Md Fahim Sikder is a Ph.D. student who works on Explainable Synthetic Time-Series Data Generation with Privacy and Fairness under the supervision of Professor Fredrik Heintz at Reasoning and Learning (ReaL) Lab, IDA, Linköping University, Sweden. Fahim was a Lecturer in the Computer Science and Engineering department at the Institute of Science, Trade, and Technology (ISTT). In addition, he was the Coordinator of the HEAP Programming Club, ISTT, and Coach of the ACM ICPC team in ISTT.
His research interests include Deep Learning, Generative Models, Time-Series Generation, Data Privacy.
Md Fahim Sikder conducted several workshops and seminars, including a Workshop on Latex and a Week-long training course on Python (Beginning to Advance including Machine Learning). He participated in several National and International Contests. He achieved many titles, including “Champion at International Contest on Programming and System Development (ICPSD), 2014”, and “Champion at NASA SPACE APPS CHALLENGE 2016” in Rajshahi Region, Bangladesh.
Ph.D. in Computer Science, Ongoing
Linköping University, Sweden
MSc in Computer Science, 2018
Jahangirnagar University, Bangladesh
BSc (Engineering) in Computer Science & Engineering, 2016
Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Bangladesh
Higher Secondary Certificate, 2012
Khilgaon Government High School, Bangladesh
Secondary School Certificate, 2010
Khilgaon Government High School, Bangladesh
Department of Computer and Information Science (IDA)
Responsibilities include:
Department of Computer Science & Engineering (CSE)
Responsibilities include:
Handwritten digit or numeral recognition is one of the classical issues in the area of pattern recognition and has seen tremendous advancement because of the recent wide availability of computing resources. Plentiful works have already done on English, Arabic, Chinese, Japanese handwritten script. Some work on Bangla also have been done but there is space for development. From that angle, in this paper, an architecture has been implemented which achieved the validation accuracy of 99.44% on BHAND dataset and outperforms Alexnet and Inception V3 architecture. Beside digit recognition, digit generation is another field which has recently caught the attention of the researchers though not many works have been done in this field especially on Bangla. In this paper, a Semi-Supervised Generative Adversarial Network or SGAN has been applied to generate Bangla handwritten numerals and it successfully generated Bangla digits.