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I’m a PhD candidate in Electrical and Systems Engineering at the University of Pennsylvania, where I’m gratefully advised by Insup Lee and James Weimer. I’m also thankful for close collaborations with Dinesh Jayaraman and Oleg Sokolsky.

My research interests are at the intersection of machine learning and sequential decision making.

Currently, I am excited about deep reinforcement and imitation learning, neurosymbolic generative models, and robust deep learning, with applications in robotics, cloud computing, cyber-physical systems (autonomous vehicles, electric grids, etc.), and operations research.

Before starting my PhD, I graduated with honors from the Indian Institute of Technology Bombay in 2019.

Research Internships

May-Aug 2022 and May-Aug 2023 - Amazon Web Services (AWS) AI Labs
May-Aug 2021 - Argo AI (Ford and VW’s self-driving partner)

Awards

Best Paper Award Nomination for CODiT at ICCPS 2023
Top Reviewer (top 10%), NeurIPS 2022
Outstanding Reviewer (top 10%), ICML 2022
NSF Travel Grant, ICCPS, 2023
Student Travel Grant, American Control Conference, 2022
The Dean’s Fellowship, University of Pennsylvania, 2019
The Howard Broadwell Fellowship, University of Pennsylvania, 2019
SN Bose Scholarship, Gov. of India, 2018
KVPY Fellowship, Gov. of India, 2015
Dan 1 (black belt), Shotokan Karate Association, 2011

My contact information is below.

Email: ksridhar@seas.upenn.edu.

My research (and publications) are listed below. My CV can be found here.

all

Large Language Models for Fixing Reward Hacking. Kaustubh Sridhar, Richard Ren, William Francis, Adam Alavi.

Course Project.

[Code]

Guaranteed Conformance of Neurosymbolic Models to Natural Constraints. Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee.

ICLR 2023 Workshop on Neurosymbolic Generative Models.
Learning for Dynamics and Control (L4DC) Conference 2023.
Invited Talk at Johns Hopkins University.

[PDF] [Code] [Video] [Poster]

Predict-and-Critic: Accelerated End-to-End Predictive Control for Cloud Computing through Reinforcement Learning. Kaustubh Sridhar, Vikramank Singh, Murali Narayanaswamy, Abishek Sankararaman.

In Preparation for the AAAI Conference on Aritifical Intelligence 2024.

[PDF]

Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates. Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani, Edgar Dobriban, James Weimer, Insup Lee, Julia Parish-Morris.

Conference on Robot Learning (CoRL) 2022.

[PDF] [Code] [Website] [Poster]

CODiT: Conformal Out-of-Distribution Detection in Time-Series Data. Ramneet Kaur, Kaustubh Sridhar, Sangdon Park, Susmit Jha, Anirban Roy, Oleg Sokolsky, Insup Lee.

ICML 2022 Workshop on Principles of Distribution Shift.
ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) 2023.
Best Paper Award Nomination at ICCPS 2023.

[PDF] [Code] [Poster]

Real-Time Data-Predictive Attack-Recovery for Complex Cyber-Physical Systems. Lin Zhang, Kaustubh Sridhar, Mengyu Liu, Pengyuan Lu, Xin Chen, Fanxin Kong, Oleg Sokolsky, Insup Lee.

IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) 2023.

[PDF] [Code]

Improving Neural Network Robustness via Persistency of Excitation. Kaustubh Sridhar, Oleg Sokolsky, Insup Lee, James Weimer.

American Control Conference (ACC) 2022.

[PDF] [Code] [Video] [Poster]

Real-Time detectors for Digital and Physical Adversarial Inputs to Perception Systems. Yiannis Kantaros, Taylor Carpenter, Kaustubh Sridhar, Yahan Yang, Insup Lee, James Weimer.

ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) 2021.

[PDF] [Website]

Fail-Safe: Securing Cyber-Physical Systems against Hidden Sensor Attacks. Mengyu Liu, Lin Zhang, Pengyuan Lu, Kaustubh Sridhar, Fanxin Kong, Oleg Sokolsky, Insup Lee.

IEEE Real-Time Systems Symposium (RTSS) 2022.

[PDF] [Code]

Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep and Exploratory Research. Pengyuan Lu, Lin Zhang, Mengyu Liu, Kaustubh Sridhar, Fanxin Kong, Oleg Sokolsky, Insup Lee.

Submitted to ACM Computing Surveys.

