CV

Education

Ph.D. in Computer Science, ETH Zurich, 2020
- Title: Scalable Automated Reasoning for Programs and Deep Learning
- John C. Reynolds Doctoral Dissertation Award
Masters in Computer Science, ETH Zurich, 2014
- ETH Medal for Outstanding Master Thesis
- ETH Excellence Scholarship
B.Tech. in Computer Science and Engineering, IIT Patna, 2012
- President of India Gold Medal
- Institute Silver Medal

Work experience

Nov 2020-Aug 2021: Researcher, VMware Research
Summer 2011: Research Assistant, University of New South Wales
Summer 2010: Research Assistant, University of Houston

Publications

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler

Zilinghan Li, Pranshu Chaturvedi, Shilan He, Han Chen, Gagandeep Singh, Volodymyr Kindratenko, Eliu A Huerta, Kibaek Kim, Ravi Madduri, Arxiv 2023.

Interpreting Robustness Proofs of Deep Neural Networks

Debangshu Banerjee, Avaljot Singh, Gagandeep Singh, WFVML@ICML 2023 (Outstanding Paper).

Incremental Randomized Smoothing Certification

Shubham Ugare, Tarun Suresh, Debangshu Banerjee, Gagandeep Singh, and Sasa Misailovic, Arxiv 2023.

CoMEt: x86 Cost Model Explanation Framework

Isha Chaudhary, Alex Renda, Charith Mendis, Gagandeep Singh, XAIA@NeurIPS 2023.

Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning

Yinglun Xu, Gagandeep Singh, Arxiv 2023.

Robust Universal Adversarial Perturbations

Changming Xu, Gagandeep Singh, Arxiv 2023.

Building Trust and Safety in Artificial Intelligence with Abstract Interpretation

Gagandeep Singh, SAS (Invited Abstract) 2023.

Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning

Yinglun Xu, Qi Zeng, Gagandeep Singh, TMLR 2023 (Featured Certification).

Synthesizing Precise Static Analyzers for Automatic Differentiation

Jacob Laurel, Siyuan Brant Qian, Gagandeep Singh, Sasa Misailovic, OOPSLA 2023.

Incremental Verification of Neural Networks

Shubham Ugare, Debangshu Banerjee, Sasa Misailovic, and Gagandeep Singh, PLDI 2023.

Toward Continuous Verification of DNNs

Shubham Ugare, Debangshu Banerjee, Tarun Suresh, Sasa Misailovic, Gagandeep Singh, WFVML@ICML 2023.

Provable Defense Against Geometric Transformations

Rem Yang, Jacob Laurel, Sasa Misailovic, Gagandeep Singh, ICLR 2023 (Spotlight).

Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems

Zikun Liu, Changming Xu, Gagandeep Singh, and Deepak Vasisht, NSDI 2023.

Property-Driven Evaluation of RL-Controllers in Self-Driving Datacenters

Arnav Chakravarthy, Nina Narodytska, Asmitha Rathis, Marius Vilcu, Mahmood Sharif, Gagandeep Singh, DMML@NeurIPS 2022.

Physically-Constrained Adversarial Attacks on Brain-Machine Interfaces

Xiaying Wang, Rodolfo Octavio Siller Quintanilla, Michael Hersche, Luca Benini, Gagandeep Singh, TSRML@NeurIPS 2022.

Scalable Verification of GNN-Based Job Schedulers

Haoze Wu, Clark Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh, OOPSLA 2022.

Proof Transfer for Fast Certification of Multiple Approximate Neural Networks

Shubham Ugare, Gagandeep Singh, Sasa Misailovic, OOPSLA 2022.

A General Construction for Abstract Interpretation of Higher-Order Automatic Differentiation

Jacob Laurel, Rem yang, Shubham Ugare, Robert Nagel, Gagandeep Singh, Sasa Misailovic, OOPSLA 2022.

Shared Certificates for Neural Network Verification

Marc Fischer, Christian Sprecher, Dimitar I. Dimitrov, Gagandeep Singh, Martin Vechev, CAV 2022.

Provably Robust Adversarial Examples

Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin Vechev, ICLR 2022.

PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations

Mark Niklas Müller, Gleb Makarchuk, Gagandeep Singh, Markus Püschel, Martin Vechev, POPL 2022.

A Dual Number Abstraction for Static Analysis of Clarke Jacobians

Jacob Laurel, Rem Yang, Gagandeep Singh, Sasa Misailovic, POPL 2022.

FIRE: Enabling Reciprocity for FDD MIMO Systems

Zikun Liu, Gagandeep Singh, Chenren Xu, Deepak Vasisht, MobiCom 2021.

