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NSERC USRA & DURA – Summer 2022

Award information

The Faculty of Science at York University is inviting undergraduate students to apply for Undergraduate Student Research Awards (USRA) for Summer 2022. USRAs are meant to nurture your interest and fully develop your potential for a research career in the natural sciences and engineering (NSE). They are also meant to encourage you to undertake graduate studies in these fields by providing research work experience that complements your studies in an academic setting. NSERC encourages qualified Indigenous students to apply for this award.

Students can find more information on the NSERC website.

Presentation Slide from the Information Session (Feb 10)

Q & A Session

Wednesday, February 23rd 2022 at 10:00 am via zoom, link below.

https://yorku.zoom.us/j/92681625241?pwd=YXZibVB2bGpYdU5kYzVVNGlUQTd0dz09

Meeting ID: 926 8162 5241
Passcode: 777138

Summer 2022 NSERC USRA Applications

Browse Projects:

Please find some of the available projects available in the Faculty of Science.  You are also welcome to find a supervisor by alternative means (ie. contacting a professor directly, through previous course/lab work, etc..)

Supervisor: Gary Sweeney
Lab Website: http://sweeneylab.ca
Contact Info:
Gsweeney@yorku.ca
Number of Positions:
2
Project Description:
Our lab investigates cellular and molecular mechanisms leading to cardiovascular disease, especially heart failure, in diabetes. In particular, for this project we will study how an excess of iron (an important nutritional dietary factor) leads to cell death. This will involve cell culture of cardiomyocytes in the laboratory and performing assays to assess lipid peroxidation, oxidative stress and apoptosis. Working with other senior laboratory members, these cellular studies will be translated to an animal model. Cellular mechanisms contributing to cardiovascular disease in diabetes;
Student Responsibilities:
The student will learn and conduct experimental protocols, attend laboratory group meetings, and be coauthor on manuscript(s) published containing the data generated.
Desired Background/Skills:
Knowledge of molecular biology, physiology.

Supervisor: Sandra Rehan
Lab Website: http://www.rehanlab.com
Contact Info:
sanrehan@yorku.ca
Number of Positions:
1
Project Description:
Wild bees provide 80% of the pollination services but are understudies and in need of conservation world wide. To date most studies have focused on agricultural systems but urban ecology is a growing area of research. The Rehan lab is looking for a summer USRA student to conduct biodiversity conservation studies of wild bees across urban landscape. This summer we will focus on downtown Toronto to continue long term annual assessments of wild bee populations to detect new invasive species, the status of native species and the relationships between wild bee populations and urban development.
Student Responsibilities:
The student will be responsible for field collection of wild bees three days a week from established sites across downtown Toronto. The remaining two days a week the student will process specimens, enter data, learn statistics and how to identify wild bee species in the lab. The student will work in collaboration with a graduate student in both the field and lab.
Desired Background/Skills:
A background in animal biology, biodiversity conservation, entomology, ecology or environmental science is desirable. An ability to conduct field work across city parks and gardens and to work with insects is required.

Supervisor: Vivian Saridakis
Lab Website: http://www.yorku.ca/vsaridak/
Contact Info:
vsaridak@yorku.ca
Number of Positions:
1
Project Description:
The Ubiquitin Proteasome System (UPS) regulates the turnover of many proteins in cells via their degradation by the 26S Proteasome. Proteins that are destined to be degraded are tagged with a protein called Ubiquitin, which acts as a degradation signal. Ubiquitin Specific Protease 7 (USP7) functions within the UPS by catalyzing the removal of ubiiquitin from specific proteins in a process called deubiquitination. Ultimately, deubiquitination prevents the protein from being degraded. To date, many USP7 substrate proteins have been discovered that are deubiquitinated by USP7. Some of these proteins include Mdm2, p53, EZH2, MCM-BP UbE2E1 and many others thus providing insight into USP7’s critical role in cellular homeostasis. The goal of the project is to identify novel USP7 substrates.
Student Responsibilities:
The responsibilities of the student will be to perform protein purification, GST pull-downs and protein interaction assays over the course of the summer. Additional responsibilities may include functional analyses to examine the role of USP7 on the regulation of turnover of the substrate protein. The results will provide further information regarding the cellular role of USP7.
Desired Background/Skills:
Having completed BIOL2070/2071 is a requirement. Previous research lab experience is desired but not essential as training will be provided.

Supervisor: Patricia Lakin-Thomas
Lab Website: https://lakinthomas.lab.yorku.ca
Contact Info:
plakin@yorku.ca
Number of Positions:
2
Project Description:
How do organisms tell time? We use the fungus Neurospora crassa as a model to study the molecular mechanisms that regulate circadian (24-hour) rhythms. We are focussing on the TOR (Target of Rapamycin) pathway that is found in all eukaryotes and that signals nutritional status to control growth. We have developed a Western-blot based assayed for TOR activity and we have found that it is rhythmic, and that mutations in TOR pathway components impair timekeeping. We want to know how TOR activity changes with nutritional conditions, and how changes in TOR might affect the circadian clock.
Student Responsibilities:
The student will learn techniques for culturing the organism, for carrying out time-based experiments to look at clock function, and Western blotting for assaying TOR activity. The student will be trained in these techniques and will then carry out independent experiments under the close supervision of the professor and graduate students.
Desired Background/Skills:
Basic lab skills from chemistry/biology labs are necessary, along with a Biology/Biomed/Biochem/Biotech academic focus and an interest in research.

Supervisor: Yi Sheng
Lab Website: www.yorku.ca/yisheng
Contact Info:
yisheng@yorku.ca
Number of Positions:
1
Project Description:
My laboratory studies the role of the ubiquitin-proteasome pathway in the regulation of DNA damage repair signaling. HUWE1 (HECT, UBA, WWE domain containing 1) is a HECT-domain E3 ligase that is involved in ubiquitin mediated degradation and signaling in a variety of cellular processes including apoptosis, DNA replication, and recently shown to be involved in DNA damage repair. PARP1 (poly-ADP ribose polymerase 1), is a key protein involved in sensing and initiating DNA damage signaling through catalyzing the attachment of poly-ADP ribose polymers (PAR chains) to its substrate proteins. Recently, we found that HUWE1 regulated the protein stability of PARP1. However, the role of HUWE1 and its interaction with PARP1 in the DNA damage response (DDR) pathway has not been well understood.
Student Responsibilities:
The USRA student will help to characterize the molecular mechanism of the HUWE1 and PARP1 interaction using co-immunoprepicipation and western blot. As HUWE1 contains a WWE1 domain, which is a putative Poly- (ADP-ribose) recognition domain, we hypothesize that that HUWE1 regulates PARP1 through the association of the WWE domain and Poly- (ADP-ribose). To achieve this goal, Greta will further characterize the molecular mechanism of the WWE1 domain dependent interaction of HUWE1 and PARP1. The outcome of this study will provide a new mechanistic insight into the signaling network of cellular DNA damage response through HUWE1 and PARP1.
Desired Background/Skills:
Complete molecular biology courses; demonstrate good critical thinking and data analysis skills. Previous lab work experience is an asset

