Olaf Yunus Laitinen Imanov olaflaitinen

  /$$$$$$  /$$            /$$$$$$        /$$     /$$                                                                                     
 /$$__  $$| $$           /$$__  $$      |  $$   /$$/                                                                                     
| $$  \ $$| $$  /$$$$$$ | $$  \__/       \  $$ /$$//$$   /$$ /$$$$$$$  /$$   /$$  /$$$$$$$                                               
| $$  | $$| $$ |____  $$| $$$$            \  $$$$/| $$  | $$| $$__  $$| $$  | $$ /$$_____/                                               
| $$  | $$| $$  /$$$$$$$| $$_/             \  $$/ | $$  | $$| $$  \ $$| $$  | $$|  $$$$$$                                                
| $$  | $$| $$ /$$__  $$| $$                | $$  | $$  | $$| $$  | $$| $$  | $$ \____  $$                                               
|  $$$$$$/| $$|  $$$$$$$| $$                | $$  |  $$$$$$/| $$  | $$|  $$$$$$/ /$$$$$$$/                                               
 \______/ |__/ \_______/|__/                |__/   \______/ |__/  |__/ \______/ |_______/                                                
                                                                                                                                         
                                                                                                                                         
                                                                                                                                         
 /$$                 /$$   /$$     /$$                                     /$$$$$$                                                       
| $$                |__/  | $$    |__/                                    |_  $$_/                                                       
| $$        /$$$$$$  /$$ /$$$$$$   /$$ /$$$$$$$   /$$$$$$  /$$$$$$$         | $$   /$$$$$$/$$$$   /$$$$$$  /$$$$$$$   /$$$$$$  /$$    /$$
| $$       |____  $$| $$|_  $$_/  | $$| $$__  $$ /$$__  $$| $$__  $$        | $$  | $$_  $$_  $$ |____  $$| $$__  $$ /$$__  $$|  $$  /$$/
| $$        /$$$$$$$| $$  | $$    | $$| $$  \ $$| $$$$$$$$| $$  \ $$        | $$  | $$ \ $$ \ $$  /$$$$$$$| $$  \ $$| $$  \ $$ \  $$/$$/ 
| $$       /$$__  $$| $$  | $$ /$$| $$| $$  | $$| $$_____/| $$  | $$        | $$  | $$ | $$ | $$ /$$__  $$| $$  | $$| $$  | $$  \  $$$/  
| $$$$$$$$|  $$$$$$$| $$  |  $$$$/| $$| $$  | $$|  $$$$$$$| $$  | $$       /$$$$$$| $$ | $$ | $$|  $$$$$$$| $$  | $$|  $$$$$$/   \  $/   
|________/ \_______/|__/   \___/  |__/|__/  |__/ \_______/|__/  |__/      |______/|__/ |__/ |__/ \_______/|__/  |__/ \______/     \_/

┌──(olaflaitinen@hpc-cluster-helsinki)-[~]
└─$ cat ~/whoami.txt

        /\_/\          Name: Olaf Yunus Laitinen Imanov
       ( o.o )         Title: Senior AI Systems & Scientific Computing Engineer
        > ^ <          Affiliation: Department of Mathematics and Statistics
       /     \         Institution: University of Helsinki (HY)
      (       )        Compute: Slurm cluster • 8x NVIDIA H100 SXM5 80GB per node (NVLink 4)
     (  )   (  )       Languages: Python • Julia • Mojo • C++20/23 • Go • Scala • R • Fortran • Rust
    (___)___(___)      Activity (last 12 months): 1.8k+ commits • 67 merged PRs • 26 repos
                       Focus: Distributed AI • SciML • Mojo SIMD • Ultra-Low-Latency Systems

