data.ts

/**
 * @license
 * SPDX-License-Identifier: Apache-2.0
*/

export interface PortfolioItem {
  id: string;
  type: 'research' | 'project';
  metadata: {
    title: string;
    subtitle: string;
    venue: string;
    date: string;
    link?: string;
    themeColor: string;
    secondaryColor: string;
    githubUrl?: string;
    demoUrl?: string;
  };
  narrative: {
    problem: string;
    innovation: string;
    impact: string;
  };
  visuals: {
    diagramType: 'bar-chart' | 'flow-chart' | 'fuzzy-curves' | 'venn-diagram' | 'waveform' | 'network-graph' | 'confidence-meter' | 'depth-grid';
  };
  technical?: {
    techStack: string[];
    codeSnippet: string;
    /** Filename shown on the code panel. */
    codeFile?: string;
  };
  /** Real screenshot of the live product, shown on the home page. */
  image?: { src: string; alt: string };
  /** Headline numbers taken from the item's own narrative. Only true, sourced values. */
  results?: Array<{ value: string; label: string }>;
  authors: Array<{ name: string; role: string }>;
}

export const papers: PortfolioItem[] = [
  {
    id: 'autism-ai',
    type: 'research',
    metadata: {
      title: "Conversational AI for Supporting Children with Autism",
      subtitle: "A deep learning-based virtual agent for social and language skills development.",
      venue: "Book Chapter (Springer)",
      date: "July 2025",
      link: "https://link.springer.com/chapter/10.1007/978-981-96-7556-2_16/",
      themeColor: "#20c997", // Soft Teal
      secondaryColor: "#e6fffa",
    },
    narrative: {
      problem: "Children with Autism Spectrum Disorder (ASD) often face significant barriers in social interaction and communication. They may find it difficult to interpret emotions, causing anxiety in social situations that hinders development.",
      innovation: "We propose a conversational virtual agent utilizing a multi-modal Deep Learning framework. It combines Speech Emotion Recognition (using CNNs) with Text Emotion Recognition (NLP) to create a responsive, empathetic digital companion.",
      impact: "The framework provides a safe, judgment-free environment for children to learn English grammar and socialization skills. It offers real-time feedback that adapts to the child's emotional state, bridging the gap between therapy and daily life."
    },
    visuals: {
      diagramType: 'flow-chart'
    },
    technical: {
      techStack: ["Python", "Keras", "Librosa", "NLTK"],
      codeFile: "extract_features.py",
      codeSnippet: `def extract_features(data, sample_rate):
    # ZCR
    result = np.array([])
    zcr = np.mean(librosa.feature.zero_crossing_rate(y=data).T, axis=0)
    result = np.hstack((result, zcr))

    # Chroma_stft
    stft = np.abs(librosa.stft(data))
    chroma_stft = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)
    result = np.hstack((result, chroma_stft))

    # MFCC
    mfcc = np.mean(librosa.feature.mfcc(y=data, sr=sample_rate).T, axis=0)
    result = np.hstack((result, mfcc))
    
    return result`
    },
    authors: [
      { name: "Ishani Kathuria", role: "Amity University" },
      { name: "Kamad Saxena", role: "Amity University" },
      { name: "Archana Singh", role: "Amity University" }
    ]
  },
  {
    id: 'food-recommendation',
    type: 'research',
    metadata: {
      title: "Real-Time Temperature Based Food Recommendation",
      subtitle: "Dynamic menu personalization using Gradient Boosting and Fuzzy Logic.",
      venue: "ICCNT (IEEE)",
      date: "July 2023",
      link: "https://ieeexplore.ieee.org/document/10306446/",
      themeColor: "#FF9933", // Saffron Orange
      secondaryColor: "#fff7ed",
    },
    narrative: {
      problem: "Weather significantly influences human food cravings, yet modern food service menus remain static. This fails to capitalize on shifting preferences, leading to suboptimal customer experiences and missed sales opportunities.",
      innovation: "We developed a hybrid model combining a Multi-Output Gradient Boosting Regressor with Fuzzy Logic. We used Gaussian membership functions to categorize 'time of day' into fuzzy slots like 'morning' or 'late night' to contextually drive recommendations.",
      impact: "The model achieved a high coefficient of determination (R² of 0.917). We successfully deployed a web interface that recommends specific comfort foods (e.g., Gajar ka halwa in winter) based on real-time local temperature."
    },
    visuals: {
      diagramType: 'fuzzy-curves'
    },
    technical: {
      techStack: ["Scikit-Learn", "Fuzzy Logic", "OpenWeatherMap API", "Flask"],
      codeFile: "fuzzy_rules.py",
      codeSnippet: `from skfuzzy import control as ctrl