Supervisor: Ryan Schott
Lab Website: https://www.yorku.ca/science/schott/
Contact Info:
schott@yorku.ca
Number of Positions:
2
Project Description:
Vertebrate visual systems adapt to different light environments through many different mechanisms. This includes morphological and optical changes to the eye and neurological changes that can affect how light signals are processed and interpreted. At the molecular level, spectral sensitivity can evolve through changes to the light-sensitive molecules of the eye (visual pigments) through gene loss and duplication, differential and co-expression, and sequence evolution. While evolution and adaptation of visual systems has been studied in many vertebrates, amphibians, and particularly salamanders, are understudied, despite providing an excellent study system due to the convergent evolution of similar activity patterns, lifestyles, and behaviours across species that are likely to influence the evolution of visual function. Our group has recently assembled large datasets of visual genes from frogs that are revealing interesting patterns of visual evolution in relation to ecology, but similar resources in salamanders do not yet exist. We will sequence and assemble the first eye transcriptomes in salamanders focusing on an initial set of 4–6 species that inhabit distinct light environments. We will use these to extract visual genes expressed in the eye in in order to produce datasets for use in future studies. We will also conduct preliminary analyses of the visual pigment genes (opsins) including comparison of gene complements among species, inferences of gene duplications and losses, and an analysis of variation at sites known to affect spectral sensitivity in other vertebrates. These datasets will form the foundation for future studies of visual evolution across salamanders that will provide a broad evolutionary context within which to test for convergent and novel visual adaptations in response to parallel selective pressures imposed by similar life histories, ecologies, and behaviours that have evolved repeatedly across amphibians.
Student Responsibilities:
The student will be responsible for extracting RNA from salamander eyes, constructing Illumina sequencing libraries and preparing them for sequencing, and checking RNA and library quality. The student will de novo assemble the resulting transcriptomes, extract visual genes, and perform preliminary molecular evolutionary analyses. The student will also have the opportunity to contribute to ongoing projects on the evolution of visual gene in frogs to gain experience with other analyses methods and manuscript writing. The student will receive high quality training in the molecular biology and computational methods needed for the project and will undertake a guided literature review to acquire important background knowledge. The student will participate in weekly lab meetings, interact with an international group of collaborators, and present the findings of the project in a scientific talk to the research group.
Desired Background/Skills:
Interested students should have basic background knowledge in molecular and evolutionary biology, some experience with molecular lab work (eg, through lab courses), and good computer skills.

Supervisor: Ryan Schott
Lab Website: https://www.yorku.ca/science/schott/
Contact Info:
schott@yorku.ca
Number of Positions:
2
Project Description:
Frogs are a highly diverse group that exhibit a variety of ecologies and behaviours. These factors are known to influence the evolution of visual systems in other vertebrates but have not been well studied in frogs. The lens is an important component of the visual system that serves to focus light onto the retina but can also filter the wavelengths of light that reach the retina. Recently our group has found that the lenses of diurnal and scansorial (climbing) frogs transmitted significantly less short-wavelength light than those of non-diurnal or non-scansorial species. We hypothesized that shortwave-absorbing lens pigments protect diurnal species from retinal damage due to bright light and provide higher visual acuity to scansorial frogs that navigate complex visual environments. However, the molecular mechanisms underlying this pattern have yet to be explored. Further, we recently found that many of the genes that encode structural lens proteins (crystallins) were differentially expressed between aquatic tadpole and terrestrial juvenile leopard frogs. We hypothesized that this change resulted in a shift in the refractive properties of the lens to support vision in air vs water. Comparison among frog species that inhabit different environments will allow us to further test this hypothesis. Using a dataset of 84 frog eye transcriptomes and 16 whole genomes, we will identify and extract lens crystallin and lens pigmentation genes, compare gene complements among species, and infer gene duplications and losses. In addition, we will conduct molecular evolutionary analyses to investigate potential functional adaptation related to lens pigmentation and refractive power in different light environments. These results of this study will provide important insights into frog visual biology and, more broadly, to the constraints and opportunities of visual systems to evolving novel solutions to new life history modes and behaviours.
Student Responsibilities:
The student will be responsible for extracting visual genes from the frog transcriptomes and genomes, recording gene presence and absence, inferring duplication and loss with synteny analyses, conducting initial molecular evolutionary analyses (which involves multiple sequence alignment, phylogenetic inference of gene trees, and conducting selection analyses using codon models of molecular evolution), and preforming preliminary interpretation of results.
Desired Background/Skills:
Interested students should have strong computer skills and basic background knowledge in molecular and evolutionary biology. The student will receive high quality training that has been adapted to a remote working environment. This includes virtual “hands-on” training in the genomic, phylogenetic, and molecular evolutionary methods needed for the project, as well as a guided literature review to acquire important background knowledge. The student will participate in weekly lab meetings, interact with an international group of collaborators, and present the findings of the project in a scientific talk to the research group.

Supervisor: Terrance Kubiseski
Lab Website: https://biology.gradstudies.yorku.ca/faculty/t-kubiseski/
Contact Info:
tkubises@yorku.ca
Number of Positions:
1
Project Description:
Intracellular oxygen radicals (or reactive oxygen species) are becoming recognized as signaling molecules, yet their levels much be tightly regulated as too much can damage DNA, proteins and lipids, and have been implicated in many age-related disease's such as Parkinson's disease, Alzheimer's and cancer. Protein signaling pathways in cells become activated to limit the damage from reactive oxygen species by generating anti-oxidant proteins that remove and limit the exposure of an organism to long-term damage. We propose to use biochemistry to look at the regulation of expression of anti-oxidant proteins. The student will carry out a biochemical analysis of the C. elegans transcription factors and mediators involved in the oxidative stress response.
Student Responsibilities:
The methodologies represent a cutting-edge approach in using the power of in vitro protein expression combined with modern genetic approaches. Specifically, the student will be involved in using molecular biology, protein chemistry, tissue culture, co-immunoprecipitation and all the ancillary techniques associated with these disciplines. The impact of the program should encourage the preparation of a high-quality publication, for which the student will be actively involved.
Desired Background/Skills:
Basic molecular biology experience such as those taught in BIOL2070 (Research Methods in Cell and Molecular Biology).

