Emad's COMBINE lab

About the PI

Dr. Emad is an Assistant Professor at the Department of Electrical and Computer Engineering at McGill University. He is also affiliated with Mila (Quebec AI Institute), the Rosalind and Morris Goodman Cancer Institute (GCI), the Quantitative Life Sciences (QLS) program, the McGill initiative in Computational Medicine (MiCM), and Meakins-Christie Laboratories. Before joining McGill, he was a Postdoctoral Research Associate at the NIH KnowEnG Center of Excellence in Big Data Computing associated with the Department of Computer Science and the Institute for Genomic Biology (IGB) at UIUC. He received his PhD from UIUC in 2015.

Current Research Interests

Open Positions

▶ Postdoctoral positions

Currently, there is opening for 1-2 postdoctoral researchers in my lab. The ideal candidate has the following qualifications:

1) A PhD degree in a related field (genetics, computational biology, computer science, machine learning, engineering, etc.)
2) Strong background in deep learning (application in molecular computational biology is a plus)
3) Strong programming skills and experience with implementing deep learning methods
4) Published first-author work in peer-reviewed journals or top-tier conferences in related fields (deep learning or computational biology)
5) Familiarity with omics data (particularly single-cell and spatially-resolved) is a plus
6) Good spoken and written communication skills in English

Application Procedure:

Interested applicants should submit 1) CV, 2) a letter of interest, 3) a research proposal and 4) a one-page summary of their most relevant publication, and 5) contact information for three references to Amin Emad (amin.emad@mcgill.ca). In the letter of interest, make sure to discuss how your background fits with the requirements of the position.

▶ PhD/MSc positions

Every semester, there are 1-2 openings for motivated graduate students (PhD and MSc) in Emad's COMBINE lab. If you are interested and have a strong background in deep learning and/or bioinformatics, send me an email and include your CV and transcripts. Additionally, you must submit a formal application to McGill. Note that I recruit students both through the Electrical and Computer Engineering (ECE) department and the Quantitative Life Sciences (QLS) program. Each of these programs have their own deadlines for official application, which you must follow. Make sure you mention my name in your application (and select Intelligent Systems if you apply to ECE.)

Women, Aboriginal persons, persons with disabilities, neurodivergent individuals, ethnic minorities, persons of minority sexual orientation or gender identity, and visible minorities are encouraged to apply.

Lab Members

Current Students

▶ Joseph Szymborski (PhD Student, ECE): Joseph has spent several years studying cancer biology from both computational and experimental perspectives. These studies were conducted as part of his Master's Degree in Experimental Medicine and Bachelor's Degree in Biochemistry, both granted by McGill University. For the period between those degrees, Joseph was employed as a Machine Learning Developer at Coveo Solutions. He is the recipient of the prestigious Les Vadasz Doctoral Fellowships in Engineering. His research interests include prediction of protein-protein interactions (PPIs) and protein large language models (pLLMs).

▶ Ali Saberi (PhD Student, ECE): Ali received his Master's degree from Sharif University of Technology (Iran) in Computer Engineering and Artificial Intelligence. He has received the McGill's International MEDA award and the doctoral FRQ-NT scholarship. Ali, who is jointly supervised by Prof. H. Najafabadi, is interested in understanding the mechanisms of RNA splicing using RNA foundation models.

▶ Chen Su (PhD Student, ECE): Chen received her Bachelor’s degree from Dalhousie University in Computer Engineering and her MSc from McGill University. She has received McGill's International MEDA award and the doctoral FRQ-NT scholarship. Her research interests include gene regulatory networks, causal AI and generative deep learning with applications in single cell omics data.

▶ Orsolya Lapohos (PhD Student, QLS): Orsolya is a PhD student in Quantitative Life Sciences at McGill University, jointly supervised by Prof. G. Fonseca. She completed her BSc and MSc in Microbiology & Immunology at McGill University. Her research interests include cis-regulatory code, gene regulatory networks, single-cell genomics, and deep learning.

▶ William Ma (PhD Student, ECE): William received his Bachelor’s degree from Carleton University in Biomedical and Electrical Engineering and his MSc from McGill University. He has received McGill's Vadasz Engineering MEDA Fellowship, the doctoral FRQ-NT scholarship, and the NSERC CGS-D scholarship. His research interests include domain adaptation for multi-omics integration and generative models for in silico simulation of -omics data.

