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Degrees:
Ph.D., Univ. of California Santa Cruz
M.S., Univ. of California Santa Cruz
B.Tech., West Bengal Univ of Technology
Chandranil “Nil” Chakraborttii, Ph.D., is an Assistant Professor of Computer Science at Trinity College. He received his Ph.D. and M.S. in Computer Science from the University of California, Santa Cruz (as a member of the Hardware Systems Collective) and his B.Tech. in Information Technology from the West Bengal University of Technology, India. Prior to his doctoral studies, he spent three years as a systems engineer in the enterprise software industry and taught computer science and logic at the Stanford Pre-Collegiate Summer Institutes for four years.
Prof. Chakraborttii’s research operates at the intersection of computer systems, cloud storage reliability, and applied artificial intelligence. His lab focuses on machine learning for storage performance and failure prediction, cryptographic data erasure and privacy in vector databases, and intelligent hybrid memory architectures. His work is supported by competitive awards including a NASA Connecticut Space Grant Consortium (CTSGC) Faculty Research Grant and enterprise computational resources from the Connecticut AI Alliance (CAIA). At Trinity, Chakraborttii directs the Cloud, Reliability, Intelligence, and Cyber-Systems (CRICS) Research Lab. For more information on current projects, publications, and student opportunities, please visit the CRICS Lab website: https://crics.domains.trincoll.edu/
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CPSC 360: Deep Learning
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CPSC 352: Artificial Intelligence
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CPSC 215 & 215-L: Data Structures and Algorithms & Laboratory
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CPSC 275 & 275-L: Introduction to Computer Systems & Systems Laboratory
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FYSM 124: Enchanted Data & Machine Minds (First-Year Seminar)
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CCUR-202
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Trinity/Tuck Business Bridge Program
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CPSC-115
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Introduction to Computer Science
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CPSC-215
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Data Structures and Algorithms
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CPSC-275
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Introduction to Computer Systems
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CPSC-352
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Artificial Intelligence
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Storage Systems
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Deep Learning
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Artificial Intelligence
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Procedural Content Generation in Video Games
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Research Lab Website: https://crics.domains.trincoll.edu/
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Publications:
- Chakraborttii, Chandranil, Jackeline García Alvarado, Sitora Abdulofizova, Talakinet Weibo, and Shivanshu Dwivedi. "Ghost Vectors: Storage-Layer Reconstruction and Cryptographic Erasure in HNSW Vector Databases." In Proceedings of the 17th ACM Symposium on Cloud Computing (SoCC '26), 2026.
- Chakraborttii, Chandranil, and Devesh Tiwari. "Failure Prediction in Cloud-Based Storage Systems: A Survey." ACM Transactions on Storage (TOS) 22, no. 1 (2026).
- Chakraborttii, Chandranil. "TAP++: A Tier-Aware Transformer for Adaptive Prefetching in Hybrid Memory Systems." Neural Computing and Applications, Springer Nature, 2026.
- Chakraborttii, Chandranil, Ana Solórzano, and Devesh Tiwari. "Synthetic Data Generation for Storage Failure Prediction in Large-Scale Systems." In Proceedings of the 26th IEEE International Symposium on Cluster, Cloud, and Internet Computing (CCGrid), Sydney, Australia, 2026.
- Mukkuzhi, Allan, Jacob Kammerzell, Noella Uwayisenga, Yeabsira Bizualem, and Chandranil Chakraborttii. "Ghost Echoes: Semantic Erasure Failure in Retrieval-Backed Applications." In Proceedings of the SHIELD-AI Workshop at ECML PKDD, 2026.
- Chakraborttii, Chandranil, and Lin Cheng. "TransPark: Leveraging Transfer Learning With Transformers for Intelligent Parking Systems." IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS) 7 (2025): 166-189.
- Alsolame, Tarek, BDS Aritra, Stephen Antogiovanni, Enock Niyonkuru, and Chandranil Chakraborttii. "Predicting Coronal Hole Activity: Key to Mitigating Space Weather Impacts." In Pan-African Artificial Intelligence and Smart Systems (PAAISS), Springer Nature, 2025.
- Lee, Hyun, Chris Yi, and Chandranil Chakraborttii. "Enhancing Graph Representation Learning with walkLM for Effective Community Detection." In Proceedings of the 11th International Conference on Networking, Systems, and Security (ICNSS), 2024.
- Ha, Kiet, Lucas Stowe, and Chandranil Chakraborttii. "Improving Insurance Fraud Detection with Generated Data." In Proceedings of the 48th IEEE International Computers, Software, and Applications Conference (COMPSAC), 2024.
- Chakraborttii, Chandranil, and Mohammad Maminur Islam. "MemFlex: A Hybrid Memory System to Boost Cost of Ownership in Data Centers." In Proceedings of the 48th IEEE International Computers, Software, and Applications Conference (COMPSAC), 2024.
- Chakraborttii, Chandranil, and Mohammad Maminur Islam. "TAP: Transformers Driven Adaptive Prefetching for Hybrid Memory Systems." In Proceedings of the 11th IEEE International Conference on Soft Computing & Machine Intelligence (ISCMI), 2024.
