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 the McGill initiative in Computational Medicine (MiCM), the Quantitative Life Sciences (QLS) program and Meakins-Christie Laboratories at McGill University as well as with the National Center for Supercomputing Applications (NCSA) at the University of Illinois (UIUC). In addition, he is an associate member of the Quebec AI Institute, MILA. 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, his MSc from the University of Alberta (UofA) in 2009 and his BSc from Sharif University of Technology (SUT) in 2007, all in Electrical and Computer Engineering.

Dr. Emad is the recipient of various awards including the NSERC Postdoctoral fellowship (2015), the UIUC CompGen Graduate Fellowship (2014), the UIUC Sundaram Seshu Summer Fellowship (2012), the NSERC Alexander Graham Bell Canada Graduate Scholarship (2010), the NSERC M.Sc. Postgraduate Scholarship (2008), the iCORE Graduate Student Scholarship in ICT (2008), the UofA Graduate Student Scholarship (2008), the UofA Walter H. Johns Graduate Fellowship (2008), the SUT Exceptional Qualification Award (2003) and the gold medal in Iran’s national Physics Olympiad (2003).

Current Research Interests

Machine Learning, Bioinformatics, Network Analysis, Cancer Genomics

Research in the COMBINE lab is focused on developing novel computational methods based on machine learning, network representation learning, and statistical methods to address various biomedical problems:

Pharmacogenomics:
We are interested in developing computational models to predict the clinical drug response of patients based on pre-clinical samples such as cell lines. In addition, we would like to identify biomarkers of drug sensitivity and novel therapeutic targets in diseases such as cancers, cystic fibrosis, COPD, etc.

▶ Key Collaborators:
Dr. J. Cairns (Center for Individualized Medicine, Mayo Clinic)
Dr. A. Cybulsky (Department of Medicine, McGill University)
Dr. M. Park (Rosalind and Morris Goodman Cancer Research Centre, McGill University)
Dr. S. Rousseau (Department of Medicine, McGill University)
Dr. N. Sidiropoulos (Department of Electrical and Computer Engineering, University of Virginia)
Dr. L. Wang (Center for Individualized Medicine, Mayo Clinic)

▶ Key Personnel:
Antoine Soulé (Postdoctoral Researcher)
David E. Hostallero (PhD Student)
Mohamed Reda El Khili (Master's Student)
Jessica Li (Master's Student)

Regulatory Systems Genomics:
We are interested in deciphering regulatory mechanisms involved in different biological processes such as human embryogenesis and diseases such as cancer and COVID-19 using multi-omics analysis.

▶ Key Collaborators:
Dr. L. McCaffrey (Department of Oncology, McGill University)
Dr. W. Pastor (Department of Biochemistry, McGill University)
Dr. S. Rousseau (Department of Medicine, McGill University)
Dr. G. Fonseca (Department of Medicine, McGill University)
Dr. J. Ding (Department of Medicine, McGill University)

▶ Key Personnel:
Joseph Szymborski (PhD Student)
Ali Saberi (PhD student)
Chen Su (Master's student)
Abdulrahman Takiddeen (Master's student)

Open Positions

Postdoctoral positions

There are two openings for postdoctoral fellows (junior or senior) in the area of Computational Biology and Machine Learning. The projects are related to precision medicine and regulatory genomics in cancer and COVID-19, and involves collaborations with McGill Goodman Cancer Center, Mayo Clinic, Quebec COVID-19 biobank and Mila (Quebec AI Institute).

Position 1 (Regulatory Systems Genomics):

The ideal candidate for this position have the following qualifications:
1) A PhD degree in a related field (genetics, computational biology, computer science, machine learning, engineering, etc.)
2) Strong proven background (published work in peer-reviewed journals) in regulatory genomics
3) Proficiency in analyzing multi-omics data including (bulk and single cell) RNA-seq, ATAC-seq, ChIP-seq, DNA-seq, etc.
4) Proficiency in probabilistic graphical models (Bayesian networks, Markov random fields, variational inference, MCMC, etc.)
5) Strong programming skills (particularly in python)
6) At least two first-authored English papers (or three if co-first authors) with submitted, accepted or published status in peer-reviewed journals
7) Good spoken and written communication skills in English

Position 2 (General Computational Biology / AI):

