Experience

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  • AI Engineer Intern, Search & Recommendation Systems | Tabhi
    Austin, TX · May 2026 – Aug 2026

    Architected an end-to-end personalized discovery feed over 450K+ items, and cut cold-feed latency from 8 s to 500 ms.

  • Research Assistant, Real-Time AI Systems | University of Southern California
    Los Angeles, CA · Nov 2025 – Present · under Prof. Laurent Itti

    Monocular SLAM for assistive smart glasses: a C++ GTSAM pose-graph backend cut trajectory error from 29–58 m to 0.09–0.24 m.

  • Grader, CSCI 585 Database Systems | University of Southern California
    Los Angeles, CA · Aug 2026 – Present · under Prof. Shahram Ghandeharizadeh

    Evaluating Database Systems coursework for 200+ students under Prof. Shahram Ghandeharizadeh.

  • Machine Learning Intern, Edge Computer Vision | YMT Medical
    India · Jan 2024 – Aug 2024

    On-device clinical vision for micro-lesion detection (0.83 mAP@0.5) and deterministic acne grading, with ~40% faster edge inference.

  • Software Engineer Intern, Backend & APIs | Technoriya ERP Solution
    India · Oct 2023 – Dec 2023

    Built backend services and APIs supporting scalable data pipelines and cloud-based ML workflows.

Research & Publications

SLAM + ToF paper Under review

with Prof. Laurent Itti · iLab, USC

Part of my research in the iLab at USC.

Blockchain Based Music Streaming Platform Using NFTs figure

Blockchain Based Music Streaming Platform Using NFTs Cited by 7

P. Baradkar et al. · IEEE ICSCSS 2023

A blockchain-based music streaming platform leveraging NFTs for rights management and royalty distribution, enabling transparent and automated compensation for artists.

Paper presentations

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks figure

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks Talk

Mireshghallah, Goyal, Uniyal, Berg-Kirkpatrick & Shokri · EMNLP 2022
Presented in CSCI 699 · Side-Channel Threats in Cloud & LLM Systems · USC · Fall 2026

Paper presentation. Loss-threshold attacks made masked LMs look safe; a likelihood-ratio test against a reference model, scored as an energy gap, lifts attack AUC on ClinicalBERT from 0.66 to 0.90, and to 0.99 per patient.

Skills

Programming Languages

PythonC++JavaJavaScriptTypeScript

AI / ML & Deep Learning

Deep LearningPyTorchTensorFlowTransformersComputer VisionNLPReinforcement LearningOffline RLscikit-learnPandasNumPy

Generative AI & Agents

LLMsRAGAgentic AI SystemsPrompt EngineeringLangChainLangGraphMCPGoogle ADKA2ALangfuse

Retrieval & Recommendation

Elasticsearch BM25Hybrid SearchVector RetrievalFAISSpgvectorTwo-Tower / SASRecOff-policy Evaluation

Computer Vision & Edge

Visual Odometry / SLAMGTSAMOpenCVYOLOv8FastSAM / SAMMediaPipeINT8 QuantizationCoreMLONNX Runtime

Privacy & Trustworthy ML

Differential PrivacydiffprivlibMembership InferenceOff-policy Evaluation

Frameworks & Development

FastAPIFlaskDjangoNode.jsExpress.jsReact.jsNext.jsStreamlitOAuth

Databases & Cloud

PostgreSQLMongoDBSQLRedisSupabaseAWSGCPAzureDockerKubernetesGitCI/CDLinux

Education