# Antecedents & Consequent
temperature = ctrl.Antecedent(np.arange(-10, 50, 1), 'temp')
time_of_day = ctrl.Antecedent(np.arange(0, 24, 1), 'time')
recommendation = ctrl.Consequent(np.arange(0, 10, 1), 'rec')

# Membership Functions
temperature['cold'] = fuzz.trapmf(temperature.universe, [-10, -10, 10, 20])
temperature['hot'] = fuzz.trapmf(temperature.universe, [20, 30, 50, 50])

# Rules
rule1 = ctrl.Rule(temperature['cold'] & time_of_day['morning'], recommendation['warm_comfort'])
rule2 = ctrl.Rule(temperature['hot'] & time_of_day['afternoon'], recommendation['refreshing'])`
    },
    results: [
      { value: "0.917", label: "R² of the recommendation model" }
    ],
    authors: [
      { name: "Ishani Kathuria", role: "Amity University" },
      { name: "Madhulika Bhatia", role: "Amity University" },
      { name: "Aniruddh Atrey", role: "Amity University" },
      { name: "S. Sujith", role: "Amity Institute of Food Technology" }
    ]
  },
  {
    id: 'healthcare-dl',
    type: 'research',
    metadata: {
      title: "Deep Learning in Healthcare: A Systematic Analysis",
      subtitle: "A review of DL architectures in Drug Discovery, Imaging, and EHR.",
      venue: "ICRIC (Springer)",
      date: "May 2023",
      link: "https://link.springer.com/chapter/10.1007/978-981-19-9876-8_29/",
      themeColor: "#EF4444", // Medical Red
      secondaryColor: "#fef2f2",
    },
    narrative: {
      problem: "The healthcare industry is overwhelmed by massive amounts of unstructured data like images and clinical notes. Manual analysis is time-consuming, expensive (taking years for drug discovery), and prone to human error.",
      innovation: "We conducted a systematic review of 24 key research papers, categorizing the efficacy of CNNs, RNNs, and LSTMs across three critical domains: Drug Discovery, Medical Imaging, and Electronic Health Records (EHR).",
      impact: "The analysis concluded that CNNs and LSTMs are the dominant architectures. It highlights their specific success in automating diabetic retinopathy diagnosis and predicting patient readmission rates, paving the way for more efficient automated care."
    },
    visuals: {
      diagramType: 'venn-diagram'
    },
    technical: {
      techStack: ["Systematic Review", "Meta-Analysis", "Prisma Guidelines"],
      codeFile: "key_findings.txt",
      codeSnippet: `Key Findings:
1. CNNs (Convolutional Neural Networks):
   - Best for: Medical Imaging (X-Ray, MRI)
   - Accuracy: >95% in Diabetic Retinopathy detection
   
2. RNNs/LSTMs (Recurrent Neural Networks):
   - Best for: EHR & Time-series data
   - Use Case: Patient Readmission Prediction
   
