Sai Sandeep Mopuri
San Francisco, California, United States
3K followers
500+ connections
About
I'm an IIT Delhi graduate currently working in Uber Palo Alto Region
Articles by Sai Sandeep
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Uber Hyderabad Hiring
Uber Hyderabad Hiring
Uber is hiring for Software developer positions in Hyderabad. Interested folks can send in your resumes.
Experience
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Education
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Volunteer Experience
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Volunteer
VR1
- Present 13 years 6 months
Civil Rights and Social Action
Volunteered for marathon, stalls and workshops of VR1 during Speranza(tech fest at IIT, 2012).
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NCC Cadet
National Cadet Corps
- 3 years 11 months
Social Services
● Attended NCC Camp held at IIT twice.
● Participated in Horse Riding and Riffle Shooting
Courses
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Analysis and Design of Algorithms
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Artificial Intelligence
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Computer Architecture
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Computer Graphics
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Computer Networks
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Data Structures and Algorithms
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Digital Hardware Design
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Digital Image Analysis
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Discrete Mathematical Structure
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Economics
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Introduction to Algebra and Matrix Analysis
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Introduction to Database Systems
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Introduction to Psychology
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Introduction to Sociology
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Machine Learning
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Numerical Optimisation
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Operating Systems
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Probability and Stochastic Processes
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Programming Languages
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Signals and Systems
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Social, Economic and Information Networks
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Projects
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Analysis and Visualisation of Parliament data
● Built a web-based application showing plots of state-wise, party-wise mash up graphs of
statistics of Parliament data using Google Charts, Prefuse.
● Made an interactive analytics dashboard for a hypothetical social network with live data
stream.Other creators -
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Architecture Simulator
● Designed architecture that simulates the branch predictor, inorder pipeline, and the cache.
● Design a tournament predictor,including a GShare predictor, and a bimodal predictor.
● Simulated inorder pipeline including the stages Fetch, Decode, Execute, Memory Access and Writeback and simulated L1, L2 caches with different cycle timing.Other creators -
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Machine Learning
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● Implemented Digit Recognition from images using Artificial Neural Networks and K Means Clustering.
● Made a Spam Classifier using SVM and using CVX package applied on dual problem.
● Implemented Newsgroup Classifier through Naive Bayes Model.
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Human Activity Recognition using Microsoft Kinect
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● Leveraged windows Kinect to identify various activities of an employee in office.
● Identified one’s activities out of one’s actions and scaled the work to two persons.
● Extended the project to identify the solid objects used by the employee considering the RGB data of the body.
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Web-based DVD renting service
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● Modelled a queuing mechanism to issue DVDs to people from large pool of IMDB data using PostgreSQL, incorporated search based on the movie attributes.
● Incorporated feature of automatic notification as soon as a new movie of user interested genre is added to the movie database.Other creators -
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K Connect Game
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● Designed K-Connect game using Mini-Max techniques incorporated with Alpha-Beta
pruning procedure along with a perfect Evaluation function.
● Improved performance of the game by introducing Monte-Carlo simulations.Other creators -
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Extending Pintos Operating System
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● Modelled argument passing, virtual memory, thread scheduling and system calls.
● Managed paging, frame table, swap spaces and memory mapped files for PintOS, employed TLB and LRU page replacement policiesOther creators -
Languages
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English
Professional working proficiency
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Hindi
Limited working proficiency
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Telugu
Native or bilingual proficiency
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Debasish Ghosh
An awesome interview, as part of SIGPLAN blog with Prof Ranjit Jhala, Professor at University of California, San Diego. The topics ranged from philosophical discussions, grad school experiences, how he developed an interest in model checking and PL research and finally down to a cool demo of refinement types in Rust. Absolutely loved the demo and surely would be playing around with Flux. Some of my rough notes from the discussion : - Model checking and Floyd Hoare logic : FH logic is symbolic model checking where u r using assertions to represent sets of states and manipulating these sets of states symbolically (preconditions, post conditions etc.) - How lazy abstraction evolved from a talk that was led to thoughts on how to reuse parts of a search space that has already been explored in previous iterations. Frugality of abstractions - don’t do stuff unless u really need it .. did I hear “Laziness by default is not my cup of tea ?” - How liquid types evolved organically as a fusion of constraint solving (that type systems do), model checking and lazy abstractions. - Thoughts on type systems in Haskell and Rust - Rust type system is extremely complex yet the compiler produces so good error messages. And there is a compelling value proposition - you do the type checking and since there is no GC, your code runs very fast. - The cool demo on Flux, a language-integrated verifier for Rust that leverages liquid types, a form of dependent type system. It was also cool to see CoPilot auto-completing flux annotations ;-). First runs rust type and borrow checker and then take the type information and then do liquid types style reasoning using SMT solvers .. Talks about the challenges of implementing refinement types in an imperative language, and here is flux. - Philosophy of Verse as a unification of functional and logic programming. Has a very very sophisticated type system - we expect to hear more in the near future. - Interesting discussion on whether CoPilot like LLMs will stifle new programming language design and research (as we don’t have many examples in the wild to train on) ... and a lot more. Well worth the one and a half hours .. Link to the interview: https://lnkd.in/g-ygZxcw
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Brian Woolfrey
Building a GenAI app? Check out Shalini Goyal Don’t just plug in a model - design it to scale, adapt, and evolve. Here’s your blueprint for future-ready GenAI systems. 👇 1. Modular Architecture Separate UI, orchestration, models, and storage to swap parts independently. Use LangChain or LlamaIndex to build pipelines. 2. Context Engineering Layer system prompts, memory, and retrieved knowledge to optimize generation. Use chunking and summarization to stay efficient. 3. Retrieval-Augmented Generation (RAG) Connect vector DBs like Pinecone or Weaviate and use hybrid search (dense + keyword) for domain-specific relevance. 4. Low-Latency Design Cut load times and delay using model distillation, quantization, and async I/O. 5. Agent-Based Systems Use CrewAI, AutoGen, or LangGraph for task decomposition and tool execution via specialized sub-agents. 6. Tool & Plugin Integration Enable LLMs to run code, hit APIs, or use external tools through OpenAI function-calling or LangChain routing. 7. Streaming & Feedback Improve experience with real-time streaming via WebSockets and user feedback for continuous refinement. 8. Memory Management Support both session and long-term memory using Redis, Postgres, or vector DBs for persistence. 9. Smart Deployment Use K8s or serverless runtimes (like AWS Lambda) to deploy GenAI apps with dynamic scaling. 10. Observability Track usage, hallucinations, and prompts using tools like LangSmith or WhyLabs for LLM monitoring. [Explore More In The Post] Here’s the takeaway? Good GenAI apps aren’t just about prompts, they’re engineered for performance, adaptability, and scale.
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