Demetri Pappas

Demetri Pappas

A summary of my formal education and honors.

  • University of Central Florida

    • B.S. in Computer Science
    • Grad. Spring 2027
  • Honors

    • President's Honor Roll Spring 2025.
    • President's Honor Roll Spring 2026.

    GPA

    3.8/4.0

  • Coursework I

    • CAP 4611

      Algorithms in Machine LearningSupervised, unsupervised, ensemble and reinforcement learning algorithms.

    • CIS 3360

      Security in ComputingNumber theory, cryptography, software security, networking.

  • Coursework II

    • COP 3402

      System SoftwareUnix, process management, compilers, macro-processors, linkers and loaders.

    • COP 3503

      CSIIDynamic programming, greedy algorithms, network flows, string matching.

  • University of Miami

    Herbert Business School

    • B.S. in Computer Science
    • Transferred 2024
  • Honors

    • Presidential Scholar.
    • National Merit Finalist.
    • Benacquisto Scholar.
    • Foote Fellow Honors Student.

Three years of making software. Three experiences that say the most.

  • Jan – May 2025

    As a Full-stack Developer Intern at Puretalk AI,

    • I led agile development of the company's landing page and onboarding flow prior to going to market through scrum cycles using Jira.
    • Improved UI/UX helped the business grow from ~40 to 150+ clients following launch, at which point I continued with CI/CD updates.
    • In addition to managing the scrum cycles, a figma designer, and communicating with the CTO and CEO on time, scope, and resources, I implemented all code changes to the frontend.
    • This included implementing API-driven interactive components and improved UI/UX, developing dashboards and AI-Agent interfaces with React, Tailwind, and Next.js, and implementing Websocket integrations with Azure endpoints to facilitate low-latency AI chatbot, voice agent, and text-to-speech interactions.
    • Next.js
    • Node.js
    • Tailwind
    • REST APIs
    • Websockets
    • CI/CD
    • UI/UX
  • May – Sep 2025

    As an ML Engineer at Puretalk AI,

    Orlando, FL

    • Built Python-based evaluation pipelines to benchmark proprietary LLM (RUTH©) performance across reasoning, retrieval, and instruction-following tasks, enabling rapid regression testing between model versions.
    • Performed large-scale error analysis across thousands of model outputs, developing failure-mode taxonomies that guided improvements to prompting and retrieval systems.
    • Conducted comparative benchmarking of proprietary transformer models and retrieval-augmented generation systems, analyzing tradeoffs between factual accuracy, inference latency, and operational cost.
    • Python
    • LLMs
    • RAG
    • Transformers
    • Benchmarking
    • Prompt Engineering
    • Error Analysis
  • May 2025 – Current

    As the Founder and Solo Dev for FlexPT,

    Orlando, FL

    • Built the frontend with React using dynamic UI components and state management for realtime data.
    • Designed backend services with Supabase (PostgreSQL), implementing authentication, database operations, and server-side business logic.
    • Deployed the application on Vercel, optimizing request routing and helper functions for scale, and deploying serverless functions for low-latency business logic operations.
    • Implemented AI workout program generation and editing with the Open AI API, designing custom system prompts and a context-retrieval pipeline to minimize token usage and reduce latency.
    • React
    • Next.js
    • PostgreSQL
    • Supabase
    • Relational Database Design
    • Software Security

Projects I'm working on.

  • Jun 2026

    CertForge – Microsoft Agents League Hackathon

    • Engineered a typed multi-agent orchestration framework in Python that coordinates project intake, knowledge retrieval, planning, assessment, and validation through modular Pydantic v2 data models and deterministic execution pipelines.
    • Implemented retrieval-augmented generation infrastructure using Azure AI Search and local semantic knowledge stores, with fail-safe fallback mechanisms, lazy-loaded cloud dependencies, and managed-identity authentication.
    • Containerized and deployed an application on Azure Container Apps with CI/CD-ready infrastructure, structured telemetry logging, RBAC, and Azure OpenAI integration for intelligent workflows.
    • Python
    • Pydantic
    • Multi-Agent Systems
    • Azure AI Search
    • RAG
    • Azure Container Apps
    • Azure OpenAI
    • CI/CD
  • Mar 2026

    High Performance Memory Allocator – C

    • Built a custom memory allocator that reclassifies blocks between size buckets and a red-black tree for larger blocks.
    • Designed test suites demonstrating 97.8% reuse rates, 1.66x faster speeds than glibc's malloc for small and random allocations, and perfect 1.0 internal fragmentation and 0.91 external fragmentation.
    • Added a union between heap pointers for small blocks and tree metadata for larger blocks in the block header, and bit-packed free and color flags for tree blocks reducing overhead from 20% to 14% without increasing fragmentation.
    • C
    • Memory Management
    • Red-Black Tree
    • Systems Programming

    high-performance-malloc

    Custom allocator (hpmalloc / hpfree) with segregated free lists for small blocks and a red-black tree for large blocks. Includes correctness tests, a stress harness, and a performance benchmark that compares against the system malloc.

