Hey there! I am an assistant professor in computer science at UofSC. My research involves advancing the state-of-the-art in AI/ML by developing novel algorithms and methods for solving some outstanding problems in: - Autonomous Systems and Robotics: e.g., (i) Our work on Autonomy for space lander missions to the Ocean Worlds, such as Europa and Enceladus. Our project, RASPBERRY-SI, is collaboration with CMU, UArk, York, as well as testbed providers: NASA JPL (physical testbed, called OWLAT) and NASA Ames (virtual testbed, called OceanWATERS). (ii) our recent project on developing a framework for designing Modular Autonomy on ROS (MARS). - Computer Systems (Distributed, Hardware-Software, Data-intensive Pipelines): e.g., (i) Our work on causal reasoning for performance debugging and optimizations in systems in collaboration with Christian Kaestner (CMU) and Baishakhi Ray (Columbia). (ii) my work on causal representation learning in the AdsAI team at Google as a visiting researcher, enhancing the explainability of learned representationas in deep neural networks via causal features for large-scale systems that rely on such represaentations for automated decision making. - Sciences and Engineering: e.g., (i) Our collaborative work on causal learning for cancer research in collaboration with Phillip Buckhaults (UofSC's College of Pharmacy) and Optimal vaccine promotion for COVID-19 in collaboration with Gregory Trevors (UofSC's College of Education). (ii) Our collaborative work on deep learning for symbolic mathematics in collaboration with Kallol Roy (University of Estonia). I am, in particular, interested in the theoretical foundations of Causal Representation Learning, Adversarial ML, AutoML, and Transfer Learning. In addition to theory, I am excited about several directions in ML for Systems and Systems for ML.
Before joining the faculty at the University of South Carolina, I was a postdoc in the School of Computer Science at Carnegie Mellon University (USA) working with Christian Kaestner, and, before that, I was a post-doc in the Department of Computing at Imperial College London (UK). I received my Ph.D. in Computer Science at Dublin City University (Ireland) and my advisor was Claus Pahl. I received an M.S. degree (research-based) in Systems Engineering from Amirkabir University of Technology (Iran) in 2006, and my master’s thesis was on symbolic reasoning and multi-objective optimizations for intelligent product design support systems under the supervision of Saeed Mansour. I also received a B.S. degree in Math & Computer Science from Amirkabir University of Technology in 2003.
With Valerie Issarny, I serve as program co-chair of SEAMS’23 in Melbourne, Australia. Please consider submitting your work on either research or artifact tracks. SEAMS is one of those awesome communities where you meet fascinating people doing great work to make systems of any kind autonomous and self-adaptive!
With Vijay Chidambaram, Romain Jacob, Neeraja J. Yadwadkar, and Ivo Jimenez, we co-founded JSys—a new diamond open-access journal for the systems community. I also co-chair two areas at JSys: Computer Architecture (with Devashree Tripathy) and Configuration Management (with Tianyin Xu). So please consider submitting your research work to JSys!
work with us? Read about AISys Lab.
know about AISys and our collaborators? Here you can find all info in one slide.
Want to join our weekly
reading group? Read about our AISys Reading Group.
join our student-led robotic team at UofSC? Read about Gamecock Robotics.
know whether I am available for AI consultancy? Please check out our Consultancy Services.
|08/30/22||A new version of FlexiBO, a Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks, with some new proofs is on ArXiv! Well done, Shahriar!|
|08/09/22||I am thrilled that our collaborative efforts with Eunsuk Kang (Carnegie Mellon University), Mehdi Mirakhorli, and Callie Babbitt (Rochester Institute of Technology) on Software-Driven Sustainability has been funded by NSF; Thank you, NSF, for funding research on Sustainability in Computing! ♡.|
|08/01/22||Three members of AISys lab at UofSC (Sonam Kharde, Abir Hossen, and I) will be at NASA JPL in Pasadena, CA from August 1st - August 21st. We are hosted in the Robotic Surface Mobility Group (Hari Nayar). We will be testing and evaluating the AI-based Autonomy, developed by the RASPBERRY-SI, with Ocean World Lander Autonomy Testbed.|
|08/01/22||Gamecock Robotics has now a website, stay tuned!|
|06/01/22||Thanks, Megan, for writing a piece about the UofSC breakthrough award.|
|03/15/22||Unicorn was awarded the Available, Functional, and Reproducible badges from EuroSys'22, thanks to dedicated work by Shahriar as well as excellent collaborators, Rahul Krishna, Mohammad Ali Javidian, and Baishakhi Ray. Since we benefited a lot by learning from previous rejections of this work, and therefore, to help other awesome researchers in our community, we release all reviews and rebuttal.|
|01/30/22||I am so delighted that Sonam Kharde has joined AISys as a postdoc; She will be working on Causal AI for Autonomous Systems. Welcome, Sonam!|
