Hi! I am Sai, a Ph.D. candidate in Computer Science at Temple University, under supervision from Prof. Slobodan Vucetic.

My CV

Research Interests

My research focuses on building efficient, generalizable, and scalable machine learning systems under real-world constraints such as limited labeled data, domain shift, and computational resource limitations. I am particularly interested in human-LLM collaboration, active learning, transfer learning, and resource-efficient ML. My work bridges scientific and social domains, including wireless sensing, remote sensing, and large-scale educational text analysis, with the goal of designing adaptive learning frameworks that enable reliable decision making from noisy, diverse, and data-scarce environments.

Education

• Ph.D in Computer Science, Temple University, 2019-2025 (GPA: 3.8/4.0)
• M.S. in Computer Engineering, Arizona State University, 2016-2018 (GPA: 3.7/4.0)
• M.S. in Electrical Engineering, University of California-Irvine, 2013-2015 (GPA: 3.7/4.0)

Work experience

• July 2025 - November 2025: Applied Scientist Intern (Amazon, Bellevue, WA)
  • Designed scalable ensemble forecasting models for last-mile logistics demand prediction using temporal and spatial signals across large operational datasets.
  • Improved production forecasting accuracy by 100 basis points, supporting capacity planning and cost-sensitive delivery decisions.
  • Built production-ready ML pipelines with optimized parallelization and memory usage for high-throughput prediction workloads.
• May 2022 - August 2022: Research Scientist Intern (NEC Laboratories America, Princeston, NJ)
  • Developed domain-adaptive deep learning models for rain detection over live telecom fiber networks, enabling robust classification across changing environmental conditions and limited labeled data.
  • Designed unsupervised domain adaptation pipelines for time-series sensing signals, improving cross-domain rain-intensity classification accuracy by 8% and supporting real-world deployment on operational infrastructure.
• March 2019 - Sep 2019: Machine Learning Engineer (AsiaInfo, Beijing, China)
  • Built an AI-based alerting system for 5G networks using pattern mining, increasing anomaly detection by 5%
  • Collaborated with product teams to integrate ML modules into production workflows

Skills

• Programming Languages: Python, SQL, R, MATLAB, C++
• Machine Learning Frameworks: PyTorch, Scikit-learn, Pandas, NumPy, Nixtla, SKTime
• Tools: Git, Docker, Tableau, PowerBI, Google Spreadsheets
• Platforms: Linux, AWS (EC2, S3, Batch), Google Cloud, Google Colab