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) and the Quantitative Life Sciences (QLS) program 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. 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:
David E. Hostallero (PhD Student)
Joseph Szymborski (PhD Student)
Ameya Bhope (MEng student)
Lulan Shen (MEng student)

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

▶ Key Collaborators:
Dr. W. Pastor (Department of Biochemistry, McGill University)
Dr. J. Cairns (Center for Individualized Medicine, Mayo Clinic)

▶ Key Personnel:
Sima Jeddi (PhD student)
Chen Su (MEng student)
Abdulrahman Takiddeen (MEng student)
Mohamed Reda El Khili (MEng student)

Lab Members

Open Positions

Postdoctoral position

There is an opening for a postdoctoral fellow in the area of AI in precision medicine. The project is focused on precision medicine in cancer and COVID-19, and involves collaborations with Mayo Clinic, Quebec COBID-19 biobank and Mila (Quebec AI Institute).

The ideal candidate will have the following qualifications:
1) A PhD degree in a related field (computer science, machine learning, bioinformatics, engineering, etc) received in the last 3 years
2) Strong background in machine learning and particularly deep learning (including network representation learning, adversarial training, domain adaptation, few-shot learning, etc.)
3) Related background in ‘omics’ and computational biology is considered an advantage
4) Strong programming skills (particularly in python)
5) At least two first-authored English papers (or three if co-first authors) with submitted, accepted or published status in journals
6) Good spoken and written communication skills in English

Interested applicants should submit CV, a letter of interest with a one-page summary of the most relevant publication, and contact information for three references to Amin Emad (amin.emad@mcgill.ca) with subject line “postdoctoral application”. The position start date can be as early as May 2020 and will remain open until filled.

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

Current Students

David E. Hostallero (PhD Student): David earned his Masters 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. His research interests include creating computational tools for the analysis of single-cell omics data, particularly as applied to the context of cancers.

Sima Jeddi (PhD Student): Sima received her Master's and Bachelor's degrees from Amirkabir University of Technology (Iran) in Electrical Engineering. She has received the prestigious MEDA award at McGill. Her research revolves around machine learning and time series prediction. Currently, she is developing machine learning methods to reconstruct causal transcriptional regulatory networks using (pseudo)-time series data.

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

Ameya Bhope (Master's Student): Ameya earned his Bachelor’s degree from the University of Pune (India) in Electronics and Telecommunications. He enjoys teaching and has received the eLATE SALTISE teaching award at McGill. His research interests include machine learning and graph mining with applications in drug discovery. Currently, he is developing novel machine learning tools to identify the best course of treatment for glomerular diseases.

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.

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 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.

Lulan Shen (Master's Student): Lulan received her MSc from the University of Oxford (United Kingdom) in Mathematical Modelling and Scientific Computing in 2016 and BEng from McGill University (Canada) in Electrical Engineering in 2015. After graduation, she worked as a primary function developer and owner of value added functions of ESP in Bosch Automotive for almost three years. Her research interests are machine learning applications in bioinformatics and autonomous driving.

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

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

▶ A. Emad, and S. Sinha, “Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study,” Under review, preprint: bioRxiv 389734, 2018.

▶ A. Emad, T. Ray, T. W. Jensen, M. Parat, R. Natrajan, S. Sinha, and P. S. Ray, “An epithelial-mesenchymal-amoeboid transition gene signature reveals subtypes of breast cancer progression and metastasis,” Submitted, preprint: bioRxiv 219410.

▶ C. Qian, A. Emad, and N. D. Sidiropoulos, “REP: Predicting the Time-Course of Drug Sensitivity,” Submitted, preprint: arXiv 1907.11911.

Peer-reviewed journal papers

▶ 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.

▶ 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