Robustness Certification for Point Cloud Models

Tobias Lorenz, Anian Ruoss, Mislav Balunović, Gagandeep Singh, Martin Vechev, ICCV 2021.

Scalable Polyhedral Verification of Recurrent Neural Networks

Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, Martin Vechev, CAV 2021.

Scaling Polyhedral Neural Network Verification on GPUs

Christoph Müller, Francois Serre, Gagandeep Singh, Markus Püschel, Martin Vechev, MLSys 2021.

Adversarial Attacks on Probabilistic Autoregressive Forecasting Models

Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev, ICML 2020.

Learning Fast and Precise Numerical Analysis

Jingxuan He, Gagandeep Singh, Markus Püschel, Martin Vechev, PLDI 2020.

Beyond the Single Neuron Convex Barrier for Neural Network Certification

Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin Vechev, NeurIPS 2019.

Certifying Geometric Robustness of Neural Networks

Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin Vechev, NeurIPS 2019.

Boosting Robustness Certification of Neural Networks

Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev, ICLR 2019.

An Abstract Domain for Certifying Neural Networks

Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev, POPL 2019.

Fast and Effective Robustness Certification

Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev, NeurIPS 2018.

Fast Numerical Program Analysis with Reinforcement Learning

Gagandeep Singh, Markus Püschel, Martin Vechev, CAV 2018.

A Practical Construction for Decomposing Numerical Abstract Domains

Gagandeep Singh, Markus Püschel, Martin Vechev, POPL 2018.

Fast Polyhedra Abstract Domain

Gagandeep Singh, Markus Püschel, Martin Vechev, POPL 2017.

Making Numerical Program Analysis Fast

Gagandeep Singh, Markus Püschel, Martin Vechev, PLDI 2015.

Past Students

Makarchuk Gleb, Dimitar Dimitrov, Clemens Giuliani, Raphaël Dang-Nhu, Jonathan Mauer, Rupanshu Ganvir, Christoph Müller, Jovan Andonov, Jakub Kotal, Afra Amini

Talks

Certified Artificial Intelligence

August 21, 2020

Invited Tutorial at WASP summer school in AI, Virtual

Certified Artificial Intelligence

May 20, 2020

Invited Talk at VMware Research, Tel Aviv University, UIUC, Cornell, Rice, NYU, MIT, and Georgia Tech,

Safe and Robust Deep Learning

October 18, 2019

Invited Talk at Workshop on Dependable and Secure Software Systems, ETH Zurich, Switzerland

Safe and Robust Deep Learning

August 29, 2019

Invited Talk at Waterloo ML + Security + Verification Workshop, University of Waterloo and New York University,

Safe and Robust Deep Learning

June 06, 2019

Invited Tutorial at DigiCosme Spring School on Formal Methods and Machine Learning, ENS Paris-Saclay, Cachan, France

An Abstract Domain for Certifying Neural Networks

January 16, 2019

Invited Talk at ACM Principles of Programming Languages (POPL), Lisbon, Portugal

Fast Numerical Program Analysis with Reinforcement Learning

July 14, 2018

Invited Talk at Computer Aided Verification (CAV), Oxford, UK

A Practical Construction for Decomposing Numerical Abstract Domains

January 12, 2018

Invited Talk at ACM Principles of Programming Languages (POPL), Los Angeles, USA

Fast Polyhedra Abstract Domain

January 18, 2017

Invited Talk at ACM Principles of Programming Languages (POPL), Paris, France

Making Numerical Program Analysis Fast

June 16, 2015

Invited Talk at ACM Programming Language Design and Implementation (PLDI), Portland, USA

Software

Teaching

Trustworthy AI Systems

Formal Methods for Software Development

Trustworthy AI Systems

Logic and Artificial Intelligence

Advanced Systems Lab

Reliable and Interpretable Artificial Intelligence

Blockchain Security Seminar

Research Topics in Software Engineering

How to Write Fast Numerical Code

Software Engineering Seminar

Program Analysis for System Security and Reliability

Algorithms and Data Structures

Program Analysis and Synthesis

Service

ICSE’24, PLDI’21,’23, ICLR’22,’23, AAAI’21,’22,’23, MLSys’23, SAS’21, ‘22 (Co-Chair), NeurIPS’,’19,’21,’22, ICML’22, CCS’21,’22, CVPR’21,’22, FOMLAS’20,’21,’22, ESOP’22, VMCAI’22, APLAS’21, ICCV’21, ATVA’21, IJCAI’21, TACAS’21, ASPLOS’21, JMLR, TMLR, FAOC, Machine Learning, IEEE TNNLS, TOPLAS