Staff ML Engineer at Latitude AI, powering Ford's vision and behavioral prediction models. Ph.D. in Computer Vision. Bridging the gap between Deep Research and Production Systems.
Structured learning modules curated for practitioners and researchers.
Latitude AI · Autonomous Driving · Palo Alto, CA
Responsible for the training ecosystem powering Ford's vision and behavioral prediction models for autonomous driving.
Adobe · Creative Cloud & Firefly · San Jose, CA
Led large-scale data pipelines, training infrastructure, and responsible generative AI initiatives that power Firefly and Creative Cloud surfaces.
Autonomous Vehicle Systems · Bay Area, CA
Shipped multi-agent prediction models for L3/L4/L5 autonomous vehicle fleets and co-designed the training framework and dataloaders that kept the stack fed with fresh data.
Qualcomm Research · San Diego, CA
Led prediction for Qualcomm's L3 highway autonomous driving stack—owning forecasting models, simulation harnesses, and post-drive analytics. Earlier built integration and test automation for the stack.
Ph.D. Computer Science & Engineering
Dissertation on collaborative perception and behavior prediction for intelligent transportation systems.
Microsoft Research · Bangalore, India
Designed AutoCalib—large-scale traffic camera calibration with <10% speed error—in Microsoft's video analytics platform.
The Ohio State University · Columbus, OH
Built SmartDashCam, Soft-Swipe, RoadView, and RoadMap; taught introductory programming; collaborated with Honda on live calibration and lane-level localization.
Standard Chartered Bank · Chennai, India
Developed reporting systems and automation scripts for global private banking infrastructure.
Tata Elxsi · Chennai, India
Optimized LTE PDCCH blind decoding algorithms and explored DSP-based radio prototyping.
Indian Institute of Technology Madras · Chennai, India
Graduated with honors; led hostel council committees.
Hand-picked deep dives worth your time.
A bird's-eye chapter for experienced ML engineers: how multimodal LLMs, text-to-image and text-to-video models are built, from data curation and tokenizing pixels, through architectures, pre-training and post-training (SFT, DPO, GRPO-style RL), to metrics and evaluation. One shared lifecycle, three very different machines.
A precise mental model for ML pipeline orchestration—mapping durable backend workflows (Temporal), data schedulers (Airflow, Prefect, Dagster), ML-native pipeline frameworks (Kubeflow, Metaflow, ZenML), and distributed compute engines (Ray). Built for engineers who need to answer 'design an ML pipeline' in interviews. Includes 2025-2026 updates: Airflow 3, KFP v2, Ray 2.x, MLflow 3.
A practitioner's guide to distributed training frameworks — the memory formulas, parallelism strategies, and failure-mode reasoning that ML infra interviews actually test. Covers DDP, FSDP, DeepSpeed ZeRO, 3D parallelism, and fault tolerance.
GPU utilization is a lagging indicator — the real battle is in the data pipeline. A practitioner's deep dive into PyTorch DataLoader internals, zero-copy data pumps, WebDataset streaming, and the exact questions this gets you in ML system design interviews.
Pre-training gives a model knowledge; post-training gives it behavior. A practitioner's breakdown of SFT, LoRA/QLoRA, DPO, and GRPO — with the memory math, concrete configs, and interview reasoning that separates candidates who've done this from candidates who've read about it.
A comprehensive guide to Python interview hacks, advanced patterns, tricky syntax, and gotchas that separate strong candidates from elite ones. Covers heapq, DP, Union-Find, Tries, and more.
From modular stacks to unified intelligence: How foundation models are reshaping AV and generalist robotics. Covers VLA models (GR00T, Pi0), Physical AI, and the 2026 embodied revolution.
A complete derivation of backpropagation for MLPs — from chain rule intuition to delta propagation, with a worked numerical example showing exactly how errors flow backward through a network.
A narrative-first walkthrough of reinforcement learning, starting with everyday intuition and ending with the math behind Q-learning and DQN.
A high level view on how modern vision-language models connect pixels and prose, from CLIP and BLIP to Flamingo, MiniGPT-4, Kosmos, and Gemini.
From naive vector search to industry-standard multimodal RAG. Master hybrid search, query rewriting, cross-encoder reranking, and the architecture of high-precision retrieval systems.
How diffusion models predict action sequences instead of pixels. Covers Diffusion Policy, world models for robotics, and connecting diffusion to reinforcement learning for autonomous systems.
A deep dive into the physics and probability of diffusion models. Learn how reversing a stochastic process became the foundation for modern generative AI, from Stable Diffusion to robotics and protein design.
The evolution of image diffusion architectures. Learn how we moved from convolutional U-Nets to scalable Diffusion Transformers (DiT), and why treating images like language changed everything.
Exploring the state-of-the-art in video generation. Learn how Sora and Veo use Spatiotemporal Transformers to simulate the physical world, and the challenges of achieving perfect motion fidelity.
How to move from visual imitation to law-governed motion. Deep dive into injecting PDEs into neural networks, implicit physics extraction, and LLM-guided physical reasoning.
How to train a world-class diffusion model. Covers the complete lifecycle: from large-scale pre-training on noisy web data to specialized post-training, alignment, and aesthetic fine-tuning.
How to accelerate diffusion sampling and steer creativity. Learn the mechanics of DDIM, DPM-Solver, Classifier-Free Guidance (CFG), and the math of negative prompting.
