{"id":3666,"date":"2016-08-05T15:21:06","date_gmt":"2016-08-05T22:21:06","guid":{"rendered":"https:\/\/blogs.ubc.ca\/karthik\/?page_id=3666"},"modified":"2025-11-02T14:24:46","modified_gmt":"2025-11-02T22:24:46","slug":"selected-papers-2010-onwards","status":"publish","type":"page","link":"https:\/\/blogs.ubc.ca\/karthik\/papers\/selected-papers-2010-onwards\/","title":{"rendered":"Selected Papers (after 2010)"},"content":{"rendered":"
\n

Top Conferences<\/A> (total = 42)<\/strong><\/p>\n

NOTE:<\/strong> Only publications in top-tier conferences with low acceptance rates, are listed here. For a list of all publications, please go here<\/a>. <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CCS’25<\/a><\/td>\n\n

Anonymity Unveiled: A Practical Framework for Auditing Data Use in Deep Learning Models<\/a>, Zitao Chen and Karthik Pattabiraman. (Acceptance Rate: 14.5%) <\/p>\n<\/td>\n<\/tr>\n

DSN’25<\/a><\/td>\n\n

ReMIX: Resilience for ML Ensembles using XAI at Inference against Faulty Training Dat<\/a>a, Abraham Chan, Arpan Gujarati, Karthik Pattabiraman and Sathish Gopalakrishnan. (Acceptance Rate: 20%)<\/p>\n<\/td>\n<\/tr>\n

DSN’25<\/a><\/td>\n\n

RAVAGE: Robotic Autonomous Vehicles\u2019 Attack Generation Engine<\/a>, Pritam Dash and Karthik Pattabiraman. (Acceptance Rate: 20%)<\/p>\n<\/td>\n<\/tr>\n

DSN’25<\/a><\/td>\n\n

Reentrancy Redux: The Evolution of Real-World Reentrancy Attacks on Blockchains<\/a>, Yuqi Liu, Rui Xi, and Karthik Pattabiraman. (Acceptance Rate: 20%)<\/p>\n<\/td>\n<\/tr>\n

NDSS’25<\/a><\/td>\n\n

A Method to Facilitate Membership Inference Attacks in Deep Learning Models<\/a>, Zitao Chen and Karthik Pattabiraman. (Acceptance Rate: 16.1%)<\/p>\n<\/td>\n<\/tr>\n

CCS’24<\/a><\/td>\n\n

AutoPatch: Automated Generation of Hotpatches for Real-Time Embedded Devices<\/a>, Mohsen Salehi and Karthik Pattabiraman. (Acceptance Rate: 16.7%) <\/p>\n<\/td>\n<\/tr>\n

CCS’24<\/a><\/td>\n\n

SpecGuard: Specification Aware Recovery for Robotic Autonomous Vehicles from Physical Attacks<\/a>, Pritam Dash, Ethan Chan and Karthik Pattabiraman. (Acceptance Rate: 16.7%) <\/p>\n<\/td>\n<\/tr>\n

IEEE S&P’24<\/a><\/td>\n\n

POMABuster: Detecting Price Oracle Manipulation Attacks in Decentralized Finance<\/a>, Rui Xi, Zehua Wang, and Karthik Pattabiraman. (Acceptance rate: 17.8%)<\/p>\n<\/td>\n<\/tr>\n

NDSS’24<\/a><\/td>\n\n

Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction<\/a>, Zitao Chen and Karthik Pattabiraman. (Acceptance Rate: 15%)<\/p>\n<\/td>\n<\/tr>\n

SC’23<\/a><\/td>\n\n

Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory Faults<\/a>, Ali Asgari, Florian Geissler, Syed Qutub, Michael Paulitsch, Prashant Nair, and Karthik Pattabiraman. (Acceptance Rate: 23.9%).<\/p>\n<\/td>\n<\/tr>\n

DSN’23<\/a><\/td>\n\n

SwarmFuzz: Discovering GPS Spoofing Attacks in Drone Swarms<\/a>, Yiangao (Elaine) Yao, Pritam Dash, and Karthik Pattabiraman (Acceptance Rate: 20%). <\/p>\n<\/td>\n<\/tr>\n

ICSE’23<\/a><\/td>\n\n

AChecker, Statically Detecting Smart Contract Access Control Vulnerabilities<\/a>, Asem Ghaleb, Julia Rubin, and Karthik Pattabiraman (Acceptance Rate: 26%). <\/p>\n<\/td>\n<\/tr>\n

ISSTA’22<\/a><\/td>\n\n

eTainter: Detecting Gas-Related Vulnerabilities in Smart Contracts<\/a>, Asem Ghaleb, Julia Rubin, and Karthik Pattabiraman. (Acceptance Rate: 24.5%). <\/p>\n<\/td>\n<\/tr>\n

