Hi, my name is Saif.

I am a researcher and an applied statistician with a PhD in statistical experimental design and a background in mechanical and manufacturing engineering. I hold Black belts in both Six Sigma and Design for Six Sigma. In my current position as a postdoctoral researcher, I research on topics concerning accurate modeling of data originating from small to large sample size experiments using statistics and AI. I provide industry training, consult for several industry leaders to provide guidance on data collection and analysis, and teach a course on regression analysis and experimental design.

Employment

Sep 2023 – present

Postdoctoral researcher

KU Leuven, Leuven, Belgium

Sep 2024 - Apr 2025

Statistical expert

EFFEX, Leuven, Belgium

Apr 2017 - Jun 2019

Manufacturing/Process Improvement Engineer

Hill Manufacturing Inc., OH, USA

Industrial certifications

2022

Lean Six Sigma (LSS)

University of Michigan

2022

Design for Six Sigma (DFSS)

University of Michigan

Education

Sep 2019 – Aug 2023

Ph.D in Bioscience Engineering (Statistical design of experiments)

KU Leuven, Leuven, Belgium

Sep 2015 – Dec 2016

M.Eng. in Manufacturing Engineering

University of Michigan, Ann Arbor, USA

Aug 2011 – Jun 2015

B.Eng. in Mechanical Engineering

Ramaiah Institute of Technology, Bangalore, India

Online certifications

2023

Neural Networks specialization

DeepLearning.AI (Coursera)

2023

Mathematics for Machine Learning specialization

Imperial College London (Coursera)

Protecting potato quality from field to storage

Measuring crunchiness in food

Efficient harvesting of corn and grass

Efficient Lithium extraction

Optimizing lithium recovery from simulated battery leachate via selective electrodialysis: Parametric and statistical insights

Ruya, P. M., Hameed, M. S. I., Swennen, A., Goos, P., Wenten, I. G., & Yang, X.

Desalination, 2026.

Optimal design of experiments when not every test is equally expensive

Hameed, M. S. I., Van Der Haar, R., Chen, Y., & Goos, P.

Chemometrics and Intelligent Laboratory Systems, 2025.

Two-level D- and A-optimal main-effects designs with run sizes one and two more than a multiple of four

Hameed, M. S. I., Núñez Ares, J., Schoen, E. D., & Goos, P.

Statistics and Computing, 2025.

Two-level D-optimal designs for run sizes that are one less than a multiple of four

Hameed, M. S. I., Núñez Ares, J., & Goos, P.

Quality Engineering, 2025.

Analysis of data from orthogonal minimally aliased response surface designs

Hameed, M. S. I., Núñez Ares, J., & Goos, P.

Journal of Quality Technology, 2023.

A complete catalog of D- and A-optimal designs with up to 20 runs

CFE-CMStatistics-2024

14 December 2024 · London, UK

Minimally aliased D- and A-optimal main-effects designs

Fall Technical Conference (FTC)

09 October 2024 · Nashville, TN, USA

Optimal experimental design when not all tests are equally expensive

European Network for Business and Industrial Statistics (ENBIS)

17 September 2024 · Leuven, Belgium

Multi-criteria evaluation and selection of experimental designs from a catalog

Fall Technical Conference (FTC)

06 October 2023 · Raleigh, NC, USA

D- and A- optimal screening designs. What if n is not a multiple of 4?

International Conference on Design of Experiments (ICODOE)

11 May 2023 · Memphis, TN, USA

D- and A- optimal main effects designs for screening experiments

DOE seminar at KU Leuven

06 December 2022 · Leuven, Belgium

One D-optimal main effects design is not the other

European Network for Business and Industrial Statistics (ENBIS)

27 June 2022 · Trondheim, Norway

A tailored analysis of data from OMARS designs

European Network for Business and Industrial Statistics (ENBIS)

14 September 2021 · Online

A study of design-based model selection methods

DOE seminar at KU Leuven

24 April 2020 · Leuven, Belgium

Finding the optimal mocktail

DOE seminar at KU Leuven

24 September 2019 · Leuven, Belgium