Supervisor: Raymond Kwong
Lab Website: https://kwong.lab.yorku.ca/
Contact Info:
rwmkwong@yorku.ca
Number of Positions:
1
Project Description:
Because of the COVID-19 pandemic, the use of disinfectants has increased exponentially and will likely remain high in the post-pandemic future. Benzalkonium chlorides (BACs) are widely used as active ingredients in disinfecting agents, and they are ubiquitously detected in various wastewater and environmental samples. The present research project is to examine the potential risk of exposure and the long-term health effects of BACs on aquatic animals. Specifically, we seek to understand the effect threshold, the life-history response to BAC exposure, and the underlying toxic mechanisms in the freshwater crustacean Daphnia magna.
Student Responsibilities:
The student will investigate the effects of exposure to BACs on physiological performance, reproductive capacity, and their underlying mechanisms in Daphnia magna. The student will perform data collection and analysis, and present the work at our weekly meetings. Finally, the student is expected to write a report after the research.
Desired Background/Skills:
The student needs to have foundational knowledge in animal biology. It is preferred that the student has completed WHMIS II and Biosafety training. Some experience in a wet lab is an asset.

Supervisor: Steven Connor
Lab Website: https://biology.gradstudies.yorku.ca/steven-connor/
Contact Info:
saconnor@yorku.ca
Number of Positions:
2
Project Description:
We study how communication zones between neurons (known as synapses) change in response to experience, and how this process is altered in autistic neural circuits. Using a combination of electrophysiology, molecular biology and transgenics we study these processes in rodent models.
Student Responsibilities:
Perform electrophysiological recordings in mouse brain slices. May also include generating wester blots of synaptic proteins and behavioral assays for learning, memory and forgetting in mice.
Desired Background/Skills:
None required but some bench skills or rodent handling would be helpful.

Supervisor: Nik Kovinich
Lab Website: http://www.kovinichlab.com
Contact Info: kovinich@yorku.ca
Number of Positions:
1
Project Description:
The vast majority of plant species biosynthesize different defensive metabolites in response to pathogen attack. These metabolites, collectively named 'phytoalexins', are diverse in biosynthetic origin among plant species and include the phenylalanine-derived glyceollins of soybean and the tyrosine-derived camalexins of Arabidopsis thaliana (Arabidopsis). Glyceollins have potent anticancer and neuroprotective activities in humans and important roles as pathogen defense in soybean agriculture. Camalexins stimulate apoptosis specifically in certain types of human cancer cells and not in non-cancerous cells. Despite that phytoalexins are biosynthesized from diverse biosynthetic pathways across plant species, their biosynthesis is stimulated by common elicitors, including certain inorganic chemicals, UV-B irradiation, and microbial pathogens. We have recently published the identification of two transcription factors (TFs) that directly activate the transcription of glyceollin biosynthesis genes in soybean by binding their promoter elements. Prior to our work, only distinct regulators of phytoalexin biosynthetic pathways were identified in other plant species. This led to the concept that the TFs were as diverse as the biosynthetic pathways that they regulate. However, the two glyceollin TFs that we identified were homologous to TFs that regulate other phytoalexin pathways. Thus, we hypothesized that our two TFs are part of a conserved network that directly regulate diverse phytoalexin biosynthetic pathways in plants. Now, the question we aim to answer is how could conserved TFs regulate divergent biochemical pathways. This work will address a longstanding question of how plants evolved lineage-specific biochemical defenses in response to common elicitors. We currently have an NSERC Discovery Grant aimed at addressing this hypothesis. A grad student in our lab has identified nine (9) TFs of the glyceollin network. A major question that remains is whether any of our 11 TFs can regulate the biosynthesis of phytoalexins in other plant species by directly binding the DNA of their biosynthetic genes.
Student Responsibilities:
The student’s role will be to determine whether any of the 11 transcription factors (TFs) from soybean can directly bind the gene promoters involved in camalexin biosynthesis from Arabidopsis. (S)he will conduct a high-throughput yeast one-hybrid assay to test whether the TF proteins can bind promoter DNA from 5 camalexin biosynthesis genes. We already have yeast strains harboring each of the five promoters and the 11 TFs cloned. The results will be validated by a PhD student using an in planta method.
Desired Background/Skills:
The student’s role will be to determine whether any of the 11 transcription factors (TFs) from soybean can directly bind the gene promoters of camalexin biosynthesis genes from Arabidopsis. (S)he will conduct a high-throughput yeast one-hybrid assay to test whether the TF proteins can bind promoter DNA of camalexin biosynthesis genes that we have already integrated into yeast strains. (S)he will also test whether a camalexin TF from Arabidopsis can bind the promoters of glyceollin biosynthesis genes from soybean. The results will be validated by a PhD student using an in planta method.

Supervisor: Nik Kovinich
Lab Website: http://www.kovinichlab.com
Contact Info: kovinich@yorku.ca
Number of Positions:
1
Project Description:
For decades regenerating plants from cultured tissues has remained a major obstacle to Cannabis bioengineering. While the plant kingdom is well known for its ability to produce a wide variety of valuable secondary metabolites, few plant species can produce those metabolites in high enough amounts to be an economical source of those molecules. Cannabis sativa (Cannabis) is a rare example of a plant species that can synthesize up to 35% of its dry weight as a single secondary metabolite, namely (−)-trans-Δ⁹-tetrahydro-cannabinol (THC). Recent gene replacement strategies hold promise to replace THC biosynthesis with that of other valuable metabolites. Yet, the inefficiency of regenerating Cannabis plants from engineered tissues remains a major obstacle. Fortunately, a major advance has been recently reported. Overexpressing a chimeric protein comprised of two developmental regulators (i.e. the transcription factors GRF and GIF) dramatically improves the regeneration efficiency of various plant species, including Cannabis. Unfortunately, constitutive expression of these factors resulted in growth defects in adult plants. To overcome this obstacle, we have added a translational fusion of a glucocorticoid receptor (GR) that renders the activity of the chimeric protein inducible. Thus, activity can be restricted solely to the time when shoots need to be regenerated from callus. Another research group recently demonstrated that infiltrating such growth regulators to wounded shoot tips was sufficient to induce the growth of transgenic shoots from several plant species. We hypothesize that transgenic Cannabis shoots regenerated using this approach could be readily rooted and propagated into full plants, avoiding the lengthy process of in vitro callus formation.
Student Responsibilities:
The student will conduct weekly transformation events. These will include callus and wounded shoot transformations using our recently described nanoparticle- and Agrobacterium-based transformation approaches. Also encoded on the DNA constructs are the CAS9 enzyme and components for replacing a THC biosynthesis gene with that of another valuable metabolite. The student will screen events for changes to the DNA and metabolites by PCR and UPLC, respectively.
Desired Background/Skills:
The ideal student would have prior education in genetics, molecular biology, and plant biology. Experience with lab techniques such as PCR and UPLC. Maintaining sterile conditions (microbiology, tissue culture, etc…).

