For a list of papers prior to 2010, go here.
| Journals | Conferences | Workshops | Patents | Talks |
| TDSC | Improving the accuracy of IR-level Fault Injection, Lucas Palazzi, Guanpeng Li, Bo Fang, and Karthik Pattabiraman, 2020. |
| TECS |
Design-level and Code-level Security Analysis of IoT Devices, Farid Molazem Tabrizi and Karthik Pattabiraman. 2019. |
| TECS |
Configurable Detection of SDC-Causing Errors in Programs, Qining Lu, Guanpeng Li, Karthik Pattabiraman, Meeta Gupta and Jude Rivers. 2017. |
| TSE |
A Study of Causes and Consequences of Client-Side JavaScript Bugs, Frolin Ocariza, Kartik Bajaj, Ali Mesbah and Karthik Pattabiraman. 2017. |
| TPDS |
A Systematic Methodology for Evaluating the Error Resilience of GPGPU Applications, Bo Fang, Karthik Pattabiraman, Matei Ripeanu and Sudhanva Gurumurthi. 2016. |
| TOSEM |
Understanding JavaScript Event-based Interactions with Clematis, Saba Alimadadi, Ali Mesbah and Karthik Pattabiraman. 2016. |
| TECS | Error Detector Placement for Soft Computing Applications, Anna Thomas and Karthik Pattabiraman. 2016. |
| STVR |
Automatic Fault Localization for Client-Side JavaScript, Frolin Ocariza, Karthik Pattabiraman and Ali Mesbah. 2016. |
| TSE |
Guided Mutation Testing for JavaScript Web Applications, Shabnam Mirshokraie, Ali Mesbah and Karthik Pattabiraman. 2015. |
| TR |
Characterizing the Impact of Intermittent Hardware Faults on Programs, Layali Rashid, Karthik Pattabiraman and Sathish Gopalakrishnan. 2015. |
| JCS |
Modular Protections against Non-control Data Attacks, Cole Schlesinger, Karthik Pattabiraman, Nikhil Swamy, David Walker, and Benjamin Zorn, 2014. |
| TC |
SymPLFIED: Symbolic Program Level Fault Injection and Error Detection Framework, Karthik Pattabiraman, Nithin Nakka, Zbigniew Kalbarczyk and Ravishankar Iyer, 2013. |
| TC |
Efficient Runtime Detection and Toleration of Asymmetric Races, Paruj Ratanaworabhan, Martin Burtscher, Darko Kirovski, Benjamin Zorn, Rahul Nagpal, Karthik Pattabiraman, 2012 |
| TDSC |
Automated Derivation of Application-aware Error Detectors using Dynamic Analysis, Karthik Pattabiraman, Giacinto Paulo Saggese, Daniel Chen, Zbigniew Kalbarczyk and Ravishankar Iyer, 2011. |
| TDSC |
Automated Derivation of Application-aware Error Detectors using Static Analysis, Karthik Pattabiraman, Zbigniew Kalbarczyk and Ravishankar Iyer, 2011. |
Refereed Conferences (Acceptance rates given where known)
| DSN’20 | Trace Sanitizer: Eliminating the Effects of Non-Determinism on Error Propagation Analysis, Habib Saissi, Stefan Winter, Oliver Schwan, Karthik Pattabiraman, and Neeraj Suri. (Acceptance Rate: 16.5%) |
| ACSAC’19 | Out of Control: Stealthy Attacks on Robotic Vehicles Protected by Control-Based Techniques, Pritam Dash, Mehdi Karimi, and Karthik Pattabiraman. (Acceptance Rate: 23%) |
| ISSRE’19 | A Tale of Two Injectors: End-to-End Comparison of IR-level and Assembly-level Fault Injection, Lucas Palazzi, Guanpeng Li, Bo Fang, and Karthik Pattabiraman. (Acceptance Rate: 31.5%). |
| SC’19 | BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems, Zitao Chen, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben. (Acceptance Rate: 21%). |
| ICS’19 | BonVoision: Leveraging Spatial Data Smoothness for Recovery from Memory Soft Errors, Bo Fang, Karthik Pattabiraman, Matei Ripeanu and Sriram Krishnamoorthy (Acceptance Rate: 23.2%) |