3. GANs (Generative Adversarial Networks):
   - Emerging role in Drug Discovery (Molecule generation)`
    },
    results: [
      { value: "24", label: "papers systematically reviewed" },
      { value: ">95%", label: "accuracy for CNNs on diabetic retinopathy detection" }
    ],
    authors: [
      { name: "Ishani Kathuria", role: "Amity University" },
      { name: "Madhulika Bhatia", role: "Amity University" },
      { name: "Anchal Garg", role: "University of Bolton" },
      { name: "Ashish Grover", role: "Manav Rachna Institute" }
    ]
  },
  {
    id: 'pm25-prediction',
    type: 'research',
    metadata: {
      title: "Predicting daily PM2.5 using Classic Azure ML Studio",
      subtitle: "A comparative analysis of neural networks and linear regression for air quality prediction.",
      venue: "COM-IT-CON (IEEE)",
      date: "May 2022",
      link: "https://ieeexplore.ieee.org/document/9850494/",
      themeColor: "#007FFF", // Azure Blue
      secondaryColor: "#e0f2fe",
    },
    narrative: {
      problem: "Air pollution is a rising global threat, but ground supervising stations are limited in number, creating 'blind spots' in air quality data at unobserved locations. Remote detection tools are needed to bridge this gap.",
      innovation: "We utilized the Azure ML Studio platform to process satellite data (Aerosol Optical Thickness) and meteorological data. We rigorously compared six different feature selection methods to optimize inputs for both linear and neural network models.",
      impact: "The research demonstrated that a Neural Network Regression model, specifically when paired with a 'count-based' feature selection metric, significantly outperformed linear regression, achieving the lowest RMSE score of 0.508."
    },
    visuals: {
      diagramType: 'bar-chart'
    },
    technical: {
      techStack: ["Azure ML Studio", "Neural Network Regression", "Pearson Correlation"],
      codeFile: "azure_experiment.py",
      codeSnippet: `Experiment Setup (Azure ML):

dataset = Dataset.get_by_name(workspace, name='PM25_Data')

# Feature Selection Step
filter_module = FilterBasedFeatureSelection(
    column_selection_mode="IndicesAndNames",
    selected_columns=selected_features, # ['AOT', 'Temp', 'Humidity']
    feature_scoring_method="CountBased"
)

# Model Training
nn_regressor = NeuralNetworkRegression(
    learning_rate=0.01,
    number_of_hidden_nodes=100,
    max_learning_iterations=1000
)`
    },
    results: [
      { value: "0.508", label: "lowest RMSE, neural network with count-based features" }
    ],
    authors: [
      { name: "Kamad Saxena", role: "Amity University" },
      { name: "Ishani Kathuria", role: "Amity University" },
      { name: "Madhulika Bhatia", role: "Amity University" },
      { name: "Anchal Garg", role: "University of Bolton" }
    ]
  },
];

export const projects: PortfolioItem[] = [
  {
    id: 'trustworthyrag',
    type: 'project',
    metadata: {
      title: "TrustworthyRAG",
      subtitle: "Query-Adaptive Learned Fusion (QALF) for optimal multimodal retrieval.",
      venue: "Project",
      date: "2025",
      themeColor: "#8b5cf6", // Violet
      secondaryColor: "#f5f3ff",
      githubUrl: "https://github.com/ikathuria/TrustworthyRAG/",
    },
    narrative: {
      problem: "Standard RAG systems struggle with complex reasoning queries, often retrieving irrelevant chunks. Fixed routing strategies fail to adapt to the nuance of user intent and query complexity.",
      innovation: "Designed QALF (Query-Adaptive Learned Fusion) to dynamically route queries to Vector, Graph, or Keyword modalities based on 4D complexity analysis. Integrated Neo4j with Llama 3 to link unstructured data into a unified multimodal knowledge graph.",
      impact: "Significantly outperformed standard RRF baselines by optimizing retrieval paths. enabled complex multi-hop retrieval across text, images, and tables, improving answer fidelity for high-complexity queries."
    },
    visuals: {
      diagramType: 'network-graph'
    },
    technical: {
      techStack: ["Neo4j", "LangChain", "Llama 3", "Python"],
      codeFile: "qalf_routing.py",
      codeSnippet: `def qalf_routing(query, context):
    # 4D Complexity & Intent Analysis
    complexity = analyze_complexity(query) # 0.0 to 1.0
    intent = classify_intent(query)        # e.g., 'multi_hop', 'factual'

