Dr. Rajappan’s research interests include fluid mechanics, soft robotics, and wearable devices for mobility assistance and rehabilitation. For information about ongoing research, publications, and opportunities in the Fluids and Soft Technologies (FAST) Lab, see https://fluids.tulane.edu.

Recent Publications

Google Scholar Link

Dr. Majed's research interests include Mechanical Engineering/Metallurgy/Nanomaterials synthesis, characterization, and electrochemical testing.

Recent Publications

Google Scholar Link

Majed, A., Torkamanzadeh, M., Nwaokorie, C.F., Eisawi, K., Dun, C., Buck, A., Urban, J.J., Montemore, M.M., Presser, V. and Naguib, M., 2023. Toward MBenes Battery Electrode Materials: Layered Molybdenum Borides for Li‐Ion Batteries. Small Methods, p.2300193.

Majed, A., Kothakonda, M., Wang, F., Tseng, E.N., Prenger, K., Zhang, X., Persson, P.O., Wei, J., Sun, J. and Naguib, M., 2022. Transition Metal Carbo‐Chalcogenide “TMCC:” A New Family of 2D Materials. Advanced Materials, 34(26), p.2200574.

Husmann, S., Torkamanzadeh, M., Liang, K., Majed, A., Dun, C., Urban, J.J., Naguib, M. and Presser, V., 2022. Layered Nano‐Mosaic of Niobium Disulfide Heterostructures by Direct Sulfidation of Niobium Carbide MXenes for Hydrogen Evolution. Advanced Materials Interfaces, 9(14), p.2102185.

Wang, L., Torkamanzadeh, M., Majed, A., Zhang, Y., Wang, Q., Breitung, B., Feng, G., Naguib, M. and Presser, V., 2022. Time‐Dependent Cation Selectivity of Titanium Carbide MXene in Aqueous Solution. Advanced Sustainable Systems, 6(3), p.2100383.

Liang, K., Tabassum, A., Majed, A., Dun, C., Yang, F., Guo, J., Prenger, K., Urban, J.J. and Naguib, M., 2021. Synthesis of new two‐dimensional titanium carbonitride Ti2C0.5N0.5Tx MXene and its performance as an electrode material for sodium‐ion battery. InfoMat, 3(12), pp.1422-1430.

Kumar, V., Yeole, P., Majed, A., Park, C., Li, K., Naguib, M., Ravindranath, P.K., Jafta, C., Spencer, R., Compton, B. and Vaidya, U., 2021. MXene reinforced thermosetting composite for lightning strike protection of carbon fiber reinforced polymer. Advanced Materials Interfaces, 8(17), p.2100803.

El-Mahallawy, N., Majed, A. and Maboud, A.A.G.A., 2020. Effect of FSP process parameters with air blowing on microstructure and hardness of NiAl Bronze alloy. Materials Research Express, 7(1), p.016590.

Subedi, B., Khatoon, N., Gaire, M., Majed, A., He, J., Zhang, X., & Chrisey, D. B. Rapid Fabrication of Binder Free Nickel Cobalt Oxide Electrodes with Forest-Like Architecture for Electrochemical Energy Storage Applications. Available at SSRN 4737632.

Dr. Hridesh Rajan is the Dean of the School of Science and Engineering at Tulane University, a position he has held since July 2024. As the leader of the second largest school at Tulane, encompassing eleven departments ranging from biological sciences and chemical engineering to physics and psychology, Dr. Rajan oversees more than 60 academic programs enrolling 2,255 undergraduate and 473 graduate/professional students. He is deeply committed to building an interdisciplinary-first, translational school of science and engineering focused on better lives. A distinguished computer scientist and academic leader, Dr. Rajan's career spans extensive contributions in research, education, and higher education administration.

Prior to joining Tulane, Dr. Rajan served for nearly two decades at Iowa State University, where he was the Kingland Professor and Chair of the Department of Computer Science. In this role, he significantly enhanced the department's national standing by overseeing substantial growth in student enrollment, faculty size, and research funding. He led a comprehensive strategic planning process that positioned the department for long-term success, emphasizing academic excellence, interdisciplinary innovation, and institutional alignment. Dr. Rajan also launched pioneering interdisciplinary data science programs and founded the Midwest Big Data Summer School, expanding access to advanced research training. Widely recognized for his administrative acumen and collaborative leadership, he increased the representation of women in computing programs by 45% and fostered cross-disciplinary research efforts that secured significant external funding. His leadership consistently emphasized student success, equitable faculty workload, and fiscal responsibility.

Dr. Rajan earned his B.Tech. from the Indian Institute of Technology (BHU), Varanasi, and completed his M.S. and Ph.D. in Computer Science at the University of Virginia. His research has advanced the fields of software engineering, programming languages, and data science, notably through the creation of the Boa programming language, designed to democratize large-scale data analysis. He has held visiting positions at the University of Texas at Austin, Harvard University, and as a U.S.-UK Fulbright Scholar at the University of Bristol. His scholarly excellence has been recognized with numerous honors, including the NSF CAREER Award, a Fulbright U.S. Scholar Award, and election as a Fellow of the American Association for the Advancement of Science (AAAS).

As Dean at Tulane, Dr. Rajan is committed to student success, fostering interdisciplinary collaboration, enhancing research capabilities, and strategically growing the School of Science and Engineering’s national and global impact. Central to his leadership at Tulane is the ambitious 1000 Days Plan, a strategic initiative aimed at accelerating interdisciplinary and translational research, expanding academic programs especially in engineering, expanding experiential opportunities for undergraduate students, and strengthening industry and community partnerships to elevate Tulane’s prominence in science and engineering education and research.
 

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.