    Build & run (WSL / Linux)

    # Correctness
    gcc -O2 -o test_correctness test_correctness.c hpallocator.c
    ./test_correctness
    
    # Stress test (pattern verification)
    gcc -O2 -o test_stress test_stress.c hpallocator.c
    ./test_stress
    
    # Performance benchmark
    gcc -O2 -o bench_perf bench_perf.c hpallocator.c
    ./bench_perf
    

    Optional benchmark tuning:

    STRESS_OPS=50000 LONG_OPS=200000 ./bench_perf
    

    Test results

    TestResult
    test_correctnessAll tests passed
    test_stress (10,000 ops)Evil test passed

    Benchmark results

    Environment: WSL2 (Ubuntu), gcc -O2
    Date: June 28, 2026
    Config: STRESS_OPS=50000, LONG_OPS=200000, LEAK_BLOCKS=100, seed 42
    Allocator: compact header (union pool links, bit-packed flags)

    Summary: hpmalloc vs system malloc

    ScenarioMetricSystemhpmallocRelative
    Random sizes / random freemalloc avg610 ns535 nshp 1.14× (faster)
    free avg222 ns214 nshp 1.04× (faster)
    realloc avg1409 ns1657 nshp 0.85× (slower)
    throughput1.42M ops/s1.18M ops/shp 0.83×
    Alternating small/largemalloc avg1003 ns1861 nshp 0.54× (slower)
    free avg318 ns398 nshp 0.80× (slower)
    realloc avg5453 ns14741 nshp 0.37× (slower)
    throughput0.98M ops/s0.46M ops/shp 0.47×
    Long-running cyclesmalloc avg160 ns94 nshp 1.71× (faster)
    free avg108 ns65 nshp 1.67× (faster)
    throughput3.99M ops/s6.61M ops/shp 1.66× (faster)

    Takeaway: hpmalloc wins on random mixed sizes and steady small-block cycles. System malloc is significantly faster on alternating small/large workloads with heavy realloc churn.


    Scenario 1: Random sizes / random free

    System malloc

    Metricavgmedianp95p99samples
    malloc610 ns80 ns752 ns6933 ns24,556
    free222 ns121 ns501 ns802 ns22,503
    realloc1409 ns431 ns6362 ns18,605 ns2,931
    • Throughput: 1,420,665 ops/sec (0.037 s wall)
    • Total allocs / frees / reallocs: 24,556 / 24,556 / 2,931
    • Peak live allocations: 2,132
    • Avg request size: 5,660 bytes
    • Largest request: 65,536 bytes
    • Total bytes requested: 138,992,682
    • Internal fragmentation: 1.001 (via malloc_usable_size)

    hpmalloc

    Metricavgmedianp95p99samples
    malloc535 ns161 ns1092 ns7804 ns24,580
    free214 ns161 ns391 ns581 ns22,479
    realloc1657 ns391 ns5560 ns27,993 ns2,931
    • Throughput: 1,184,781 ops/sec (0.044 s wall)
    • Total allocs / frees / reallocs: 24,580 / 24,580 / 2,931
    • Peak live allocations: 2,121
    • Avg request size: 5,771 bytes
    • Largest request: 65,536 bytes
    • Total bytes requested: 141,860,630
    • Peak heap: 12,316,880 bytes
    • Heap growth: 12,316,880 bytes
    • sbrk calls: 597
    • Metadata overhead: 0.1400 (header bytes / user bytes)
    • Internal fragmentation: 1.000
    • External fragmentation: 0.789 (largest free / total free)
    • Reuse rate: 0.978 (pool allocs / hpmalloc calls)