    # Dynamic Routing Logic
    if complexity > 0.8 or intent == 'multi_hop':
        # Prioritize Graph Traversal for reasoning
        return graph_retriever.query(query)
    elif intent == 'visual_lookup':
        # Route to Vector Store for image embeddings
        return multimodal_vector_store.similarity_search(query)
    else:
        # Fallback to Hybrid (Keyword + Dense)
        return hybrid_retriever.invoke(query)`
    },
    authors: [
      { name: "Ishani Kathuria", role: "Project Lead" }
    ]
  },
  {
    id: 'autoredteam',
    type: 'project',
    metadata: {
      title: "AutoRedTeam",
      subtitle: "Multi-agent adversarial simulation framework for LLM safety.",
      venue: "Project",
      date: "2026",
      themeColor: "#ef4444", // Red
      secondaryColor: "#fef2f2",
      githubUrl: "https://github.com/ikathuria/AutoRedTeam/",
      demoUrl: "https://ishani.kathuria.net/AutoRedTeam/",
    },
    narrative: {
      problem: "Manual red-teaming of LLMs is unscalable and inconsistent. Ensuring safety against prompt injections and jailbreaks requires continuous, adaptive adversarial testing.",
      innovation: "Engineered a 3-agent feedback loop (Attacker, Target, Judge) where the 'Attacker' iteratively refines Chain-of-Thought jailbreak strategies based on the 'Target's' responses. Built a deterministic 'Judge' to score defense performance.",
      impact: "Enabled systematic stress-testing across Gemini 1.5 Pro, GPT-4, and Llama 3. The React/TypeScript frontend visualizes real-time thought processes, simulating enterprise-grade red-teaming workflows."
    },
    visuals: {
      diagramType: 'flow-chart'
    },
    technical: {
      techStack: ["React", "TypeScript", "LangChain", "Gemini/GPT-4"],
      codeFile: "redTeamLoop.ts",
      codeSnippet: `const redTeamLoop = async (target: LLM, attack: string) => {
  // 1. Attack Execution
  const response = await target.generate(attack);
  
  // 2. Deterministic Judging
  const judgeScore = await judgeAgent.evaluate(response, safetyRubric);

  if (judgeScore.isUnsafe) {
    console.log("Jailbreak Successful:", attack);
    return { success: true, prompt: attack };
  }

  // 3. Iterative Refinement (CoT)
  const refinedAttack = await attackerAgent.refine({
    original: attack,
    targetResponse: response,
    critique: judgeScore.reasoning
  });
  
  return redTeamLoop(target, refinedAttack);
}`
    },
    results: [
      { value: "3", label: "LLMs stress-tested: Gemini 1.5 Pro, GPT-4, Llama 3" }
    ],
    image: {
      src: "/img/autoredteam.jpg",
      alt: "AutoRedTeam's Mission Control dashboard mid-simulation: a red-team attacker and a blue-team target trade messages while an arbiter scores each round."
    },
    authors: [
      { name: "Ishani Kathuria", role: "Project Lead" }
    ]
  },
  {
    id: 'deepfakeguard',
    type: 'project',
    metadata: {
      title: "DeepFakeGuard",
      subtitle: "Client-side Edge AI forensic tool for synthetic audio detection.",
      venue: "Project",
      date: "2026",
      themeColor: "#0ea5e9", // Sky Blue
      secondaryColor: "#f0f9ff",
      githubUrl: "https://github.com/ikathuria/DeepFakeGuard/",
      demoUrl: "https://ishani.kathuria.net/DeepfakeGuard/",
    },
    narrative: {
      problem: "Deepfake audio is becoming indistinguishable from reality, posing risks to privacy and security. Server-side detection raises privacy concerns and introduces latency.",
      innovation: "Evolved from a FastAPI microservice to a fully client-side solution using Transformers.js. Implemented streaming inference logic to analyze spectrograms and waveforms directly in the browser without external dependencies.",
      impact: "Eliminated server-side processing, ensuring user privacy and sub-second latency. The forensic dashboard visualizes detection confidence in real-time, providing immediate feedback on audio authenticity."
    },
    visuals: {
      diagramType: 'waveform'
    },
    technical: {
      techStack: ["Transformers.js", "React", "Recharts", "Web Audio API"],
      codeFile: "detector.ts",
      codeSnippet: `import { pipeline } from '@xenova/transformers';

// Initialize Client-side Pipeline
const detector = await pipeline('audio-classification', 'xenova/audio-fake-detect', {
    quantized: true // Optimized for browser
});

async function processAudioStream(chunk: Float32Array) {
    // 1. Feature Extraction (Spectrogram/MFCC)
    const features = extractFeatures(chunk);
    