`[0x01]` ENTERPRISE LLM & INFERENCE PIPELINE

               +-------------------------------------------------------+
               |             Agent Request / User Query                |
               +---------------------------+---------------------------+
                                           |
                        [ Input Guardrails -> Semantic Cache ]
                                           |   (cache miss)
               +---------------------------v---------------------------+
               |        Intent Router & Hybrid Retrieval (RAG)         |
               +-------------+---------------------------+-------------+
                             |                           |
                 (Vector + BM25 Hybrid)             (Direct Prompt)
                             |                           |
               +-------------v-------------+             |
               | Qdrant / Milvus Cluster   |             |
               | Re-ranking: Cross-Encoder |             |
               +-------------+-------------+             |
                             |                           |
                             +-------------+-------------+
                                           |
               +---------------------------v---------------------------+
               |          High-Throughput Model Serving Engine         |
               |         [ vLLM • TensorRT-LLM • Triton Server ]       |
               +---------------------------+---------------------------+
                                           |
                     +---------------------+---------------------+
                     |                                           |
                     v                                           v
        +-------------------------+                 +-------------------------+
        | PagedAttention + Chunked|                 | Custom Kernels          |
        | Prefill (FlashAttn-3)   |                 | CUDA • Triton • Mojo    |
        +------------+------------+                 +------------+------------+
                     |                                           |
                     +---------------------+---------------------+
                                           |
               +---------------------------v---------------------------+
               | Quantized Weights: FP8 / INT4 (AWQ, GPTQ)             |
               +---------------------------+---------------------------+
                                           |
                                [ Output Guardrails ]
                                           |
                                [ SSE Token Streaming ]

`[0x02]` TECH RADAR & SPECIALIZED MATRIX

+-----------------------+-------------------------------------------------------------------+
| DOMAIN                | CORE STACK, TOOLS & FRAMEWORKS                                    |
+-----------------------+-------------------------------------------------------------------+
| Core Languages        | Python, Julia, Mojo, C++ (20/23), Go, Scala, R, Fortran (90/08),  |
|                       | Rust, POSIX Shell (Zsh/Bash), TeX / LaTeX                         |
| Scientific & SciML    | Julia SciML, NumPy, SciPy, R (Tidyverse/Bioconductor), JAX        |
| High-Perf & Hardware  | CUDA, OpenAI Triton, OpenMP, MPI, BLAS/LAPACK, SIMD/AVX-512,      |
|                       | NVLink/NVSwitch, MLIR                                             |
| LLM Inference Serving | vLLM, TensorRT-LLM, Triton Server, TGI, ONNX Runtime, Ollama      |
| Deep Learning Core    | PyTorch, DeepSpeed, Megatron-LM, Hugging Face, FlashAttention-3   |
| Distributed & BigData | Apache Spark (Scala), Ray, Slurm HPC, Apache Arrow, Kafka, Flink  |
| Vector & Retrieval    | Qdrant, Milvus, FAISS, PGVector, Hybrid Search, Cross-Encoders    |
| Cloud & Infrastructure| Kubernetes, KServe, Docker, Terraform, Helm, Linux Kernel (eBPF)  |
| Tooling & Compilers   | LLVM, Clang, GCC, GFortran, CMake, Cargo, Go Toolchain            |
| MLOps & Observability | Weights & Biases, MLflow, Prometheus, Grafana, OpenTelemetry, DVC |
+-----------------------+-------------------------------------------------------------------+

`[0x03]` ENGINEERING PHILOSOPHY & FIRST PRINCIPLES

/*
 *  1. "Python for expressiveness, Mojo/Julia/C++ for hardware saturation, Fortran for raw math."
 *  2. "Attention is all you need, but memory bandwidth is all you pay for."
 *  3. In high-scale inference: PagedAttention > Naive Allocation.
 *  4. Hardware is the only source of truth; optimize down to the cache line and FLOP.
 *  5. Theory without benchmarks is speculation; benchmarks without theory are anecdotes.
 */