| SAC’19 | Failure Prediction in the Internet of Things due to Memory Exhaustion, Mohammad Rafiuzzaman, Julien Gascon-Samson, Karthik Pattabiraman, and Sathish Gopalakrishnan. (Acceptance Rate: 27.5%) |
| PRDC’18 | DynPolAC: Dynamic Policy-based Access Control for IoT Systems, Mehdi Karimbuiki, Ekta Aggarwal, Karthik Pattabiraman and Andre Ivanov. (Acceptance Rate: 45%) |
| ECOOP’18 | ThingsMigrate: Platform-Independent Migration of Stateful JavaScript IoT Applications, Julien Gascon-Samson, Kumseok Jung, Shivanshu Goyal, Armin Rezalean-Asel, and Karthik Pattabiraman. (Acceptance Rate: 39%) |
| DSN’18 | Modeling Soft-Error Propagation in Programs, Guanpeng Li, Karthik Pattabiraman, Siva Kumar Sastry Hari, Michael Sullivan, and Timothy Tsai. (Acceptance Rate: 25%) Best paper Award Runner Up (one of three papers nominated for the award) |
| DSN’18 | Modeling Input-Dependent Error Propagation in Programs, Guanpeng Li, and Karthik Pattabiraman. (Acceptance Rate: 25%) |
| ICSE’18 | Inferring Hierarchical Motifs from Execution Traces, Saba Alimadadi, Ali Mesbah, and Karthik Pattabiraman. (Acceptance Rate: 21%) |
| ASE’17 | Detecting Unknown Inconsistencies in Web Applications, Frolin Ocariza, Karthik Pattabiraman, and Ali Mesbah. (Acceptance Rate: 21%) |
| SC’17 | Understanding Error Propagation in Deep learning Neural Networks (DNN) Accelerators and Applications, Guanpeng Li, Siva Hari, Michael Sullivan, Timothy Tsai, Karthik Pattabiraman, Joel Emer, Stephen Keckler. (Acceptance Rate: 19%) |
| FSE’17 | ARTINALI: Dynamic Invariant Detection for Cyber-Physical System Security, Maryam Raiyat Aliabadi, Amita Kamath, Julien Samson, and Karthik Pattabiraman. (Acceptance Rate: 24%) |
| HPDC’17 | LetGo: A Lightweight Continuous Framework for HPC Applications Under Failures, Bo Fang, Qiang Guan, Nathan Debardeleben, Karthik Pattabiraman, and Matei Ripeanu. (Acceptance Rate: 19%) |
| DSN’17 | One Bit is (Not) Enough: An Empirical Study of the Impact of Single Bit and Multiple Bit-Flip Errors, Behrooz Sangchoolie, Karthik Pattabiraman and Johan Karlsson. (Acceptance Rate: 23%) |
| ICST’17 | IPA: Error Propagation Analysis of Multi-threaded Programs Using Likely Invariants, Abraham Chan, Stefan Winter, Habib Saissi, Karthik Pattabiraman and Neeraj Suri. (Acceptance Rate: 27%) |
| ACSAC’16 |
Formal Security Analysis of Smart Embedded Systems, Farid Molazem Tabrizi and Karthik Pattabiraman. (Acceptance Rate: 23%). |
| SC’16 |
Understanding Error Propagation in GPGPU Applications, Guanpeng Li, Karthik Pattabiraman, Chen-Yong Cher and Pradip Bose. (Acceptance Rate: 18%) |
| EDCC’16 |
Finding Resilience-Friendly Compiler Optimizations Using Meta-Heuristic Search Techniques, Nithya Narayanamurthy, Karthik Pattabiraman, and Matei Ripeanu. (Acceptance Rate: 41%) (Best Paper Award – one of three) |
| SafeComp’16 |
FIDL: Fault Injection Description Language for Compiler-Based SFI Tools, Maryam Raiyat Ailabadi and Karthik Pattabiraman. (Acceptance Rate: 34%) |
DSN’16 |
ePVF: An Enhanced Program Vulnerability Factor Methodology for Cross-Layer Resilience Analysis, Bo Fang, Qining Lu, Karthik Pattabiraman, Matei Ripeanu and Sudhanva Gurumurthi. (Acceptance Rate: 21%) |
| ICST’16 |