The fundamentals of video diffusion models. Learn how we extend 2D diffusion to time, the mechanics of temporal attention, and the architectural shifts required for motion consistency.
From stateless inference to tool-augmented AI agents. Learn how the Model Context Protocol (MCP), secure sandboxes, and holistic versioning enable the next generation of AI systems.
Why modern AI teams are handcrafting GPU kernels—from FlashAttention to Triton code—and how silicon-level tuning is the new frontier of MLOps.
Standard MLOps advice tells you to learn Git and Docker. But for the next generation of AI Engineers, that's just the baseline. This roadmap focuses on the Infrastructure Round—deep-diving into how data is structured for speed, how it's fed into models, how those models scale across clusters, and how we squeeze every drop of performance out of the silicon.
A comprehensive deep-dive into production inference optimization, tracing the path of a request through LLM and diffusion model serving systems. Understanding the bottlenecks from gateway to GPU kernel execution.
The unsung hero of modern data processing is how we structure data itself. Learn how Apache Parquet and Apache Arrow solve the fundamental trade-off between storage efficiency and compute speed.
How PagedAttention, Continuous Batching, Speculative Decoding, and Quantization unlock lightning-fast, reliable large language model serving.
The raw senses of an autonomous vehicle: What data does each sensor provide? Covers cameras, radar, LiDAR, ultrasonics, and microphones—their physics, strengths, weaknesses, and why fusion is necessary.
From GPS to centimeter accuracy: How autonomous vehicles know their exact position. Covers GNSS, IMU, wheel odometry, scan matching, and Factor Graphs.
How autonomous vehicles remember the world. Covers HD maps, lane graphs, offline vs. online mapping, MapTR, and the map-heavy vs. map-light debate.
Why L5 autonomy is harder than a moon landing. Understanding ODD, latency loops, compute constraints, and the modern Hybrid Architecture (Modular vs. End-to-End).
From pixels to 4D realities: How AVs understand their environment. Deep dive into BEV Transformers, Panoptic Occupancy, Scene Flow, and Foundation Models for open-world perception.
The hardest problem in AV: predicting human irrationality. From physics-based Kalman Filters to Joint Autoregressive Distributions, Generative Motion Diffusion, and World State Propagations.
From perception to action: How autonomous vehicles make decisions. Covers cost functions, game-theoretic planning, MPC, and the "End-to-End" debate.
If you don't know where your eyes are relative to your feet, you trip. Covers intrinsics, extrinsics, SE(3) transforms, online vs. offline calibration, and time synchronization.
A deep dive into XGBoost — how second-order Taylor approximations and sophisticated regularization make it the dominant algorithm for structured data, bridging mathematical rigor with system engineering excellence.
Part 4 of a comprehensive guide to agentic AI design patterns. Covers common failure modes, safety mechanisms, verifiable pipelines, and how to build reliable production systems.
Part 3 of a comprehensive guide to agentic AI design patterns. Covers specialized patterns: embodied agents, 3D scene understanding, imagination loops, multi-agent societies, error recovery, and self-debugging.
Part 5 of a comprehensive guide to agentic AI design patterns. Covers 2025 trends, cost optimization, case studies, production checklist, and the state of the field.
Part 1 of a comprehensive guide to agentic AI design patterns. Covers the fundamentals: ReAct loops, planning, tool use, self-consistency, and graph-based reasoning.
Part 2 of a comprehensive guide to agentic AI design patterns. Covers production-ready patterns: memory management, supervisor/orchestrator, parallel tool execution, and hidden reasoning.
An exploration of modern agent systems, with math, analogies, and examples. From ReAct loops to multi-agent societies, discover the design patterns that make AI agents think, act, and fix themselves.
An intuitive introduction to the Transformer architecture — from the attention mechanism to self-attention and cross-attention, using language translation as a concrete example.
An intuitive introduction to Variational Autoencoders — how compressing data into probabilistic codes enables machines to generate realistic images, sounds, and structures.
Reflections on building production-grade behavior prediction systems for autonomous vehicles — and why closed-loop reasoning is the bridge between perception and planning.
My research journey from wireless communication foundations to solving the camera calibration bottleneck that enables autonomous vehicle vision.
How we used deep learning to automatically calibrate traffic cameras by observing vehicle motion—work that won Best Paper Award at ACM BuildSys 2017.
Latest additions from the knowledge base.
Why Ray, when a process pool or torchrun is enough, and every Ray concept an ML engineer is expected to know: tasks, actors, objects and ownership, scheduling and placement groups, fault tolerance, Ray Data, Train, Tune, Serve, RLlib, compiled graphs and direct GPU transport, KubeRay, observability, and the anti-patterns that waste GPU hours. Mapped onto PyTorch DDP, NCCL and Kubernetes, with self-check questions at the end.
Parquet was built for scans; ML needs random access, fat embedding columns, cheap column backfills, and versioned datasets. A practitioner's look at the Lance format and LanceDB: what the peer-reviewable numbers actually show, where the hype outruns the evidence, and how it changes the dataloader and RAG designs from my earlier posts.
A structured articulation and pacing warm-up designed to help technologists speak with clarity and confidence in high-stakes meetings.
A collaborative 45-minute thinking algorithm tuned for Google-style coding interviews—classify the problem, co-design an optimal approach, code with confidence, and handle follow-ups with ease.
A curated list of book recommendations covering personal development, philosophy, psychology, and life lessons from my personal library.