`[0x04]` HPC NODE TOPOLOGY & INTERCONNECT FABRIC

+-------------------------------------------------------------------------------+
|                  2x AMD EPYC 9654 (192 Cores / 384 Threads)                   |
|        Memory: 1.5 TB DDR5-4800 (12 channels per socket, 2 NUMA nodes)        |
+---------------------------------------+---------------------------------------+
                                        |
                PCIe Gen5 switches: one x16 link per GPU and per NIC
                (~64 GB/s per direction, ~128 GB/s bidirectional)
                                        |
                 +----------------------+----------------------+
                 |                                             |
+----------------v----------------+           +----------------v----------------+
|  GPU 0..3  (NVIDIA H100 SXM5)   |           |  GPU 4..7  (NVIDIA H100 SXM5)   |
|  4x 80 GB HBM3, 3.35 TB/s each  |           |  4x 80 GB HBM3, 3.35 TB/s each  |
+----------------+----------------+           +----------------+----------------+
                 |                                             |
                 +=====================+=======================+
                                       |
         [ 4x NVSwitch (3rd gen): all-to-all NVLink 4, 900 GB/s per GPU ]

   Scale-out (via PCIe, not NVSwitch): 4x ConnectX-7 400 Gb/s NDR InfiniBand
   -> Quantum-2 switches • GPUDirect RDMA for GPU-to-GPU traffic across nodes

`[0x05]` DISTRIBUTED 3D PARALLELISM ARCHITECTURE (TRAINING)

                                [ 70B - 405B PARAMETER MODEL ]
                                              │
               ┌──────────────────────────────┼──────────────────────────────┐
               ▼                              ▼                              ▼
    [ TENSOR PARALLELISM ]        [ PIPELINE PARALLELISM ]         [ DATA PARALLELISM ]
           (TP = 8)                       (PP = 4)                (ZeRO-1 / Dist. Optimizer)
               │                              │                              │
  Intra-node Megatron split      Inter-node layer partitioning   Optimizer states sharded
  GEMMs across NVLink/NVSwitch   1F1B schedule across stages     across DP replicas

            World size = TP × PP × DP = 8 × 4 × DP  →  32 GPUs per model replica

`[0x06]` SCIENTIFIC ML (SciML) & NUMERICAL STACK

               +-----------------------------------------------+
               |   High-Dimensional Dynamical Systems / SciML  |
               +-----------------------+-----------------------+
                                       |
               +-----------------------v-----------------------+
               | Julia DifferentialEquations.jl & ForwardDiff  |
               | (Automatic Differentiation, Stiff ODE Solvers)|
               +-----------------------+-----------------------+
                                       |
                     +-----------------+-----------------+
                     |                                   |
                     v                                   v
        +-------------------------+         +-------------------------+
        | Legacy Numerical Kernels|         | Statistical & Empirical |
        | Fortran 90 BLAS / LAPACK|         | R (Bioconductor / Tidy) |
        +------------+------------+         +------------+------------+
                     |                                   |
                     +-----------------+-----------------+
                                       |
               +-----------------------v-----------------------+
               | Zero-Copy Bridge (PythonCall.jl / juliacall)  |
               |          -> Python / PyTorch Runtime          |
               +-----------------------------------------------+

`[0x07]` KERNEL & SYSTEM MICRO-BENCHMARKS

+----------------------+--------------+------------------+-----------------+---------+-----------+
| OPERATION            | LANGUAGE     | BACKEND          | RESULT          | % PEAK* | BOUND     |
+----------------------+--------------+------------------+-----------------+---------+-----------+
| GEMM FP8 (4096³)     | Mojo (MAX)   | Tensor Cores     | 1,480 TFLOPS    |  74.8%  | Compute   |
| GEMM BF16 (8192³)    | C++ / CUDA   | cuBLASLt         | ~760 TFLOPS     |  76.8%  | Compute   |
| FlashAttn-3 Fwd FP8  | CUDA C++     | CUTLASS / CuTe   | 820 TFLOPS      |  41.4%  | Compute   |
| Stiff ODE Solve      | Julia        | DiffEq.jl / LLVM | 142 MFLOPS      |    —    | Latency   |
| Cholesky (dpotrf)    | Fortran 90   | OpenBLAS         | 118 GFLOPS      |    —    | Compute   |
| Async RPC Gateway    | Go           | netpoll (epoll)  | 2.4M req/s      |    —    | I/O       |
| Distributed Agg      | Scala        | Spark / JVM      | 850 GB/s (agg.) |    —    | Network   |
+----------------------+--------------+------------------+-----------------+---------+-----------+
  * Dense Tensor Core peak per H100 SXM5: BF16/FP16 989 TFLOPS, FP8 1,979 TFLOPS.

