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.”

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Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. - Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. - Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. - Develop offline policy estimation tools and integrate with measurement systems/econometric models. - Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. - Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. - Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. - Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems. Key job responsibilities Key job responsibilities - Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. - Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. - Develop offline policy estimation tools and integrate with measurement systems/econometric models. - Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. - Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. - Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. - Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
US, WA, Seattle
We are seeking a Principal Applied Scientist to own the scientific vision across Agentic WorkSpaces. This is a foundational role spanning the full portfolio — Personal, Applications, and Core, and the agentic surfaces (WS4Builders and WorkSpaces for Agents). You will define how we measure, improve, and guarantee the performance of AI agents and human-AI teams. A core part of the role is defining the science agenda itself — identifying which problems are most worth solving and where the highest-leverage bets lie. Directions worth exploring might include Organizational Intelligence (turning institutional knowledge into agent-consumable skills), AI Agent Experience / AiAX (agent observability and autonomous remediation), and contextual, behavioral security that adapts enforcement in real time for human and agent sessions — but these are illustrative examples, not a fixed roadmap, and many other directions are possible. You will help define which ones we pursue. The problems you will solve do not have established industry patterns. You will set the direction for the science of how AI agents and people perceive, reason about, and act reliably within computing environments at enterprise scale. Key job responsibilities - Set the long-term scientific vision: Define what best-in-class agent performance, evaluation, and learning look like across Agentic WorkSpaces — for computer-using agents and human-AI teams alike. Identify the unsolved scientific problems, chart a multi-year research roadmap, and secure buy-in from VP-level leadership. - Solve highly ambiguous, novel problems: Independently frame and deliver solutions to foundational challenges in agent perception, reasoning, evaluation, reliability, and human-AI collaboration — problems where neither the approach nor the success criteria are pre-defined. - Own the evaluation and measurement foundation: Build the benchmarks, datasets, and metrics that quantify agent and team accuracy, cost, productivity, and safety across the portfolio and diverse enterprise workflows, and that gate what we ship. - Drive cross-organizational scientific alignment: Work across partner teams (AgentCore, Bedrock model teams, Identity, Security, the MCP ecosystem) and across the Applied AI Solutions product portfolio to shape how models and agent frameworks are applied, and ensure scientific decisions compose into a coherent system. - Deliver measurable business impact: Ensure research translates to customer outcomes: higher task accuracy, lower cost-per-action, faster time-to-production, measurable productivity for human-AI teams, and the trust that lets enterprises scale agent workflows. - Raise the scientific bar: Establish rigor in experimentation, evaluation, and reproducibility. Mentor and grow senior scientists and engineers. Set the standard for applied science quality across the organization. - Advance the state of the art: Contribute to the external technical community through publications, patents, and open-source contributions that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork. About the team AWS Applied AI Solutions' (AAIS) vision is every business innovating with Amazon AI teammates. Our mission is to build delightful AI solutions that improve human capabilities and business outcomes. The Agentic WorkSpaces organization within AAIS envisions a world where people, teams, and AI collaborate securely from anywhere to create unprecedented value for every organization. We build lovable products that empower every business to unlock the full potential of human-AI teamwork, driving smarter decisions, greater creativity, more value, and faster innovation with confidence. Amazon Agentic WorkSpaces (AAWS) is building the world's most lovable, secure, and trusted always-on workspace where AI agents and humans work as partners behind enterprise-grade security. Our portfolio spans persistent desktops (Personal), application streaming (Applications), and Core, and is evolving into the governed operating environment for the hybrid workforce: humans get AI-native desktops for their role, and agents get governed desktops scoped to their task, with administrators managing both as one. This surface includes WS4Builders (an AI-native environment for builders) and WorkSpaces for Agents (W4A) — enabling AI agents to work the way humans do, with access to real applications, real interfaces, and real computing environments. Enterprises want to use AI agents for critical business workloads that touch legacy desktop applications and mainframes, yet 75% of organizations run legacy applications that lack modern APIs, and 90% of corporate data remains locked in systems never designed for agents. Agentic WorkSpaces solves this: it gives enterprises a secure, governed environment where agents and humans operate both legacy and modern applications directly, just as an employee would, without costly migrations.
