Insup Lee Elected 2022 AAAS Fellows
January 31, 2023

Insup Lee, Warren D. Seider and Karen I. Winey are among the eight Penn scholars that have been named to the 2022 class of American Association for the Advancement of Science Fellows. They are among more than 500 researchers honored for their “scientifically and socially distinguished achievements.”

Congratulations, Insup!

"Stroke Detection Wristband" selected Time’s 100 best technologies in 2022
November 14, 2022

James Weimer co-founded Neuralert Technologies in 2019 as a spin-out from the University of Pennsylvania. Its mission is to transform the method of monitoring for stroke symptoms in hospitalized patients. Their "Stroke Detection Wristband" Named to TIME’s List of the Best Inventions of 2022


Mayur Naik and his team won ESEC/FSE 2022 Test of Time Award
October 24, 2022

A very warm congratulations to Mayur, Saswat Anand, Mary Jean Harrold, and Hongseok Yang! Their paper titled "Automated Concolic Testing of Smartphone Apps" was selected for the ESEC/FSE 2022 Test of Time Award.  This is a great recognition of the outstanding and impactful work he's doing!

PRECISE Team Wins ACM TECS Best Paper Award at ESWeek 2022
October 12, 2022

Radoslav Ivanov, Taylor J. Carpenter, James Weimer, Rajeev Alur, George J. Pappas, and Insup Lee won ACM Transactions on Embedded Computing Systems (TECS) Best Paper Award at ESWeek 2022. Their paper addresses the problem of verifying the safety of autonomous systems with neural network (NN) controllers; and provides evaluations on four benchmarks.

Congratulations on your well-deserved achievement!

Insup Lee received ACM SIGMBED Distinguished Leadership Award 2022
October 12, 2022

ACM SIGBED established the Distinguished Leadership Awards to recognize individuals who have exemplary and substantive leadership in leading and implementing activities over the communities relevant to SIGBED at regional, national, and/or international level.

PRECISE Director, Insup Lee, was named the recipient of the ACM SIGBED Inaugural Distinguished Leadership Award this year for his leadership in promoting cross-fertilization of ACM and IEEE communities in Cyber-Physical Systems, Embedded Systems, and Real-Time Systems.

Warmest congratulations to Insup!


Linh Thi Xuan Phan Builds New Defenses for Cyber-physical Infrastructure
June 30, 2022

PRECISE Faculty, Linh Thi Xuan Phan, will be collaborating with an international team of researchers that will work to unify the design of software and hardware components in cyber-physical transportation systems.

This five-year, $5.7 million project, is being funded by the National Science Foundation (NSF), and will be led by researchers at the University of California, Santa Cruz. It will feature collaborators from the University of California, Berkeley; Vanderbilt University; the University of Colorado, Boulder; the Norwegian University of Science Technology and Italy’s IMT School for Advanced Studies, Lucca.

A major goal of this research is to help self-driving cars and other autonomous transportation systems achieve increased efficiency and performance, as well as better safety guarantees.

PRECISE Team Wins Best Paper Award at ICCPS 2022
May 6, 2022

Yahan Yang, Ramneet Kaur, Souradeep Dutta, and Insup Lee won the Best Paper Award at the 13th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2022). Below is the title and abstract for their paper.

"Interpretable Detection of Distribution Shifts in Learning Enabled Cyber-Physical Systems"

Deep neural networks have allowed us to use high dimensional real world signals generated from sensors like camera and LiDAR. However, this  comes with its potential perils. The pitfalls arise from possible over-fitting, and subsequent unsafe behavior when exposed to unknown environments. In this paper, our proposal is to build good representations for in-distribution data. We introduce the idea of a memory bank to store prototypical samples from the input space. We use these memories to compute     probability density estimates using kernel density estimation techniques. We evaluate our technique on two challenging scenarios :  a self-driving car setting implemented inside the simulator CARLA with image inputs, and an autonomous racing car navigation setting, with LiDAR inputs. An added benefit of using training samples as memories to detect out-of-distribution inputs is that the system is interpretable to a human operator. Explanation of this nature is generally hard to obtain from pure deep learning based alternatives.  

Congratulations on your well-deserved achievement!


Welcome our new postdoc, Kuk Jin Jang!
December 22, 2021

We’re thrilled to announce our newest postdoc, Kuk Jang, is joining PRECISE!

Kuk recently completed his doctorate in the Electrical and Systems Engineering Department and has been working with Dr. Rahul Mangharam in mLab since 2014. His research interests include domain adaptation and reinforcement learning for medical and robotics applications.

Kuk will be working with Drs. Insup Lee and Jim Weimer on data design for robust machine learning in our “MURI” and “Smart Alarms 2.0” projects. We are glad he decided to extend his time at Penn and look forward to seeing his continued contributions.

Welcome, Kuk!

PRECISE Team Wins Best Paper Award at Winter Simulation Conference 2021
December 14, 2021

Alan Ismaiel, Ivan Ruchkin, Jason Shu, Oleg Sokolsky, and Insup Lee won the Best Contributed Theoretical Paper Award at the 54th Winter Simulation Conference (WSC 2021). Below is the title and abstract for their paper:

Title: Data Generation with PROSPECT: a Probability Specification Tool

Abstract: Stochastic simulations of complex systems often rely on sampling dependent discrete random variables. Currently, their users are limited in expressing their intention about how these variables are distributed and related to each other over time. This limitation leads the users to program complex and error-prone sampling algorithms. This paper introduces a way to specify, declaratively and precisely, a temporal distribution over discrete variables. Our tool PROSPECT infers and samples this distribution by solving a system of polynomial equations. The evaluation on three simulation scenarios shows that the declarative specifications are easier to write, 3x more succinct than imperative sampling programs, and are processed correctly by PROSPECT. 

Congratulations on your well-deserved achievement!

StrokeDetectAI is now licensed
December 13, 2021

StrokeDetectAI, a Python library for detecting onset of asymmetric movement in hospitalized patients with risk factors of stroke and those with no baseline asymmetric upper extremity weakness, has officially been licensed to Neuralert! This is a technology developed out of our Assured Autonomy and Smart Alarms projects.