The ideal candidate for this position have the following qualifications:
1) A PhD degree in a related field (genetics, computational biology, computer science, machine learning, engineering, etc.)
2) Strong proven background (published work in peer-reviewed journals) in computational biology, machine learning, and omics data analysis
3) Proficiency in different areas of machine learning including deep learning and/or probabilistic graphical models
5) Strong programming skills (particularly in python)
6) At least two first-authored English papers (or three if co-first authors) with submitted, accepted or published status in peer-reviewed journals
7) 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). If application is for position 1, the subject line of the email should read ''Postdoctoral Application: Regulatory Systems Genomics'', and if it is for position 2 it should read ''Postdoctoral Application: General Computational Biology / AI". In the letter of interest, make sure to discuss how your background fits with the requirements of the position. The start date can be as early as Oct. 2020 and positions will remain open until filled.

PhD/Master's positions

Every semester, there are 1-2 openings for motivated graduate students (PhD and Master's) in the COMBINE. If you are interested in the research in COMBINE lab and have a strong programming background and knowledge in machine learning and/or bioinformatics, send me an email. Please keep in mind that the departmental deadline for the Winter semester (January) is August 1st for international students and October 15 for domestic students. Also, for the Fall semester (September), the departmental deadline for all students is December 15th.


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

Antoine Soulé (Postdoctoral Researcher): Antoine received his PhD from McGill University in Computational Biology. His current research interests are focused on developing deep learning algorithms for prediction of response of cancer patients to immunotherapies and for prediction of response to drug combinations in cancer.

David E. Hostallero (PhD Student): David earned his Master's degree from Korea Advanced Institute of Science and Technology (KAIST) in Electrical Engineering and his Bachelor's degree from the University of the Philippines - Diliman in Computer Science. He is the recipient of the prestigious McGill MEDA award. His research interests include machine learning and graph mining in pharmacogenomics. Currently, he is developing novel computational models to predict the drug response and identify therapeutic targets in cancers and respiratory diseases such as cystic fibrosis.

Joseph Szymborski (PhD Student): Joseph has spent the last 7 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 creating computational tools for the analysis of single-cell omics data, particularly as applied to the context of cancers.

Ali Saberi (PhD Student, co-supervised by Prof. Hamed Najafabadi): Ali received his Master's degree from Sharif University of Technology (Iran) in Computer Engineering and Artificial Intelligence. He has received the prestigious MEDA award at McGill. His research is focused on understanding the mechanisms of RNA splicing using deep learning models.

Yitian Zhang (PhD Student, jointly supervised by Prof. Mark Coates)

Chen Su (Master's Student): Chen received her Bachelor’s degree from Dalhousie University (Canada) in Computer Engineering. Her research interests include computational regulatory genomics and machine learning. Currently, she is developing novel machine learning tools to reconstruct lineage-relevant transcriptional regulatory networks in human embryogenesis and COVID19-relevant regulatory networks.

Abdulrahman Takiddeen (Master's Student): Abdulrahman earned his Bachelor’s Degree in Electrical Engineering from the American University of Sharjah (United Arab Emirates). Before joining McGill, Abdulrahman worked as a graduate research and teaching assistant at Khalifa University (United Arab Emirates). Abdulrahman is the recipient of the prestigious NSERC CGS-M scholarship and McGill's Graduate Excellence Fellowship award. His research interests include biomedical applications of machine learning. Currently, his research focuses on the inference of causal transcriptional regulatory networks.

Mohamed Reda El Khili (Master's Student): Reda graduated with a Bachelor’s Degree in Electrical Engineering from McGill University (Canada). His research interests include several applications of Machine Learning in real world systems and more specifically in the biological and medical fields. Currently his research focuses on the implication of differentially expressed biological molecules in different diseases.

Jessica (Yihui) Li (Master's Student): Jessica received her BEng from McGill University in Electrical Engineering in 2020. Her research interests are machine learning applications in bioinformatics and pharmacogenomics. She is the recipient of Faculty of Engineering’s MEUSMA award.

Alumni

Ameya Bhope

Lulan Shen

Sima Jeddi Khajeh

Software

KowEnG

KnowEnG (Knowledge Engine for Genomics) is a cloud-based platform for genomic analysis.

KnowEnG Website

InPheRNo

Reconstruction of phenotype-relevant transcriptional regulatory networks.

GitHub

TG-LASSO

Tissue-guided LASSO to predict the drug response of cancer patients using cell line training samples.

GitHub

foRWaRD

Prioritization of gene sets on heterogenous networks using random walk with restarts.

