Deema Abayawardena

4th year PhD in Biology

PROJECT: An “omics” approach to unravel the molecular basis for a fundamental polarity in animal eggs

Deema is a fourth-year PhD student in Dr. Athula Wikramanayake’s lab in the Biology Department at the University of Miami. Her research focuses on uncovering the molecular mechanisms that establish polarity in animal oocytes, a process critical for proper embryonic development. By bisecting Patiria miniata (sea star) oocytes, she uses RNA sequencing and mass spectrometry to identify and analyze molecules involved in the localized activation of the evolutionarily conserved Wnt/β-catenin signaling pathway. As an IDSC fellow, Deema plans to apply deep learning techniques to analyze RNA-seq and mass spectrometry data to identify differentially enriched factors within the oocytes.

Nimay Mahajan

Junior, BA/BSc in Meteorology + Mathematics

PROJECT: Exploring the Dynamics of a Changing Climate: A Machine Learning Analysis on Indian Monsoon and Atlantic Ocean Interactions under Elevated CO₂ Conditions

Nimay is currently a junior at the University of Miami, double majoring in Meteorology and Mathematics. Under the guidance of Dr. Ben Kirtman, he has been analyzing two datasets from a climate model, one of which simulates doubled atmospheric carbon dioxide levels. Using these datasets, his research has focused on examining variability within the Indian Monsoon and its potential impact on vorticity patterns and overall hurricane activity in the East Atlantic through variables such as precipitation. Using the resources at the IDSC, Nimay hopes to apply machine learning techniques to analyze a wide collection of variables and gain insights into their roles and interactions within the Atlantic Ocean.

Cait Martinez

3rd year PhD in Atmospheric Sciences

PROJECT: ENSO Preconditioning versus Noise as the Forecast Evolves: A Machine Learning Diagnosis

MENTOR:
Samuel Goldberg, PhD | Assistant Professor, Department of Marine Geosciences, Rosenstiel School | IDSC Core Faculty Member

Cait is a third-year PhD student in Atmospheric Sciences, advised by Dr. Ben Kirtman at the Rosenstiel School of Marine, Atmospheric, and Earth Science. Cait’s research aims to disentangle the dual influences of deterministic dynamics and stochastic processes on the predictability of the El Niño-Southern Oscillation (ENSO). Prior to joining the Kirtman Group, Cait worked as watershed modeler for Daniel B. Stephens & Associates. She has a M.S. in Hydrologic Science and Engineering from the Colorado School of Mines and a B.A. in Geology from the University of Colorado at Boulder. As an IDSC Fellow, Cait’s objective is to develop a machine learning-based diagnostic tool to assess whether subsurface precursors or atmospheric noise play a more dominant role in determining ENSO event outcomes, with the goal of enhancing our understanding of the dynamic drivers of ENSO variability.

Katarina Micin

2nd year PhD in Human Genetics + Genomics

PROJECT: Development of Single-cell RNA Sequencing-Guided Chronic Myelomonocytic Leukemia (CMML) Classification Model

MENTOR:
Vanessa Aguiar-Pulido, PhD | Assistant Professor, Department of Computer Science, College of Arts & Sciences | P.I., Data Science and Computational Biology Lab

Katarina is a second-year PhD student in the Human Genetics and Genomics program and is a member of Dr. Justin Taylor’s leukemia-focused research laboratory. They graduated from the University of Tennessee with a B.S. in Biology with a concentration in Biochemistry and Cellular and Molecular Biology, and later graduated from the George Washington University with a M.S. in Anatomical and Translational Sciences. As an IDSC fellow, they hope to explore the integration of machine learning algorithms with single-cell RNA sequencing (scRNA) technologies in the context of Chronic Myelomonocytic Leukemia (CMML). More specifically, they are interested in the development of  a scRNA-guided multinomial CMML classification model with drug response prediction capabilities.

Jessica Okutsu

2nd year PhD in Biology

PROJECT: Therapeutic Potential of Selenium Against Cadmium Toxicity in Zebrafish

ADVISOR: Delia S. Shelton, PhD | Department of Biology

Jessica Okutsu’s research examines how selenium pre-exposure may protect against cadmium-induced toxicity in zebrafish larvae, with a focus on photomotor behavior, mechanosensory behavior, developmental morphology, and cardiovascular function, the latter of which has resulted in a publication in Cardiovascular Toxicology. As an IDSC Fellow, she aims to leverage machine learning techniques to analyze her complex dataset of over 1 million behavioral data points from 1,900+ fish across 18 unique treatment combinations over 4 time points to uncover patterns in selenium-cadmium interactions between multiple datasets. Her work has already demonstrated promising results showing selenium’s protective effects against cadmium-induced malformations. Jessica’s research has implications for understanding potential therapeutic approaches to heavy metal toxicity.

Alejandra Planells Devesa

2nd year PhD in Biochemistry + Molecular Biology

PROJECT: Exploring Peptide-Small Molecule Interactions with AI and Large Language Models for Versatile Molecular Detection and Biosensor Design

MENTOR:
Ravi Vadapalli, PhD | Director, Advanced Computing, Frost Institute for Data Science and Computing (IDSC)

Alejandra Planells Devesa is a second-year Ph.D. student in Biochemistry and Molecular Biology in the Sylvia Daunert Lab at the Miller School of Medicine. She graduated with a B.Sc. in Biotechnology from the high academic program at the Universidad Politécnica de Valencia in Spain and has gained extensive international research experience in labs across Spain, France, and Switzerland. As an IDSC Fellow, Alejandra aims to advance her work on designing adaptable peptide-based biosensors, leveraging machine learning algorithms and large language models (LLMs). Her focus is on developing a streamlined computational pipeline for the rapid identification and optimization of peptide binders with high specificity and affinity, applicable to diverse molecular targets. This approach has the potential to address a broad spectrum of challenges in molecular recognition across biological, clinical, and environmental contexts.