Supervisor: Sylvie Morin
Lab Website: https://www.yorku.ca/science/morinresearchgroup/research/
Contact Info:
smorin@yorku.ca
Number of Positions:
1
Project Description:
Pressing issues such as climate change and the need for alternative energy sources can be addressed through the use of electrochemical technology. It already plays an important role in energy storage and conversion devices such as fuel cells and batteries. Hydrogen gas is viewed as one of the energy carriers of the future as it is environmentally friendly and inexhaustible. However, at present, hydrogen is mainly produced from steam methane reforming and coal gasification, which lead to carbon dioxide emissions. Water electrolysis is an efficient and sustainable way to produce H2. However, the practical application of water electrolysis has been limited due to its high cost. Water splitting involves two half-reactions, hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The OER is particularly kinetically sluggish and this causes significant energy loss in the water splitting process. Thus, a lot of efforts have gone into developing materials that can decrease the energy needed to carry out this reaction. We have recently identified that iron-containing copper-cobalt oxide and nickel-cobalt oxide materials display good oxygen production properties. Thus, the goal of this project is to identify and understand what make iron containing copper-cobalt oxide and nickel-cobalt oxide materials so good at producing oxygen gas. We will be using state of the art techniques to achieve this goal and identify the structural, electronic and mechanistic characteristics that make these materials work the way they do. This research would be highly beneficial to our understanding of their mode of operation and will enable their application in new water electrolysis technology, opening the door to practical applications for the production of hydrogen via water electrolysis. Transition metal oxides as efficient electrocatalysts;
Student Responsibilities:
Together with my graduate students you will learn to prepare and characterize transition metal oxide materials using X-ray diffraction, scanning electron microscopy, and electrochemical techniques.
You will become familiar with electrode kinetics measurements.
You will learn how to analyze the data and present your work in the form of written reports, posters and talks.
Desired Background/Skills:
Successful progress towards an undergraduate degree in Chemistry

Supervisor: Gerald Audette
Lab Website: http://audettelab.ca
Contact Info:
audette@yorku.ca
Number of Positions:
1
Project Description:
The gram negative bacteria Coxiella burnetii and Francisella tularensis are the causative agents of Q fever and tularaemia, respectively. Infection by either organism results in high mortality rates, especially in immuno-compromised individuals, and there are very limited options for detection of both C. burnetii and F. tularensis. Recent evidence shows that both bacteria have functional type II secretion system (T2SS) and type IV pilus (T4P), and that functional T4P affect virulence. Sequence analysis indicates that proteins cbu0156 and ftn0389 are the major pilins of C. burnetii and F. tularensis, respectively; several other proteins, including cbu0155, cbu1891, ftn0116, and ftn1137 have been identified as T2SS effector proteins. However, little is understood of these proteins at the structural level, how they affect virulence, or how they can be used for detection of C. burnetii or F. tularensis infection. The aim of this research is (1) the structural and biophysical analysis of Coxiella and Francisella pilins and T2SS-mediated effector proteins, and (2) coupling structural and functional data to provide a clearer understanding of these proteins within the infective cycle of C. burnetti and F. tularensis. These studies will provide detailed structural information into the pilins and multiple T2SS effector proteins, leading to novel therapeutics, and provide a framework for the development of protein-specific biosensors for C. burnetii and F. tularensis.
Student Responsibilities: The student's research will focus on the expression and purification of a recombinant protein from C. burnetti, and initial characterization of the protein using liquid chromatography, dynamic light scattering and other biophysical methods. These studies will lead towards the crystallization of the protein and bioinformatics comparison to similar proteins in the Protein Data Bank.
Desired Background/Skills:
Molecular biology/biochemistry lab experience (such as Biol 2070, Chem 2050 etc.). And an enthusiastic attitude!

Supervisor: Derek Wilson
Lab Website: http://yorku.ca/dkwilson
Contact Info:
dkwilson@yorku.ca
Number of Positions:
2
Project Description:
Bcl2 family proteins play a central role in mitochondrial-induced apoptosis and are regulated via a complex web of pro- and anti-apoptotic protein-protein interactions. Anti-apoptotic regulation within the Bcl2 family system is known to be dysregulated in many cancers, and understanding the nature of interactions amongst Bcl2 family members could lead to new therapeutic approaches.The aim of the proposed work will be to characterize the interactions between Bcl2 family proteins Bcl2 and Mcl1 in terms of the biophysical drivers that confer specificity and thermodynamic stability in these interactions. To do this, students will use our unique millisecond hydrogen deuterium exchange technology, linked to mass spectrometry. Students will learn to carry out hydrogen deuterium exchange-MS experiments and interpret the resulting data. Findings will be incorporated into a broader project that examines the broader set of Bcl2 family interactions, and explores the molecular evolution of this system.Each project will focus on interactions involving Bcl2 or Mcl1 with binding partners Bid, Bad, Bax and others currently available in the lab.
Student Responsibilities:
The student will: Learn to express their tagged protein in E. coli and purify; Learn and conduct millisecond hydrogen deuterium exchange (HDX) measurements using the lab's unique TRESI-HDX technology; Learn to interpret HDX-MS data to understand what they say about protein dynamics; Provide updates on progress at weekly group meetings; - Contribute to writing papers/reports on the experiments conducted.
Desired Background/Skills:
Interest in Biochemistry at the molecular level.

Supervisor: Sergey Krylov
Lab Website: http://www.yorku.ca/skrylov/
Contact Info: skrylov@yorku.ca
Number of Positions:
2
Project Description:
This research project deals with Ideal-Filter Capillary Electrophoresis (IFCE). IFCE is a novel and highly-enabling technology for screening DNA-encoded molecular libraries (DELs) for drug leads. IFCE is ten million times more efficient that typical surface-based screening techniques! The development of IFCE into a practical drug-development technology is a focus of a NSERC SPG-P grant awarded to me and my collaborator, Dr. Hili. A central part to this project is a sub-project on adopting IFCE to real DELs (the proof-of-principle work was done with DEL-mimicking libraries). In essence, the electrophoretic behavior of a DEL (provided by GlaxoSmithKline (GSK), which is an industrial partner on the project) will be tested under IFCE conditions and the non-binder background will be measured. IFCE-based screening of the DEL for binders to Carbonic Anhydrase, a protein known to be druggable, will be then carried. Further, DNA tags on the collected binders will be sequenced at the GSK site. Finally, the sequences will be analyzed and the results will be compared with those of GSK’s conventional surface-based screening. The expected output is a set of performance parameters of IFCE in DEL screening. The work will also serve as a proof-of-principle for IFCE applicability to DELs. The results are expected to be published.
Student Responsibilities:
Conduct experiment, perform lab duties, read the literature, write reports
Desired Background/Skills:
It is preferable the the student(s) will have taken General Chemistry, General Physics, Analytical Chemistry, and Calculus by the time he/she joins the project