GitHub

ProGENI

Gene prioritization by combining transcriptomic data with prior network information.

GitHub

C3

A method based on community detection to identify cancer driver mutation modules.

GitHub

CaSPIAN

Gene regulatory network reconstruction using times-series transcriptomic data.

Code

Publications

Patent

▶ A. Emad, C. J. Nuzman, and E. Soljanin, “Methods and systems for determining crosstalk for a line in a vectored system,” United States patent granted, US9948769B2, 2018.

Book Chapter

▶ D. M. Malioutov, K. R. Varshney, A. Emad, and S. Dash, “Learning Interpretable Classification Rules with Boolean Compressed Sensing,” in Transparent Data Mining for Big and Small Data, Studies in Big Data, 11, Springer, 2017, pp. 95-121.

Preprints

▶ C. Su, S. Rousseau, and A. Emad “Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets,” Submitted, preprint: bioRxiv 2020.12.23.424177.

▶ J. Ding, D. E. Hostallero, et al., “A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil Extracellular Traps as mediators of thrombosis in COVID-19,” Submitted, preprint: medRxiv 2020.07.01.20144121.

Peer-reviewed journal papers

▶ A. Emad, and S. Sinha, “Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study,” In Press, NPJ Systems Biology and Applications, 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. *Contributed equally

▶ 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, T. Ray, et al., “Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature,” Breast Cancer Research, 22(1), pp. 1-13, 2020.

▶ C. Qian, A. Emad, and N. D. Sidiropoulos, “A recursive framework for predicting the time-course of drug sensitivity,” Scientific Reports, 10 (1), 1-12, 2020.

▶ K. O’Dowd, M. Emam, et al., “Distinct miRNA Profile of Cellular and Extracellular Vesicles Released from Chicken Tracheal Cells Following Avian Influenza Virus Infection,” Vaccines 8 (3), 438, 2020.

▶ S. Tabe Bordbar*, A. Emad*, S. D. Zhao, S. Sinha, “A closer look at cross-validation approaches for assessing the accuracy of gene regulatory networks and models,” Nature Scientific Reports, 8, 2018. *Contributed equally

▶ 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.

▶ J. P. Hou*, A. Emad*, G. J. Puleo, J. Ma, and O. Milenkovic, “A new correlation clustering method for cancer mutation analysis,” Bioinformatics, 32 (24), pp. 3717-3728, 2016. *Contributed equally

▶ A. Emad and O. Milenkovic, “Code Construction and Decoding Algorithms for Semi-Quantitative Group Testing with Nonuniform Thresholds,” IEEE Trans. Inf. Theory, vol. 62, pp. 1674-1687, 2016.

▶ A. Emad and O. Milenkovic, “Poisson Group Testing: A Probabilistic Model for Boolean Compressed Sensing,” IEEE Trans. Signal Proc., vol. 63, pp. 4396-4410, 2015.

▶ A. Emad and O. Milenkovic, “Semiquantitative Group Testing,” IEEE Trans. Inf. Theory, vol. 60, pp. 4614-4636, 2014.

▶ A. Emad and O. Milenkovic, “CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks,” PLOS ONE, vol. 9, no. 3, e90781, March 2014. doi:10.1371/journal.pone.0090781.

▶ A. Emad and N. C. Beaulieu, “On the Performance of an Automatic Frequency Control Loop in Dissimilar Fading Channels in the Presence of Interference,” IEEE Trans. Commun., vol. 59, pp. 3234-3239, Dec. 2011.

▶ A. Emad and N. C. Beaulieu, “Lower Bounds to the Performance of Bit Synchronization for Bandwidth Efficient Pulse-Shaping,” IEEE Trans. Commun., vol. 58, pp. 2789-2794, Oct. 2010.

▶ A. Emad and N. C. Beaulieu, “Performance of an AFC Loop in the Presence of a Single Interferer in a Fading Channel,” IEEE Trans. Commun., vol. 58, pp. 3386-3391, Dec. 2010.

Peer-reviewed conference papers

▶ C. Qian, N. D. Sidiropoulos, M. Amiridi, and A. Emad, “From Gene Expression to Drug Response: A Collaborative Filtering Approach,” Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP’19), 2019, pp. 7465-7469.

▶ A. Emad, K. R. Varshney, and D. Malioutov, “Learning Interpretable Clinical Prediction Rules using Threshold Group Testing,” NIPS Workshop on Machine Learning in Healthcare, 2015.

