principal updates on homepage, welcome, and healthcare applications

Author: ngisolfi

_includes/footer.html

@@ -20,7 +20,7 @@ <h5>FOLLOW US</h5>
 	  <div class="footer-col" style="width:1000px">
         <h5>COME VISIT</h5>
         <p>Carnegie Mellon University<br/>
-Newell Simon Hall 3128 <br/>
+Newell Simon Hall 3100 <br/>
 5000 Forbes Ave <br/>
 Pittsburgh PA 15213-3815</p>
       </div>

application_areas.md

@@ -3,10 +3,10 @@ layout: page
 title: "Application Areas"
 ---
 
-Here at the lab, we believe that the most useful concepts in AI and ML reveal themselves through constant exposure to domain-specific challenges and constraints.
-It is these limitations that force us to get creative and think about how to address the hurdles that stand in the way of widespread adoption of AI technology.
-Our goal is to assuage these pain points to best meet the needs of potential users of AI technology, whether they be subject matter experts, developers of AI systems, or general users.
-To get a flavor for the type of work happening at the lab, here is a selection of five broad application areas where we are applying our research.
+At the Auton Lab, we believe that the most useful concepts in AI and ML reveal themselves through frequent exposure to domain-specific challenges and constraints.
+It is these limitations that motivate us to engage creatively and think about how to address the hurdles that stand in the way of widespread, beneficial adoption of AI technology.
+Our goal is to assuage these pain points to best meet the needs of potential users of AI, whether they be subject matter experts, developers of AI systems, or general users.
+To get a flavor for the type of work happening at the lab, here is a selection of five broad application areas where we are applying and proving our research.
 
 {% for slide in site.applications %}
 <div class="row checker" style="margin:auto;justify-content:center;width:100%;max-width:1000px">

