About
As of March 2024, I'm a full time Software Engineer at Google.

I live and work in Philadelphia, Pennsylvania.
Technical Skills
Software Development; proficient in Python, Java, C++.
Test-Driven Development, including unit testing and integration testing.
Machine Learning/Artificial Intelligence, including data set management, feature extraction, model training/tuning, and evaluation.
Languages Spoken: English (native); Spanish (fluent); French (elementary)
Employment
2022 to 2024: Senior Software Engineer
Contributor, Android System UI: My team is responsible for the Notification stack, from UI down to on-device system server managing notification lifetime and behavior. My current focus is on system server improvements, feature development, and maintaining compatibility APIs.
2018 to 2022: Senior Software Engineer
Tech lead, Google Assistant Personal Knowledge Graph: built and maintained scalable microservices processing and serving structured personal data to power personalization. Featured at 2019 Google I/O.
TL of 10 engineers, set team direction, created and managed relationships with dozens of internal clients, from speech biasing to fulfillment.
Grew service to handle hundreds of thousands of QPS.
2016 to 2017: Software Engineer
Contributor, Assistant Memory: built Assistant Memory Parking feature, helped build Assistant Memory infrastructure.
Assistant Memory feature was featured in Google's 2020 Superbowl Ad.
Education
University of Pennsylvania - Ph.D., Computer and Information Science
Dissertation: Parameter Invariant Statistics and Their Application to Clinical Decision Support
Advised by Dr. Insup Lee
Degree awarded August 2016
University of Pennsylvania - M.S., Computer and Information Science
Degree awarded Summer 2014
University of Miami - B.S., Computer Science, B.S., Applied Mathematics
Departmental GPA: 3.99/4.0; Robert L Kelly Award for Outstanding Junior Mathematics Major; UM Honor Roll
Advised by Dr. Geoff Sutcliffe
Degree awarded 2009, with Departmental Honors in Computer Science
Research (2009-2016) [Click to Expand]
Summary
Member, PRECISE Center's Smart Alarm research group.
Focused on building sytems to apply machine learning to high-frequency, multi-source physiologic data to improve clinical care through safe and effective smart alarms and decision support.
Machine Learning for Physiologic Data:
Machine learned discriminative models over big physiologic critical care patient data sets.
Extracting features from high to medium-frequency temporal waveforms with confounding external care events/comorbidities.
Development of new mechanisms for utilizing physiologic data streams in machine learned models to improve clinical decision support.
Decision Support System Development:
Working with clinicians to identify use-cases where access to machine-analyzed clinical data could improve care (sepsis, hypovolemia, diabetes, cerebral vasospasm).
Collecting and synthesizing data and applying machine learning techniques to develop clinical decision support systems (CDSS) as tools for use in clinical care.
Parameter Invariant Statistics:
Development of parameter-invariant detectors, using sufficient statistics that are invariant to unknown parameters to achieve a constant false alarm rate across different systems.
Publications [Click to Expand]
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 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
Talks [Click to Expand]
PRECISE Industry Day 2015 University of Pennsylvania, October 2015 Short 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
Teaching [Click to Expand]
TA for CIS 400/401 (Senior Design Project) Taught by Dr. Insup Lee, Fall 2014 and Spring 2015
TA for CIS 400/401 (Senior Design Project) Taught by Dr. Insup Lee, Fall 2011 and Spring 2012
TA for CIS 160 (Mathematical Foundations of Computer Science) Taught by Jean Gallier, Fall 2010
Was honored with the 2011Penn Prize for Excellence in Teaching by Graduate Students.
Volunteering
PRIDE 🏳️‍🌈 at Google ERG, Cambridge Chapter Lead 2017-2024
Organize and Coordinate quarterly events for the Cambridge chapter of Google's LGBTQ+ Employee Resource Group
Physical Easter Eggs Group Lead 2018-2022
Lead a team that worked with Google Real Estate and Workplace Services and local artists to embed multimedia puzzle experiences in new build offices.
Advancing Women in Engineering Board Member 2010-2015
Logistical support and Volunteering
Penn GEMS: Girls in Engineering, Math and Science Workshop Designer and Leader 2011-2014
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 2011-2014
Taught 10th and 11th grade girls basic programming constructs and binary through LOGO. Co-taught with Dr. Katherine Gibson
Internships
Undergraduate Student Researcher NASA Ames Research Center Summer 2009
Software Testing Intern ACAS/Altimeter Groups, Rockwell Collins Summer 2007, Summer 2008