M. Sohel Rahman

2020–2023 Distinguished Speaker
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Dr. M. Sohel Rahman is a Professor of the CSE department of BUET. He had worked as a Visiting Research Fellow of King’s College London, UK during 2008-2011 and again as a Visiting Senior Research Fellow there during 2014-15. He is a Senior Member of both IEEE and ACM; member of American Mathematical Society (AMS) and London Mathematical Society
(LMS). He is also a Peer-review Associate College Member of EPSRC, UK.

Dr. Rahman received different scholarships and fellowships including Commonwealth Scholarship, Commonwealth Fellowship, ACU Titular Fellowship, University College London-Big Data Institute visiting grant, London Mathematical Society Visiting Grant etc. He is also a
recipient of the Bangladesh Academy of Sciences Gold Medal and UGC Award. He has led research and development projects funded by British Council, UGC-World Bank, ICT Division, Government of Bangladesh and BUET. He has so far published 86 peer-reviewed international journal papers. Among his notable results are the work on high dimensional Knapscak problems, sequence alignment problems, data structures and string combinatorics, sufficient conditions for Hamiltoninicity, Machine Learning based predictors in Bioinformatics, and
metaheuristics solutions for hard problems.

He is an Academic Editor of PLOS One, Associate Editor of BMC Research Notes and had edited special issues as guest editors in Theoretical Computer Science, Journal of Graph Algorithms and Applications, Journal of Discrete Algorithms, Fundamenta Informaticae etc. He has also served as Program Committee members in a number of conference series of international repute. Dr. Rahman regularly writes reviews at Mathematical Review and ACM Computing Review. He is currently an ACM Distinguished Speaker (2019-22) and IEEE Computer Society Distinguished Speaker (2020-22). Very recently, he has been elected as a Fellow of Bangladesh Academy of Sciences.

Department of CSE, BUET
ECE Building, West Palasi
Dhaka-1205, Bangladesh
e-mail: msrahman@cse.buet.ac.bd; sohel.kcl@gmail.com
cell: +8801552389480
URL: http://msrahman.buet.ac.bd

DVP term expires December 2023


Prediction based on biological sequences (where Machine Learning meets Life Sciences)

Due to the rapid development of fast sequencing technologies, we now have tremendous amount data on different biological sequences. For example, the number of sequence-known proteins has grown exponentially in recent years. On the contrary, the biochemical experiments to learn the attributes of proteins are expensive and time consuming. A large gap thus exists between the number of sequence-known proteins and that of attribute-known proteins. To catch up, researchers have started to rely on state of the art computational intelligence based methods (e.g., Machine Learning) to predict different attributes of proteins and other biological sequences.

In this lecture, we will discuss Machine Learning based methods for a number of prediction tasks in the domain of life sciences. We will discuss predictors that have been developed based on a machine learning based framework where the features are extracted from the primary sequence only. Overall, our research empirically asserts the natural belief that the functional and structural information of a biological sequence are intrinsically encoded within its primary sequence. This assertion culminates in generalizing a framework for sequence based feature extraction and selection that can be applied to any prediction problem in life sciences.

An agent-based model to examine the impact of Malaria vector control interventions

Malaria is one of the most devastating global health issues. Would not it be wonderful if we could model the Malaria vectors (i.e., different mosquito species) so as to check which intervention technique would be the most appropriate in a particular area? In this talk, we discuss the design and implementation of a spatial agent based model based on the biological attributes of a Malaria vector called Anopheles vagus, which is widely distributed in Asia and a dominant vector in Bandarban, Bangladesh. Real-life field data of Bandarban have been used to generate landscapes to run the simulations. Validation of the model has been done using several standard techniques. Also, verification and validation of the model was done against real-life field data.
Using artificial landscapes, the individual and combined efficacies of existing vector control interventions have been modeled, applied, and examined. Thus this agent based model now can aid us in deciding what sort of interventions would be most appropriate to prevent or contain Malaria. For example, for Anopheles vagus, and based on the real-life data of Bandarban, this research output suggests that combined interventions will have the best effect.
[This is a joint work with Alam, Arifin, Al-Amin and Alam, which was published in BMC Malaria Journal (2017)]

What Metaheuristics can do for you

Often we are provided with multiple options and we want to pick the best one. This is true for our daily life, but also for many scenarios in different branches of science and engineering (e.g., industry, management, planning, design, medical services, logistics etc.). Unfortunately- or perhaps fortunately for us, i.e., Metaheuristics community, most of the problems that deal with such situation are “hard” to solve. Metaheuristics are an approach to solve such hard problems. In this talk, after briefly introducing metaheuristics techniques and what sort of problems it can solve, we will take a journey to different domains to explore how different metaheuristics techniques are solving diverse range of problems. In the sequel, I hope, we will answer the question asked in the title enthusiastically, as metahueristics indeed can do many things for you irrespective of what domain you are working in.


Prediction based on biological sequences (where Machine Learning meets Life Sciences)
An agent-based model to examine the impact of Malaria vector control interventions
What Metaheuristics can do for you

Read the abstracts for each of these presentations