DSN’22<\/a><\/td>\n\n

The Fault in Our Data Stars: Studying Mitigation Techniques against Faulty Training Data in ML Applications<\/a>, Abraham Chan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan. (Acceptance Rate: 18.7%)<\/p>\n<\/td>\n<\/tr>\n

DSN’21<\/a><\/td>\n\n

PID-Piper: Recovering Robotic Vehicles from Physical Attacks<\/a>, Pritam Dash, Guanpeng Li, Zitao Chen, Mehdi Karimibiuki, and Karthik Pattabiraman. (Acceptance Rate: 16.5%). Best paper award<\/strong>. <\/p>\n<\/td>\n<\/tr>\n

DSN’21<\/a><\/td>\n\n

A Low Cost Fault Corrector for Deep Neural Networks through Range Restriction<\/a>, Zitao Chen, Guanpeng Li, and Karthik Pattabiraman. (Acceptance Rate: 16.5%). Best paper award runner up (one of two papers)<\/strong>. Chosen as a IEEE top picks in Test and Reliability<\/a>, 2024.<\/strong><\/p>\n<\/td>\n<\/tr>\n

SC’20<\/a><\/td>\n\n

GPU-TRIDENT: Efficient Modeling of Error Propagation in GPU Programs<\/a>, Abdul Rehman Anwer, Guanpeng Li, Karthik Pattabiraman, Michael Sullivan, Timothy Tsai and Siva Hari. (Acceptance Rate: 25.1%) <\/p>\n<\/td>\n<\/tr>\n

ISSTA’20<\/a> <\/td>\n\n

How Effective are Smart Contract Analysis Tools? Evaluating Smart Contract Static Analysis Tools Using Bug Injection<\/a>, Asem Ghaleb and Karthik Pattabiraman. (Acceptance Rate: 26%) <\/p>\n<\/td>\n<\/tr>\n

DSN’20<\/a> <\/td>\n\n

Trace Sanitizer: Eliminating the Effects of Non-Determinism on Error Propagation Analysis<\/a>, Habib Saissi, Stefan Winter, Oliver Schwan, Karthik Pattabiraman, and Neeraj Suri. (Acceptance Rate: 16.5%)<\/p>\n<\/td>\n<\/tr>\n

SC’19<\/a><\/td>\n\n

BinFI: An Efficient Fault Injector for Safety-Critical Machine Learning Systems<\/a>, Zitao Chen, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben. (Acceptance Rate: 21%). Finalist for SC reproducibility challenge (1 of 3)<\/strong><\/p>\n<\/td>\n<\/tr>\n

DSN’18<\/a><\/td>\n\n

Modeling Soft-Error Propagation in Programs<\/a>, 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<\/strong>. <\/p>\n<\/td>\n<\/tr>\n

DSN’18<\/a><\/td>\n\n

Modeling Input-Dependent Error Propagation in Programs<\/a>, Guanpeng Li, and Karthik Pattabiraman. (Acceptance Rate: 25%)<\/p>\n<\/td>\n<\/tr>\n

ICSE’18<\/a><\/td>\n\n

Inferring Hierarchical Motifs from Execution Traces<\/a>, Saba Alimadadi, Ali Mesbah, and Karthik Pattabiraman. (Acceptance Rate: 21%)<\/p>\n<\/td>\n<\/tr>\n

ASE’17<\/a><\/td>\n\n

Detecting Unknown Inconsistencies in Web Applications<\/a>, Frolin Ocariza, Karthik Pattabiraman, and Ali Mesbah. (Acceptance Rate: 21%)<\/p>\n<\/td>\n<\/tr>\n

SC’17<\/a><\/a><\/td>\n\n

Understanding Error Propagation in Deep learning Neural Networks (DNN) Accelerators and Applications,<\/a> 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.<\/strong> <\/a><\/p>\n<\/td>\n<\/tr>\n

FSE’17<\/a><\/td>\n\n

ARTINALI: Dynamic Invariant Detection for Cyber-Physical System Security<\/a>, Maryam Raiyat Aliabadi, Amita Kamath, Julien Samson, and Karthik Pattabiraman. (Acceptance Rate: 24%)<\/p>\n<\/td>\n<\/tr>\n

HPDC’17<\/a><\/td>\n\n

LetGo: A Lightweight Continuous Framework for HPC Applications Under Failures<\/a>, Bo Fang, Qiang Guan, Nathan Debardeleben, Karthik Pattabiraman, and Matei Ripeanu. (Acceptance Rate: 19%)<\/p>\n<\/td>\n<\/tr>\n

DSN’17<\/a><\/td>\nOne Bit is (Not) Enough: An Empirical Study of the Impact of Single Bit and Multiple Bit-Flip Errors<\/a>, Behrooz Sangchoolie, Karthik Pattabiraman and Johan Karlsson. (Acceptance Rate: 23%)
\n<\/tr>\n		SC’16<\/a><\/td>\n\n

