Researcher in Reliable Machine Learning

Tanveer Hossain Munim

Reliable Machine Learning · Uncertainty Quantification · Trustworthy Evaluation

I study how calibration and evaluation choices affect model comparisons, and develop uncertainty methods for deployment.

View Research Download CV

Research Profile

Research Focus & Background

My current research covers calibration-aware evaluation of rare-event forecasts, distribution-free prediction intervals for crowd counting, exact robustness certificates for forecast rankings, and conformal risk under vocabulary shift.

I am a Senior Research Engineer at AI GeoLAB, where I work on evaluated, human-in-the-loop systems for Bengali land-record digitisation. Previously, at RIMES, I led backend, data-pipeline, and ML architecture for national-scale meteorological and early-warning systems. I hold a B.Sc. in Computer Science and Engineering from BUET.

Research Interests

Reliable Machine Learning Uncertainty Quantification Calibration Trustworthy AI Evaluation Conformal Prediction
Tanveer Hossain Munim

Contact

Academic Foundation

Education

  • April 2018 – September 2023

    B.Sc. in Computer Science and Engineering

    Bangladesh University of Engineering and Technology (BUET)

    CGPA 3.62/4.00 · Undergraduate thesis: ShasthoSheba · Supervisor: Prof. A. B. M. Alim Al Islam · GRE 329 (Q170, V159) · IELTS 8.0

Research Output

Publications & Manuscripts

Work on reliable evaluation, uncertainty quantification, geospatial ML, and earlier human-centred computing.

Submitted and under review

  • 2026

    A Free Knob: Decoupling Calibration and Predictive Skill in Threshold-Based Evaluation

    Munim, T. H., Saiem, B. A., Sany, A.-A., & Hashem, T.

    ICLR 2027 submission · Datasets & Benchmarks

    A matched-calibration audit for thresholded rare-event benchmarks. On released CasCast checkpoints, the cascade-over-backbone CSI gap falls from 0.1601 to 0.0339 after monotone recalibration; across 450 SEVIR contrasts, 51 reverse sign.

    CalibrationPythonReliable ML
  • 2026

    Counting People with Coverage: Distribution-Free Prediction Intervals for Crowd Counting via Crop-Consistency

    Munim, T. H., Ikram, F. Z., & Hassan, A. M.

    Manuscript

    A post-hoc conformal wrapper for frozen crowd counters. Across four counters and four datasets, crop-consistency improves hardest-region coverage in every evaluated model–dataset pair; at matched coverage, intervals are 15% narrower than a label-trained uncertainty model and 29% narrower than isotonic recalibration.

    Computer VisionConformal PredictionReliable ML
  • 2026

    ShasthoSheba: Leveraging an mHealth Solution for Providing Healthcare Service to Orphanages

    Munim, T. H., Anan, M. T. T., Galib, F.-Z.-I., Saiem, B. A., & Islam, A. B. M. A. A.

    ACM Transactions on Computer-Human Interaction · Under review

    Reliable ML

Peer-reviewed publication

  • 2024

    eDakterBari: A human-centered solution enabling online medical consultation and information dissemination for resource-constrained communities in Bangladesh

    Eliza, I. J., Urmi, M. A., Anan, M. T. T., Munim, T. H., Galib, F.-Z.-I., & Islam, A. B. M. A. A.

    Heliyon, 10(1), e23100

    Python DOI ↗

Work in progress

  • 2026

    Anchored Risk Profiles: Exact Robustness Certificates for Fixed-Threshold Forecast Rankings under Acceptable Corrections

    Munim, T. H.

    Manuscript in preparation

    Exact certificates for whether forecast rankings survive every acceptable calibration-improving monotone correction.

    CalibrationPythonReliable ML

Invited Talks & Presentations

  • 29–30 April 2026

    “Climate–Agriculture Risk Modeling: Sri Lanka”

    Climate Services User Forum, South Asian Hydromet Forum (SAHF) · Malé, Maldives

    On behalf of RIMES

  • 9 October 2025

    “Machine Learning for Extreme-Event Detection in Agrometeorology”

    Capacity Building Training, National Center of Meteorology · UAE

    On behalf of RIMES

Research in Practice

Selected Research & Applied Work

Selected empirical research, applied research, and research software.

