Selected Papers (from 2010)


Top Conferences (total = 38)

NOTE: Only publications in top-tier conferences with low acceptance rates, are listed here. For a list of all publications, please go here.

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%)

NDSS’24

Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction, Zitao Chen and Karthik Pattabiraman. (Acceptance Rate: 15%)

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%).

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%).

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%)

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 (one of two papers).

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%)

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%). Finalist for SC reproducibility challenge (1 of 3)

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 two papers.

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%)
SC’16

Understanding Error Propagation in GPGPU Applications, Guanpeng Li, Karthik Pattabiraman, Chen-Yong Cher and Pradip Bose. (Acceptance Rate: 18%)

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%)

ICSE’16

Understanding Asynchronous Interactions in Full-Stack JavaScript, Saba Alimadadi, Ali Mesbah and Karthik Pattabiraman. (Acceptance Rate: 19%)

ASE’15

Synthesizing Web Element Locators, Kartik Bajaj, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 20.8%)

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%)

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%)

DSN’13

Error Detector Placement for Soft Computation,
Anna Thomas and Karthik Pattabiraman. (Acceptance rate: 20%)

DSN’12

BLOCKWATCH: Leveraging Similarity in Parallel Programs for Error Detection, Jiesheng Wei and Karthik Pattabiraman. (Acceptance Rate: 17%).

ASPLOS’11

Flikker: Saving DRAM Refresh-power through Critical Data Partitioning , Song Liu, Karthik Pattabiraman, Thomas Moscibroda and Benjamin Zorn. (Acceptance Rate: 20% )