For a list of selected papers, go here.
For a list of papers prior to 2010, go here.
| Journals | Conferences | Workshops | Patents | Talks |
| TECS |
Co-Approximator: Enabling Performance Prediction in Colocated Applications, Mohammad Rafiuzzaman, Sathish Gopalakrishnan and Karthik Pattabiraman. |
| TCPS |
Characterizing and Improving Resilience of Accelerators to Memory Errors in Autonomous Robots, Deval Shah, Zi Yu Xue, Karthik Pattabiraman and Tor Aamodt. |
| JPDC | Mixed Precision Support in HPC Applications: What About Reliability?, Alessio Netti, Yang Peng, Patrik Omland, Michael Paulitsch, Jorge Parra, Gustavo Espinosa, Udit Agarwal, Abraham Chan, and Karthik Pattabiraman. | SPE | A Large-scale Empirical Study of Low-level Function Use in Ethereum Smart Contracts and Automated Replacement, Rui Xi and Karthik Pattabiraman. |
| TDSC |
Fault Injection for TensorFlow Applications, Niranjhana Narayanan, Zitao Chen, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben. |
| SPE |
ThingsMigrate: Platform Independent Migration of Stateful JavaScript Applications, Kumseok Jung, Julien Gascon-Samson, Shivanshu Goyal, Armin Rezalean-Asel, Karthik Pattabiraman. |
| TDSC |
An Empirical Study of the Impact of Single and Multiple Bit-Flip Errors in Programs, Behrooz Sangchoolie, Karthik Pattabiraman and Johan Karlsson. |
| DTRAP |
Stealthy Attacks Against Robotic Vehicles Protected by Control-Based Intrusion Detection Techniques, Pritam Dash, Mehdi Karimibuiki, and Karthik Pattabiraman. |
| 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. (Best paper award for 2018-2020) |
| 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 (total = 85)
| SAC’25 |
D-semble: Efficient Diversity-Guided Search for Resilient ML Ensembles, Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan. (Acceptance Rate: TBD) |
| NDSS’25 |
A Method to Facilitate Membership Inference Attacks in Deep Learning Models, Zitao Chen and Karthik Pattabiraman. (Acceptance Rate: TBD) |
| CCS’24 |
AutoPatch: Automated Generation of Hotpatches for Real-Time Embedded Devices, Mohsen Salehi and Karthik Pattabiraman. (Acceptance Rate: 16.7%) |
| CCS’24 |
SpecGuard: Specification Aware Recovery for Robotic Autonomous Vehicles from Physical Attacks, Pritam Dash, Ethan Chan and Karthik Pattabiraman. (Acceptance Rate: 16.7%) |
| IEEE S&P’24 |
POMABuster: Detecting Price Oracle Manipulation Attacks in Decentralized Finance, Rui Xi, Zehua Wang, and Karthik Pattabiraman. (Acceptance rate: 17.8%) |
| CHASE’24 |
Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems, Mohammad ElNawawy, Mohammadreza Hallajiyan, Gargi Mitra, Shahrear Iqbal, and Karthik Pattabiraman. (Acceptance Rate: 28.4%) |
| IoTDI’24 |
ImmunoPlane: Middleware for Providing Adaptivity to Distributed Internet-of-Things Applications, Kumseok Jung, Gargi Mitra, Sathish Gopalakrishnan and Karthik Pattabiraman. (Acceptance Rate: 36.7%) |
| AsiaCCS’24 |
Diagnosis-guided Attack Recovery for Securing Robotic Vehicles from Sensor Deception Attacks, Pritam Dash, Guanpeng Li, Mehdi Karimibiuki, and Karthik Pattabiraman. (Acceptance Rate: 21.9%) |
| NDSS’24 |
Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction, Zitao Chen and Karthik Pattabiraman. (Acceptance Rate: 15%) |
| SEC’23 |
EdgeEngine: A Thermal-Aware Optimization Framework for Edge Inference, Amirhossein Ahmadi, Hazem Abdelhafez, Karthik Pattabiraman and Matei Ripeanu. (Acceptance Rate: 25%) |
| ISSRE’23 |
Resilience Assessment of Large Language Models under Transient Hardware Faults, Udit Agarwal, Abraham Chan, and Karthik Pattabiraman. (Acceptance Rate: 29.5%) |
| ISSRE’23 |
Evaluating the Effect of Common Annotation Faults on Object Detection Techniques, Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan. (Acceptance Rate: 29.5%) |
| SC’23 |
Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults, Ali Asgari, Florian Geissler, Syed Qutub, Michael Paulitsch, Prashant Nair, and Karthik Pattabiraman. (Acceptance Rate: 23.9%). |
