Ali Ghodsi


Ali Ghodsi
Contact Information:
Ali Ghodsi

Ali Ghodsi's personal website

Research interests

Machine learning, Deep learning, Computational statistics, Dimensionality reduction, Natural language processing, Bioinformatics.

Professor Ghodsi's current research sweeps across a broad swath of AI encompassing machine learning, deep learning, and dimensionality reduction.  He studies theoretical frameworks and develops new machine-learning algorithms for analyzing large-scale data sets, with applications in natural language processing, bioinformatics, and computer vision. Dr. Ghodsi's work has been published extensively in high-quality proceedings and journals. He is the co-author of the "Elements of Dimensionality Reduction and Manifold Learning" (Springer)  and several US patents. His popular lectures on YouTube have more than one million views. View a complete list of his online lectures.

Selected publications

Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices

R Qiao, NH Tran, L Xin, X Chen, M Li, B Shan, A Ghodsi

Nature Machine Intelligence 3 (5), 420-425 (2021)

Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry

NH Tran, R Qiao, L Xin, X Chen, C Liu, X Zhang, B Shan, A Ghodsi, M Li

Nature methods 16 (1), 63-66 (2019)

Sentiment analysis based on improved pre-trained word embeddings

SM Rezaeinia, R Rahmani, A Ghodsi, H Veisi

Expert Systems with Applications 117, 139-147 (2019)

Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds

E Barshan, A Ghodsi, Z Azimifar, MZ Jahromi

Pattern Recognition 44 (7), 1357-1371 (2011)

An efficient greedy method for unsupervised feature selection

AK Farahat, A Ghodsi, MS Kamel

2011 IEEE 11th International Conference on Data Mining, 161-170 (2011)

Nonnegative matrix factorization via rank-one downdate

M Biggs, A Ghodsi, S Vavasis

Proceedings of the 25th international conference on Machine learning, 64-71(2008)