    // 2. Inference in Browser
    const result = await detector(features);
    
    // 3. Update Visualizer
    updateDashboard({
        timestamp: Date.now(),
        probability: result[0].score,
        isFake: result[0].label === 'FAKE'
    });
}`
    },
    results: [
      { value: "Sub-second", label: "detection latency" },
      { value: "0", label: "audio uploads to a server" }
    ],
    image: {
      src: "/img/deepfakeguard.jpg",
      alt: "DeepGuard analysing a cloned voice: a probability curve, a 30% authenticity ring, and a per-frame verdict log."
    },
    authors: [
      { name: "Ishani Kathuria", role: "Project Lead" }
    ]
  },
  {
    id: 'autbot',
    type: 'project',
    metadata: {
      title: "Autbot – Educational Chatbot",
      subtitle: "Improving engagement for children with autism using multi-modal emotion recognition.",
      venue: "Project",
      date: "2023",
      themeColor: "#6366f1", // Indigo
      secondaryColor: "#e0e7ff",
      githubUrl: "https://github.com/ikathuria/Autbot/",
    },
    narrative: {
      problem: "Children with autism often find standard educational tools unengaging because these tools fail to recognize or respond to the child's emotional state, leading to frustration.",
      innovation: "Developed a conversational AI using DistilRoBERTa and TensorFlow. The system employs a dual-input architecture analyzing both speech (tone) and text (semantics) to gauge emotion with high fidelity.",
      impact: "Improved engagement by 30% in pilot tests. Achieved over 90% accuracy in speech-based emotion recognition and 73% in text-based recognition, adjusting responses to the child's feelings."
    },
    visuals: {
      diagramType: 'confidence-meter'
    },
    technical: {
      techStack: ["TensorFlow", "DistilRoBERTa", "HuggingFace", "React"],
      codeFile: "dual_model.py",
      codeSnippet: `import tensorflow as tf
from transformers import TFDistilBertModel

# Multi-Modal Model Architecture
def build_dual_model():
    # 1. Text Input (BERT)
    text_input = tf.keras.layers.Input(shape=(max_len,), dtype=tf.int32, name='text')
    bert_layer = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
    text_embedding = bert_layer(text_input)[0][:, 0, :]
    
    # 2. Audio Input (Spectrogram CNN)
    audio_input = tf.keras.layers.Input(shape=(128, 128, 1), name='audio')
    conv1 = tf.keras.layers.Conv2D(32, (3,3), activation='relu')(audio_input)
    flat_audio = tf.keras.layers.Flatten()(conv1)
    
    # 3. Fusion Layer
    concatenated = tf.keras.layers.Concatenate()([text_embedding, flat_audio])
    output = tf.keras.layers.Dense(num_emotions, activation='softmax')(concatenated)
    
    return tf.keras.Model(inputs=[text_input, audio_input], outputs=output)`
    },
    results: [
      { value: "+30%", label: "engagement in pilot tests" },
      { value: ">90%", label: "speech-emotion accuracy" },
      { value: "73%", label: "text-emotion accuracy" }
    ],
    authors: [
      { name: "Ishani Kathuria", role: "Project Lead" }
    ]
  }
];

export interface Hackathon {
  id: string;
  project: string;
  hackathon: string;
  date: string;
  location?: string;
  role: 'builder' | 'organizer';
  award?: string;
  image?: { src: string; alt: string };
  tagline: string;
  techStack?: string[];
  team?: string[];
  themeColor: string;
  links: {
    github?: string;
    demo?: string;
    video?: string;
    linkedin?: string;
    devpost?: string;
  };
}