    Scenario 2: Alternating small/large

    System malloc

    Metricavgmedianp95p99samples
    malloc1003 ns210 ns4819 ns8586 ns24,955
    free318 ns220 ns651 ns921 ns22,872
    realloc5453 ns621 ns30,858 ns78,488 ns2,167
    • Throughput: 978,410 ops/sec (0.053 s wall)
    • Peak live allocations: 2,151
    • Avg request size: 20,282 bytes
    • Largest request: 65,533 bytes
    • Total bytes requested: 506,135,385

    hpmalloc

    Metricavgmedianp95p99samples
    malloc1861 ns431 ns6733 ns14,858 ns24,919
    free398 ns311 ns832 ns1433 ns22,908
    realloc14,741 ns872 ns103,635 ns173,305 ns2,166
    • Throughput: 463,694 ops/sec (0.112 s wall)
    • Peak live allocations: 2,118
    • Avg request size: 20,285 bytes
    • Largest request: 65,527 bytes
    • Total bytes requested: 505,473,278
    • Peak heap: 31,606,912 bytes
    • sbrk calls: 620
    • Metadata overhead: 0.0037
    • Internal fragmentation: 1.000
    • External fragmentation: 1.000
    • Reuse rate: 0.977

    Scenario 3: Long-running cycles

    512 allocations per batch, repeated LONG_OPS / 512 times. Small blocks only (16–2063 bytes).

    System malloc

    Metricavgmedianp95p99samples
    malloc160 ns110 ns271 ns441 ns199,680
    free108 ns90 ns180 ns290 ns199,680
    • Throughput: 3,988,609 ops/sec (0.100 s wall)
    • Peak live allocations: 512
    • Avg request size: 1,040 bytes
    • Total bytes requested: 207,750,406
    • Internal fragmentation: 1.008

    hpmalloc

    Metricavgmedianp95p99samples
    malloc94 ns70 ns170 ns291 ns199,680
    free65 ns60 ns80 ns121 ns199,680
    • Throughput: 6,613,934 ops/sec (0.060 s wall)
    • Peak live allocations: 512
    • Avg request size: 1,040 bytes
    • Total bytes requested: 207,652,012
    • Internal fragmentation: 1.000
    • External fragmentation: 1.000
    • Reuse rate: 1.000 (all allocations served from pool after warmup)

    Leak check

    Allocates 100 blocks, frees 50, reports unfreed remainder, then cleans up.

    AllocatorLeaked blocks (before cleanup)Leaked bytes
    system malloc50102,014
    hpmalloc50100,232

    Both allocators correctly reported the intentional leaks; all memory was freed after measurement.


    Notes

    • Platform: hpmalloc uses sbrk() and requires Linux/WSL. It does not build natively on Windows.
    • Realloc: hpmalloc has no native realloc; the benchmark implements it as malloc + memcpy + free.
    • System allocator limits: libc malloc does not expose peak heap, sbrk count, external fragmentation, or reuse rate; those metrics are n/a for system runs.
    • Tests: test_stress.c runs 10,000 ops with byte-pattern integrity checks. test_correctness.c covers edge cases (zero-size alloc, double free, split/coalesce, etc.).
    • Compact header: bucket and tree pointers share a union; free and color flags are packed into size. Metadata overhead on random workloads dropped from ~20% to ~14% with no change to split/coalesce policy.

Parts Inventory.

Technologies and skills I specialize in.

SKL-AN=8REV.A

Languages

BIN A
  • Python
  • TypeScript
  • JavaScript
  • C
  • C++
  • Java
  • Go
  • Kotlin
SKL-BN=6REV.A

AI / ML

BIN B
  • LLMs
  • RAG
  • Transformers
  • Multi-Agent Systems
  • Prompt Engineering
  • Benchmarking
SKL-CN=6REV.A

Web & Backend

BIN C
  • React
  • Next.js
  • Node.js
  • REST APIs
  • Websockets
  • Tailwind
SKL-DN=4REV.A

Mobile

BIN D
  • React
  • Responsive UI
  • UI/UX
  • Realtime State
SKL-EN=6REV.A

Cloud & DevOps

BIN E
  • Docker
  • Azure Container Apps
  • Azure OpenAI
  • CI/CD
  • Vercel
  • RBAC
SKL-FN=5REV.A

Data Engineering

BIN F
  • Azure AI Search
  • Semantic Search
  • PostgreSQL
  • Supabase
  • Pydantic
SKL-GN=4REV.A

Databases

BIN G
  • PostgreSQL
  • Supabase
  • Relational Database Design
  • Auth & RLS
SKL-HN=4REV.A

Security

BIN H
  • Software Security
  • RBAC
  • Managed Identity
  • Auth Flows
SKL-IN=4REV.A

Domains

BIN I
  • Agentic AI
  • Full-stack Web
  • Cloud Infrastructure
  • Systems Programming

Get in touch.

Open to opportunities and collaborations. Send a note below or reach out through any of my profiles.