| SafeComp’23 | A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks, Florian Geissler, Syed Qutub, Michael Paulitsch and Karthik Pattabiraman. (Acceptance Rate: 20%) |
| DSN’23 |
SwarmFuzz: Discovering GPS Spoofing Attacks in Drone Swarms, Yiangao (Elaine) Yao, Pritam Dash, and Karthik Pattabiraman (Acceptance Rate: 20%). |
| ICSE’23 |
AChecker: Statically Detecting Smart Contract Access Control Vulnerabilities, Asem Ghaleb, Julia Rubin, and Karthik Pattabiraman (Acceptance Rate: 26%). |
| AsiaCCS’23 |
Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks, Zitao Chen, Pritam Dash, and Karthik Pattabiraman. (Acceptance Rate: 17%) |
| SEC’22 |
Characterizing Variability in Heterogeneous Edge Systems: A Methodology & Case Study, Hazem A. Abdelhafez, Hassan Halawa, Amr Almoallim, Amirhossein Ahmadi, Karthik Pattabiraman, and Matei Ripeanu. (Acceptance Rate: 27%) |
| ISSRE’22 | LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications (Tools and Artifact Track), Udit Agarwal, Abraham Chan, and Karthik Pattabiraman (Acceptance Rate: 29%). |
| ISSTA’22 |
eTainter: Detecting Gas-Related Vulnerabilities in Smart Contracts, Asem Ghaleb, Julia Rubin, and Karthik Pattabiraman. (Acceptance Rate: 24.5%). |
| DSN’22 |
The Fault in Our Data Stars: Studying Mitigation Techniques against Faulty Training Data in ML Applications, Abraham Chan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan. (Acceptance Rate: 18.7%) |
| IoTDI’22 |
π-Configurator: Enabling Efficient Configuration of Pipelined Applications on the Edge, Mohammad Rafiuzzaman, Sathish Gopalakrishnan and Karthik Pattabiraman. (Acceptance Rate: 33%) |
SANER’22 |
When They Go Low: Automated Replacement of Low-level Functions in Ethereum Smart Contracts, Rui Xi and Karthik Pattabiraman. (Acceptance Rate: 36%) |
| QRS’21 |
Understanding the Resilience of Neural Network Ensembles against Faulty Training Data, Abraham Chan, Niranjhana Narayananan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan. (Acceptance Rate: 25.1%). Best Paper Award (1 of 3) |
| SEC’21 |
OneOS: Middleware for Running Edge Computing Applications as Distributed POSIX Pipelines, Kumseok Jung, Julien Gascon-Samson, and Karthik Pattabiraman. (Acceptance Rate: 27%). Best Demo Award. |
| SEC’21 |
MIRAGE: Machine Learning-based Modeling of Identical Replicas of the Jetson AGX Embedded Platform, Hazem A. Abdelhafez, Hassan Halawa, Mohamed Ahmed, Karthik Pattabiraman, and Matei Ripeanu. (Acceptance Rate: 27%). |
| PRDC’21 |
Are you for Real? Authentication in Dynamic IoT Systems, Mehdi Karimibiuki, Karthik Pattabiraman, and Andre Ivanov. (Acceptance Rate: 43%). |
| DSN’21 |
PID-Piper: Recovering Robotic Vehicles from Physical Attacks, Pritam Dash, Guanpeng Li, Zitao Chen, Mehdi Karimibiuki, and Karthik Pattabiraman. (Acceptance Rate: 16.5%). Best paper award . |
| DSN’21 |
A Low Cost Fault Corrector for Deep Neural Networks through Range Restriction, Zitao Chen, Guanpeng Li, and Karthik Pattabiraman. (Acceptance Rate: 16.5%). Best paper award runner up (1 of 2), IEEE Top Pick in Test and Reliability (TPTR), 2024. |
| ISSRE’20 |
TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications, Zitao Chen, Niranjhana Narayanan, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben. (Acceptance Rate: 26%) |
| ISSRE’20 |
How Far Have We Come in Detecting Anomalies in Distributed Systems? An Empirical Study with a Statement-level Fault Injection Method, Yong Yang, Yifan Wu, Karthik Pattabiraman, Long Wang, and Ying Li. (Acceptance Rate: 26%) |
| SC’20 |
GPU-TRIDENT: Efficient Modeling of Error Propagation in GPU Programs, Abdul Rehman Anwer, Guanpeng Li, Karthik Pattabiraman, Michael Sullivan, Timothy Tsai and Siva Hari. (Acceptance Rate: 25.1%) |
| ISSTA’20 |
