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keerthanap8898/README.md

Keerthana Purushotham

◯ ☽ Computer Scientist ◐ Software Developer ◑ Research Engineer ❨ ☼

✧ Keerthana currently works at Amazon Linux, AWS in the Threat, Security & Vulnerability Management* team ,in EC2's Kernels & Operating Systems org ( KaOS ) 🙆🏻‍♀️.

✧ As a Software developer at Amazon Linux ( AL ), she, along with the rest of her team drive CVE( Common Vulnerabilities & exposures ) Management; i.e., the vulnerability life-cycle across all AWS OS instances, shells, VMs, Hypervisors, EC2 servers (& containers), etc.; spanning distros non-exclusively including including- AL12, AL1, AL2, AL2023, bare metal instances etc., amongst others.

  • She works at the intersection of AI, Distributed Systems, & Correctness; exploring how large-scale intelligent systems can be made more reliable, interpretable, & aligned with design intent.
  • Her work integrates research-driven inquiry with production-grade engineering.

✧ Keerthana has developed deep expertise in threat modeling & remediation, i.e, detecting new bugs i.e., CVE(s) & patching them; across more than 1,500 CVEs for multiple Amazon Linux (AL) distributions.

  • These threat detections & patches regularly touched every single one of the millions of AWS instances deployed globally including all of EC2 servers, AWS hypervisors, etc., during AL's fortnightly security releases.
  • Also involved orchestrating automated tests spanning various linux VM instances offered by AWS for packages whose vulnerability lifecycles she's managed end-to-end;
  • This non-exclusively includes packages like docker, kernel, openssl, nss, python, java, mozilla, etc., amongst all packages seen on AL2023, & more.

❄️🏂🏻 She seeks impactful roles where she can drive innovation at scale. 🤶🏻⛄


Key Links ( x2 )

⎯⎯✧ ˚.☆°。𓆉 ྀ○°𓆝˚○。𓆡☆。⋆.݁݁✧˚𓆞。𓇼𓆝˚𓆟。༚⋅ ✧⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

[˖˚⋆. ݁݁ ⏾˚ ˖ ྀ ⋆] : See this Google-LM notebook, pre-trained on her profile @ AI-chatbot .

⎯⎯✧ 𓋼𖧧˚°⚘𓃦。𓃙˚𓃠○𓃥°𓃚'⚘.𓏲˚𓍊𓋼✧。༚⋅ ✧⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

Networking:

⎯⎯✧ 𓂇𖧧𖠰ᨒ↟𓃬﹏↟𓂃𓃮ᨒ˚𖠰࣪↟𓃮﹏𓃮‿་༘ ✧⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

Focus Areas & Technical Interests:

Non-exclusively,

  • 🌕 AI System Reliability: Correctness & robustness in AI & distributed systems,
  • 🌔 Distributed Computing: Scalable, fault-tolerant architecture design,
  • 🌓 Scalable Machine Learning Infrastructure: Systems reasoning, verification, & interpretability,
  • 🌒 Program Analysis & Dataflow Optimization: Research-informed engineering practice,
  • 🌑 Systems for Alignment & Verification: Architecting provably-correct intelligent systems through formal methods, test-driven reasoning, & algorithmic accountability.
⎯⎯✧ 𓇢𓆸𓇗⚘𖤣𖥧𓏲°✾.𓅰.𓅭.𓅮.𓅯.𖡼˚↟𖠰✧𖤣𖥧𓅪𖧧⋆ ✧⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

Career:

She is open to conversations & new opportunities around AI systems research, reliability engineering, & correctness-oriented design.

✧ She is a full-stack SDE with expertise in cybersecurity, cloud, NLP, & statistics.