Supervisor: Ozzy Mermut
Lab Website: https://omermut.lab.yorku.ca/
Contact Info:
omermut@yorku.ca
Number of Positions:
1
Project Description:
Azobenzene molecules are nature inspired biological mimics for a variety of important natural photochromes in vision. For example, photo-responsive Retinal is the chemical basis of visual phototransduction involved in visual perception through the eye, and is responsible for signaling that enables vision. The geometric photo-induced isomerization of mimetic azo-benzene molecules are therefore important to study in a variety of biophysical phenomena and to develop new sensors for emerging new biophotonic phototherapies for devasting visual diseases and cancer.  In this project, the goal is to build on our library of synthesized molecules by synthesizing new derivatives of these biomimetic azobenzene photo-switches and to determine their physical-optical properties (specifically rate constants, and relative activation energies) through density functional theory calculations of these newly synthesized derivatives. The transdisciplinary nature of the project, supervised by Prof. Mermut, implicates a highly collaborative environment wherein this NSERC USRA project the student is co-supervised by Prof. Pietro (York U), and performed in collaboration with Prof. Barrett (McGill)
Student Responsibilities:
Depending on the students’ background, the NSERC USRA student in this project shall be responsible for either synthesis and spectroscopic optical characterization of four azobenzene molecules OR density functional theory modeling calculations (DFT) of four candidate photo-switches. In either case the NSERC USRA student’s primary role is to determine the relatives rate of the photo-switches, dihedral angles implicated in the geometric photo-isomerization and the relative changes in activation energies. Specifically, the dihedral angle studies will lead to the determination of activation barriers, so one can calculate relative kinetics of the molecules. The NSERC USRA student will conduct photo-switching experiments for optical characterizations with the synthesized molecules in our labs and data analysis to determine photo-switching rates. Depending on the skill set of the candidate, the chemistry or biochemical physics NSERC USRA student should have some background or be comfortable with relatively simple organic synthesis and/or DFT computational modeling.
Desired Background/Skills:
organic synthesis and/or DFT computational modeling

Supervisor: Gino Lavoie
Lab Website: http://www.yorku.ca/glavoie
Contact Info: glavoie@yorku.ca
Number of Positions:
1
Project Description:
At the heart of most chemical transformations is the use of catalysts to mediate reactions under mild conditions. Over the past decades, improvements have been made to catalysis thanks to well-defined transition metal complexes. Polymerization is one of those transformations that makes use of catalysts. Polymers have played a significant role in today's society, with applications in medical devices, electronics, sporting goods, construction and transportation, to name a few. With approximately 400 millions metric tonnes of polymers produced worldwide per year, it is critical that catalysts with better performance be developed.  Our group has thus been developing new modular bidentate ligands, focusing on highly versatile guanidine donors. These allow us to easily tune their electronic and steric contributions to the catalytic system. The performance of the resulting complexes in the polymerization of lactide and olefins is then explored. A typical research project for undergraduate students involve the synthesis of small organic molecules (ligands) and transition metal complexes, and testing of these complexes in polymerization.  Researchers in the Lavoie group gain valuable hands-on experience in (i) the synthesis of both inorganic and organic compounds, including working under oxygen and moisture-free conditions, (ii) their characterization by NMR spectroscopy and X-ray crystallography, (iii) the assessment of catalysts performance in polymerizations, and (iv) the use of computational chemistry to gain further insight and develop structure–activity relationships. Students work in state-of-the-art facilities touching on all four classical divisions of chemistry (inorganic, organic, analytical and physical chemistry) while developing skills transferable to their future research endeavours.
Student Responsibilities:
- synthesis organic and inorganic compounds
- characterize all compounds by 1H NMR spectroscopy
- test the activity of transition metal complexes for the polymerization of olefins and lactide
- document all experimental procedures in a laboratory notebook
- participate in group meetings
- write interim reports and a final report
- have good housekeeping skills
- abide by the safety rules
Desired Background/Skills:
organic and inorganic chemistry at an intermediate (3000) level, including some laboratory synthetic skills (ideally with CHEM 3000 and 3001 completed); basic 1H NMR spectroscopy knowledge

Supervisor: Amenda Chow
Lab Website: https://amchow.info.yorku.ca/
Contact Info: amchow@yorku.ca
Number of Positions:
1
Project Description:
Magnetism can be described by ordinary and partial differential equations. Controlling its stability behaviour is often desirable for various physical reasons. This can be difficult to achieve and heavily dependent on the differential equation. Some mathematical tools and concepts related to the analysis of stability of differential equations are:  (i) linearization (ii) Lyapunov theory  (iii) modelling (iv) norms and Lp spaces (v) vector operations,  (vi) simulations in MATLAB, and lastly (vii) a general willingness to talk to others about what you don’t know. This project will be co-supervised by Amenda Chow (York) and Kirsten Morris (Waterloo).
Student Responsibilities:
Independent reading and learning with good time management, conducting literature searches, applying mathematical knowledge creatively and correctly, attending teaching and research meetings, writing mathematical solutions, programming in MATLAB.
Desired Background/Skills:
Completed several applied math courses especially Math 2270, Math 3271, Math 4271, and proficient knowledge with functional analysis, MATLAB and LaTeX

Supervisor: Yuejiao Fu
Lab Website: https://yuejiao.info.yorku.ca/
Contact Info:
yuejiao@yorku.ca
Number of Positions:
2
Project Description:
Statistical depth, which measures the center-outward rank of a given sample with respect to its underlying distribution, has become a popular and powerful tool in nonparametric inference for big data. In the project, we will examine and compare different statistical depth functions for multivariate and functional observations. The use of statistical depth in classification problems and outlier detection will be studied as well.
Student Responsibilities:
Conduct literature review, simulation studies and data analysis
Desired Background/Skills:
Completed 3131 and 3330, strong R programming skills

