Dr Goldenberg is a Scientist in Genetics and Genome Biology program at the SickKids Research Institute, an Assistant Professor in the Department of Computer Science at the University of Toronto, cross appointed in the Department of Statistics, faculty member at Vector and a fellow at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group. Dr Goldenberg trained in machine learning and statistics at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. She is an expert in developing machine learning approaches for biological data, network methods and most recently, data integration of omics and clinical data. The current focus of her lab is on developing methods that capture heterogeneity and identify disease mechanisms in complex human diseases. Her translational focus is on methods that efficiently combine many types of patient measurements to refine diagnosis, improve prognosis and personalise drug response prediction for patients with complex human diseases. She was recently awarded an Early Researcher Award from the Ministry of Research and Innovation and a Canada Research Chair in Computational Medicine.
Ladislav Rampášek is a PhD candidate in the Department of Computer Science at the University of Toronto. He is mostly interested in development of machine learning methods and their applications in life sciences. His research has been focused on deep generative models and drug response prediction since he started working with Dr. Anna Goldenberg in 2014. He previously authored papers on computational methods in non-invasive prenatal testing and for fast RNA motif search. Ladislav holds bachelor’s and master’s degree with honours in Computer Science from the Comenius University in Bratislava.
Read more about Ladislav on his LinkedIn page
Lauren Erdman is the Project Manager for SickKids Hospital’s new Data Science team and a PhD student in Computer Science at University of Toronto under the supervision of Anna Goldenberg. Previously she completed a MSc in Computer Science and a MSc in Biostatistics at University of Toronto under the supervision of Anna Goldenberg and Lisa Strug, respectively. Her research is focused on developing and applying machine learning methods primarily for data integration and improved translational discovery in the areas of population genetics, genome biology, and complex disease.
Daniel Hidru is a Computer Science PhD student at the University of Toronto. He has previously completed a MSc in Computer Science and a BSc in Mathematics and Statistics at the same institution. His research focus is the development of models for drug response prediction. He has previously worked on the pharmacogenomic prediction of anti-TNF drug response in rheumatoid arthritis patients and is currently working on drug sensitivity prediction in cancer cell lines. He is more generally interested in the development of computational and statistical techniques to solve scientific problems, especially in the domains of biology and medicine.
Kingsley was a double major at National Taiwan University, earning a bachelors degree in Electrical Engineering and Life Sciences. Currently he is earning his PhD in Computer Science at the University of Toronto. He is interested in Machine Learning and its application in genomics and healthcare. He has a wide range of interestes such as interpretability, uncertainty, counterfactual inference and time-series model, graph NN, etc. He loves good tea and plays volleyball from time to time.
Mingjie earned his undergraduate in both Laboratory Medicine & Pathology as well as computer science. He is currently working towards his masters in computer science and is interested in modeling and prediction on large-scale and high dimensional biological datasets, as well as on complex temporal health care data using natural language processing, recurrent neural networks and reinforcement learning
Valli graduated with an honors Bachelor of Science degree in Biomedical Science & Computer Science. She is currently pursuing a Masters of Science in Medical Biophysics at the University of Toronto to further her interests at the intersection of healthcare and computer science. Her current project involves predictive modelling of age of onset and tumor type in Li-Fraumeni Syndrome using data analysis techniques and machine learning.
Alex graduated with a Bachelor's degree in computer science from U of T, and is currently pursuing a Master's degree. Alex is interested in creating machine learning models that are able to fail gracefully. A fundamental part of this is analyzing what relationships a model is actually learning by testing it against pathological examples. He's also interested in non-conventional loss functions since he believes that current loss functions are used mainly for convenience and don't represent underlying concepts that a model should learn. He has experience working with several types of data including pharmacogenomics data (both drugs and cell lines), time series data (not health related), and imaging data. Alex enjoys cooking, creating workout programs, and the occasional game of Starcraft 2.
Sana graduated with a major in electrical engineering and a minor in bioengineering form University of Toronto. In her undergraduate studies she was involved in biomedical engineering research and now, she is pursuing her master’s degree in computer science with a focus on computational medicine. She is particulalry interested in applying machine learning knowledge to healthcare data, and is a big believer of AI and the impact it can have on healthcare. She is passionate about cinema and music, and enjoys playing the piano. In her spare time, she loves playing basketball and tennis.