Inferring Unit Oracles from GUI Test Cases, Shabnam Mirshokraie, Ali Mesbah and Karthik Pattabiraman. (Acceptance Rate: 27%) |
| ICSE’16 |
Understanding Asynchronous Interactions in Full-Stack JavaScript, Saba Alimadadi, Ali Mesbah and Karthik Pattabiraman. (Acceptance Rate: 19%) |
| ISSRE’15 |
Experience Report: An Application-specific Checkpointing Technique for Minimizing Checkpoint Corruption, Guanpeng Li, Karthik Pattabiraman, Chen-Yong Cher and Pradip Bose. (Acceptance Rate: 32%) |
| ASE’15 |
Synthesizing Web Element Locators, Kartik Bajaj, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 20.8%) |
| EDCC’15 |
Flexible Intrusion Detection Systems for Memory-Constrained Embedded Systems, Farid Molazem Tabrizi and Karthik Pattabiraman. (Acceptance Rate: 45%). Best Paper Award – one of three. |
| ECOOP’15 |
Hybrid DOM-sensitive Change Impact Analysis for JavaScript, Saba Alimadadi, Ali Mesbah and Karthik Pattabiraman. (Acceptance Rate: 22.8%) |
| DSN’15 |
Fine-Grained Characterization of Faults Causing Long Latency Crashes in Programs, Guanpeng Li, Qining Lu and Karthik Pattabiraman. (Acceptance Rate: 22%) |
| ICSE’15 |
Finding Inconsistencies in JavaScript MVC Applications, Frolin Ocariza, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 18.5%) |
| ICST’15 |
JSEFT: Automated JavaScript Unit Test Generation, Shabnam Mirshokraie, Ali Mesbah and Karthik Pattabiraman. (Acceptance Rate: 25%) |
| ISSRE’14 |
Failure Analysis of Jobs in Compute Clouds: A Google Cluster Case Study, Xin Chen, Charng-da Lu and Karthik Pattabiraman. (Acceptance Rate: 25%) |
| CASES’14 |
SDCTune: A Model for Predicting the SDC-Proneness of an Application for Configurable Protection, Qining Lu, Karthik Pattabiraman, Meeta Gupta and Jude Rivers. (Acceptance Rate: 30%). |
| DSN’14 |
Integrated Hardware-Software Diagnosis for Intermittent Hardware Faults, Majid Dadashi, Layali Rashid, Karthik Pattabiraman and Sathish Gopalakrishnan. (Acceptance Rate: 30%). |
| DSN’14 |
Quantifying the Accuracy of High-Level Fault Injection Techniques for Hardware Faults, Jiesheng Wei, Anna Thomas, Guanpeng Li, and Karthik Pattabiraman. (Acceptance Rate: 30%). |
| ICSE’14 |
Vejovis: Suggesting Fixes for JavaScript Faults, Frolin S. Ocariza, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 20%). |
| ICSE’14 |
Understanding JavaScript Event-Based Interactions, Saba Alimadi, Sheldon Sequira, Ali Mesbah and Karthik Pattabiraman (Acceptance Rate: 20%). ACM SIGSOFT Distinguished Paper Award (one of nine papers) |
| ASE’14 |
Dompletion: DOM-Aware Code Completion, Kartik Bajaj, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 20%) |
| MSR’14 |
Mining Questions Asked by Web Developers, Kartik Bajaj, Karthik Pattabiraman and Ali Mesbah. (Acceptance rate: 34%) |
| DATE’14 |
GPGPUs: How to combine high performance with high reliability, L. Bautista Gomez, F. Cappello, L. Carro, N. DeBardeleben, Bo Fang, S. Gurumurthi, Karthik Pattabiraman, P. Rech, M. Sonza Reorda, Embedded Tutorial. |
| ISPASS’14 |
GPU-Qin: A Methodology for Evaluating the Error Resilience of GPGPU Applications, Bo Fang, Karthik Pattabiraman, Matei Ripeanu and Sudhanva Gurumurthi. (Acceptance rate: 30%) |
| HASE’14 |
A Model-based Intrusion Detection System for Smart Meters, Farid M. Tabrizi and Karthik Pattabiraman. (Acceptance rate: 30%) |