- Chakraborttii, Chandranil, and Lucas Ferreira. "Towards Generating Surprising Content in 2D Platform Games." In Proceedings of the 19th ACM International Conference on the Foundations of Digital Games (FDG), 2024.
- Jaliminche, Lokesh, Chandranil Chakraborttii, Changho Choi, and Heiner Litz. "Enabling Multi-Tenancy on SSDs with Accurate IO Interference Modeling." In Proceedings of the 14th ACM Symposium on Cloud Computing (SoCC '23), 2023, pp. 216–232.
- Y. Zhu, B. Hudson, C. Chakraborttii, Y. -H. Su and K. Huang. "A Deep-Learning Approach to Marble-Burying Quantification: Image Segmentation of Marbles and Bedding," 2023 IEEE/SICE International Symposium on System Integration (SII), Atlanta, GA, USA, 2023, pp. 1-8, doi: 10.1109/SII55687.2023.10039320.
- Chakraborttii, Chandranil, and Boettner, Jonas. “Leveraging Temporality of Data to Improve Failure Predictions for Solid State Drives in Data Centers,” The 11th IEEE International Workshop on Consumer Devices, Systems, and Services (CDS 2023), co-located with COMPSAC 2023, pp. 1272-1278.
- Chakraborttii, Chandranil, and Osborne, Anya. "Characterizing player responses to surprising events in 2D platform games." Entertainment Computing 45 (2023): 100542.
- Chakraborttii, Chandranil, and Ganji, Dhiraj. “Towards data generation to alleviate privacy concerns for cybersecurity applications.” 1st IEEE International Workshop on Data Science & Machine Learning for Cybersecurity, IoT & Digital Forensics (DSML 2023), co-located with COMPSAC 2023.
- Chakraborttii, Chandranil, and Heiner Litz. "Deep Learning based Prefetching for Flash." Nonvolatile Memory Workshop (NVMW). 2022.
- Chakraborttii, C., and Litz, H. "Improving the accuracy, adaptability, and interpretability of SSD failure prediction models." Proceedings of the 12th ACM Symposium on Cloud Computing (SoCC ’21), pp. 120-133, 2021.
- Chakraborttii, C, and Litz, H. "Reducing Write Amplification in Flash by Death time prediction of Logical Block Addresses." 14th ACM International Systems and Storage Conference (SYSTOR ’21), Haifa, Israel, June 14-16, 2021.
- Chakraborttii, C, and Litz, H. "Explaining SSD Failures using Machine Learning." Proceedings of "Non-Volatile Memories Workshop," University of California San Diego, CA, March 7-10, 2021. (Extended Abstract).
- Chakraborttii, C, and Litz, H. "Learning I/O access patterns to improve prefetching in SSDs." Applied Data Science Track, ECML-PKDD, Joint European Conference on Machine Learning and Knowledge Discovery in Databases (September 14-16, 2020). Springer, Ghent, 2020.
- Chakraborttii, C., Sinha, V., & Litz, H. "SSD QoS improvements through machine learning." Proceedings of the ACM Symposium on Cloud Computing (SoCC ’18), 511-511, Carlsbad, CA, October 11-13, 2018.
- Mobramaein, A., Whitehead, Chakraborttii, C. "Talk to me about pong: On using conversational interfaces for mixed-initiative game design." AAAI Spring Symposium Series, The Design of the User Experience for Artificial Intelligence (the UX of AI), Palo Alto, CA, March 26-28, 2018.
- Chakraborttii, C., Ferreira, L. & Whitehead, J. "Towards generative emotions in games based on cognitive modeling." Proceedings of the Eighth Workshop on Procedural Content Generation (PCG 2017), Hyannis, MA, August 14-16, 2017. (Foundations of Digital Games)
- Compton, K., Logas, H., Osborn, J. C., Chakraborttii, C., Coffman, K., Fava, D., ... & Pagnutti, J. "Design lessons from binary fission: A crowd sourced game for precondition discovery." Proceedings of 1st International Joint Conference of Digital Games Research Association and Foundation of Digital Games, Dundee, Scotland, August 1-6, 2016.
Patents:
- Method for accelerating image storing and retrieving differential latency storage devices based on access rates (Patent ID: 16/708429)
Inventors: Olarig, Paul; Schwaderer, David; Chakraborttii, Chandranil
- A machine learning method for SSD based image processing targeting self-driving vehicles (Patent ID: 16/826066)
Inventors: Olarig, Paul; Chakraborttii, Chandranil; Sharma, Manali
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Research Grants & Computing Awards:
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Connecticut AI Alliance (CAIA) AI Computing Access Program Award: Auditing Deletion Residues in AI Storage (Enterprise AI Computing Allocation), June 2026
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NASA Connecticut Space Grant Consortium (CTSGC) Faculty Research Grant: Analyzing Anomalies from Solar Observations, March 2023
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Google Skills Educational Credits Award: Hands-on Cloud Systems Computing Instruction
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Digital Health Course Development Grant, Trinity College, June 2021
Fellowships & Recognition:
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Student Travel Grant, ACM Symposium on Cloud Computing (SoCC), 2019
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Teaching Recognition Award, Stanford Pre-Collegiate Summer Institutes, 2018
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Regents Fellowship, University of California, Santa Cruz, 2014
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