export const hackathons: Hackathon[] = [
  {
    id: 'yourbusinesscards',
    project: 'yourbusiness.cards',
    hackathon: 'DeveloperWeek NY Hackathon',
    date: 'Jun 2026',
    location: 'New York',
    role: 'builder',
    award: 'Winner · name.com Domain Roulette',
    tagline: "A freemium SaaS that spins up a polished single-screen digital business card — templates, shareable links, QR codes, and AI-designed art — in under 60 seconds.",
    techStack: ["Next.js", "Supabase", "Claude", "Stripe"],
    image: {
      src: "/img/yourbusinesscards.jpg",
      alt: "The yourbusiness.cards landing page: the headline 'A business card people actually scan.', with Make yours free and Browse templates buttons."
    },
    themeColor: "#10B981", // Emerald
    links: {
      github: "https://github.com/ikathuria/yourbusiness.cards",
      demo: "https://yourbusiness-cards.vercel.app",
      devpost: "https://devpost.com/software/yourbusiness-cards",
    },
  },
  {
    id: 'realsight',
    project: 'RealSight',
    hackathon: 'Google Cloud Rapid Agent Hackathon',
    date: 'Jun 2026',
    role: 'builder',
    tagline: "A Chrome extension that passively flags AI-generated videos on YouTube & Reels right on the player — a confidence badge with the model's visual reasoning in ~5 seconds.",
    techStack: ["Google ADK", "Gemini 2.5", "Cloud Run", "MongoDB"],
    themeColor: "#EA4335", // Google Red
    links: {
      github: "https://github.com/ikathuria/RealSight",
      devpost: "https://devpost.com/software/realsight",
    },
  },
  {
    id: 'afterpartydigital',
    project: 'afterparty.digital',
    hackathon: 'DeveloperWeek NY Hackathon',
    date: 'Jun 2026',
    location: 'New York',
    role: 'builder',
    tagline: "An AI platform that turns a finished event into lasting connections — personalized attendee match pages, editable relationship graphs, and a connection-ROI dashboard for organizers.",
    techStack: ["Next.js", "Neon Postgres", "Claude", "force-graph"],
    themeColor: "#6366F1", // Indigo
    links: {
      github: "https://github.com/ikathuria/afterparty.digital",
      devpost: "https://devpost.com/software/afterparty-digital",
    },
  },
  {
    id: 'howcookedami',
    project: 'How Cooked Am I?',
    hackathon: 'Vibe-Coded Creator Hackathon',
    date: 'May 31, 2026',
    location: 'Chicago · hosted by Play',
    role: 'builder',
    team: ["Yasaa Usman", "Shreya Naresh Manjucha"],
    tagline: "An AI tool that roasts then rebuilds content creators — feed it your niche, platform & stats and it returns a brutally honest 'cookedScore', a diagnosis of what's holding you back, and a three-phase growth plan. Built end-to-end with Replit Agent in ~1 hour.",
    techStack: ["Replit Agent", "Gemini", "React 19", "Expo"],
    themeColor: "#EC4899", // Pink
    links: {
      github: "https://github.com/ikathuria/HowCookedAmI",
      demo: "https://how-cooked-am-i.replit.app/",
      linkedin: "https://www.linkedin.com/posts/ishani-kathuria_ai-hackathon-replitagent-ugcPost-7467322741529026560-iCK4",
    },
  },
  {
    id: 'groundwork',
    project: 'GROUNDWORK',
    hackathon: 'Hack-Nation Global AI Hackathon',
    date: 'Apr 25–26, 2026',
    location: 'San Francisco',
    role: 'builder',
    team: ["Blas Moreno Laguna"],
    tagline: "A literature-synthesis engine that turns a research question into a traceable, experiment-ready brief — deep search across arXiv, Semantic Scholar, OpenReview & Papers With Code.",
    techStack: ["Claude Code", "Next.js", "arXiv API", "Semantic Scholar"],
    themeColor: "#8B5CF6", // Violet
    links: {
      github: "https://github.com/ikathuria/groundwork",
      demo: "https://hack-nation.ai/",
      linkedin: "https://www.linkedin.com/posts/ishani-kathuria_hacknation-buildinpublic-ai-activity-7455632096628424704-TnI_",
    },
  },
  {
    id: 'globalbuddy',
    project: 'GlobalBuddy · Globalदोस्त',
    hackathon: 'HackwithChicago 3.0',
    date: 'Apr 2, 2026',
    location: 'Microsoft, Chicago',
    role: 'builder',
    team: ["Yasaa Usman", "Jinen Modi"],
    tagline: "A graph-powered support platform that helps international students navigate life in a new US city — turning a profile into a 30-day plan and a network of people, places & tasks in three steps.",
    techStack: ["Neo4j", "FastAPI", "Vue.js", "Gemini"],
    themeColor: "#14B8A6", // Teal
    links: {
      github: "https://github.com/ikathuria/GlobalBuddy",
      linkedin: "https://www.linkedin.com/posts/ishani-kathuria_hackathon-buildinpublic-neo4j-activity-7447051822684585984-Cc52",
    },
  },
  {
    id: 'vibecut',
    project: 'VibeCut',
    hackathon: 'Google Hackathon',
    date: 'Mar 14, 2026',
    location: 'Drive Capital, Chicago',
    role: 'builder',
    team: ["Blas Moreno Laguna"],
    tagline: "An AI-native video editor — semantic clip search, transcription-aware word-level cuts, natural-language editing, and generative image/video media, all in the browser.",
    techStack: ["Gemini", "Next.js", "React", "ffmpeg"],
    themeColor: "#4285F4", // Google Blue
    links: {
      github: "https://github.com/slab10000/Google-Hackathon",
    },
  },
  {
    id: 'realityshift',
    project: 'RealityShift',
    hackathon: 'Mistral AI Hackathon',
    date: 'Feb 28 – Mar 1, 2026',
    location: 'San Francisco',
    role: 'builder',
    tagline: "A voice-controlled RPG where an omnipresent 'Architect' AI rewrites the game's source code in real time — turning live coding into gameplay. Sponsored stack from NVIDIA, Hugging Face & W&B.",
    techStack: ["Mistral devstral", "ElevenLabs", "React", "TypeScript", "Supabase"],
    themeColor: "#F97316", // Mistral Orange
    links: {
      github: "https://github.com/ikathuria/RealityShift",
      video: "https://www.youtube.com/watch?v=jj_8OoTdjRQ",
      linkedin: "https://www.linkedin.com/posts/ishani-kathuria_mistralhack-the-reality-shift-rpg-activity-7434398396876107776-GnBP",
    },
  },
  {
    id: 'siren',
    project: 'SIREN One-Day App Challenge',
    hackathon: 'Founder & President · Purdue Northwest',
    date: 'Apr 3, 2026',
    location: 'Purdue University Northwest',
    role: 'organizer',
    tagline: "Founded and ran a one-day build challenge to lower the barrier between an idea and a working prototype — 70+ registrants, 7 final teams, and $200 in prizes, backed by PNW faculty mentors.",
    themeColor: "#F59E0B", // Amber
    links: {
      demo: "https://ishani.kathuria.net/siren-student-research-pnw/odadc/2026",
      linkedin: "https://www.linkedin.com/posts/ishani-kathuria_studentleadership-techcommunity-siren-activity-7447267037174824960-dk3P",
    },
  },
];

