Resume

• 2015

  Doctor of Philosophy (Ph.D.)

  Computer Systems Networking and Telecommunications

  University at Buffalo

  Large-Scale Distributed Systems

  Analysis of Algorithms

  Data Intensive Computing

  Modern Networking Concepts

  Seminar course on BigData

  Parallel and Distributed File Systems

  Machine Learning

  Fundamentals of Programming Languages

  Parallel and Distributed Processing

  Microsoft Certified Professional

  Microsoft

  Cisco

  Cisco Certified Network Associate

  Red Hat

  Red Hat Certified Engineer

• 2012

  Master’s Degree

  Brain Computer Interfacing and machine learning

  North South University

• 2005

  Bachelor’s Degree

  Computer Science and Engineering

  Military institute of science and Technology

• 2003

  High School

  Science

  Notre Dame College

• Verbal Reasoning - 155/170\nQuantitative Reasoning - 161/170\n

  Hive

  Data mining

  HTML

  C#

  Apache Pig

  C++

  Bash scripting

  R

  Python

  OpenMP

  High Performance Computing

  Spark

  Java

  Supercomputing

  C

  Hadoop

  Perl

  SQL

  Matlab

  Machine Learning

  Fuzzy logic based dynamic load balancing in virtualized data centers

  Rashedur M Rahman

  Saad Abdullah

  Md Abul Kalam Azad

  Cloud Computing helps to provide quality of service to the end users within required time frame. Serving user requests using distributed network of virtualized data centers is a challenging task as response time increases significantly without a proper load balancing strategy. There are many algorithms proposed in the literature to support load balancing in cloud environment. All of them have their merits and demerits. The inherent structure of load balancing is rather imprecise. In this research

  we model the imprecise requirements of memory

  bandwidth and disk space through the use of fuzzy logic. Then we design and evaluate an efficient dynamic fuzzy load balancing algorithm which could efficiently predict the virtual machine where the next job will be scheduled. We implement a cloud model in simulation environment and compare the result of our novel approach with existing techniques. Simulation results demonstrate that our fuzzy algorithm outperforms other scheduling algorithms with respect to response time

  data center processing time

  etc.

  Fuzzy logic based dynamic load balancing in virtualized data centers

  Mahedi H. Hoque

  M. A. K. Khan

  M. Ameer Ali

  Nikhil C. Shil2

  Vendor selection using fuzzy integration

  Mohammad Alaul Haque Monil

  Saad Abdullah

  Md Abul Kalam Azad

  Grid computing is the most popular infrastructure in many emerging field of science and engineering where extensive data driven experiments are conducted by thousands of scientists all over the world. Efficient transfer and replication of these peta-byte scale data sets are the fundamental challenges in Scientific Grid. Data grid technology is developed to permit data sharing across many organizations in geographically disperse locations. Replication of data helps thousands of researchers all over the world to access those data sets more efficiently. Data replication is essential to ensure data reliability and availability across the grid. Replication ensures above mentioned criteria by creating more copies of same data sets across the grid. In this paper

  we proposed a data mining based replication to accelerate the data access time. Our proposed algorithm mines the hidden rules of association among different files for replica optimization which proves highly efficient for different access patterns. The algorithm is simulated using data grid simulator

  OptorSim

  developed by European Data Grid project. Then our algorithm is compared with the existing approaches where it outperforms others.

  Application of association rule mining for replication in scientific data grid

  Rashedur M. Rahman

  Saad Abdullah

  Abul Kalam Azad

  Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider

  M. Ameer Ali

  Mahedi Hasanul Hoque

  Md. A. K. Khan

  Vendor selection using fuzzy C means algorithm and analytic hierarchy process

  Cognitive Task Classificaiton from Wireless EEG

  M. Ashraful Amin

  Mahady Hasan

  Shuvo Kumar Paul

  Human brain uses a complex electro-chemical signaling pattern that creates our imagination

  memory and self-consciousness. It is said that Electroencephalography better known as EEG contains signatures of various tasks that we perform. In this paper we study the possibility of categorizing tasks conducted by humans from EEG recordings. The novelty of this study mainly lies in the use of very cost effective consumer grade wireless EEG devices. Three cognitive tasks were considered: text reading and writing

  Math problem solving and watching videos. Twelve subjects were used in this experiment. Initial features were calculated from Discrete Wavelet Transform (DWT) of raw EEG signals. After application of appropriate dimensionality reduction

  Support Vector Machine (SVM) was used for classification of tasks. DWT + Kernel PCA with SVM based classifier showed 86.09 % accuracy.

  Cognitive Task Classificaiton from Wireless EEG

  Road traffic accident is one of the major causes of death in Bangladesh. In this article

  we propose a method that uses road side video data to learn the traffic pattern and uses vision based techniques to track and determine various kinetic features of vehicles. Finally

  the proposed method detects anomaly (possibility of accident) and accidents on the road. The proposed method shows approximately 85% accuracy level in detecting special situations.