FreeKnob Audit — Calibration vs Predictive Skill

A model-agnostic audit for threshold-based rare-event evaluation. Matched monotone calibration separates predictive skill from threshold placement without retraining or changing spatial order.

Calibration Python Reliable ML

Distribution-Free Uncertainty for Crowd Counting

A post-hoc conformal wrapper for frozen crowd counters using a label-free crop-consistency difficulty signal. Evaluated across four counters and four datasets, including cross-dataset weighted conformal prediction.

Computer Vision Conformal Prediction Reliable ML

Anchored Risk Profiles

An exact rational-arithmetic framework for testing whether a fixed-threshold forecast ranking survives every acceptable monotone recalibration. Manuscript in preparation.

Calibration Python Reliable ML

Vocabulary Shift in Open-Vocabulary Segmentation

Ongoing research on when conformal guarantees break under vocabulary change and how the failure can be predicted from calibration data before deployment.

Computer Vision Conformal Prediction Reliable ML

Engineering

Selected Production Systems

Deployed infrastructure, applied ML systems, and open-source contributions.

Reliable Bengali Land-Record Digitisation

At AI GeoLAB, a QLoRA-tuned Qwen2.5-VL-3B reaches 0.965 core-field F1 on 10,509 held-out records. A separate segmentation and number-reading pipeline recovers and correctly numbers 91.7% of plots on 22 held-out cadastral sheets.

Document AIGeospatial MLPyTorchQwen2.5-VL

GCF CDIS — Timor-Leste National Meteorological Platform

Led backend, data-pipeline, and ML architecture for a national meteorological platform processing more than 1 TB/day from five NWP models and two satellites. The system also supports operational CAP v1.2 alerting.

Apache AirflowDjango / DRFDockerPostgreSQL / PostGISxarray / NetCDF

Research Toolkit

Methods & Technical Foundations

Reliability & Evaluation

  • Conformal prediction
  • Calibration and benchmark auditing
  • Robust model comparison
  • Pre-registration and reproducibility

Statistical Methods

  • Bootstrap inference
  • Weighted and Mondrian conformal methods
  • Bayesian modelling with PyMC
  • Exact rational-arithmetic analysis

Vision & Language Models

  • PyTorch and Hugging Face
  • VLM fine-tuning with QLoRA
  • Segmentation, OCR, and crowd counting
  • Hybrid retrieval and local inference

Geospatial & Production Systems

  • xarray, GDAL, Rasterio, and PostGIS
  • ERA5, IMERG, GloFAS, and SEVIR
  • Airflow, Celery, and Docker
  • Python, Rust, TypeScript, and SQL

Research & Practice

Research and Professional Experience

  • April 2026 – Present

    Senior Research Engineer

    AI GeoLAB Ltd

    Developing evaluated, human-in-the-loop methods for handwritten Bengali land records and cadastral map digitisation.

  • July 2024 – July 2026

    Senior Software Engineer

    Regional Integrated Multi-Hazard Early Warning System (RIMES)

    Led backend, data-pipeline, and ML architecture for Timor-Leste CDIS; designed CAP v1.2 alerting and the data unification behind Bangladesh FFWC's live 15-day flood forecasts.

  • June 2021 – August 2022; June 2023 – August 2024

    Backend Developer; later Tech Lead

    Interactive Cares

    Led an eight-person engineering team as the learning platform grew from 8,000 to 100,000 users.

Credentials & Recognition

Awards & Affiliations

NST Fellowship Bangladesh National Science and Technology Fellowship · 2026
NVIDIA Inception MIRA AI · 2025
Accelerating Asia Selection, team member · Top 9 of 500+ · 2023
Bangladesh Mathematical Olympiad Champion · IMO team shortlist · 2016

Let's Connect

Get in Touch

I welcome conversations about graduate research and collaborations in reliable machine learning.

The quickest way to reach me is directly by email. I usually respond within 24 hours.

Email Me

Also find me on

Open To

Graduate Research Opportunities Research Collaborations Reliable ML Climate & GeoAI Research