As of September 2016, I have joined Google as a Software Engineer.

In August 2016, I received my PhD from the department of Computer and Information Science at the University of Pennsylvania. I was advised in my work by Dr. Insup Lee, and was a member of the PRECISE Center, specifically the Smart Alarm research group. My graduate work focused on applying machine learning to high-frequency, multi-source physiologic data to improve clinical care. In particular, I designed and implemented smarter alarm systems and clinical decision support systems with the goal of helping doctors and nurses to detect and prevent adverse medical events in the ICU, and which could operate safety and effectively in the hospital environment.
Highlighted Research Projects
Parameter Invariant Statistics: There exist numerous approaches to designing medical monitors ranging from simple sensor thresholding techniques to more complex machine learning approaches. While all the current design approaches have different strengths and weaknesses, their performance degrades when underlying models contain unknown parameters and training data is scarce. Under this scenario, an alternative approach that performs well is the parameter-invariant detector, which utilizes sufficient statistics that are invariant to unknown parameters to achieve a constant false alarm rate across different systems.
Decision Support System Development: In the modern hospital, patients are often connected to a multitude of medical devices, each of which records some data about the patient. Much of this data is currently inaccessible, or is underutilized. As part of our efforts to improve hospital care, I work with doctors and nurses to identify specific use-cases in which better access to clinical data provides a promising possibility of increased care. Once use-cases are identified, I work with clinicians to determine what data is currently being collected, how it is being stored, and how we can gather that data and apply straightforward machine learning techniques develop clinical decision support systems (CDSS), software systems that provide as tools for use in clinical care.
Machine Learning on Physiologic Data: Utilizing the aformentioned large amount of patient data generated in hospitals requires the ability to extract patterns from and learn over physiologic data. These data take the form of high to medium-frequency, temporal waveforms, and are often influenced by external care events. My research incorporates investigations into novel mechanisms for utilizing these physiologic data streams in machine learned models to improve CDSS.
Generic Clinical Decision Support Systems In working with clinicians to develop clinical decision support sytems, we have identified design methodologies and implementation commonalities that we see as being key to the development of robust CDSSs. The Generic Smart Alarm and Generic Clinical Decision Support Systems represent the establishment of reconfigurable pipelines and component libraries to facilitate the development of smarter alarms and CDSSs, to allow them to be quickly customized and reused in creation of these systems.
Parameter Invariant Statistics and their Application to Clinical Decision Support Alexander Roederer Thesis, Degree of Doctor of Philosophy, University of Pennsylvania August 2016
Parameter-Invariant Monitor Design for Cyber Physical Systems James Weimer, Radoslav Ivanov, Sanjian Chen, Alexander Roederer, Oleg Sokolsky, Insup Lee Proceedings of the IEEE September 2017
Clinician-in-the-Loop Annotation of ICU Bedside Alarm Data Alexander Roederer, Joseph Dimartino, Jacob Gutsche, Margaret Mullen-Fortino, Sachin Shah, C. William Hanson, Insup Lee IEEE Conference on Connected Health: Applications, Systems, and Engineering Technologies 2016 April 2016
Robust Monitoring of Hypovolemia in Intensive Care Patients using Photoplethysmogram Signals - Alexander Roederer, James Weimer, Joseph DiMartino, Jacob Gutsche, Insup Lee IEEE Engineering in Medicine and Biology Society 2015 August 2015
Parameter Invariant Design of Medical Alarms - James Weimer, Radoslav Ivanov, Alexander Roederer, Sanjian Chen, Insup Lee IEEE Design and Test June 2015