collections/_applications/healthcare_public_health.md

@@ -1,29 +1,29 @@
 ---
 layout: research
 title:  "Health Care & Public Health"
-summary: "Research at the intersection of medicine and machine learning. The Auton Lab works closely with clinicians at UPMC and the University of Pittsburgh to identify opportunities for AI and ML to make an impact in medical practice and clinical research. Lab history of impact in healthcare applications goes back decades. Our lab is leading the field in ways to deploy AI in ways that are clinically relevant and help save lives."
+summary: "Research at the intersection of medicine and machine learning. The Auton Lab works closely with clinicians to identify opportunities for AI and ML to make an impact in medical practice and clinical research. Lab history of impact in healthcare applications goes back decades. Our lab aims to lead the field towards deploying AI in ways that are clinically relevant and help save lives."
 splash: "/assets/images/health-care.jpg"
 projects:
   - 
-    name: Public Health Surveillance
+    name: Public Health and Epidemiology
     anchor: phs
     image: "assets/images/mcba.png"
-    blurb: "We develops statistical inference model in a long-term joint research collaboration with epidemiologists and infection prevention experts from the University of Pittsburgh. Our algorithms detect systematic outbreaks and identify root causes by joining disparate sources of information such as genetic tests, patient electronic health records (EHRs), and other epidemiological information. Leveraging multiple data sources, our algorithms establish corroborating evidence to support or dismiss hypothetical outbreak scenarios, both increasing detectability and speed of analysis while maintaining low false alert rates. We also perform analytics to detect and forecast new positive cases of COVID-19 using microbiological testing of wastewater and develop a systematic analysis capability that the Allegheny County Health Department will use in daily practice for public health surveillance."
+    blurb: "We develop statistical inference models in a long-term joint research collaboration with epidemiologists and in-hospital infection prevention experts. Our algorithms simultaneously detect emerging outbreaks and identify their most likely spread pathways by joining disparate sources of information such as genetic tests of microbial isolates, patient electronic health records, and other epidemiological information. Leveraging multiple data sources, our algorithms establish corroborating evidence to support or dismiss hypothetical outbreak scenarios, both increasing detectability and speed of analysis while maintaining low false alert rates. We also perform analytics to detect and forecast new positive cases of COVID-19 using microbiological testing of wastewater and we develop a systematic analysis capability that the Allegheny County Health Department could use in daily practice for public health surveillance."
   -
     name: Critical Care Medicine
     anchor: icm
     image: "/assets/images/auviewer_screenshot.png"
-    blurb: "Critical Care Medicine, from initial incident and response to emergency room discharge or hospital admission in urban, rural, and field settings, is a challenging domain for machine learning. These incidents arise in routine emergency medicine operations as well as in large scale crises, inlcluding natural disasters, mass casualty incidents, emerging pandemics, and terrorist events, each putting unique demands and stresses on provision of the necessary care. The nature of the emergency involves unpredictability of demand for services, varying severity of cases, need for rapid assessment, and required persistent availability of stand-by resources. It puts a physical, cognitive and emotional strain on performers, exacerbating risk of human errors."
+    blurb: "In a critical care environment, adverse events tend to escalate quickly, patients are monitored with increasingly powerful sensors that generate large streams of data, and the clinicians are expected to make correct treatment decisions rapidly, while facing information overload and cognitive and emotional pressure. The margin for mistakes is tight, and their consequences can be deadly. It is one of the potentially most impactful areas of meaningful application of AI, which can help sift through large volumes of data generated at the bedside of critically ill, as well as their medical histories, to quickly and reliably extract operationally useful insights to inform the clinicians, preventing them from missing important clues, and reducing their fear of drowning in the ocean of data they do not have the capacity to internalize and reliably process.  Medicine, from initial incident and response to emergency room discharge or hospital admission in urban, rural, and field settings, is a challenging domain for machine learning. These incidents arise in routine emergency medicine operations as well as in large scale crises, inlcluding natural disasters, mass casualty incidents, emerging pandemics, and terrorist events, each putting unique demands and stresses on provision of the necessary care. The nature of the emergency involves unpredictability of demand for services, varying severity of cases, need for rapid assessment, and required persistent availability of stand-by resources. It puts a physical, cognitive and emotional strain on performers, exacerbating risk of human errors."
   -
     name: Field Medicine
     anchor: tracir
     image: "/assets/images/battlefield-health.jpg"
-    blurb: "Using cutting edge research and technologies, CMU develops techniques and systems intended to rapidly triage and stabilize injuries sustained in high-risk areas, particularly the battlefield or during military operations. Automated diagnosis and detection of injuries can quickly identify problems, with the eventual goal of automatic medical kits to stabilize soldiers and victims of disasters until qualified help can arrive."
+    blurb: "AI and robotics can be of great help assisting emergency medicine workers from the initial medical intervention at the incident site, through evacuation or transportation, to hospital admission or discharge. Either supporting triage of casualty cases using machine learning driven assessment of severity of their condition, or using robotic devices to automate routine tasks such as needle insertion for venous fluid resuscitation, or automatically stabilizing resuscitated patients while they await more comprehensive treatment at the appropriate healthcare facility, we are developing AI and robotics solutions to support field medicine. The attainable positive impact includes empowering medics to save more lives and scaling up scarce field medicine resources. The beneficial applications include combat field care, responding to traffic or massive casualty accidents, natural disasters, or rescuing subjects who are stranded, injured, wounded or ill in austere, remote or inaccessible locations."
   -
     name: Biomedical Imaging
     anchor: bmi
     image: "/assets/images/angiogram.png"
-    blurb: "The proven ability of AI to learn from images encounters new challenges in biomedical imaging modalities, where there is a large amount of variance between individual patients due to both anatomical structure as well as a different definitions of health for different cohorts of patients. Furthermore, videos contain rich information and potential that has yet to be fully unlocked in clinical applications. Our work focuses on supporting expert decision-making in order to improve clinical outcomes."
+    blurb: "The proven ability of AI to learn from images encounters new challenges in biomedical imaging modalities, primarily due to a large amount of variance between individual patients due to both differences in their anatomical structure and differing definitions of health for distinct cohorts of patients. Furthermore, medical images and videos contain rich information and potential that has yet to be fully unlocked in clinical applications. Our work in this area focuses on supporting expert decision-making in order to improve clinical outcomes."
 ---
 