Jaryd previously completed a BFA Specialized Honours in Theatrical Production from York University. He then started working in the entertainment industry around Toronto, but after a few years he decided to return to school to pursue a career that would give his working life more stability. Ultimately, he chose to take computer science at the university of Toronto where he is currently working towards completing a BSC specialist in Computer Science. He still enjoys going to see live performances around the city, and he tries to see smaller shows that are locally written. His main hobbies include, playing piano, guitar, and singing in welsh. He's also a member (on leave) with the Toronto Welsh Male Vocal Choir (TWMVC, you should check them out!). Finally, he enjoys playing board/video games, especially the strategic variety.
Walter is currently an undergraduate student pursuing a degree in Computational Biology and Neuroscience at the University of Toronto. His interests include integrating biological and clinical predictors of psychological disorders, statistical methods for analyzing genomic data, software tools for bioinformatics and reproducibility, as well as graph algorithms.
Jennifer is an undergraduate student, majoring in Machine Intelligence in the Engineering Science program at U of T. She is interested in applying machine learning techniques to healthcare and is studying methods to predict treatment effect. In her spare time she likes to sing, play piano, and watch Star Trek. She isn't very good at describing herself, and wishes she could create a neural net that can write better blurbs about her personality.
Soren is currently a 4th year Engineering Science student majoring in Aerospace Engineering at the University of Toronto. His interest in medicine and computers led him to pursue a research project on the use of Machine Learning for diagnosing Hepatic Cirrhosis. After graduation, he plans to pursue a career as an entrepreneur. In his spare time, he enjoys composing instrumental music and sharing it on his YouTube channel Revanchist1.
Hannah is a fourth year Computer Science student at the University of Toronto. She is interested in machine learning applications in healthcare and is currently working on models for drug response prediction. In her free time, she enjoys playing with her 2 cats and paddling for the New Dragons dragon boat team.
Ben is originally from Gainesville, Florida and earned a BA in Economics from The University of the South in Tennessee (2012). He graduated with a Master’s in Applied math and Economics from the Paris School of Economics and the Sorbonne in France (2015) and spent the following summer as a data science fellow at the University of Chicago's Data Science for Social Good program. He is currently working on building a machine learning alorithm to predict age of onset in patients with Li Fraumeni Syndrome. In his free time he enjoys watching the Toronto Raptors, eating pizza, and hanging out with his wife Xing (preferably all at the same time).
Lebo graduated with double major in Computer Science and Statistics, with a minor in African Studies. She then went on to complete her MSc in Applied Computing (Data Science concentration). She is interested in applying data science to social good, particularly in the field of healthcare.
Erik grew up in British Columbia and obtained a BA and an MA in Economics from Simon Fraser University and Queen's, respectively. After working as an Economist at the Bank of Canada for two years, Erik obtained an MSc in Statistics, taking courses in biostatistics and bioinformatics, and then worked as a Bioinformatician at the Boutros Lab in the OICR. Erik's research interests are focused on the intersection of statistics and machine learning and include high-dimensional data analysis and survival modelling. In his free time Erik enjoys reading, listening to podcasts, and playing tennis.
Farnush has a BA in computer engineering from Sharif University of Technology. Her M.Sc. is in Bioinformatics from the University of British Columbia. Her research interests are applying machine learning and statistical methods to multiomics data (genotype, gene expression, DNA methylation, and etc.) to model biological phenomenas (diseases like mental disorders or dynamics of networks). Other than academia, She is interested in singing, dancing, and playing board games. Also, she's interested in discussing about psychology-related topics.
Shalev Lifshitz is a 16-year-old high school student passionate about Machine Learning and Computer Vision. His current research focuses on developing a Computer Vision system to automatically diagnose Ciliopathy in patients using cell microscopy images. Shalev loves teaching other students about innovation and technology as well as speaking at conferences about ground-breaking technologies! In his spare time, Shalev enjoys playing the piano, basketball, watching Netflix and reading lots of papers!
Shalev's LinkedIn page