`[0x08]` CONFERENCES & PREPRINTS (2026)

/*
 *  Affiliation for all works: Department of Mathematics and Statistics, University of Helsinki.
 *
 *  ════════════════════════════════════════════════════════════════════════════
 *  [PEER-REVIEWED CONFERENCES & WORKSHOPS]
 *  ════════════════════════════════════════════════════════════════════════════
 *
 *  [01] "Asymmetric Federated Agentic Intelligence."
 *       40th Conference on Neural Information Processing Systems (NeurIPS 2026), to appear.
 *       Workshop: Agentic AI Benchmarks and Applications for Enterprise Tasks (AABA4ET).
 *       Laitinen-Imanov, O. Y., et al. (2026).
 *       DOI: —   |   URL: —
 *
 *  [02] "Hypotheses, Not Methods: What Machine Unlearning Can Give Back to the Study of Human Unlearning."
 *       3rd International Congress on Data Science and Information Technologies (INFTEC'26).
 *       Laitinen-Imanov, O. Y., Gunalp, O., Tugrul, A. T., Erisken, D., Turhan, R. I., Turhan, T. C., Pehlivan, S. (2026).
 *       DOI: —   |   URL: —
 *
 *  [03] "From Evidence Sufficiency to Abstention: A Conceptual Framework for Biomedical RAG Decision Support."
 *       3rd International Congress on Data Science and Information Technologies (INFTEC'26).
 *       Gunalp, O., Laitinen-Imanov, O. Y., Turhan, R. I., Erisken, D., Turhan, T. C. (2026).
 *       DOI: —   |   URL: —
 *
 *  [04] "Spatiotemporal Transferability of Open-Data Machine Learning Models for Precipitation pH Prediction."
 *       3rd International Congress on Data Science and Information Technologies (INFTEC'26).
 *       Tugrul, A. T., Gunalp, O., Erisken, D., Laitinen-Imanov, O. Y., Turhan, R. I., Turhan, T. C. (2026).
 *       DOI: —   |   URL: —
 *
 *  [05] "Beyond Neuronal Plasticity: Microglia as a Bounded Biological Regularization Layer for Long-Term Organoid Intelligence."
 *       3rd International Congress on Data Science and Information Technologies (INFTEC'26).
 *       Gunalp, O., Laitinen-Imanov, O. Y., Akyol, A., Turhan, R. I., Erisken, D., Turhan, T. C. (2026).
 *       DOI: —   |   URL: —
 *
 *  [06] "EKG Skalograflarının CLIP Tabanlı Sıfır-Atışlı Sınıflandırmada Spektrogramlara Üstünlüğü."
 *       [The Superiority of ECG Scalograms over Spectrograms in CLIP-Based Zero-Shot Classification]
 *       2026 34th Signal Processing and Communications Applications Conference (SIU), İstanbul, Türkiye, pp. 1–4.
 *       Fidan, M., Kulali, D. U., Laitinen-Imanov, O. Y. (2026).
 *       DOI: 10.1109/SIU71813.2026.11637094   |   URL: https://doi.org/10.1109/SIU71813.2026.11637094
 *
 *  [07] "To Retrieve or to Memorize? Analyzing the Cost-Benefit Tradeoff of Weight-Time vs. Context-Time Memory in Multi-Session Agents."
 *       Conference on Language Modeling (COLM 2026).
