Lukman Enegi ISMAILA

Postdoctoral Researcher at Johns Hopkins University
I am postdoctoral researcher at The Johns Hopkins University, School of Medicine. I reside at The F.M. Kirby Research Center for Functional Brain Imaging at Kennedy Krieger Institute, under the supervision of Dr. Ann Choe and Prof. Jim Pekar. I earned my Ph.D degree in Computer Science with speciality on Image, Signals and Vision under the supervision of Prof. David Rousseau, Dr. Pejman Rasti and Dr. Jean-Michel Lemée at Angevin Laboratory for Research in Systems Engineering (LARIS), University of Angers, France. My doctoral work focused on developing machine learning algorithms for automatic recognition of functional brain networks towards preservation of patients neurological functions in brain tumor resection procedure using resting-state functional magnetic resonance imaging (rs-fMRI). Previously, I have the honor of doing my Master thesis under the supervision of Prof. Mohamed Hamada at University of Aizu, Japan in collaboration with African University of Science and Technology (AUST) Abuja, Nigeria. I completed my B.Sc. in computer science at Nile University of Nigeria, under the supervision of Prof. Steve A. Adeshina where I got the opportunity to embark on early fruitful scientific endeavors like eGovernance and election management system. My professional experience as a software engineer has afforded me the opportunity to confidently interact with cutting-edge technologies for advancing strategic research projects. My university teaching experience has strengthened my understanding of complex theoretical concepts, while providing me the opportunity to support and mentor students.
Research Interests
While my research interest is an intersection of machine learning and medical imaging, I have keen passion for understanding how the brain compensates for functional connectivity loss, towards the design of neuro-rehabilitation protocols tailored to individual patient needs. As well as, understand complex interaction between the brain and the gastrointestinal system to reveal how alterations in gastric rhythm impact various brain regions involved in neurological functions towards improved patient outcomes. My current work involves fMRI data preprocessing using advanced medical imaging tools, and developing computational models to better understand functional connectivity in the brain using anatomical neuroimaging methods. I am passionate about medical-imaging, image reconstruction, graph representation learning, reinforcement learning, predictive modeling and scalable algorithms.
Honors and Awards
  • Datasphere Research Fellowship: Multi-stakeholder research fellowship program to investigate the status medical imagery dataset of African context, for scientific research | Oct 2022-Aug 2023
  • Doctoral Research Scholarship: Petroleum Technology Development Fund (PTDF), Nigeria | Sept 2020-Aug 2023
  • Award of Excellence: Software for Student Representative Counsel (SRC) Election | 2018
  • African Development Bank (ADB) Scholarship: Full Scholarship for Masters program in Computer Science | Aug 2016-Dec 2017
  • 3rd Overall Best Graduating Students in Class-of-2015
  • 2nd Best Graduating Student in Computer Science | 2015
University Teaching Summary
  • Numbers, Set theory, limits and continuity of functions, differentiation of functions, applications of the derivative, integration of functions, double integrals, triple integrals, line integrals and surface integrals etc.
  • Algorithms and data structures, computer networking, computer architecture, numerical analysis, databases, mobile & web application programming.