 

collections/_carousel/healthcare_public_health.md

@@ -1,29 +1,29 @@
 ---
 layout: research
 title:  "Health Care & Public Health"
-summary: "Research at the intersection of medicine and machine learning. The Auton Lab works closely with clinicians at UPMC and the University of Pittsburgh to identify opportunities for AI and ML to make an impact in medical practice and clinical research. Lab history of impact in healthcare applications goes back decades. Our lab is leading the field in ways to deploy AI in ways that are clinically relevant and help save lives."
+summary: "Research at the intersection of medicine and machine learning. The Auton Lab works closely with clinicians to identify opportunities for AI and ML to make an impact in medical practice and clinical research. Lab history of impact in healthcare applications goes back decades. Our lab aims to lead the field towards deploying AI in ways that are clinically relevant and help save lives."
 splash: "/assets/images/health-care.jpg"
 projects:
   - 
-    name: Public Health Surveillance
+    name: Public Health and Epidemiology
     anchor: phs
     image: "assets/images/mcba.png"
-    blurb: "We develops statistical inference model in a long-term joint research collaboration with epidemiologists and infection prevention experts from the University of Pittsburgh. Our algorithms detect systematic outbreaks and identify root causes by joining disparate sources of information such as genetic tests, patient electronic health records (EHRs), and other epidemiological information. Leveraging multiple data sources, our algorithms establish corroborating evidence to support or dismiss hypothetical outbreak scenarios, both increasing detectability and speed of analysis while maintaining low false alert rates. We also perform analytics to detect and forecast new positive cases of COVID-19 using microbiological testing of wastewater and develop a systematic analysis capability that the Allegheny County Health Department will use in daily practice for public health surveillance."
+    blurb: "We develop statistical inference models in a long-term joint research collaboration with epidemiologists and in-hospital infection prevention experts. Our algorithms simultaneously detect emerging outbreaks and identify their most likely spread pathways by joining disparate sources of information such as genetic tests of microbial isolates, patient electronic health records, and other epidemiological information. Leveraging multiple data sources, our algorithms establish corroborating evidence to support or dismiss hypothetical outbreak scenarios, both increasing detectability and speed of analysis while maintaining low false alert rates. We also perform analytics to detect and forecast new positive cases of COVID-19 using microbiological testing of wastewater and we develop a systematic analysis capability that the Allegheny County Health Department could use in daily practice for public health surveillance."
   -
     name: Critical Care Medicine
     anchor: icm
     image: "/assets/images/auviewer_screenshot.png"
-    blurb: "Critical Care Medicine, from initial incident and response to emergency room discharge or hospital admission in urban, rural, and field settings, is a challenging domain for machine learning. These incidents arise in routine emergency medicine operations as well as in large scale crises, inlcluding natural disasters, mass casualty incidents, emerging pandemics, and terrorist events, each putting unique demands and stresses on provision of the necessary care. The nature of the emergency involves unpredictability of demand for services, varying severity of cases, need for rapid assessment, and required persistent availability of stand-by resources. It puts a physical, cognitive and emotional strain on performers, exacerbating risk of human errors."
+    blurb: "In a critical care environment, adverse events tend to escalate quickly, patients are monitored with increasingly powerful sensors that generate large streams of data, and the clinicians are expected to make correct treatment decisions rapidly, while facing information overload and cognitive and emotional pressure. The margin for mistakes is tight, and their consequences can be deadly. It is one of the potentially most impactful areas of meaningful application of AI, which can help sift through large volumes of data generated at the bedside of critically ill, as well as their medical histories, to quickly and reliably extract operationally useful insights to inform the clinicians, preventing them from missing important clues, and reducing their fear of drowning in the ocean of data they do not have the capacity to internalize and reliably process.  