Abraham Chan, EIT

About

I am a PhD candidate at the University of British Columbia. My advisors are Professors Karthik Pattabiraman and Sathish Gopalakrishnan. My research interests are in Machine Learning Reliability, where I aim to develop the most effective ML detection and tolerance techniques against faulty training data in safety-critical applications.
I have previously worked in fault-tolerant computing systems, compiler optimizations, and software security techniques for IoT devices.

Education

• University of British Columbia
  Master of Applied Science, Computer Engineering, 2015-2017


• University of British Columbia
  Bachelor of Applied Science (with 20 months of software development internships), Computer Engineering, 2010-2015

Conference Papers

1. Evaluating the Effect of Common Annotation Faults on Object Detection Techniques
   Abraham Chan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan.
   ISSRE'23 (Acceptance Rate: 29.5%)


2. Resilience Assessment of Large Language Models under Transient Hardware Faults
   Udit Agarwal, Abraham Chan, and Karthik Pattabiraman.
   ISSRE'23 (Acceptance Rate: 29.5%)


3. Towards Reliability Assessment of Systolic Arrays against Stuck-at Faults
   Udit Agarwal, Abraham Chan, Ali Asgari, and Karthik Pattabiraman.
   SELSE'23 [Best of SELSE Award (1 of 3)]


4. LLTFI: Framework Agnostic Fault Injection for Machine Learning Applications
   Udit Agarwal, Abraham Chan, and Karthik Pattabiraman.
   ISSRE'22 (Acceptance Rate: 29%)


5. 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.
   DSN'22 (Acceptance Rate: 18.7%)


6. Understanding the Resilience of Neural Network Ensembles against Faulty Training Data
   Abraham Chan, Niranjhana Narayananan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan.
   QRS'21 (Acceptance Rate: 25.1%) [Best Paper Award (1 of 3)]


7. BalloonJVM: Dynamically Resizable Heap for FaaS
   Abraham Chan, Kai-Ting Amy Wang, Vineet Kumar.
   Cloud Computing 2019 (Acceptance Rate: 29%)


8. IPA: Error Propagation Analysis of Multi-threaded Programs Using Likely Invariants
   Abraham Chan, Stefan Winter, Habib Saissi, Karthik Pattabiraman and Neeraj Suri.
   ICST'17 (Acceptance Rate: 27%)

Journal Publications

1. 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.
   Journal of Parallel and Distributed Computing

Workshop / Short Papers

1. Harnessing Explainability to Improve ML Ensemble Resilience
   Abraham Chan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan.
   Disrupt'24 @ DSN


2. Building Resilient ML Applications using Ensembles against Faulty Training Data
   Abraham Chan.
   ISSRE'23 Doctoral Symposium


3. Towards Building Resilient Ensembles against Training Data Faults
   Abraham Chan, Arpan Gujarati, Karthik Pattabiraman, and Sathish Gopalakrishnan.
   DSN-DSML'22


4. (WiP) LLTFI: Low-Level Tensor Fault Injector
   Abraham Chan, Udit Agarwal, and Karthik Pattabiraman.
   WoSoCER'21


5. Automated Program Diversity using Program Synthesis
   Abraham Chan.
   DSN'17 Student Forum

Courses I have TAed at UBC

• CPEN 322: Software Construction II
• CPEN 400A: Building Modern Web Applications
• CPEN 400P: Topics in Computer Engineering - Program Analysis
• EECE 513: Error Resilient Computing Systems
• CPEN 502: Architecture for Learning Systems
• CPEN 522: Software Verification and Testing

Contact

• LinkedIn
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