  Computer vision based road traffic accident and anomaly detection in the context of Bangladesh

  M. Ashraful Amin

  Bruce Poon

  Mridul Khan

  In this paper

  we propose a novel algorithm that can classify the electroencephalogram (EEG) signals extracted by a single sensor EEG device. The data are captured when subjects were doing three different tasks like – reading/writing texts

  solving math problems and watching videos. Our proposed algorithm is capable of extracting the useful information as feature from an EEG signal. The Discrete Wavelet Transform (DWT) is performed to extract frequency band information. To reduce the dimensionality of the higher dimensional feature

  we have used Principle Component Analysis (PCA). The K-nearest neighbor classifier is applied finally for EEG signal classification. Our novel approach is capable to operate with 73.12% accuracy.

  Human Computer Interaction Through Wireless Brain Computer Interfacing Device

  Dynamic Load Sharing to Maximize Resource Utilization Within Cloud Federation

  Nova Ahmed

  Saad Abdullah

  Md. Abul Kalam Azad

  It is evident in recent years that cloud has resource constraints. Client requests are at the highest priority of cloud services. Denial of client services will not only hamper profits but also tarnish reputation of the provider. In these cases an effort can be made to provision resources from the other cloud providers so that they can serve the request using their unused resources. In this way the idea of cloud federation has emerged. The idea is

  if a cloud saturates its computational and storage resources

  or it is requested to use resources in a geography where it has no footprint

  it would still be able to satisfy such requests for service allocations sent from its clients. Our work contributes by offering a model for enacting the cloud federation. More precisely

  we have introduced a cloud broker which decides whether a job sent to a particular cloud provider should be served there or routed to another provider. Our proposed model selects the best option to outsource the request without violating Service Layer Agreement (SLA).

  Dynamic Load Sharing to Maximize Resource Utilization Within Cloud Federation

  M Ashraful Amin

  B Poon

  H Monil

  M Alaul

  Optical character recognition (OCR) is a widely used technology to convert text images to editable text. Researchers already proposed many machine learning algorithms to address this problem. However

  Bangla text recognition is highly challenging job for its complicated writing style

  compound characters and highly diversified fonts. To address the segmentation problem we have proposed an algorithm namely Blob-Labeled character Segmentation (BLCS) that initiates with an extensive preprocessing to extract the characters from text. Our novel character segmentation procedure extracts characters maintaining 97.5% accuracy. Unsupervised feature learning becomes a powerful tool in machine learning nowadays. To increase the recognition rate of the characters

  we have introduced a fuzzy unsupervised feature learning algorithm to learn features of individual characters. We then use Artificial Neural Network (ANN) and Support Vector Machine (SVM) to classify the characters. The SVM provides 99.4% accuracy which outperforms all other approaches.

  Bangla text processing and recognition based on Fuzzy unsupervised Feature Extraction and SVM

  et. al.

  kemal guner

  Big data transfer optimization based on offline knowledge discovery and adaptive sampling

  Kemal Guner

  The achievable throughput for a data transfer can be determined by a variety of factors such as network bandwidth

  round trip time

  background traffic

  dataset size

  and end-system configuration. For the best-effort optimization of the transfer throughput

  three application-layer transfer parameters -- pipelining

  parallelism and concurrency -- have been actively used in the literature. However

  it is highly challenging to identify the best combination of these parameter settings for a specific data transfer request. In this paper

  we analyze historical data consisting of 70 Million file transfers; apply data mining techniques to extract the hidden relations among the parameters and the optimal throughput; and propose a novel approach based on hysteresis to predict the optimal parameter settings.

  Hysteresis-based optimization of data transfer throughput

  MD S Q Zulkar

  Nine

  IBM

  University at Buffalo

  North South University

  Buffalo/Niagara

  New York Area

  Teaching a full course CSE 250 - Data Structures with 48 students.

  Instructor

  University at Buffalo

  Buffalo/Niagara

  New York Area

  Graduate Research Assistant

  University at Buffalo

  Dhaka

  Bangladesh

  Courses: \n(1) Programming in C\n(2) Object Oriented Programming (Java)\n\nResponsibilities:\nTeach students programming languages in lab. Preparing and grading homework. Maintain office hour to help students who have difficulty understanding the courses.

  Laboratory Instructor

  North South University

  Dhaka

  Bangladesh

  Courses: \n(1) Advanced Neural Network\n(2) Machine Learning\n(3) Computer Vision\n\nResponsibilities: \nTeaching Matlab

  Python. Grading homework

  and Maintain office hour to help students who have difficulty understanding the courses.

  Teaching Assistant

  North South University

  Buffalo

  NY

  Courses: \n(1) Introduction to Algorithm

  Fall

  2014\n(2) Engineering Computation

  Spring

  2015\n(3) Introduction to Algorithm

  Fall

  2015\n(4) Discrete Structures

  Spring

  2016\n(5) Operating Systems

  Fall

  2016\n(6) Distributed Systems

  Spring

  2017\n\nResponsibilities:\n\nTeaching Undergrad students algorithms

  and programming mostly C

  C++

  Java

  Matlab

  Python

  R etc. Also take recitations. Preparing and grading homeworks. Maintain office hour to help students who have difficulty understanding the courses.

  Teaching Assistant

  University at Buffalo

  Yorktown Heights

  NY

  Research Intern

  IBM

  Student Member

  IEEE

  English

  Bangla

  Summa Cum Laude

  North South University

  Full Tuition Scholarship

  Awarded full tuition scholarship for \nacademic year 2014 - 2015\nacademic year 2015 - 2016\nacademic year 2016 - 2017

  University at Buffalo

  Tuition waiver on merit

  North South University