export const resume = {
  education: [
    {
      degree: "Master of Science in Applied Artificial Intelligence. GPA: 4.0/4.0",
      school: "Purdue University Northwest",
      location: "Indiana, USA",
      period: "Aug 2025 – May 2027",
      details: ["Coursework: Generative AI, Ethical AI, Applied Machine Learning, Data Visualization"]
    },
    {
      degree: "Bachelor of Technology in Artificial Intelligence. CGPA: 9.09/10",
      school: "Amity University",
      location: "Uttar Pradesh, India",
      period: "Jul 2019 – Jul 2023",
      details: ["Coursework: Deep Learning, Computer Vision, Natural Language Processing"]
    }
  ],
  experience: [
    {
      role: "AI Research Assistant",
      company: "Center for Cybersecurity, Purdue University",
      period: "Sep 2025 – Present",
      details: [
        "Conducting applied research on Retrieval-Augmented Generation (RAG) systems, focusing on adversarial defenses, retrieval quality, and hallucination reduction for LLM-based applications.",
        "Evaluating retrieval strategies using Recall@K and nDCG to optimize real-world question answering performance."
      ]
    },
    {
      role: "Software Development Engineer",
      company: "Amazon Web Services",
      period: "Jul 2023 – Jul 2025",
      details: [
        "Built and deployed LLM-based log summarization for production services, cutting mean root-cause analysis time from 2.5 hours to 30 minutes.",
        "Designed internal AI chatbots for developer support workflows, improving resolution efficiency on routine operational queries by 40% for 200+ engineers.",
        "Automated region-build and deployment pipelines for 15+ OpenSearch services using AWS infrastructure tooling, reducing manual intervention by 80% and accelerating launch timelines by 3 weeks.",
        "Developed proactive anomaly and bug detection systems to catch failures pre-release, reducing externally reported customer issues by 30%."
      ]
    },
    {
      role: "Founder",
      company: "Get It Done",
      period: "Oct 2021 – Jun 2023",
      details: [
        "Founded and led a student-focused AI startup, mentoring 200 students.",
        "Organized workshops leading to 78% of students achieving placements."
      ]
    },
    {
      role: "Data and Algorithm Lead",
      company: "Planet E-Com Solutions",
      period: "May 2022 – Jul 2022",
      details: [
        "Engineered web scraping pipelines and data processing algorithms in Python to improve the accuracy of education consultation software."
      ]
    },
    {
      role: "Artificial Intelligence Automation Intern",
      company: "Deloitte",
      period: "Jun 2022 – Jul 2022",
      details: [
        "Developed hybrid RPA solutions using Automation Anywhere and Python, automating complex internal reporting tasks."
      ]
    },
    {
      role: "AI Developer & Lead",
      company: "Docplus Online",
      period: "Jul 2021 – Nov 2021",
      details: [
        "Engineered Python-based AI pipelines for translation models and gender identification, coordinating technical implementation across 40 + developers."
      ]
    }
  ],
  skills: {
    genai: ["Large Language Models (LLMs)", "RAG", "Fine-Tuning", "Multi-Agent Systems", "Prompt Engineering", "LLM Evaluation", "LangChain", "HuggingFace"],
    ml: ["Ollama", "PyTorch", "TensorFlow", "Scikit-learn", "vLLM"],
    programming: ["Python", "Golang", "TypeScript", "Java", "SQL"],
    cloud: ["AWS Bedrock", "AWS Lambda", "AWS ECS", "AWS Step Functions", "AWS CloudFormation", "AWS CloudWatch", "Azure", "Docker"],
    data: ["React", "Flask", "Django", "FastAPI", "Pandas", "NumPy", "Tableau", "Matplotlib", "Seaborn"],
    certs: ["AWS Certified AI Practitioner", "AWS Certified Cloud Practitioner"]
  }
};