[PDF]

Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations. Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee.

arXiv:2206.06496, 2022.

[PDF] [Code]

A Framework for Checkpointing and Recovery of Hierarchical Cyber-Physical Systems. Kaustubh Sridhar, Radoslav Ivanov, Marcio Juliato, Manoj Sastry, Vuk Lesi, Lily Yang, James Weimer, Oleg Sokolsky, Insup Lee.

arXiv:2205.08650.

[PDF] [Code]

Finite-time, Event-triggered Tracking Control of Quadrotors. Kaustubh Sridhar, Srikant Sukumar.

Conference on Guidance, Navigation, and Contol (EuroGNC) 2019.

[PDF]

learning for dynamics and control

Finite-time, Event-triggered Tracking Control of Quadrotors. Kaustubh Sridhar, Srikant Sukumar.

Conference on Guidance, Navigation, and Contol (EuroGNC) 2019.

[PDF]

cps safety and security

Real-Time Data-Predictive Attack-Recovery for Complex Cyber-Physical Systems. Lin Zhang, Kaustubh Sridhar, Mengyu Liu, Pengyuan Lu, Xin Chen, Fanxin Kong, Oleg Sokolsky, Insup Lee.

IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) 2023.

[PDF] [Code]

Fail-Safe: Securing Cyber-Physical Systems against Hidden Sensor Attacks. Mengyu Liu, Lin Zhang, Pengyuan Lu, Kaustubh Sridhar, Fanxin Kong, Oleg Sokolsky, Insup Lee.

IEEE Real-Time Systems Symposium (RTSS) 2022.

[PDF] [Code]

Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep and Exploratory Research. Pengyuan Lu, Lin Zhang, Mengyu Liu, Kaustubh Sridhar, Fanxin Kong, Oleg Sokolsky, Insup Lee.

Submitted to ACM Computing Surveys.

[PDF]

A Framework for Checkpointing and Recovery of Hierarchical Cyber-Physical Systems. Kaustubh Sridhar, Radoslav Ivanov, Marcio Juliato, Manoj Sastry, Vuk Lesi, Lily Yang, James Weimer, Oleg Sokolsky, Insup Lee.

arXiv:2205.08650.

[PDF] [Code]

robust deep learning

CODiT: Conformal Out-of-Distribution Detection in Time-Series Data. Ramneet Kaur, Kaustubh Sridhar, Sangdon Park, Susmit Jha, Anirban Roy, Oleg Sokolsky, Insup Lee.

ICML 2022 Workshop on Principles of Distribution Shift.
ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) 2023.
Best Paper Award Nomination at ICCPS 2023.

[PDF] [Code] [Poster]

Improving Neural Network Robustness via Persistency of Excitation. Kaustubh Sridhar, Oleg Sokolsky, Insup Lee, James Weimer.

American Control Conference (ACC) 2022.

[PDF] [Code] [Video] [Poster]

Real-Time detectors for Digital and Physical Adversarial Inputs to Perception Systems. Yiannis Kantaros, Taylor Carpenter, Kaustubh Sridhar, Yahan Yang, Insup Lee, James Weimer.

ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) 2021.

[PDF] [Website]

Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations. Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee.

arXiv:2206.06496, 2022.

[PDF] [Code]

reinforcement learning

Large Language Models for Fixing Reward Hacking. Kaustubh Sridhar, Richard Ren, William Francis, Adam Alavi.

Course Project.

[Code]

Predict-and-Critic: Accelerated End-to-End Predictive Control for Cloud Computing through Reinforcement Learning. Kaustubh Sridhar, Vikramank Singh, Murali Narayanaswamy, Abishek Sankararaman.

In Preparation for the AAAI Conference on Aritifical Intelligence 2024.

[PDF]

Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates. Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani, Edgar Dobriban, James Weimer, Insup Lee, Julia Parish-Morris.

Conference on Robot Learning (CoRL) 2022.

[PDF] [Code] [Website] [Poster]

neurosymbolic generative models

Guaranteed Conformance of Neurosymbolic Models to Natural Constraints. Kaustubh Sridhar, Souradeep Dutta, James Weimer, Insup Lee.

ICLR 2023 Workshop on Neurosymbolic Generative Models.
Learning for Dynamics and Control (L4DC) Conference 2023.
Invited Talk at Johns Hopkins University.

[PDF] [Code] [Video] [Poster]

Please find my earlier undergraduate research in motion plannning and control at this link.