Supervisor: Jude Kong
Lab Website: https://judekong.mathstats.yorku.ca/
Contact Info: jdkong@yorku.ca
Number of Positions:
2
Project Description:
The still ongoing “Coronavirus Disease 2019”(COVID-19) pandemic has disproportionately affected and is still affecting long-term care facilities. This study aimed to identify predictors of the timing of the first COVID-19 case and death in Long-term care homes across Ontario during the 1st, 2nd, 3rd, 4th and 5th pandemic waves and to test for associations with the preparedness of health systems and government pandemic responses. We manually developed and curated a dedicated database, specifically based in Ontario, Canada. The dataset consists of 74 covariates collated from over 30 sources verified by the Ontario Ministry of Health. This database contain all the factors that have been suspected to be associated with COVID-19 case and death in  Long-term care  homes in Ontario.
Student Responsibilities:
The student will perform Cox proportional hazard analyses for timing of the first case and death during the various waves as well as a generalized linear mixed model analysis for the factors associated with cases and mortality in LTCHs during all the 5 waves. Prepare a manuscript for publication. Attend research group  meetings every week as well as weekly one-on-one meetings with me. Present at least once in a lab meeting and once in a national/international conference/symposium /webinar and give trivias at least once in research group meetings.
Desired Background/Skills:
Background in Survival analysis/regression/machine learning/Mathematical Biology/Applied Mathematics/Statistics/Computer Science.

Supervisor: Jude Kong
Lab Website: https://judekong.mathstats.yorku.ca/
Contact Info: jdkong@yorku.ca
Number of Positions:
2
Project Description:
Despite advances in medical care, vaccine development, and public health tools, outbreaks like COVID- 19, continue to easily spread, overwhelming plans and government institutions, leading to significant loss of lives, economic and social displacement, and undermined political institutions. The aim of this project is to use publicly available data  to enhance Canada's capacities in detecting and predicting the emergence of novel human pathogens.  Usually before an outbreak grow from a local community to an entire region/country/world, there may be a spike in the mention of the word outbreak in Community News papers, google searches or social media posts. We will extract these signal, study the correlation between them and investigate if they can be used to warn of any upcoming outbreak.
Student Responsibilities:
The student is expected to extract the signals and investigate the correlation between them. Prepare a manuscript for publication. Attend research group  meetings every week as well as weekly one-on-one meetings with me. Present at least once in a lab meeting and once in a national/international conference/symposium /webinar and give trivias at least once in research  group meetings.
Desired Background/Skills:
Application Programming Interface/Natural language processing/Artificial Intelligence.

Supervisor: Kevin McGregor
Lab Website: https://www.kevmcgregor.com/
Contact Info: kevinmcg@yorku.ca
Number of Positions:
1
Project Description:
Statistical genetics, Bayesian statistics, compositional data, high-dimensional data
Student Responsibilities:
Data analysis in R, designing an R package, running simulations on Compute Canada
Desired Background/Skills:
Completed several upper level statistics courses (at least Math 3131 and Math 3330, preferably more), strong R programming skills, independent worker with attention to detail.

Supervisor: Pavlos Motakis
Lab Website: https://pmotakis.mathstats.yorku.ca/
Contact Info: pmotakis@yorku.ca
Number of Positions:
1
Project Description:
The purpose is to study the connections between topological K-theory and the behaviour of a matrix function A defined on a subset U of a Euclidean space of dimension at least two and takes values in a space of nxn matrices. Topological K-theory is a field that is traditionally established from a scope of algebraic topology. To each compact topological space, an abelian group is assigned using the notion of a vector bundle. A variation of this approach uses continuous matrix functions instead. By studying the topological properties of a domain U, the final goal is to distill information regarding how well a continuous matrix function defined on it preserves certain information.
Student Responsibilities:
Throughout the duration of the project the student researcher will meet with the faculty mentor approximately twice a week. They will initially be tasked with studying bibliography and research papers relevant to the topic. In direct collaboration with the faculty mentor, they will explore connections between the tools developed in K-theory and factorizing continuous matrix functions of a high-dimensional domain. Step by step they will be guided towards asking the right questions and developing answers that may lead to gradual improvements. In the end of the project they will be asked to write a report or a short article.
Desired Background/Skills:
The student researcher needs to be familiar with the following subjects, in addition to being skilled in Latex: Differential Calculus (Math 1300 and Math 1310), Linear Algebra (Math 1021 and Math 2022), Real Analysis (at least Math 2001 and desirably Math 3001), Algebra (Math 3021), Vector Calculus (Math 2310 and desirably Math 3010), Complex Variables (Math 3410) and Topology (Math 4081)

Supervisor: Iain Moyles
Lab Website: http://www.yorku.ca/imoyles/
Contact Info:
imoyles@yorku.ca
Number of Positions:
1
Project Description:
One of the characteristic features of an infectious disease becoming endemic is the rate at which immunity wanes in its host. This is currently very prominent with COVID-19 as vaccine protection and immunity from infection degrade over time. There are many complex models that consider waning immunity in their predictions for COVID-19 numbers. Instead, this project will look at a simple model of susceptibility, infection, and return to susceptibility. Waning immunity rates will be determined by matching the model to case count data and will be compared to other values in epidemiological literature. Simple models are fast to compute and analyze and therefore estimating inferred immunity from them may help guide real-time policy on scheduling vaccine supply and distribution.
Student Responsibilities:
The students will: Read literature to understand the modelling of infectious diseases; Write down differential equations that model the spread of infectious diseases; Derive basic reproduction numbers and write code to solve models; Use data fitting techniques to compare model to data; Communicate their findings and discuss implications;
Desired Background/Skills:
Students should be comfortable with mathematical equations (Math 2270 is helpful) and have some experience coding in MATLAB or python.

Supervisor: Iain Moyles
Lab Website: http://www.yorku.ca/imoyles/
Contact Info:
imoyles@yorku.ca
Number of Positions:
1
Project Description:
Prior to the implementation of vaccines in the mitigation of COVID-19, non-pharmaceutical interventions (NPIs) such as distancing, masking, and other public health measures were the primary tool for preventing infection. However, prolonged use of these measures cause societal fatigue and reduce their effectiveness. This project will use an infectious disease model with behaviour considered and determine the optimal public health measures that should have been used in each province in the early waves of the pandemic. This will be compared to actual citizen response and the results will help guide policy decisions for future use of NPIs.
Student Responsibilities:
The students will: Read literature to understand the modelling of infectious diseases; Write down differential equations that model the spread of infectious diseases; Derive basic reproduction numbers and write code to solve models; Use data fitting techniques to compare model to data; Communicate their findings and discuss implications;
Desired Background/Skills:
Students should be comfortable with mathematical equations (Math 2270 is helpful) and have some experience coding in MATLAB or python.