| ASE’13 |
PYTHIA: Generating Test Cases with Oracles for JavaScript Applications, Shabnam Mirshokraie, Ali Mesbah and Karthik Pattabiraman. New Ideas Track (Acceptance rate: 23%) |
| ESEM’13 |
An Empirical Study of Client-Side JavaScript Bugs, |
| DSN’13 |
Error Detector Placement for Soft Computation, |
| ICST’13 |
Efficient JavaScript Mutation Testing, |
| QEST’12 |
Intermittent Errors Recovery: Modeling and Evaluation, Layali Rashid, Karthik Pattabiraman and Sathish Gopalakrishnan. (Acceptance Rate: Unknown). |
| DSN’12 |
BLOCKWATCH: Leveraging Similarity in Parallel Programs for Error Detection, Jiesheng Wei and Karthik Pattabiraman. (Acceptance Rate: 17%). |
| ICST’12 |
AutoFlox: An Automatic Fault Localizer For JavaScript, Frolin Ocariza Jr., Karthik Pattabiraman and Ali Mesbah. Best Paper Award Nominee . (Acceptance rate: 27 %). |
| ISSRE’11 |
JavaScript Errors in the Wild: An Empirical Study, Frolin Ocariza Jr., Karthik Pattabiraman and Ben Zorn. (Acceptance Rate: 25 %). |
| CSF’11 |
Yarra: An Extension to C for Data Integrity and Partial Safety, Cole Schlesinger, Karthik Pattabiraman, Nikhil Swamy, David Walker and Ben Zorn.(Acceptance Rate: 26 %). Forwarded to Journal of Computer Security as one of the best papers at the conference |
| ASPLOS’11 |
Flikker: Saving DRAM Refresh-power through Critical Data Partitioning , Song Liu, Karthik Pattabiraman, Thomas Moscibroda and Benjamin Zorn. (Acceptance Rate: 20% ) |
| PRDC’10 |
Modeling the Propagation of Intermittent Hardware Faults in Programs, Layali Rashid, Karthik Pattabiraman and Sathish Gopalakrishnan. (Acceptance Rate: 42 %) |
| ISSRE’10 |
DoDOM: Leveraging DOM Invariants for Web 2.0 Application Robustness Testing, Karthik Pattabiraman and Benjamin Zorn. (Acceptance Rate: 31 %) |
| HotEdge’19 | OneOS: IoT Platform based on POSIX and Actors, Kumseok Jung, Julien Gascon-Samson, and Karthik Pattabiraman |
| SELSE’19 | Towards analytically evaluating the error resilience of GPU Programs, Abdul Rehman Anwer, Guanpeng Li, Karthik Pattabiraman, Siva Kumar Sastry Hari, Michael Sullivan and Timothy Tsai |
| CPS-SPC 2018 | CORGIDS: A Correlation-based Generic Intrusion Detection System, Ekta Aggarwal, Mehdi Karimbuiki, Karthik Pattabiraman and Andre Ivanov |
| WoSoCER 2018 | TensorFI: A Configurable Fault Injector for TensorFlow Applications, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben |
| M4IoT 2017 | ThingsJS: Towards a Flexible and Self-Adaptable Middleware for Dynamic and Heterogeneous IoT Environments, Julien Gascon-Samson, Mohammad Rafiuzzaman, and Karthik Pattabiraman |
| RSDA’2016 | SDC is in the eye of the beholder: A survey and preliminary study, Bo Fang, Panruo Wu, Qiang Guan, Nathan Debardeleben, Laura Monroe, Sean Blanchard, Zhizong Chen, Karthik Pattabiraman, and Matei Ripeanu |
| Middleware’15-Student forum | Intrusion Detection for Embedded Systems, Farid Molazem and Karthik Pattabiraman |
| ISSRE’15-Industry Track | FIDL: A Fault Injection Description Language for Compiler-based Tools, Maryam Raiyat Aliabadi, Karthik Pattabiraman, Nematollah Bidokhti |
| ASE’15-Tools Track | LED: Tool for Synthesizing Web Element Locators, Kartik Bajaj, Karthik Pattabiraman and Ali Mesbah |