Towards Non-Invasive Monitoring of Hypovolemia in Intensive Care Patients Alexander Roederer, James Weimer, Joseph Dimartino, Jacob Gutsche Insup Lee Medical Cyber Physical Systems Workshop 2015 April 2015
Wandering Data: A Scalable, Durable System for Effective Visualization of Patient Health Data Alexander Roederer, Andrew King, Sanjian Chen, Margaret Mullen-Fortino, Soojin Park, Oleg Sokolsky, Insup Lee IEEE Computer Based Medical Systems May 2014
Prediction of Significant Vasospasm in Aneurysmal Subarachnoid Hemorrhage Using Automated Data Alexander Roederer, John H. Holmes, Michelle J. Smith, Insup Lee, Soojin Park Neurocritical Care 2014
A Survey of Active Learning for Classification of Medical Signals Alexander Roederer University of Pennsylvania Written Preliminary Examination II Presented November 2012
Clinical Decision Support for Integrated Cyber-Physical Systems: A Mixed Methods Approach Alex Roederer, Andrew Hicks, Enny Oyeniran, Insup Lee and Soojin Park IHI 2012 1;5S Demo Presented January 2012
Challenges and Research Directions in Medical Cyber-Physical Systems Insup Lee, Oleg Sokolsky, Sanjian Chen, John Hatcliff, Eunkyoung Jee, BaekGyu Kim, Andrew L. King, Margaret Mullen-Fortino, Soojin Park, Alexander Roederer, Krishna K. Venkatasubramanian Proceedings of the IEEE, 2012
Limitations of Threshold-Based Brain Oxygen Monitoring for Seizure Detection Soojin Park, Alexander Roederer, Ram Mani, Sarah Schmitt, Peter D. LeRoux, Lyle H. Ungar, Insup Lee and Scott E. Kasner Neurocritical Care, November 2011
GSA: a framework for rapid prototyping of smart alarm systems Andrew L. King, Alex Roederer, David Arney, Sanjian Chen, Margaret Mullen-Fortino, Ana Giannareas, William Hanson III, Vanessa Kern, Nicholas Stevens, Jonathan Tannen, Adrian Viesca Trevino, Soojin Park, Oleg Sokolsky, Insup Lee IHI 2010
Demo of the Generic Smart Alarm: a framework for the design, analysis, and implementation of smart alarms and other clinical decision support systems Andrew L. King, Alex Roederer, Sanjian Chen, Nicholas Stevens, Philip Asare, Oleg Sokolsky, Insup Lee, Margaret Mullen-Fortino, Soojin Park Wireless Health 2010
Divvy: An ATP Meta-system Based on Axiom Relevance Ordering Alex Roederer, Yury Puzis, Geoff Sutcliffe CADE 2009
PRECISE Industry Day 2015 University of Pennsylvania, October 2015 2-Minute Presentation, Poster Presentation
Conference of the IEEE Engineering and Medicine in Biology Society Milano, Italy, August 2015 Oral Presentation of Work
PennApps Health Symposium University of Pennsylvania, January 2015 Invited Panelist
PRECISE Industry Day 2014 University of Pennsylvania, October 2014 2-Minute Presentation, Poster Presentation
Smart Connected Medical Home Retreat University of Pennsylvania, June 2014 Invited Speaker
TA for CIS 400/401 Taught by Insup Lee, Fall 2014 and Spring 2015
TA for CIS 400/401 Taught by Insup Lee, Fall 2011 and Spring 2012
TA for CIS 160 Taught by Jean Gallier, Fall 2010 Was honored with the 2011Penn Prize for Excellence in Teaching by Graduate Students.
Software Engineer Google, Inc. September 2016-Current
Undergraduate Student Researcher at NASA Ames Research Center Summer 2009
Intern, Software Testing for ACAS/Altimeter Groups at Rockwell Collins Summer 2007, Summer 2008
University of Pennsylvania - PhD, Computer and Information Science
Research: Machine Learning, Decision Support, and Smart Alarms in Critical Healthcare scenarios, under the advise of Dr. Insup Lee
University of Pennsylvania - M.S. in Engineering, Computer and Information Science
Completed Summer 2014
University of Miami - B.S. Computer Science, B.S. Applied Mathematics
Research Area: Latent Semantic Analysis and Automated Theorem Provers, with Professor Geoff Sutcliffe.
Advancing Women in Engineering Board Member
Penn GEMS: Girls in Engineering, Math and Science Workshop Designer and Leader
Taught 7th and 8th grade girls about binary, sorting, stacks/queues and cryptography. Co-taught with Katherine Gibson
Penn GEARS Day: Girls in Engineering and Related Sciences Workshop Designer and Leader
Taught 10th and 11th grade girls basic programming constructs and binary through LOGO. Co-taught with Katherine Gibson (We're about halfway down the page here.)