Supervisor: Paul Skoufranis
Lab Website: https://pskoufra.info.yorku.ca/
Contact Info:
pskoufra@yorku.ca
Number of Positions:
1
Project Description:
Learn a variety of related results in analysis and linear algebra and try to generalize those results to n-tuples of continuous matrix operators.
Student Responsibilities:
Throughout the duration of the project the student researcher will meet with the faculty mentor approximately twice a week. They will initially be tasked with studying bibliography and research papers relevant to the topic. In direct collaboration with the faculty mentor, they will explore connections between the tools developed in K-theory and factorizing continuous matrix functions of a high-dimensional domain. Step by step they will be guided towards asking the right questions and developing answers that may lead to gradual improvements. In the end of the project they will be asked to write a report or a short article.
Desired Background/Skills:
Math 2022 and Math 3001 required with grades of B+ or better.  Math 4011 would be optimal, but is not required.

Supervisors: Woldegebriel Assefa & Jude Kong
Lab Website: https://www.yorku.ca/professor/waw/
Contact Info:
wassefaw@yorku.ca
Number of Positions:
1
Project Description:
It is not always possible to measure all state variables and estimate time-dependent parameters in a mathematical model repressing a real world-phenomenon.  One approach to address such problems is to consider state reconstruction problem (or inverse system design). So, the main objective of this work is to construct an observer (method from control theory) that will help us to estimate time-dependent parameters and unmeasurable state variables in a simple model of Covid-19. The model will consider susceptible- infectious-recovered with waning immunity model applied to real data.   An observer (Auxiliary system) whose solutions converge exponentially to those of an original system and solely utilizes known inputs and output of the model will be constructed. The project will be co-supervised by Woldegebriel Assefa and Jude Kong.
Student Responsibilities:
The students will: Read existing simple mathematical models of Covid-19, and related literature; Read and understand the process of constructing ab observer from existing epidemic models that applied observer construction to other infectious diseases; Formulate SIR model with waning immunity using system of differential equations; Construct an auxiliary system (called an observer); Estimate time-dependent variables and not accessible variables using real data; Possibly write manuscript. This will be decided during the process;
Desired Background/Skills:
Completed courses related with differential equations and linear algebra, particularly Math 2270 or Math 2271, Math 1021 or Math 1025, and  has experience with  Python or MATLAB,  Latex Typing (or Microsoft word).

Supervisors: Woldegebriel Assefa & Jianhong Wu
Lab Website: https://www.yorku.ca/professor/waw/
Contact Info:
wassefaw@yorku.ca
Number of Positions:
1
Project Description:
In this project we will present different neural network methods for solving ordinary differential equations (ODEs), which provides several attractive features towards the behavior of the solution. The idea of solving an ODE using a Neural Network (NN) is basically based on training a neural network to satisfy the conditions required by a differential equation. In other words, we need to find a (loss) function whose derivative satisfies the ODE conditions. In this project, we will use artificial neural networks  to solve initial and boundary value problems  of a single ODE or simple system of ODEs. We will apply this method to a simple SI epidemic model using real data. The project will be co-supervised by Woldegebriel Assefa and Prof. Jianhong Wu.
Student Responsibilities:
The students will: Review literature on different types of neutral networks, and particularly with focus on ODE neutral networks; Understand the foundations different NN methods for solving ODEs, and system of ODES; Compare ODE networks  with other types of Networks; Solve some protype examples ODEs, and two by two system of ODES using ODE NN; Apply ODE networks for a simple epidemic model and make predictions;
Desired Background/Skills:
Completed courses related with differential equations and linear algebra, particularly Math 2270 or Math 2271,  Math 1021 or Math 1025, and  has experience with  Python or MATLAB,  Latex Typing (or Microsoft word). Experience of machine learning, particularly, neural networks is good but not necessary.

Supervisor: Ozzy Mermut
Lab Website: https://omermut.lab.yorku.ca/
Contact Info:
omermut@yorku.ca
Number of Positions:
1
Project Description:
In this project, the aim is to analyze theoretical derivative molecules of biomimetic azobenzene photo-switches and ascertain their physical, optical, and kinetic properties therefrom. Specifically, we will determine rate constants, and relative activation energies through density functional theory (DFT) computational modeling.  The transdisciplinary nature of the project, supervised by Prof. Mermut, involves a highly collaborative environment wherein this NSERC USRA project the student is co-supervised by Prof. William Pietro (York U), and in collaboration with Prof. Christopher J. Barrett (McGill).
Student Responsibilities:
The NSERC USRA student in this project shall be responsible for density functional theory modeling calculations (DFT) of six to eight candidate azobenzene photo-switches. The NSERC USRA student’s primary role is to determine the relatives rate of the photo-switches, dihedral angles implicated in the geometric photo-isomerization and the relative changes in activation energies. Specifically, the analyses of the dihedral angle studies will lead to the determination of activation energy barriers, and thereby obtain kinetic information with respect to the photo-switching mechanism(s). If possible, the NSERC USRA student will compare and correlate their theoretical computational modeling results from DFT, to data obtained from biophotonics experiments. Depending on the skill set of the candidate, the physics or biophysics NSERC USRA student should have some background or be comfortable with DFT computational modeling.
Desired Background/Skills:
Density Functional Theory computational Modeling.

Supervisor: Eric Hessels
Lab Website: http://edmcubed.com
Contact Info:
hessels@gmail.com
Number of Positions:
2
Project Description:
The student will participate in a major initiative at York University (EDMcubed, which stands for Electron Dipole Measurement using Molecules in a Matrix) in which the electric dipole moment of the electron will be measured to unprecedented precision. The measurement takes advantage of the large electric field that an electron experiences inside of a polar molecule (BaF in this case), and takes advantage of the large number of these molecules that can be embedded into a cryogenic sample of solid argon. The electron's electric dipole moment is key to understanding the asymmetry between matter and antimatter in the universe.
Student Responsibilities:
The student’s research will focus around designing, planning and building and optimizing one of the systems needed to make the measurement. Several systems are required, including a cryogenic system, a vacuum system, a molecular ion beam system, a magnetic field system, a radio-frequency system, and an optical detection system. The student will focus on one of these systems, but the choice of which one will be made based on the progress EDMcubed in the intervening months, and in consultation with the student. The student will take away valuable experience in design, building and testing a complex scientific apparatus, as well as being part of a very exciting and high-profile research effort.
Desired Background/Skills:
Successful progress towards an undergraduate degree in physics.