Understanding Error Propagation in GPGPU Applications<\/a>, Guanpeng Li, Karthik Pattabiraman, Chen-Yong Cher and Pradip Bose. (Acceptance Rate: 18%)<\/p>\n<\/td>\n<\/tr>\n

DSN’16<\/a><\/td>\n\n

ePVF: An Enhanced Program Vulnerability Factor Methodology for Cross-Layer Resilience Analysis<\/a>, Bo Fang, Qining Lu, Karthik Pattabiraman, Matei Ripeanu and Sudhanva Gurumurthi. (Acceptance Rate: 21%)<\/p>\n<\/tr>\n

ICSE’16<\/a><\/td>\n\n

Understanding Asynchronous Interactions in Full-Stack JavaScript<\/a>, Saba Alimadadi, Ali Mesbah and Karthik Pattabiraman. (Acceptance Rate: 19%)<\/p>\n<\/td>\n<\/tr>\n

ASE’15<\/a><\/td>\n\n

Synthesizing Web Element Locators<\/a>, Kartik Bajaj, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 20.8%)<\/p>\n<\/td>\n<\/tr>\n

DSN’15<\/a><\/td>\n\n

Fine-Grained Characterization of Faults Causing Long Latency Crashes in Programs<\/a>, Guanpeng Li, Qining Lu and Karthik Pattabiraman. (Acceptance Rate: 22%)<\/p>\n<\/td>\n<\/tr>\n

ICSE’15<\/a><\/td>\n\n

Finding Inconsistencies in JavaScript MVC Applications<\/a>, Frolin Ocariza, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 18.5%)<\/p>\n<\/td>\n<\/tr>\n

DSN’14<\/a><\/td>\n\n

\nIntegrated Hardware-Software Diagnosis for Intermittent Hardware Faults<\/a>, Majid Dadashi, Layali Rashid, Karthik Pattabiraman and Sathish Gopalakrishnan. (Acceptance Rate: 30%).\n<\/p>\n<\/td>\n<\/tr>\n

DSN’14<\/a><\/td>\n\n

Quantifying the Accuracy of High-Level Fault Injection Techniques for Hardware Faults<\/a>, Jiesheng Wei, Anna Thomas, Guanpeng Li, and Karthik Pattabiraman. (Acceptance Rate: 30%).<\/p>\n<\/td>\n<\/tr>\n

ICSE’14<\/a><\/td>\n\n

Vejovis: Suggesting Fixes for JavaScript Faults<\/a>, Frolin S. Ocariza, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 20%). <\/p>\n<\/td>\n<\/tr>\n

ICSE’14<\/a><\/td>\n\n

Understanding JavaScript Event-Based Interactions<\/a>, Saba Alimadi, Sheldon Sequira, Ali Mesbah and Karthik Pattabiraman (Acceptance Rate: 20%). ACM SIGSOFT Distinguished Paper Award (one of nine papers)<\/p>\n<\/td>\n<\/tr>\n

ASE’14<\/a><\/td>\n\n

Dompletion: DOM-Aware Code Completion<\/a>, Kartik Bajaj, Karthik Pattabiraman and Ali Mesbah. (Acceptance Rate: 20%)<\/p>\n<\/td>\n<\/tr>\n

DSN’13<\/a><\/td>\n\n

Error Detector Placement for Soft Computation<\/a>,
\nAnna Thomas and Karthik Pattabiraman. (Acceptance rate: 20%)<\/p>\n<\/td>\n<\/tr>\n

DSN’12<\/a><\/td>\n\n

BLOCKWATCH: Leveraging Similarity in Parallel Programs for Error Detection<\/a>, Jiesheng Wei and Karthik Pattabiraman. (Acceptance Rate: 17%). <\/p>\n<\/td>\n<\/tr>\n

ASPLOS’11<\/a><\/td>\n\n

Flikker: Saving DRAM Refresh-power through Critical Data Partitioning <\/a>, Song Liu, Karthik Pattabiraman, Thomas Moscibroda and Benjamin Zorn. (Acceptance Rate: 20% ). Won the Jean Claude Laprie Award<\/a> in Dependable Computing in 2025.<\/strong> <\/p>\n<\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

Top Conferences (total = 42) NOTE: Only publications in top-tier conferences with low acceptance rates, are listed here. For a list of all publications, please go here. CCS’25 Anonymity Unveiled: A Practical Framework for Auditing Data Use in Deep Learning … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":120,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-3666","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/pages\/3666","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/comments?post=3666"}],"version-history":[{"count":83,"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/pages\/3666\/revisions"}],"predecessor-version":[{"id":5984,"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/pages\/3666\/revisions\/5984"}],"up":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/pages\/120"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/karthik\/wp-json\/wp\/v2\/media?parent=3666"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}