The curriculum vitae, in proof

Masthead

Summary

1. Title

   MSc, Computer Science and Engineering — BRAC University, Dhaka

   Date

   Fall 2026 —

   Detail

   Thesis proposal accepted: HCGT-PG. Supervisor: Dr. Badhan Das.
2. Title

   BSc, Computer Science and Engineering — BRAC University, Dhaka

   Date

   Fall 2021 – Summer 2026

   Detail

   CGPA 3.30 / 4.00. Thesis: HierarchiRetina, supervised by Rafeed Rahman, deposited in the BRAC University institutional repository. Coursework in machine learning, deep learning, compilers, theory of computation, DBMS, operating systems, algorithms and software engineering.
3. Title

   Higher Secondary Certificate — Uttara High School and College, Dhaka

   Date

   2020

   Detail

   GPA 5.00 / 5.00. Science.
4. Title

   Secondary School Certificate — Ullapara Merchant Pilot High School, Sirajganj

   Date

   2018

   Detail

   GPA 4.72 / 5.00. Science.

1. Title

   HierarchiRetina

   Date

   2026

   What it does

   Interpretable diabetic retinopathy grading from retinal photographs

   Detail

   Supervisors: Rafeed Rahman, Niloy Farhan, Md. Golam Rabiul Alam. Deposited.
2. Title

   Rank Radii Transfer as Quantiles

   Date

   2026

   What it does

   Placing unseen species inside the taxonomy, not in flat clusters

   Detail

   Supervisor: Niloy Farhan. In preparation.
3. Title

   Automated Renal Analysis

   Date

   2026

   What it does

   Automated Renal Measurement, Kidney Stone and Cyst Segmentation, and Radiology Report Generation from Thick-Slice Non-Contrast CT

   Detail

   Supervisor: Niloy Farhan. In preparation.
4. Title

   HCGT-PG

   Date

   2026

   What it does

   What would happen if you knocked the gene down

   Detail

   Supervisor: Dr. Badhan Das. Proposal accepted.

1. Title

   HierarchiRetina: A Multi-Stage Deep Learning Pipeline for Diabetic Retinopathy Severity Assessment

   Date

   Published 2026

   Detail

   BSc thesis, BRAC University
2. Title

   Rank Radii Transfer as Quantiles: Taxonomic Placement and Sub-Taxonomy Construction for Unseen Species

   Date

   In preparation 2026
3. Title

   Automated Renal Analysis

   Date

   In preparation 2026

1. Title

   Languages

   Detail

   Python (primary). C (coursework level).
2. Title

   Deep learning

   Detail

   PyTorch, timm, segmentation_models_pytorch. Mixed-precision training with GradScaler tuning, EMA, test-time augmentation, k-fold CV with resumable per-fold checkpointing, class-imbalance handling (pos_weight, weighted samplers), threshold calibration on validation only, ordinal regression (CORN), mixture-of-experts, cross-attention fusion, attention gates, ASPP, GeM pooling, deep supervision, frozen-backbone transfer, Grad-CAM.
3. Title

   Architectures used hands-on

   Detail

   ConvNeXt V2, Swin UNETR V2, DINOv2/v3, EfficientNet, U-Net, U-Net++, Attention U-Net, SwinHRUNetPP, HSMoE-AUNet, image-text encoders.
4. Title

   Medical imaging

   Detail

   Fundus photography, non-contrast CT, NIfTI and DICOM handling, FOV masking with morphological erosion, CLAHE on the green channel, retinal cropping, patch extraction and stitching, lesion-level connected-component evaluation, de-identification, radiologist report parsing.
5. Title

   Evaluation

   Detail

   QWK, ICC, AUC-ROC, AUC-PR, Dice, IoU, NMI, lesion-level and image-level recall, sensitivity/specificity tradeoff analysis, per-dataset breakdowns, held-out test discipline and leakage assertions.
6. Title

   Causal inference and computational biology

   Detail

   Causal graphs and DAGs, d-separation, interventions, counterfactual reasoning, gene perturbation analysis, gene regulatory network inference, multi-omics integration and biomarker discovery.
7. Title

   Data

   Detail

   pandas, NumPy, Albumentations, OpenCV, Matplotlib, Seaborn, scikit-learn, nbformat.
8. Title

   Backend

   Detail

   FastAPI, REST APIs, SQLAlchemy, JWT auth, Alembic, AWS S3, CloudFront.
9. Title

   MODEL FAMILIARITY

   Detail

   Beyond the architectures I have used hands-on, I regularly study modern vision, multimodal, language, medical imaging and computational biology models. This includes SigLIP 2, EVA-CLIP, SAM 3, nnU-Net, ViT, Qwen, Llama, DeepSeek, Kimi, MedGemma and genomic models such as AlphaGenome and Geneformer. I focus on understanding their architecture, training objective, scaling behavior, benchmarks, limitations and practical use rather than treating familiarity with a model as equivalent to hands-on experience. The model notes I keep are part of this ongoing study.
10. Title

    READING

    Detail

    I love reading books and research papers. My current reading is centered on machine learning, causality and computational biology. Key books include The Book of Why, Causality: Models, Reasoning, and Inference, Causal Inference in Statistics: A Primer, and Elements of Causal Inference: Foundations and Learning Algorithms. The reading is not separate from the research work. It is mainly about understanding what can be inferred from data, where the assumptions enter, and how interventions can provide stronger evidence than observation alone.
11. Title

    Other

    Detail

    Git, Jupyter, LaTeX, lex/yacc, CUDA-level debugging.

1. Title

   CSMMS — Campus Student Management and Marketplace System

   Detail

   FastAPI backend: REST API design, filtered and paginated marketplace, S3 and CloudFront image upload, service impact metrics, SQLAlchemy, JWT, Alembic.
2. Title

   Leveraging CNN and Random Forest for Accurate Food Expiry Prediction

   Detail

   AI-driven food expiry prediction model combining CNNs for image analysis with Random Forest and MLP algorithms to evaluate environmental metadata (temperature, humidity, storage duration) and minimize food waste.
3. Title

   Compiler symbol table

   Detail

   Scoped symbol table for a compiler front end using lex and yacc/bison, with cross-platform builds on Linux and macOS.

1. Detail

   Languages. Bangla, native. English, full professional proficiency and the medium of instruction throughout undergraduate study.