Supervisor: Sean Tulin
Lab Website: http://www.yorku.ca/stulin
Contact Info:
stulin@yorku.ca
Number of Positions:
2
Project Description:
Small dark matter structures (minihalos) provided the gravitational seeds for the first stars in the Universe to collapse and ignite. This research will study how dark matter's microphysical properties, such as its possible interactions and forces, can impact the structure of minihalos and the formation of the first stars. A goal of this research, which is theoretical and computational in nature, will be to perform simplified simulations of star-forming gas collapsing in minihalos.
Student Responsibilities:
Student will assist with developing theoretical ideas related to hydrodynamical equations for gas and dark matter evolution in the early Universe. Student will write, run, and debug Python code for implementing these ideas, based on an existing codebase. Student will work in a collaborative and vibrant team environment and will be expected to contribute to group activities, such as giving presentations and sharing results with the team.
Desired Background/Skills:
Prior experience in Python, or enthusiasm for learning Python if no previous experience.

Supervisor: Patrick Hall
Lab Website: http://www.yorku.ca/phall/HOME/astro.html
Contact Info: phall@yorku.ca
Number of Positions:
1
Project Description:
Quasars are disks of matter around supermassive black holes in galaxy cores. My research group has access to large database of spectroscopy and photometry of quasars from the SDSS survey. We are modelling the emission from quasars to compare to the predictions of models of disks and their outflows.
Student Responsibilities:
Work with Prof. Hall and his group on scientific programming using MATLAB and Python, compile and analyze scientific results, and contribute significantly to writing up the results for publication in a peer-reviewed journal.
Desired Background/Skills:
has taken undergraduate astronomy courses, has some experience in MATLAB or Python

Supervisor: Ananthraman Kumarakrishnan
Lab Website: http://datamac.phys.yorku.ca
Contact Info: akumar@yorku.ca
Number of Positions:
2
Project Description:
My group has developed a new class of low cost, homebuilt, vacuum-sealed, auto- locking laser systems that can be frequency stabilized with respect to atomic, molecular, and temperature tunable solid state frequency markers without human intervention.
Summer research projects will focus on the applications of these laser systems in several exciting experiments that include:
1) Ultra cold atom sensors that measure gravitational acceleration with high precision
2) Optical lattices that can realize the most accurate measurement of a diffusion coefficient-a parameter that is required to model the performance of the most sensitive magnetometers
3) Coherent transient experiments that are capable of realizing the most precise measurements of atomic lifetimes
4) Free space optical tweezers that trap dielectric particles, and rapidly determine their masses by investigating kinematics on fast time scales
Student Responsibilities:
My group has developed a new class of low cost, homebuilt, vacuum-sealed, auto-locking laser systems that can be frequency stabilized with respect to atomic, molecular, and temperature tuneable solid state frequency markers without human intervention. Summer research projects will focus on the applications of these laser systems in several exciting experiments that include:  (1) Ultra cold atom sensors that measure gravitational acceleration with high precision (2) Optical lattices that can realize the most accurate measurement of a diffusion coefficient-a parameter that is required to model the performance of the most sensitive magnetometers (3) Coherent transient experiments that are capable of realizing the most precise measurements of atomic lifetimes (4) Free space optical tweezers that trap dielectric particles, and rapidly determine their masses by investigating kinematics on fast time scales
Desired Background/Skills: Introductory courses on electromagnetism (PHYS 2020), Optics (PHYS 2060), Modern Physics (PHYS 3040), aptitude for experimental physics and data analysis

Supervisor: Deborah Harris
Lab Website: https://www.yorku.ca/science/profiles/faculty/deborah-harris/
Contact Info: deborahh@yorku.ca
Number of Positions:
1
Project Description:
The MINERvA experiment has recorded over million-event samples of neutrino and antineutrino interactions in a fine-grained well-understood detector composed primarily of plastic scintillator augmented by thin passive targets of iron, lead, carbon, and water.  The collaboration is preparing a public release of its data and a simulation of the data, and the Undergraduate Research project will be to exercise the prototype version of this "Data Preservation" product to contribute to an antineutrino cross section measurement.  These cross section measurements are important inputs to long baseline neutrino oscillation experiments, which need accurate models of both neutrino and antineutrino interactions to correctly interpret their data and measure oscillation probabilities as a function of neutrino energy.

Student Responsibilities: The student will exercise a new Data Preservation Package that the MINERvA collaboration is assembling for broad use within the field of particle physics.  The student will work to extract an antineutrino cross section on hydrocarbon scintillator using this package, and may also contribute to data and simulation processing associated with producing this package. 
Desired Background/Skills:
Python, C++, PHYS 4040 or its equivalent

Supervisor: Joel Zyberberg
Lab Website: https://www.yorku.ca/science/profiles/faculty/joel-zylberberg/
Contact Info: joelzy@yorku.ca
Number of Positions:
Project Description:
Prof. Zylberberg is involved in a clinical study to develop adaptive Deep Brain Stimulation implants for Parkinson’s Disease (PD). These implantable devices apply electrical pulses to the brain to mitigate the motor symptoms of PD, and the adaptive device being studied uses algorithms (developed in Prof. Zylberberg’s lab) to infer the person’s sleep state from electrical signals recorded by the implanted electrodes. These algorithms work fairly well on the patients for which data was provided to train the algorithm, but do not always generalize well to new patients. To improve this generalization, the USRA student will implement an ensemble approach: using bootstrap aggregating (bagging), she will generate multiple training datasets, by drawing from the existing patient dataset with replacement. Each training dataset will be used to train a different neural network (NN) algorithm. After training, to infer sleep stages on new data, the data will be passed through each of these different NNs, and their outputs will be averaged together to yield the ensemble output. We hypothesize that this ensemble will generalize better than our current approach, that uses a single NN instead of the ensemble. The anticipated performance gain should arise because each member of the ensemble should make different kinds of errors in inference: by combining outputs from the members, any uncorrelated errors can be “averaged away” to yield more robust inference. This effect is known as the “wisdom of crowds”
Student Responsibilities:
The student will be responsible for writing python scripts that will train and test ensembles of artificial neural networks for sleep stage prediction. They will also be responsible for summarizing their findings into a report and a presentation.
Desired Background/Skills:
Strong python programming skills are required, ideally including experience with tensorflow and/or pytorch. Experience with processing electrophysiological brain signals (e.g., local field potentials) is beneficial but not required.

Supervisor: William Van Wijngaarden
Lab Website: https://www.yorku.ca/science/profiles/faculty/william-van-wijngaarden/
Contact Info: wavw@yorku.ca
Number of Positions:
1
Project Description:
This study will look at how radiation is transferred from the Earth's surface through a cloudy atmosphere to space. The effect of changing greenhouse gases, most notably carbon dioxide, has been calculated for the case of a clear sky. Work is underway to extend these calculations to consider scattering by clouds.
Student Responsibilities:
The student would be exposed to extensive programming using MATLAB and gain background in various numerical approximations
Desired Background/Skills: A background in computer programming is essential.