US, WA, Seattle
We are seeking a Senior Manager, Applied Science to build and lead the science organization across Agentic WorkSpaces. This is a foundational leadership role spanning the full portfolio — Personal, Applications, and Core, and the agentic surfaces (WS4Builders and WorkSpaces for Agents). You will hire, grow, and lead a team of applied scientists who define how we measure and improve the performance of AI agents and human-AI teams. A core part of the role is defining the science agenda itself — identifying which problems are most worth solving and where the highest-leverage bets lie. Directions worth exploring might include Organizational Intelligence (turning institutional knowledge into agent-consumable skills), AI Agent Experience / AiAX (agent observability and autonomous remediation), and contextual, behavioral security that adapts enforcement in real time for human and agent sessions — but these are illustrative examples, not a fixed roadmap, and many other directions are possible. You and your team will define which ones we pursue. The problems your team will solve do not have established industry patterns. You will set the scientific direction and build the team that determines how AI agents and people perceive, reason about, and act reliably within computing environments at enterprise scale. What You Will Do Build and lead the applied science team. Hire, develop, and retain a high-caliber team of applied scientists spanning the Agentic WorkSpaces portfolio. Set the bar for scientific talent, create the growth paths, and build the culture that makes AAWS a destination for the best agent and human-AI researchers. Own the science strategy across the portfolio. Direct the research agenda for how we measure and improve agents and human-AI teams: the benchmarks, task suites, and metrics (accuracy, cost-per-task, task completion, productivity) that turn subjective "it works" judgments into rigorous, reproducible measurement that gates what we ship. Define and drive high-leverage research directions. Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS, and engage directly with enterprise customers on how agent performance, safety, and human-AI productivity are measured and earned. Key job responsibilities Build and lead the applied science team. Hire, develop, and retain a high-caliber team of applied scientists spanning the Agentic WorkSpaces portfolio. Set the bar for scientific talent, create the growth paths, and build the culture that makes AAWS a destination for the best agent and human-AI researchers. Own the science strategy across the portfolio. Direct the research agenda for how we measure and improve agents and human-AI teams: the benchmarks, task suites, and metrics (accuracy, cost-per-task, task completion, productivity) that turn subjective "it works" judgments into rigorous, reproducible measurement that gates what we ship. Define and drive high-leverage research directions. Work with your team to identify the problems most worth solving and shape the science agenda. Directions worth exploring might include how agents combine deterministic tool use (MCP) with visual reasoning from computer use; Organizational Intelligence and workflow learning (learning from expert recordings, voice annotations, and SOPs); and AI Agent Experience / AiAX (detecting when agents are stuck or degrading productivity and autonomously remediating) — these are illustrative starting points, and your team will weigh them against many other possibilities. Translate science into shipped product. Partner with engineering, product, and program leaders to move models, evaluation, and learning systems from prototype into a decade-old production service operating at massive scale, without compromising the reliability that customers depend on. Represent science in leadership and to customers. Be the scientific voice in org-level planning and roadmap decisions across AAWS, and engage directly with enterprise customers on how agent performance, safety, and human-AI productivity are measured and earned. Set the long-term scientific vision and team strategy: Define what best-in-class agent performance, evaluation, and learning look like across Agentic WorkSpaces — for computer-using agents and human-AI teams alike. Chart a multi-year research roadmap, and build the team and plan to deliver it. Secure buy-in from VP-level leadership. Hire and grow scientific talent: Own recruiting, calibration, development, and retention for the science team. Mentor scientists toward senior and principal scope, and raise the scientific bar across the organization. Direct research on highly ambiguous, novel problems: Guide the team through foundational challenges in agent perception, reasoning, evaluation, reliability, and human-AI collaboration — problems where neither the approach nor the success criteria are pre-defined. Drive cross-organizational alignment: Work across partner teams (AgentCore, Bedrock model teams, Identity, Security, the MCP ecosystem) and across the Applied AI Solutions product portfolio, with product and engineering leadership, to ensure scientific decisions compose into a coherent product. Deliver measurable business impact: Ensure your team's research translates to customer outcomes: higher task accuracy, lower cost-per-action, faster time-to-production, measurable productivity for human-AI teams, and the trust that lets enterprises scale agent workflows. Establish scientific rigor and operational excellence: Set the standard for experimentation, evaluation, and reproducibility, and the mechanisms that keep the science organization productive and accountable. Advance the state of the art: Enable and champion contributions to the external technical community through publications, patents, and open-source work that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork. About the team AWS Applied AI Solutions' (AAIS) vision is every business innovating with Amazon AI teammates. Our mission is to build delightful AI solutions that improve human capabilities and business outcomes. The Agentic WorkSpaces organization within AAIS envisions a world where people, teams, and AI collaborate securely from anywhere to create unprecedented value for every organization. We build lovable products that empower every business to unlock the full potential of human-AI teamwork, driving smarter decisions, greater creativity, more value, and faster innovation with confidence. Amazon Agentic WorkSpaces (AAWS) is building the world's most lovable, secure, and trusted always-on workspace where AI agents and humans work as partners behind enterprise-grade security. Our portfolio spans persistent desktops (Personal), application streaming (Applications), and Core, and is evolving into the governed operating environment for the hybrid workforce: humans get AI-native desktops for their role, and agents get governed desktops scoped to their task, with administrators managing both as one. This surface includes WS4Builders (an AI-native environment for builders) and WorkSpaces for Agents (W4A) — enabling AI agents to work the way humans do, with access to real applications, real interfaces, and real computing environments. Enterprises want to use AI agents for critical business workloads that touch legacy desktop applications and mainframes, yet 75% of organizations run legacy applications that lack modern APIs, and 90% of corporate data remains locked in systems never designed for agents. Agentic WorkSpaces solves this: it gives enterprises a secure, governed environment where agents and humans operate both legacy and modern applications directly, just as an employee would, without costly migrations.