▶ Shayan Hajhashemi (PhD Student, QLS): Shayan is a PhD student in Quantitative Life Sciences at McGill University, awarded the NSERC CGS-D scholarship. He holds a B.Sc. in Interdepartmental Immunology from McGill, where he explored theoretical questions in pattern formation using reaction-diffusion modeling. His current research, jointly supervised by Prof. M. Craig at Université de Montréal, focuses on applying mechanistic modeling and differentiable programming to optimal control in systems pharmacology.

▶ Kiri Stern (PhD Student, QLS): Kiri completed her Bachelor’s at Concordia University with a major in Biology and a minor in Sustainability Studies before completing her MSc at the University of Montreal in Quantitative and Computational Biology. Kiri, who is jointly supervised by Prof. S. Rousseau, is interested in leveraging probabilistic deep learning and generative models to infer the causal biomarkers in diseases using high-dimensional, multi-modal biological data.

▶ Cedrique Shum-Tim (PhD Student, BBME): Cedrique received his Bachelor’s degree from the University of Waterloo in Computer Engineering and his MSc in Computer Science from the Georgia Institute of Technology. Cedrique, who is jointly supervised by Prof. S. Prakash, is interested in the human microbiome and its role in disease development, graph representation learning, time series representation learning, dynamical systems, and SciML.

▶ Kinaan Aamir Khan (PhD Student, ECE): Kinaan received his BSc from National University of Computer and Emerging Sciences, Islamabad Pakistan. He received his MSc from University of Saarland, Germany. He has received the McGill's International MEITA award. He is interested in precision cancer medicine based on single cell molecular omics datasets and protein-protein interactions.

▶ Yazdan Zinati (MSc Student, ECE): Yazdan graduated with a Bachelor’s Degree in Honours Electrical Engineering and a minor in Software Engineering from McGill University. He is the recipient of the Master’s FRQ-NT scholarship and the Faculty of Engineering’s MEUSMA award. His research interests include causal AI and generative modelling, regulatory genomics, and network biology. Currently, his research focuses on developing causal generative tools for single-cell omics data.

Alumni

▶ Postdoctoral Fellows: Antoine Soulé (2024)

▶ Doctorate: David Earl Hostallero (PhD, 2024)

▶ Master's: Safyan Memon (MSc, 2024); William Ma (MSc, 2023); Sin Young Kwon (MSc, 2022); Jessica (Yihui) Li (MSc, 2022); Mohamed Reda El Khili (MSc, 2022); Abdulrahman Takiddeen (MSc, 2022); Chen Su (MSc, 2021); Lulan Shen (MSc, 2020); Ameya Bhope (MEng, 2020)

Software

Check out our GitHub page: Emad-COMBINE-lab

Publications

For the full and updated list of publications see my Google Scholar profile.

Sample Publications

▶ J. Szymborski and A. Emad, “INTREPPPID—an orthologue-informed quintuplet network for cross-species prediction of protein–protein interaction,” Briefings in Bioinformatics, 25 (5), bbae405, 2024.

▶ Y. Zinati, A. Takiddeen, A. Emad, “GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks,” Nature Communications, 15(1), 4045, 2024.

▶ D.E. Hostallero, L. Wei, L. Wang, J. Cairns, and A. Emad, “Preclinical-to-clinical anti-cancer drug response prediction and biomarker identification using TINDL,” Genomics, Proteomics & Bioinformatics, 21 (3), pp. 535-550, 2023.

▶ M.R. El Khili, S.A. Memon, and A. Emad, “MARSY: A multitask deep learning framework for prediction of drug combination synergy scores,” Bioinformatics, 39 (4), btad177, 2023.

▶ D.E. Hostallero, Y Li, and A. Emad, “Looking at the BiG picture: Incorporating bipartite graphs in drug response prediction,” Bioinformatics, 38 (14), 3609-3620, 2022.

▶ A. Emad, and S. Sinha, “Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study,” NPJ Systems Biology and Applications, 7(9), 2021.

▶ C. A. Blatti*, A. Emad*, et al., “Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform,” PLoS Biology, 18 (1), e3000583, 2020.

▶ E. W. Huang, A. Bhope, J. Lim, S. Sinha, and A. Emad, “Tissue-guided LASSO for prediction of clinical drug response using preclinical samples,” PLoS Computational Biology, 16(1): e1007607, 2020.

▶ A. Emad, J. Cairns, K. R. Kalari, L. Wang, and S. Sinha, “Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance,” Genome Biology, 18(1), 153, 2017.

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