 *       Workshop: Context Beyond the Window: Persistent Knowledge in Language Models.
 *       Laitinen-Imanov, O. Y., Turhan, R. I. (2026).
 *       DOI: —   |   URL: https://openreview.net/forum?id=KmJi1O0sUV
 *
 *  ════════════════════════════════════════════════════════════════════════════
 *  [RESEARCH PREPRINTS]
 *  ════════════════════════════════════════════════════════════════════════════
 *
 *  [08] "Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization."
 *       Laitinen-Imanov, O. Y., Kulali, D. U., Yilmaz, T., Erisken, D., Turhan, R. I. (2026).
 *       DOI: 10.48550/arXiv.2602.02439   |   URL: https://arxiv.org/abs/2602.02439
 *
 *  [09] "Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning."
 *       Laitinen-Imanov, O. Y. (2026).
 *       DOI: 10.48550/arXiv.2601.18699   |   URL: https://arxiv.org/abs/2601.18699
 *
 *  [10] "Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility."
 *       Laitinen-Imanov, O. Y. (2026).
 *       DOI: 10.48550/arXiv.2603.05581   |   URL: https://arxiv.org/abs/2603.05581
 *
 *  [11] "Autonomous AI Agents for Real-Time Affordable Housing Site Selection: Multi-Objective Reinforcement Learning Under Regulatory Constraints."
 *       Laitinen-Imanov, O. Y., Erisken, D., Kulali, D. U., Yilmaz, T., Turhan, R. I. (2026).
 *       DOI: 10.48550/arXiv.2602.03940   |   URL: https://arxiv.org/abs/2602.03940
 *
 *  [12] "Transparency-First Medical Language Models: Datasheets, Model Cards, and End-to-End Data Provenance for Clinical NLP."
 *       Laitinen-Imanov, O. Y., Yilmaz, T., Tugrul, A. T., Zaman, M. N., Gunalp, O., et al. (2026).
 *       DOI: 10.48550/arXiv.2601.19191   |   URL: https://arxiv.org/abs/2601.19191
 *
 *  [13] "Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review."
 *       Laitinen-Imanov, O. Y., Gunalp, O. (2026).
 *       DOI: 10.48550/arXiv.2601.00990   |   URL: https://arxiv.org/abs/2601.00990
 *
 *  [14] "Verifiable Provenance and Watermarking for Generative AI: An Evidentiary Framework for International Operational Law and Domestic Courts."
 *       Laitinen-Imanov, O. Y., Abdullayeva, N. (2026).
 *       DOI: 10.48550/arXiv.2605.21002   |   URL: https://arxiv.org/abs/2605.21002
 *
 *  [15] "A Transport Geometry of Benign Overfitting."
 *       Laitinen-Imanov, O. Y., Turhan, R. I., Jafarli, I. (2026).
 *       DOI: 10.48550/arXiv.2604.08625   |   URL: https://arxiv.org/abs/2604.08625
 *
 *  [16] "When Does Cheap Physics Help? Gated Multi-Fidelity Physics-Informed Networks with Provable Sample Savings and Conformal Guarantees."
 *       Laitinen-Imanov, O. Y., Turhan, R. I., Jafarli, I. (2026).
 *       DOI: 10.48550/arXiv.2602.01176   |   URL: https://arxiv.org/abs/2602.01176
 */