| ISSRE’14-Industry Track | Soft-LLFI: A Comprehensive Framework for Software Fault Injection, Maryam Raiyat Aliabadi, Karthik Pattabiraman, Nematollah Bidokhti |
| QRS’15 | LLFI: An Intermediate Code-Level Fault Injection Tool for Hardware Faults
Qining Lu, Mostafa Farahani, Jiesheng Wei, Anna Thomas and Karthik Pattabiraman. |
| RSDA’14 |
Failure Prediction of Jobs in Compute Clouds: A Google Cluster Case Study, Xin Chen, Charng-da Lu and Karthik Pattabiraman. |
| FTXS’14 |
Evaluating the Error Resilience of Parallel Programs, Bo Fang, Karthik Pattabiraman, Matei Ripeanu and Sudhanva Gurumurthi. |
| AER’13 |
Effect of Compiler Optimizations on the Error Resilience of Soft Computing Applications, Anna Thomas, Jacques Clapach and Karthik Pattabiraman. |
| RSDA’13 |
Predicting Job Completion Times Using System Logs in Supercomputing Clusters, Xin Chen, Charng-da Lu and Karthik Pattabiraman. |
| SELSE’13 |
LLFI: An Intermediate Code Level Fault Injector for Soft Computing Applications, Anna Thomas and Karthik Pattabiraman. |
| SELSE’13 |
SCRIBE: A Hardware Infrastructure Enabling Fine-Grained Software Layer Diagnosis, Majid Dadashi, Layali Rashid and Karthik Pattabiraman. |
| WRA’12 |
Towards Building Error Resilient GPGPU Applications, Bo Fang, Jiesheng Wei, Karthik Pattabiraman and Matei Ripeanu. |
| WRAITS’12 |
A Model for Security Analysis of Smart Meters, Farid M. Tabrizi and Karthik Pattabiraman. |
| SELSE’12 |
DIEBA: Diagnosing Intermittent Errors by Backtracing Application Failures, Layali Rashid, Karthik Pattabiraman and Sathish Gopalakrishnan |
| SELSE’12 |
BlockWatch: Leveraging Similarity in Parallel Programs for Error Detection, Jiesheng Wei and Karthik Pattabiraman. |
| WDSN’11 |
Comparing the Effects of Transient and Intermittent Faults on Programs, Jiesheng Wei, Layali Rashid, Sathish Gopalakrishnan and Karthik Pattabiraman. |
| WDSN’10 |
Towards Understanding the Effects of Intermittent Hardware Faults on Programs, Layali Rashid, Karthik Pattabiraman and Sathish Gopalakrishnan. |
| SELSE’10 |
Formal Diagnosis of Hardware Transient Errors in Programs , Layali Rashid, Karthik Pattabiraman, and Sathish Gopalakrishnan, Workshop on Silicon Errors in Logic, System Effects (SELSE), 2010, Palo Alto, CA. |
|
Providing Hardware Resources having Different Reliabilities for Use by an Application, with Benjamin Zorn, Thomas Moscibroda and Song Liu, Sep 2011, USA (patent number 20110231601A1). |
|
Leveraging On-Chip Variability, B Zorn, D Kirovski, R Bittner, K Pattabiraman, Dec 2011 (Patent# 20110314210). |
Talks
Modeling Hardware Error Propagation in Programs for Low-Cost Dependability
Resilience and Security in Cyber-Physical Systems: Self-Driving Cars and Smart Devices
(Video)
Tolerating Hardware Faults in Commodity Software: Problems, Solutions and a Roadmap
Security and Reliability of the Internet of Things: A Smart Meter Case Study
Does Error Resilience Matter in the age of Approximate Computing?
Approximate Computing and Error Resilient Systems: Conjoined Twins Separated at Birth?
Tolerating Silent Data Corruption (SDC) causing Hardware Faults Through Software Techniques
Model-Based Intrusion Detection System (IDS) for Smart Meters
Good Enough Systems: Tolerating (most) hardware Errors using Software
Towards Improving the Reliability of JavaScript-Based Web Applications
