Working under Vikram K. Mulligan & P. Doug Renfrew to build deep learning models that increase the in-silico capabilities of generating heterochiral macrocycles in conjunction with Rosetta. Also, working on development projects.

Continuing in the Hosseinzadeh Lab for my doctorate. My work involves designing de-novo macrocycles and speeding up the design of therapeutic drugs, such as peptides and cyclic peptides, by developing new machine learning models or computational tools.

-Working in the Hosseinzadeh Lab to design Peptides to inhibit pathogenic viruses.-Using Rosetta to model/design peptides and peptide docking to targets to induce inhibition.-Setup, maintain, and be an administrator for the high-performance computer (HPC) cluster used in the lab.-Bash, Python, XML, and R used daily

- Work on a team to develop/apply convolutional neural network models to analyze photos pulled from Facebook and pull out relevant research question information.- Develop/apply deep learning Named Entity Recognition (NER) Natural Language Processing (NLP) model to pull out keep information from scraped Facebook Posts. - Develop a script to web scrape Facebook and pull relevant information into a JSON file format for future processing.

- Used Python to generate a Protein-Protein Interaction Network between human macrophage proteins and Mycobacterium Tuberculosis (Mtb) proteins to assess how Mtb affects protein interactions- Community detection algorithms run on the disease and healthy networks to develop intuitions on how Mtb changed the network- Applied deep learning link/edge prediction methods to the network to generate undocumented protein-protein interactions- Modeled predicted protein-protein interactions using AlphaFold 2

• Independently generated a chimeric capsid library, from AAV, which will be used for delivery of gene therapy• Developed an assay to analyze immune response to different serotypes of AAV for gene therapy screening purposes.• Created protocols for company’s neutralizing antibody assay (NAb) and production of a chimeric library.• Screened 120 monoclonal antibodies• Performed Western and ELISA analysis of dog protein from company experiments• Transfected and transduced cell lines• Created virus for NAb assay screening and of my chimeric capsid library• Utilized Gateway and T4 DNA ligation methods, Gateway was used to create the chimeric library, while T4 ligase was used to make our treatment plasmids• Familiar with ethanol precipitation and drop dialysis, which was essential for downstream electroporations• Carried out routine PCR, qPCR, Mini, Maxi, and Giga preps

• Overexpressed lentiviral vectors encoding ORF’s of the envelope of an endogenous retroviral element in LLC1 cells• Injected LLC1 overexpressed cells into mice and measured cancer progression• Cultured and maintained cell lines 293T, Hela, U2OS, PSN1, BXPC, MYC driven and pancreatic cancer lines, and immortalized primary mammalian lines.• Designed plasmids using SnapGene• Designed primers, such as Gibson, adding restriction sites, and creating type II enzyme restriction sites ordered through IDT• Carried out enzyme restriction digestions for creation and confirmation of plasmids.• Utilized T4 DNA ligase to ligate and create new plasmids for transfection and experimental purposes• Mini & Maxi prepped the created lentiviral vectors for transfection.

• Familiarized self with creation of buffers and gels used for biological experiments• Learned how to carry out PCR & qPCR, and how to run and interpret results• Managed cell lines and created stocks of cells for later use, particularly used 293T cells, and cancer derived cell lines• Extracted RNA and protein from cells, to be used in qPCR/PCR and • Westerns, respectively• Learned BCA assay and western blot techniques to analyze protein concentration extracted from cell lines• Western blots were used to distinguish differences between experimental groups• Transfection of cell lines 293T and cancer cell lines

• MRI brain scan review and quality control follow ups• used Freesurfer and familiarized self with ICD data servers