`[0x09]` AI KERNEL & MODEL OPS TRACE LOG

$ git log --graph --oneline -8
*   9a2f1c4 (HEAD -> main, origin/main) Merge branch 'feat/heterogeneous-kernel-pipeline'
|\
| * 3e8a4d1 feat(sciml): interface Fortran BLAS routines with Julia SciML solvers
| * 5e2b09c perf(mojo): accelerate vector dot-product with Mojo SIMD primitives
| * b61f09d perf(go): async high-throughput gRPC model gateway with zero-alloc pool
|/
* 7b1c34e perf(vllm): tune prefix-caching hit ratio for multi-turn chats
* 3c9b20f feat(data): optimize distributed ingestion pipeline via Scala/Spark
* 2a8f90b docs(paper): finalize camera-ready LaTeX manuscript
* 1f4a08e refactor(serving): migrate distributed worker pool to Ray on Slurm

`[0x0A]` GITHUB ACTIVITY

┌──[ GITHUB ACTIVITY SNAPSHOT • OCT 2026 ]────────────────────────────────────┐
│                                                                             │
│  [LAST 12 MONTHS]                                                           │
│  ├─ Total Commits       : 1,800+ commits (High-Frequency Contributor)       │
│  ├─ Merged Pull Requests: 67 PRs                                            │
│  ├─ Ecosystem Reach     : 26 Repositories Contributed To                    │
│  └─ Stars Earned        : 56 Stargazers                                     │
│                                                                             │
│  [MULTI-PARADIGM RUNTIMES & DOMAINS]                                        │
│  ├─ AI & Modeling       : Python • Julia • Mojo (MAX Engine) • PyTorch      │
│  ├─ Systems & Low-Level : C++20 • Fortran (HPC Linear Algebra) • CUDA • Go  │
│  ├─ Distributed Compute : Scala (Spark Engine) • Ray Cluster • Slurm        │
│  ├─ Statistical Math    : R (Bioconductor / Tidyverse) • Numerical SciPy    │
│  └─ Research Formalisms : TeX / LaTeX (Academic Paper Publications)         │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

`[0x0B]` CONTACT & ACADEMIC IDENTITY

┌──(identity)─────────────────────────────────────────────────────────────────┐
│ Name        : Olaf Yunus Laitinen Imanov                                    │
│ Cited As    : Laitinen-Imanov, O. Y.                                        │
│ Role        : Senior AI Systems & Scientific Computing Engineer             │
│ Department  : Department of Mathematics and Statistics                      │
│ Institution : University of Helsinki                                        │
│ Contact     : [email protected]                                    │
│ GitHub      : https://github.com/olaflaitinen                               │
│ Research    : Low-Latency LLM Serving, Machine Unlearning & Distributed AI  │
└─────────────────────────────────────────────────────────────────────────────┘

[EXECUTION COMPLETE] -- Model weights loaded into GPU VRAM (0 bytes leaked)

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Olaf Yunus Laitinen Imanov olaflaitinen

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`[0x01]` ENTERPRISE LLM & INFERENCE PIPELINE

`[0x02]` TECH RADAR & SPECIALIZED MATRIX

`[0x03]` ENGINEERING PHILOSOPHY & FIRST PRINCIPLES

`[0x04]` HPC NODE TOPOLOGY & INTERCONNECT FABRIC

`[0x05]` DISTRIBUTED 3D PARALLELISM ARCHITECTURE (TRAINING)

`[0x06]` SCIENTIFIC ML (SciML) & NUMERICAL STACK

`[0x07]` KERNEL & SYSTEM MICRO-BENCHMARKS

`[0x08]` CONFERENCES & PREPRINTS (2026)

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Olaf Yunus Laitinen Imanov olaflaitinen

Achievements

Achievements

Highlights

Organizations

[0x01] ENTERPRISE LLM & INFERENCE PIPELINE

[0x02] TECH RADAR & SPECIALIZED MATRIX

[0x03] ENGINEERING PHILOSOPHY & FIRST PRINCIPLES

[0x04] HPC NODE TOPOLOGY & INTERCONNECT FABRIC

[0x05] DISTRIBUTED 3D PARALLELISM ARCHITECTURE (TRAINING)

[0x06] SCIENTIFIC ML (SciML) & NUMERICAL STACK

[0x07] KERNEL & SYSTEM MICRO-BENCHMARKS

[0x08] CONFERENCES & PREPRINTS (2026)

[0x09] AI KERNEL & MODEL OPS TRACE LOG

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`[0x01]` ENTERPRISE LLM & INFERENCE PIPELINE

`[0x02]` TECH RADAR & SPECIALIZED MATRIX

`[0x03]` ENGINEERING PHILOSOPHY & FIRST PRINCIPLES

`[0x04]` HPC NODE TOPOLOGY & INTERCONNECT FABRIC

`[0x05]` DISTRIBUTED 3D PARALLELISM ARCHITECTURE (TRAINING)

`[0x06]` SCIENTIFIC ML (SciML) & NUMERICAL STACK

`[0x07]` KERNEL & SYSTEM MICRO-BENCHMARKS

`[0x08]` CONFERENCES & PREPRINTS (2026)

`[0x09]` AI KERNEL & MODEL OPS TRACE LOG

`[0x0A]` GITHUB ACTIVITY

`[0x0B]` CONTACT & ACADEMIC IDENTITY