About
I am a Ph.D. candidate in Computer & Information Science at the University of Pennsylvania, advised by Prof. Boon Thau Loo and co-advised by Prof. Mohammad Javad Amiri. I also collaborate closely with Prof. Ryan Marcus on adaptive data management and distributed learning systems.
My research focuses on high-performance distributed databases, intelligent blockchains, and machine learning for systems optimization. I develop algorithms and systems that make data-intensive applications more efficient, adaptive, and reliable—combining rigorous quantitative analysis with practical engineering solutions.
📢 Currently on the job market! I'm seeking full-time industry positions starting 2025! Please reach out at bhavanam@upenn.edu
Research Interests
My research interests span distributed systems, machine learning for systems, and high-performance computing:
- Adaptive Database Systems: Designing self-tuning database architectures that dynamically adjust partitioning strategies, indexing, and replication in response to changing workloads and system conditions
- Quantitative System Optimization: Applying mathematical modeling, reinforcement learning, and statistical methods to optimize distributed system performance and resource allocation
- Scalable Distributed Systems: Creating highly available, fault-tolerant protocols with strong consistency guarantees for mission-critical applications
- ML for Systems Infrastructure: Developing ML-powered techniques that automatically tune and optimize system configurations to maximize throughput and minimize latency
Selected Projects
Developed a self-tuning query optimizer that uses reinforcement learning to dynamically adjust execution plans based on workload patterns, reducing query latency by 40% in high-concurrency environments.
Technologies: PostgreSQL, TensorFlow, C++Designed and implemented a Byzantine fault-tolerant consensus protocol that scales horizontally while maintaining strong consistency guarantees, enabling secure transactions in untrusted environments.
Technologies: Go, Rust, Distributed SystemsBuilt a low-latency streaming data processing framework capable of handling 100K+ events per second with sub-millisecond processing guarantees for time-sensitive financial applications.
Technologies: Java, Kafka, CUDACreated statistical models to predict system performance under varying loads, optimizing resource allocation and reducing infrastructure costs while maintaining SLAs.
Technologies: Python, PyTorch, Time Series AnalysisSelected Publications
- Paper on Adaptive Distributed Systems Under Submission
- Towards Full Stack Adaptivity in Permissioned Blockchains VLDB '24
- Towards Adaptive Fault-Tolerant Sharded Databases AIDB @ VLDB '23
- AdaChain: A Learned Adaptive Blockchain VLDB '23
Experience
Research Assistant
2019 - Present
University of Pennsylvania
- Conducting research in adaptive distributed databases and machine learning for systems.
- Worked on TCP migration projects in collaboration with Microsoft Research.
Teaching Assistant
2020 - 2024
University of Pennsylvania
- CIS 471: Computer Architecture (Instructor: Joe Devietti)
- CIS 548: Operating Systems (Instructor: Boon Thau Loo)
Undergraduate Research Assistant
2015 - 2018
Nirma Institute of Technology
- Research in embedded systems, IoT optimization, and human action recognition using deep learning.
Research Intern
Summer 2017
RISE Lab, IIT Madras
- Worked on FPGA-based high-speed divider architectures (radix-4 SRT dividers).
Design Engineer
2018 - 2019
Bluespec Inc.
- Architected and optimized RISC-V cores with a focus on pipelining and timing closure.
- Automated deployment of hardware cores from high-level specifications
- Developed testing frameworks for hardware validation