|01/22/22||UofSC's College of Engineering and Computing published an interview about our NSF project on Causal AI for Systems.|
|01/10/22||Unicorn has been accepted EuroSys'22; We are grateful to all who provided feedback on this work, including Christian Kaestner, Sven Apel, Yuriy Brun, Emery Berger, Tianyin Xu, Vivek Nair, Jianhai Su, Miguel Velez, Tobius Durschmied, and the anonymous Eurosys'21&22 reviewers.|
|01/10/22||I am honored and humbled to be among the recipient of UofSC's 2022 Breakthrough Stars Award. I owe this recognition to so many people, including brilliant graduate students and postdocs at AISys, my colleagues at UofSC, my collaborators around the globe, and my dear family without their support none of these were even imaginable.|
|12/05/21||Two papers were accepted at ICSE 2022 (On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support) and NeurIPS WHY-21 (Scalable Causal Transfer Learning). Congrats, Miguel Velez, Om Pandey, and Mohammad Ali Javidian!|
|10/27/21||I am honored to become the academic mentor of Gamecock Robotics--A team consist of more than a dozen students who compete in international robotics leagues, including VEX Robotics.|
|08/20/21||A postdoc position (up to 3 years) is available at AISys on Causal AI for Systems. Please apply here.|
|08/09/21||I am thrilled Causal Performance Debugging for Highly-Configurable Systems has been funded by NSF ♡. This is a collaborative project on Causal AI for Systems with Christian Kaestner (CMU) and Baishakhi Ray (Columbia) with total funding of $1,200,000.|
|07/29/21||We have released a demo about our NASA RASPBERRY-SI project on AI-based autonomy for Europa Lander to find life in Jupiter's moon Europa; thanks, NASA ♡.|
|06/22/21||I am thrilled RTG: Mathematical Foundation of Data Science at University of South Carolina has been funded by NSF ♡. This is a collaborative training project with my genius colleagues in mathematics, Wolfgang Dahmen, Linyuan Lu (PI) Wuchen Li, and Qi Wang, on the Mathematical Foundation of AI and ML.|
|06/18/21||A new way to ‘see’: A story about our NSF SmartSight project on AI for Social Good, has been published at UofSC's research magazine|
|06/04/21||I am so delighted that I (together with Valerie Issarny) will serve as the PC co-chair of SEAMS 2023 colocated with ICSE 2023 in Melbourne. Meanwhile, please do consider submitting to SEAMS 2022!|
|05/10/21||I am so delighted to announce that I am now a Visiting Researcher at Google! I will be working on Causal Representation Learning, Adversarial ML, and Self-Supervised Learning.|
AI for Symbolic Math (2021–)
Mathematics is one of the most precious human heritage. To preserve mathematical knowledge even beyond the human era, in collaborations with the University of Tartu, we develop new methods to solve symbolic math tasks (differentiation, integration, PDE, linear algebra, etc.) with machine learning models such as neural networks. The key ideas behind our approach are to exploit the underlying structures (e.g., tree-like structures) behind symbolic math tasks and transform the symbolic math tasks into tasks such as language translations that deep learning models can do well.
NASA RASPBERRY-SI (2020–)
In this collaborative project (USC, CMU, York, UArk, NASA JPL/Ames/GRC), we develop technologies that enable learning-based autonomous planning and adaptation of space landers.
In this project, we are addressing the following important questions from theoretical and empirical perspectives: (i) How to learn reliable causal structures from data and how to use the learned causal structures for identifying causal invariances across environments? (ii) How to use the causal structure learning and counterfactual reasoning based on causal invariances for systems problems? In particular, we are extremely interested to apply these theoretical advancements for an explainable and guided performance debugging of highly-configurable systems.
Despite achieving state-of-the-art performance across many domains, machine learning systems are highly vulnerable to subtle adversarial perturbations. In this project, we propose Athena, an extensible framework for building effective defenses to adversarial attacks against machine learning systems.
One of the key challenges in designing machine learning systems is to determine the right balance amongst several objectives, which also oftentimes are incommensurable and conflicting. For example, when designing deep neural networks (DNNs), one often has to trade-off between multiple objectives, such as accuracy, energy consumption, and inference time. We developed FlexiBO, a flexible Bayesian optimization method, to address this issue.
BRASS MARS (2016–)
How do you build a software system that can function for a century without being touched by a human engineer? This is the herculean task being undertaken by the DARPA Building Resource Adaptive Software System’s (BRASS) program. We, at UofSC with several collborators at CMU, have been developing learning mechanisms to be integrated with quantitative planning to adapt/reconfigure robots to overcome environmental changes at runtime.