Genomic Annotation & Recombination Mapping of Feline Coronavirus (FCoV/FIPV)Context & Reference: Managed high-density genomic sequence and phenotypic data annotation pipelines for the groundbreaking LIU College of Veterinary Medicine study, "Emerging Feline Infectious Peritonitis Virus strains in Feral Cats exhibit extensive genomic recombination and ongoing viral evolution" co-authored by Dr. Abid Ullah Shah, Dr. Csaba Varga, Nadine Moawad, Dr. Megan Rosen, Diane Levitan, and Dr. Maged Hemida [1].Annotation Task: Executed precision sequence-tagging across 143 isolate sequences, hand-labeling 76 distinct amino acid substitutions in the S1 region and 12 highly specific mutations within the viral receptor-binding domain [1]
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Data CollectionOver the past several years, my work has directly spanned high-level biomedical data curation, machine learning model training, and advanced clinical data visualization across elite research, academic, and clinical institutions in New York. Below are the primary data labeling and annotation projects I have managed:Project 1: Genomic Annotation & Recombination Mapping of Feline Coronavirus (FCoV/FIPV)Context & Reference: Managed high-density genomic sequence and phenotypic data annotation pipelines for the groundbreaking LIU College of Veterinary Medicine study, "Emerging Feline Infectious Peritonitis Virus strains in Feral Cats exhibit extensive genomic recombination and ongoing viral evolution" co-authored by Dr. Abid Ullah Shah, Dr. Csaba Varga, Nadine Moawad, Dr. Megan Rosen, Diane Levitan, and Dr. Maged Hemida [1].Annotation Task: Executed precision sequence-tagging across 143 isolate sequences, hand-labeling 76 distinct amino acid substitutions in the S1 region and 12 highly specific mutations within the viral receptor-binding domain [1]. Mapped complex breakpoints in the ORF1b, spike, and NSP-3abc genomic regions to track recombination patterns mimicking the Cyprus FIP epidemic [1].AI/Data Output: Created the structured genomic training datasets utilized by machine learning models to predict target protease inhibitors, accelerate pharmaceutical development, and forecast cross-species viral jump risks.Project 2: In Silico Vaccine Epitope Mapping & Machine Learning CurationContext: Collaborated on immunoinformatic data curation aligning with the LIU Molecular Virology Department workflow, focusing on the in silico design of multi-epitope DNA vaccines against feline lentiviruses (FIV) using machine learning tools.Annotation Task: Standardized, reviewed, and hand-labeled predicted B-cell and T-cell linear and discontinuous epitopes within major viral structural proteins. Tagged and annotated conserved regions of the gag, pol, and env lentivirus genes to evaluate strong immune binding responses while minimizing allergenicity profile scores.AI/Data Output: Generated structural data parameters used to train machine learning tools to predict Toll-like receptor (TLR) binding affinities and automate next-generation vaccine construct evaluations.Project 3: Mock Clinical Simulation & Case-Based DVM Curriculum ArchitectureContext: Formulated and annotated digital clinical case-study structures for Years 1–3 DVM students within the LIU College of Veterinary Medicine Core Curriculum, collaborating on pedagogy data structures alongside LIU Faculty like Dr. Megan Rosen [1].Annotation Task: Designed, tagged, and ingested multi-tiered mock simulation cases (focusing on triage, anesthesia caseload distributions, and emergency pharmacology) into the institutional Learning Management System (LMS). Hand-labeled student diagnostic decision-trees within the software framework to measure logical compliance with board-certified DACVAA evidence-based parameters.AI/Data Output: Provided an engineered diagnostic training queue used to fine-tune educational large language models (LLMs) to automatically evaluate veterinary student clinical reasoning competence and auto-generate logically sound board exam prompts.Project 4: Neural Spike Train Electrophysiology & Histogram ProgrammingContext: Managed raw neuroscientific data collection and clinical signal curation for human and non-human primate (NHP) neurosurgical epileptic cohorts at the NYU Center for Neural Science.Annotation Task: Utilized MATLAB to program, clean, and filter out high-volume electrical artifacts from intracranial electrocorticography (eCOG) trace lines. Labeled distinct seizure-onset sequences, spike intervals, and wave patterns, translating complex physiological signals into frequency distribution histograms.AI/Data Output: Produced clean, deeply annotated signal arrays used to train computer vision models and deep neural networks to forecast epileptic seizure events in real-time.Summary of Core Technical QualificationsData Science & Software: MATLAB, Statistical Data Visualization, Histogram Frequency Analysis, Learning Management Systems (LMS - Canvas/Blackboard data ingestion).Biomedical Domain Expertise: Genomics, Molecular Virology, Electrophysiology (eCOG), Non-Human Primate (NHP) Methodologies, Human Clinical Research, Advanced Surgical Caseload Management, DACVAA Collaboration Protocols, Years 1–3 DVM Pedagogy.AI Training Skillset: Expert-tier Reinforcement Learning from Human Feedback (RLHF), medical LLM prompt engineering, edge-case validation, and strict logical error correction.