  • At AWS, she has integrated AWS CDK, C, Rust, Python, JavaScript, node.js, most major AWS tools & services, APIs, containers & shells, load-balanced edge-APIs & Lambdas, etc., running high frequency, global, federated, throttled workflows orchestrating async requests, to collect critical threat data as soon as they're released; to streamline, plus reliably execute engineering workflows.
  • She's also tasked with accurately evaluating thse bugs, finding &/or designing their corresponding patches from scratch if unavailable- to design, plan & prove remediation plan, without delays.
  • A large part of her work also involves building predictive automation tools for CVE evaluation, designs scalable cloud infrastructure, & supporting threat detection & mgmt for Amazon Linux.

✧ Keerthana's strong computer science foundation from UCSD, enabled her to build deep expertise & skills across NLP, recommender systems, Algorithms & Complexity Theory, Statistics, cloud architectures, etc.

  • She has contributed significantly to system design efforts, ensuring that critical security information is incorporated effectively into real-world defenses.
  • Her niche in AI, NLP, & computational statistics enables her to apply rigorous statistical methods to security analysis, threat modeling, & security R&D.
Between 2021 & 2025, she's successfully published multiple research articles that have accumulated about 50 citations as of 2025, in conferences & journals like IEEE, ACL, OpenAire, etc.
⎯⎯✧﹌𓆤༉𖧧𖥧𖤣. ༘༝ၴ( ၴႅၴ˖𓏲⚘ཐི༏ཋྀˎ ྀ𓏲𓇗𖤣﹏𓆏࿐⚘𖥧𖤣𓇗ˎˊˎˊ𓆈ˊˎ゛✧⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

Other Links:

# Category Links
❶. Matrix ( Element ) / Pagure @keepur:fedora.im / pagure.io/user/keepur
❷. Fedora / fedora:WIKI / Redhat accounts.fedoraproject.org/user/keepur / fedoraproject.org/wiki/user:keepur / access.redhat.com/account/57599301
❸. Website ( personal ) / LinkedIn keerthanap8898.github.io/keerthanap8898 / linkedin.com/in/keerthanapurushotham
❹. GitHub / github-Bio / github-Repositories github.com/keerthanap8898 / github.com/keerthanap8898/bio / github.com/keerthanap8898?tab=repositories
❺. Mastodon / Bluesky @[email protected] / @keepur8.bsky.social
❻. Google-Scholar / ResearchGate scholar.google: user=tWzF13sAAAAJ / ResearchGate: Keerthana Purushotham
❼. Medium / Substack Medium: @keerthanapurushotham / Substack: @keerthanapurushotham
❽. X ( twitter ) / Discord X: keepur8 / Discord: 747152507184349195 - ( keepur8 )
❾. AI Chatbot notebooklm.google.com/notebook/fe2125af-e6e0-4815-8181-041b267e3b8b

P.S.

Someday she'll quit messing with unicode symbols. Not today though.

Pinned Loading

  1. TextToVideoAPI TextToVideoAPI Public template

    Async text2video API with the Genmo Mochi-1 model on 8×H100 GPU K8 worker nodes. Backend will handle job submission, tracking, & retrieval via JSON endpoints. Basic frontend for prompt submission, …

    Rust

  2. CveToad CveToad Public

    WIP to help catch bugs and whatnot

    2

  3. bio bio Public

    This is where it's all at 💫 (˙ᵕ˙) ྀི —— Scroll down to the README

  4. NeuralCRFs_for_ConstituencyParsing NeuralCRFs_for_ConstituencyParsing Public

    I present the CKY algorithm for constituency parsing on the PTB_LE10 & PTB_first_2000 subsets plus the full PTB dataset; to compute model features needed to calculate the partition function in the …

    Jupyter Notebook

  5. Neural-CRF_NER-Tagger Neural-CRF_NER-Tagger Public

    How to build a baby-BERT : I analyze BiLSTMs combined with Conditional Random Fields for Named Entity Recognition & contrasts a Neural-CRF tagger against a baseline BiLSTM model, exploring how prob…

    Jupyter Notebook

  6. context-based-comment-filtering context-based-comment-filtering Public

    This repository contains a sub-project to filter out irrelevant comments based on the context of the initial search in a data-set of web scraped comments and replies from social media websites.

    Jupyter Notebook