplinary teams to mechanize your solution into scalable and automated frameworks. Data Infrastructure - Build data pipelines, and identify novel data sources to leverage in analytical work - both from within Alexa and from cross Amazon - Acquire data by building the necessary SQL / ETL queries Communication - Excel at communicating complex ideas to technical and non-technical audiences. - Build relationships with stakeholders and counterparts. Work with stakeholders to translate causal insights into actionable recommendations - Force multiply the work of the team with data visualizations, presentations, and/or dashboards to drive awareness and adoption of data assets and product insights - Collaborate with cross-functional teams. Mentor teammates to foster a culture of continuous learning and development
US, WA, Seattle
This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services. You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance. We are looking for an experienced Principal Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide. You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.
US, NY, New York
Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products. This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Applied Scientist to drive innovation in our Ads Foundational Model. In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As an Applied Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals. 2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3. Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5. Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.