Medicine, from initial incident and response to emergency room discharge or hospital admission in urban, rural, and field settings, is a challenging domain for machine learning. These incidents arise in routine emergency medicine operations as well as in large scale crises, inlcluding natural disasters, mass casualty incidents, emerging pandemics, and terrorist events, each putting unique demands and stresses on provision of the necessary care. The nature of the emergency involves unpredictability of demand for services, varying severity of cases, need for rapid assessment, and required persistent availability of stand-by resources. It puts a physical, cognitive and emotional strain on performers, exacerbating risk of human errors."
   -
     name: Field Medicine
     anchor: tracir
     image: "/assets/images/battlefield-health.jpg"
-    blurb: "Using cutting edge research and technologies, CMU develops techniques and systems intended to rapidly triage and stabilize injuries sustained in high-risk areas, particularly the battlefield or during military operations. Automated diagnosis and detection of injuries can quickly identify problems, with the eventual goal of automatic medical kits to stabilize soldiers and victims of disasters until qualified help can arrive."
+    blurb: "AI and robotics can be of great help assisting emergency medicine workers from the initial medical intervention at the incident site, through evacuation or transportation, to hospital admission or discharge. Either supporting triage of casualty cases using machine learning driven assessment of severity of their condition, or using robotic devices to automate routine tasks such as needle insertion for venous fluid resuscitation, or automatically stabilizing resuscitated patients while they await more comprehensive treatment at the appropriate healthcare facility, we are developing AI and robotics solutions to support field medicine. The attainable positive impact includes empowering medics to save more lives and scaling up scarce field medicine resources. The beneficial applications include combat field care, responding to traffic or massive casualty accidents, natural disasters, or rescuing subjects who are stranded, injured, wounded or ill in austere, remote or inaccessible locations."
   -
     name: Biomedical Imaging
     anchor: bmi
     image: "/assets/images/angiogram.png"
-    blurb: "The proven ability of AI to learn from images encounters new challenges in biomedical imaging modalities, where there is a large amount of variance between individual patients due to both anatomical structure as well as a different definitions of health for different cohorts of patients. Furthermore, videos contain rich information and potential that has yet to be fully unlocked in clinical applications. Our work focuses on supporting expert decision-making in order to improve clinical outcomes."
+    blurb: "The proven ability of AI to learn from images encounters new challenges in biomedical imaging modalities, primarily due to a large amount of variance between individual patients due to both differences in their anatomical structure and differing definitions of health for distinct cohorts of patients. Furthermore, medical images and videos contain rich information and potential that has yet to be fully unlocked in clinical applications. Our work in this area focuses on supporting expert decision-making in order to improve clinical outcomes."
 ---
 
 

index.md

@@ -16,7 +16,7 @@ permalink: /
     {%- endif -%}
     <h4>Intelligent systems that work, are useful, and make economic sense.</h4>
     <div class="row" style="margin:auto;max-width:1000px">
-    <p>We are an academic lab that studies theory, algorithms, and applications of Artificial Intelligence, Machine Learning, and Robotics.  We are part of the School of Computer Science at Carnegie Mellon University. By constantly exposing our work to domain-specific constraints and challenges, we identify gaps in the science of AI and our research focuses on developing new methods to improve the application of AI in practice.</p>
+    <p>We are an academic lab that studies theory, algorithms, and applications of Artificial Intelligence, Machine Learning, and Robotics.  We are part of the School of Computer Science at Carnegie Mellon University. By constantly exposing our work to application domain specific constraints and challenges that occur in the real world, we identify gaps in the science of AI and our fundamental research focuses on developing new methods to improve the application of AI in practice.</p>
     </div>
 	<br/>
     <h1>{{ page.carousel }}</h1>