Mahdi Vasighi

Mahdi Vasighi is currently serving as an Assistant Professor at the Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran, a position he has held since February 2012. Prior to this, he was a Post-doc Researcher at the same institution from February 2011 to February 2012. He has also served as a Visiting Researcher at the Milano Chemometrics and QSAR Research Group, University of Milano - Bicocca, Milan, Italy from September to October 2009, and as a Guest Lecturer at the Pasteur Institute, Tehran, Iran since September 2016.

Dr. Vasighi earned his educational qualifications from the Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran, where he completed his Ph.D. in Chemometrics in May 2010 and his M.Sc. in Analytical Chemistry between 2002 and 2005. His undergraduate education was in Pure Chemistry at Imam Khomeini International University, Qazvin, Iran, from 1998 to 2002.

Dr. Vasighi's primary research interests lie at the intersection of bioinformatics, machine learning, and data analysis. His work focuses on structural bioinformatics, particularly on modeling relationships between biological sequences and their corresponding structure or function. He has made significant contributions to the field of self-organizing maps with dynamic structure, developing innovative approaches like the Directed Batch Growing Self-Organizing Map (DBGSOM) that enhance topology preservation and visualization of high-dimensional data. His research spans multiple domains including protein structural classification, cancer diagnostics using fluorescence spectroscopy, and drug discovery for diseases like COVID-19.

Dr. Vasighi's publication record demonstrates a strong trajectory in applying machine learning techniques to solve complex problems in bioinformatics and medical diagnostics. His recent work shows an increasing focus on applying computational approaches to healthcare challenges, including cancer detection, protein analysis, and drug discovery for viral diseases. He has successfully bridged the gap between theoretical machine learning advancements and practical applications in biology and medicine, with a particular emphasis on developing interpretable models that can be used by domain experts.

Dr. Vasighi has actively contributed to the academic community through teaching and conference organization. He has served as Local Chair for the International Conference on Contemporary Issues in Data Science 2019 (CiDaS 19) and as Scientific Committee Member and Organizing Chair for previous CICIS conferences. His teaching portfolio includes graduate courses in Artificial Neural Networks, Computational Data Mining, Bioinformatics, Statistical Pattern Recognition, and Multimedia Systems.

Dr. Vasighi has supervised numerous MSc students, with over twenty graduated students and nine current students listed in his profile. His research has been supported through collaborations with institutions like the Pasteur Institute, where he worked on projects related to nuclear magnetic resonance-based screening of thalassemia and determination of coronary heart disease risk using NMR spectra of plasma lipoproteins.

Through his Directed Batch Growing Self-Organizing Map (DBGSOM) package and other software contributions, Dr. Vasighi has made his research tools accessible to the broader scientific community. His work continues to push the boundaries of how machine learning can be applied to solve challenging problems in bioinformatics and medical diagnostics.