Hridesh Rajan

Hridesh Rajan is the Dean of the School of Science and Engineering at Tulane, overseeing a wide range of departments and programs. Before joining Tulane, he was the Kingland Professor at Iowa State University, where he served as the Department Chair of Computer Science. He also held the role of founding Professor-in-Charge of Data Science Programs from 2017 to 2019, during which he established the annual Midwest Big Data Summer School and led several key data science educational initiatives.

As an academic, Rajan is well-regarded for his contributions to Software Engineering and Programming Languages. He is the creator of the Ptolemy programming language, which improved modular reasoning about crosscutting concerns, and the Boa programming language, which simplifies data-driven software engineering. His research has been recognized with numerous awards, including the NSF CAREER award, the LAS Early Achievement in Research Award, and the Facebook Probability and Programming Award. Rajan’s academic influence extends through his editorial work with IEEE Transactions on Software Engineering and ACM SIGSOFT Software Engineering Notes, and his advisory role with the Proceedings of the ACM on Programming Languages.

Rajan’s leadership at Iowa State University saw significant growth in the computer science department, with increases in student enrollment, faculty numbers, research funding, and philanthropic support. He spearheaded efforts in diversity and inclusion, resulting in a notable increase in female enrollment. He also led the development of new academic programs, including the M.S. degree in Artificial Intelligence and the B.S. degree in Data Science.

Educated at the University of Virginia, Rajan holds a Ph.D. and an MS in Computer Science. He earned his B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Varanasi. His professional journey includes a tenure as a Member of Technical Staff at Bell Labs, Lucent Technologies in Bangalore, India.

Rajan is a Fellow of the American Association for the Advancement of Science (AAAS), a Fulbright Scholar, and an ACM Distinguished Scientist. His ongoing research continues to push the boundaries of software engineering and programming languages, especially in making AI and ML-enabled systems more reliable and trustworthy.

Research Interests

Software Engineering and Programming Languages

Selected Books and Publications

• An Experiential Introduction to Principles of Programming Languages, MIT Press, May 2022, 304 pp., ISBN 9780262045452.
• On Decomposing a Deep Neural Network into Modules, ESEC/FSE’2020.
• Fairify: Fairness Verification of Neural Networks. ICSE’2023.

Selected Awards and Recognitions

• 2022 ISU Award for Early Achievement in Departmental Leadership
• 2020 American Association for the Advancement of Science (AAAS) Fellow
• 2020 Facebook Probability and Programming Award
• 2018-19 Fulbright U.S. Scholar
• 2017 ACM Distinguished Scientist
• 2009 US National Science Foundation (NSF) CAREER Award
• Various best paper and distinguished paper awards

Recent Service Roles

• General Chair, SPLASH 2021, the ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity
• General Chair, SPLASH 2020, the ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity
• Program Committee, 47th International Conference on Software Engineering (ICSE 2025)
• Program Committee, 46th International Conference on Software Engineering (ICSE 2024)
• Program Committee, 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024)

Teaching

Programming Languages  

Object-oriented Analysis and Design

Advanced Topics in Programming Languages: Type Systems 

Selected Publications

Shibbir Ahmed, Hongyang Gao, and Hridesh Rajan, "Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment," ICSE’2024: The 46th International Conference on Software Engineering, April, 2024.

David OBrien, Sumon Biswas, Sayem Mohammad Imtiaz, Rabe Abdalkareem, Emad Shihab, and Hridesh Rajan, "Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot," ICSE’2024: The 46th International Conference on Software Engineering, April, 2024.

David OBrien, Robert Dyer, Tien Nguyen, and Hridesh Rajan, "Data-Driven Evidence-Based Syntactic Sugar Design," ICSE’2024: The 46th International Conference on Software Engineering, April, 2024.

Ali Ghanbari, Deepak-George Thomas, Muhammad Arbab Arshad, and Hridesh Rajan, "Mutation-based Fault Localization of Deep Neural Networks," ASE’2023: 38th IEEE/ACM International Conference on Automated Software Engineering, September, 2023.

Samantha Syeda Khairunnesa, Shibbir Ahmed, Sayem Mohammad Imtiaz, Hridesh Rajan, and Gary T. Leavens, "What Kinds of Contracts Do ML APIs Need?," Empirical Software Engineering, March, 2023.

Shibbir Ahmed, Sayem Mohammad Imtiaz, Samantha Syeda Khairunnesa, Breno Dantas Cruz, and Hridesh Rajan, "Design by Contract for Deep Learning APIs," ESEC/FSE’2023: The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, December, 2023.

Giang Nguyen, Sumon Biswas, and Hridesh Rajan, "Fix Fairness, Don’t Ruin Accuracy: Performance Aware Fairness Repair using AutoML," ESEC/FSE’2023: The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, December, 2023.

Sumon Biswas and Hridesh Rajan, "Fairify: Fairness Verification of Neural Networks," ICSE’23: The 45th International Conference on Software Engineering, May, 2023.

Sayem Mohammad Imtiaz, Fraol Batole, Astha Singh, Rangeet Pan, Breno Dantas Cruz, and Hridesh Rajan, "Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement," ICSE’23: The 45th International Conference on Software Engineering, May, 2023.

Usman Gohar, Sumon Biswas, and Hridesh Rajan, "Towards Understanding Fairness and its Composition in Ensemble Machine Learning," ICSE’23: The 45th International Conference on Software Engineering, May, 2023.

David OBrien, Sumon Biswas, Sayem Mohammad Imtiaz, Rabe Abdalkareem, Emad Shihab, and Hridesh Rajan, "23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software," Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, November, 2022.
 

Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, and Hongyang Gao, "A Global Convergence Theory for Deep ReLU Implicit Networks via Over-Parameterization," ICLR’22: The 10th International Conference on Learning Representations, April, 2022.

Sumon Biswas, Mohammad Wardat, and Hridesh Rajan, "The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large," ICSE’22: The 44th International Conference on Software Engineering, May, 2022.

Rangeet Pan and Hridesh Rajan, "Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules," ICSE’22: The 44th International Conference on Software Engineering, May, 2022.

Mohammad Wardat, Breno Dantas Cruz, Wei Le, and Hridesh Rajan, "DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs," ICSE’22: The 44th International Conference on Software Engineering, May, 2022.

Giang Nguyen, Johir Islam, Rangeet Pan, and Hridesh Rajan, "Manas: Mining Software Repositories to Assist AutoML," ICSE’22: The 44th International Conference on Software Engineering, May, 2022.

Menglu Yu, Bo Ji, Hridesh Rajan, and Jia Liu, "On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenan GPU Clusters with Communication Contention," Proc. ACM MobiHoc, October, 2022.
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Shibbir Ahmed, Md Johirul Islam, and Hridesh Rajan, "Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data," ACM/IMS Transactions on Data Science, January, 2022.

Sumon Biswas and Hridesh Rajan, "Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline," ESEC/FSE’2021: The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, August, 2021.

Mohammad Wardat, Wei Le, and Hridesh Rajan, "DeepLocalize: Fault Localization for Deep Neural Networks," ICSE’21: The 43nd International Conference on Software Engineering, May, 2021.

Hamid Bagheri, Andrew J. Severin, and Hridesh Rajan, "Detecting and correcting misclassified sequences in the large-scale public databases ," Oxford Bioinformatics, August, 2020.