Chandranil “Nil” Chakraborttii received his Ph.D. and master’s degree in computer science from the University of California Santa Cruz and an undergraduate degree in information technology from West Bengal State University, India. Before starting his Ph.D. program, Chakraborttii worked for three years as a systems engineer in the software industry. During graduate school, he taught at the Stanford Pre-Collegiate Summer Institutes for four years, where he discovered his passion for teaching. He has collaborated with industry partners, Samsung and Intel on research projects related to cloud storage systems, and has jointly authored patents and publications. His research interests lie at the intersection of artificial intelligence and storage systems. More specifically, Chakraborttii is interested in the performance optimization of flash-based solid-state drives for cloud systems using machine learning techniques. At Trinity, Chakraborttii heads the CRICS research lab which work on applications of artificial intelligence (AI) for improving the performance of cloud based systems. For more information, please visit the CRICS website @ https://crics.domains.trincoll.edu/

curriculum vitae

Publications:

• 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 (2023), co-located with COMPSAC 2023.
• 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.”  IEEE International Workshop on Data Science & Machine Learning for Cybersecurity, IoT & Digital Forensics (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." Symposium on Cloud Computing (SoCC ’20), New York, NY, October 19-21, 2020.
• 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, 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., 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