- Full-Stack Systems Design: Architect end-to-end product systems across web, mobile, and API layers—applying CS fundamentals in modular design, type-safe contracts, data modeling, and scalable service boundaries.
- Frontend Engineering: Build high-performance interfaces with Next.js, React, and Vue—covering component architecture, SSR/SSG, state management, rendering performance, and accessible interaction design.
- Backend & Distributed Services: Develop reliable APIs and domain services with Node.js, NestJS, FastAPI, and Django—REST/GraphQL design, authentication & authorization, concurrency-aware request handling, and transactional integrity.
- Mobile Cross-Platform: Deliver production mobile apps with React Native, Expo, Flutter, Swift, and Kotlin—sharing business logic while preserving platform-native UX and offline-capable data sync.
- Cloud, Data & DevOps: Operate cloud workloads on AWS and Azure with Docker/Kubernetes; design persistence on PostgreSQL, MongoDB, and Supabase; automate CI/CD, observability, and infrastructure as code.
- Workflow & API Automation: Orchestrate cross-system business processes via n8n, Zapier, Make.com, and custom API pipelines—event-driven integrations that reduce manual ops and enforce reliable handoffs between SaaS tools.
- End-to-End Autonomous Driving: Design and implement unified perception-planning-control pipelines using large-scale vision-language models, replacing modular architectures with monolithic neural approaches for robust driving policy learning.
- Multimodal Sensor Fusion: Integrate camera, LiDAR, radar, and IMU data through deep fusion networks, leveraging BEV (Bird's Eye View) representation and transformer-based encoders for comprehensive scene understanding.
- World Model & Scene Understanding: Build predictive world models using diffusion-based and autoregressive approaches for trajectory prediction, causal reasoning, and temporal scene comprehension in dynamic driving environments.
- Foundation Models for Driving: Adapt and fine-tune vision-language foundation models (LLaVA, GPT-4 V, etc.) for driving-specific tasks including scene description, rule compliance, and zero-shot generalization to unseen scenarios.
- Real-time Inference Optimization: Deploy efficient end-to-end models with model compression, quantization, and knowledge distillation techniques to meet automotive safety-critical latency requirements (≤100 ms inference time).
- Data Closed-Loop: Build end-to-end data flywheels from fleet logging and upload through scenario/corner-case mining, auto-labeling, curation, training, offline evaluation, and OTA redeployment—closing the loop so production failures continuously improve the next model iteration.
- Simulation & Data Engine: Develop synthetic data generation pipelines using generative AI, create photorealistic simulation environments, and design active learning strategies for data-efficient training and edge case coverage—feeding hard cases back into the closed-loop dataset.
- LLM Application Frameworks: Deep integration with DSPy, LangChain, AutoGen, CrewAI, and the ReAct paradigm.
- Advanced RAG Systems: Build enhanced retrieval pipelines incorporating vector databases, hybrid search, and custom retrievers.
- Agentic & Autonomous Systems: Develop multi-agent systems for research, process automation, and trading.
- AI Content Detection: Apply stylometric analysis and embedding techniques for AI-generated content identification.
- Internal Automation: Built a Slack → Notion → API → LLM automation workflow, reducing support response time by 60%.
- Multimodal Intelligence: Integration of CLIP for image tagging, YOLOv 8 for content moderation, Whisper for ASR, and Tacotron 2 for TTS.
- LLM Evaluation & Benchmarking: Design and implement comprehensive evaluation systems (leveraging LLM-as-a-Judge, human annotation) to track core metrics: accuracy, hallucination rate, latency, and cost.
- Prompt Engineering & Optimization: Systematic prompt iteration, chain-of-thought design, and few-shot learning to maximize model performance.
- Model Fine-tuning & Alignment: Proficient in full pipelines including Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for domain-specific model optimization and safety alignment.
- Emerging Architecture & Applied Research: In-depth exploration of cutting-edge areas: long-context optimization, multimodal understanding, agent collaboration, and AI memory systems.
- Venture Focus: Found and build AI products at the intersection of autonomous driving and embodied intelligence—turning research capability into shippable platforms for industry teams.
- Data Closed-Loop Toolchain: Develop full-stack toolchains that close the data flywheel—collection, mining, labeling, curation, training, evaluation, and redeployment—purpose-built for AD and embodied AI workloads.
- Agent Customization: Design and deliver domain-specific agents tailored to customer workflows—covering scenario mining, annotation assist, simulation ops, and decision support—so teams can automate high-friction loops without rebuilding from scratch.
- Productization & Delivery: Package models, pipelines, and agents into production-ready systems with APIs, dashboards, and deployment paths that customers can adopt and iterate on.