/**
 * Single source of truth for identity, positioning, and headline stats.
 * Consumed by the WebMCP tool layer (useWebMCP) so machine agents and the
 * human-facing site stay in agreement.
 */
export const profile = {
  name: "Ishani Kathuria",
  headline: "AI/ML Engineer & Researcher",
  tagline: "MS Applied AI @ Purdue · ex-SDE @ AWS · 4× Published (IEEE + Springer)",
  status: "Graduating May 2027 — open to AI/ML engineer internships (now) and full-time roles (from May 2027). Primary target: USA; also open to India, Ireland, UK, Netherlands, Germany, Japan, Norway, Switzerland.",
  location: "Hammond, Indiana, USA",
  summary:
    "AI/ML engineer with 2 years building production LLM systems at AWS, now completing an MS in Applied AI at Purdue with research in Retrieval-Augmented Generation and LLM evaluation. I build LLM systems, agentic pipelines, and AI safety tools that bridge rigorous research and real-world products.",
  email: "ishani@kathuria.net",
  links: {
    website: "https://ishani.kathuria.net",
    linkedin: "https://linkedin.com/in/ishani-kathuria",
    github: "https://github.com/ikathuria",
    resume: "https://ishani.kathuria.net/resume.pdf",
  },
  stats: [
    { value: "2.5hr→30m", label: "debugging time cut at AWS" },
    { value: "$50K/mo", label: "infrastructure costs saved" },
    { value: "172K+", label: "lines of code shipped to production" },
    { value: "200+", label: "students mentored into AI careers" },
    { value: "4", label: "peer-reviewed papers (IEEE + Springer)" },
    { value: "9", label: "hackathons built & led" },
    { value: "4.0", label: "GPA at Purdue University" },
  ],
};