▶ A. Emad, K. R. Varshney, and D. Malioutov, “A Semiquantitative Group Testing Approach for Learning Interpretable Clinical Prediction Rules,” Signal Processing with Adaptive Sparse Structured Representations (SPARS’15), 2015.

▶ A. Emad and O. Milenkovic, “Group Testing for Non-Uniformly Quantized Adder Channels,” Proc. IEEE Int. Symp. Inf. Theory (ISIT’14), July 2014, pp. 2351-2355.

▶ A. Emad and O. Milenkovic, “Poisson Group Testing: A Probabilistic Model for Nonadaptive Streaming Boolean Compressed Sensing,” Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP’14), 2014, pp. 3335-3339.

▶ M. Kim, J. G. Ligo, A. Emad, F. Farnoud (Hassanzadeh), O. Milenkovic, V. V. Veeravalli, “MetaPar: Metagenomic Sequence Assembly via Iterative Reclassification,” Proc. IEEE GlobalSIP, 2013, pp. 43-46.

▶ J. G. Ligo, M. Kim, A. Emad, O. Milenkovic, V. V. Veeravalli, “MCUIUC – A New Framework for Metagenomic Read Compression,” Proc. IEEE Inf. Theory Workshop (ITW’13), 2013.

▶ A. Emad and O. Milenkovic, “Compression of Noisy Signals with Information Bottlenecks,” Proc. IEEE Inf. Theory Workshop (ITW’13), 2013.

▶ P. Johnstone, A. Emad, O. Milenkovic, and P. Moulin, “RFIT: A New Algorithm for Matrix Rank Minimization,” Signal Processing with Adaptive Sparse Structured Representations (SPARS’13), 2013.

▶ M. Deng, A. Emad and O. Milenkovic, “Causal Compressive Sensing for Gene Network Inference,” Proc. IEEE Statistical Signal Processing Workshop (SSP’12), Aug. 2012, pp. 696 – 699.

▶ A. Emad and O. Milenkovic, “Semi-quantitative Group Testing,” Proc. IEEE Int. Symp. Inf. Theory (ISIT’12), July 2012, pp. 1847 – 1851.

▶ A. Emad and O. Milenkovic, “Information Theoretic Bounds for Tensor Rank Minimization over Finite Fields,” Proc. IEEE Global Commun. Conf. (Globecom’11), Dec. 2011, pp. 1-5.

▶ A. Emad and O. Milenkovic, “Symmetric Group Testing and Superimposed Codes,” Proc. IEEE Inf. Theory Workshop (ITW’11), Oct. 2011, pp. 20 – 24, invited.

▶ A. Emad, W. Dai, and O. Milenkovic, “Protein-Protein Interaction Prediction using Non-Linear Matrix Completion Methods,” 15th Int. Conf. Research Computational Molecular Biology (RECOMB’11), March 2011.

▶ A. Emad and N. C. Beaulieu, “On the Performance of Bit-Synchronizers in an ISI Channel and a Related Lower Bound,” Proc. IEEE Global Commun. Conf. (Globecom’09), Dec. 2009, pp. 1-6.

▶ A. Emad and N. C. Beaulieu, “Mean Time to Loss of Lock and Average Switching Rate of an Automatic Frequency Control Loop with an Interferer and Noise in a Fading Channel,” Proc. IEEE Int. Conf. Commun. (ICC’09), June 2009, pp. 1-6.

▶ A. Emad and N. C. Beaulieu, “Effect of a Cochannel Interferer on an Automatic Frequency Control Loop in Fading Channels,” Proc. IEEE Wireless Commun. Netw. Conf. (WCNC’09), April 2009, pp. 1-6.

▶ A. Emad and N. C. Beaulieu, “Performance Measures of Automatic Frequency Control Corrupted by Interference and Fading in Dual Dissimilar Channels,” Proc. IEEE 69th Veh. Technol. Conf. (VTC’09), April 2009, pp. 1-5.

▶ A. Emad and M. B. Shamsollahi, “ECG Denoising Using M-Band Wavelet Transform,” 12th Int. Conf. Biomed. Engin. (ICBME’05), Dec. 2005.

Contact

  • Room 755, McConnell Engineering Building
  • 3480 University Street
  • Montreal, Quebec, Canada
  • H3A 0E9
  • amin(dot)emad(at)mcgill(dot)ca
  • +1(514) 398-1847