How Effective are Smart Contract Analysis Tools? Evaluating Smart Contract Static Analysis Tools Using Bug Injection, Asem Ghaleb and Karthik Pattabiraman. (Acceptance Rate: 26%) |
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 Karimibuiki, 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%). Reproducibility Initiative Finalist (1 of 3) |
| 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 Karimibuiki, 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%). Chosen for IEEE Top Picks in Test and Reliability (TPTR), 2023. |
| 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%). Chosen as “Highlights of ISSRE” in 2019 – one of 26 papers out of more than 1000. |
| 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 %) |
| FITML’24 | Hierarchical Unlearning Framework for Multi-Class Classification, Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan. |
| RICCS’24 | Targeting the Blind Spot: Evaluating Modern ICS Security Against A Novel Denial of Service (DoS) Attack, Gargi Mitra, Pritam Dash, Yingao (Elaine) Yao, Aastha Mehta, Karthik Pattabiraman. |
| HealthSec’24 | SAM: Foreseeing Inference-Time False Data Injection Attacks on ML-enabled Medical Devices, Mohammadreza Hallajiyan, Athish Pranav Dharmalingam, Gargi Mitra, Homa Alemzadeh, Shahrear Iqbal and Karthik Pattabiraman. |
| AISafety’24 | Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models, Qutub Syed, Michael Paulitsch, Karthik Pattabiraman, Korbinian Hagn1, Fabian Oboril, Cornelius Buerkle, Kay-Ulrich Scholl, Gereon Hinz and Alois Knoll. (Acceptance Rate: TBD) |
| DSN’24-Disrupt | Harnessing Explainability to Improve ML Ensemble Resilience, Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan. (Acceptance Rate: 45%) |
| EdgeSys’23 | Hot Under the Hood: An Analysis of Ambient Temperature Impact on Heterogeneous Edge Platforms, Amirhossein Ahmadi, Hazem AbdelHafez, Shaswat Jaiswal, Karthik Pattabiraman, and Matei Ripeanu. (Acceptance Rate: 42%). Best Paper Award |
| SELSE’23 | Towards Reliability Assessment of Systolic Arrays against Stuck-at Faults, Udit Agarwal, Abraham Chan, Ali Asgari and Karthik Pattabiraman, Chosen as “Best of SELSE’23” and invited to present at DSN’23 – one of three papers. |
| DSML’22 | Towards Building Resilient Ensembles against Training Data Faults, Abraham Chan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan. |
| WoSoCER’21 |
(WiP) Low Level Tensor Fault Injector (LLTFI), Abraham Chan, Udit Agarwal, and Karthik Pattabiraman. |
| AI Safety’21 |
Towards a safety case for hardware-fault tolerance in convolutional neural networks using activation range supervision, Florian Geissler, Syed Qutub, Sayanta Roychowdhury, Ali Asgari, Yang Peng, Akash Dhamasia, Ralf Graefe, Karthik Pattabiraman and Michael Paulitsch, Best Paper Award Nominee (1 of 4) |
| EdgeSys’21 |
Snowflakes at the Edge — A Study of Variability among NVIDIA Jetson AGX Xavier Boards, Hazem A. Abdelhafez, Hassan Halawa, Karthik Pattabiraman, Matei Ripeanu |
| DeepTest’21 |
TF-DM: Tool for Studying ML Model Resilience to Data Faults, Niranhana Narayanan and Karthik Pattabiraman |
| WoSoCER’20 |
New Wine in an Old Bottle: N-Version Programming for Machine Learning Components, Arpan Gujarati, Sathish Gopalakrishnan, and Karthik Pattabiraman |
| 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 Karimibuiki, 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 |
| FIDL: A Fault Injection Description Language for Compiler-based Tools, Maryam Raiyat Aliabadi, Karthik Pattabiraman, Nematollah Bidokhti | |
| LED: Tool for Synthesizing Web Element Locators, Kartik Bajaj, Karthik Pattabiraman and Ali Mesbah | |
| 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. |
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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). |
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Leveraging On-Chip Variability, B Zorn, D Kirovski, R Bittner, K Pattabiraman, Dec 2011 (Patent# 20110314210). |
