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Genomics Cohort Training—2026-2027

Summer 2026

Students who needed preparation for their fall matriculation into the program took a summer bridging course DSCC 162: Data Structures in Python with Andrea Cogliatti.

At the end of the summer, students were invited to participate in the annual biomedical hackathon.

Fall 2027

Students participated in 13 credits of coursework. During the first semester the students took the following courses:

BIO 457: Applied Genomics, Professor Amanda Larracuente
DSCC 462: Computational Introduction to Statistics, Professor Anson Kahng
DSCC 440: Data Mining, Professor Monika Polak
IND 501: SMD Research Ethics, Professor Jonathan Herrington

Professor Justin Fay initiated a professional development workshop series that covered individual development plans, professional network building, career paths in genomics, and navigating the job/internship market.

A pre-program survey was done by Cathy Cerosaletti to assess the cohort’s understanding and experiences and competency with research, ethics, equity and inclusion, genomics and career development skills.

Other events that happened during the semester included:

Attending poster presentations by the previous cohort during Meliora Weekend.
Connecting with mentors at a mentor meetup.

Winter 2026

A sequence of three extra-curricular, hands-on workshops aimed at building core competencies in genomic data analysis were offered by the Genomics Research Center (GRC) in conjunction with the Goergen Institute for Data Science GIDS-REM fellowship program and the Center for Integrated Research Computing (CIRC). These workshops covered nine core competencies were required for GIDS-REM fellows were open to any researchers in the University of Rochester and Medical Center communities.

An introductory session on Monday, January 11 from 10 a.m.-3:30 p.m. by Professor Brendan Mort and staff of CIRC at the Vista Collaboratory in Carlson Library was required for those who needed Blue Hive access or basic knowledge of Linux clusters, running Jobs and Slurm, Modules, Software, and Jupyter.

The workshops were two hours each and led by John Ashton and staff of GRC:

Tuesday, January 12: Experimental Design and GRC deliverables. This workshop introduces experimental design considerations in the generation of high throughput sequencing data including RNAseq, ChIP-seq (and related -seq technologies).
Wednesday, January 13: Bulk RNAseq. This workshop introduces quality control in bulk RNA sequencing experiments and best practices in RNA seq analysis.
Thursday, January 14: Single cell sequencing. This workshop introduces single cell RNAseq and ATACseq data generation and analysis.

Spring 2026

Students returned to studies with 12 credits of coursework taking:

DSCC 465: Introduction to Statistical Machine Learning, Professor Cantay Caliskan
BST 434: Genomic Data Analysis, Professor Matthew McCall
BIOL 453: Computational Biology, Professor Justin Fay

Mentor Meetings were used to start discussions about summer internship placements.

Summer 2026

Students engage full-time in their internships and lab’s research assignments.

Professor Fay will lead the Summer Seminar series—a weekly venue to learn and engage in applied problems in genomics. The series includes short presentations from students, trainees and faculty on current research problems with an emphasis on pragmatic solutions. Presentations will be followed by a discussion to enhance practical understanding and assessment of genomics approaches, methods and computational analyses. The seminar series will be run as part of the Goergen Institute for Data Science MS program in applied genomics and open to students and faculty interested in application of genomics and genome technologies to problems in biology.

The seminar series will also included a professional development component for students in the GIDS-REM training program. For this component students learn and assess presentation strategies and research communication skills to enhance their career goals in either academics or industry.

Seminar schedule (TBA):

At the end of the summer, students will participate in the annual Biomedical Data Science Hackathon.

Fall 2026

Students enroll in:

DSCC 483: Practicum Project, Professors Ajay Anand and Cantay Caliskan
DSCC 461: Database Systems, Professor Eustrat Zhupa

Conference Travel

The cohort is considering attending one of the following conferences: TBD

Practicum Project

Fall presentations will be posted as they become available.

Genomics Cohort 2026-2027

Seren Castellano

HOMETOWN: Des Moines, IA

UNDERGRAD DEGREE: BS Biology with Honors, BS Anthropology with Honors, University of Iowa, 2026

WHY ROCHESTER: The GIDS-REM curriculum at the University of Rochester allows me to take my background in biology and expand it to the realm of data science and computational biology. I have always been interested in this field but did not have the coursework to be able to teach me the fundamentals of data science and statistics, which could be applied to genomic data. The hands-on research and mentorship from this program allow me to not only build a new repertoire of skills in data science and bioinformatics but also provides support for my future professional career.

PREVIOUS EXPERIENCE: During my undergraduate degree, I was involved in several research labs focused on studying snail sleep and evolution, viral mutation rates, Antarctic worm biodiversity, and brow ridge variation in humans. From these opportunities, I built a diverse skill set and wanted to dive deeper into bioinformatics, as the research techniques aligned with my overall goals and interests.

SELECTED MENTORS:

FUTURE PLANS: My long-term goal is to pursue a PhD focused on studying human archaic admixture and introgression across population groups and related hominin species. From this, I hope to uncover population history and shared genetic characteristics over the course of human evolution. Additionally, I am interested in expanding into the biotechnology industry, focusing on human genetic diversity and diseases for a few years before pursuing my doctoral studies.

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Giovanni De La Luz

HOMETOWN: Los Angeles, CA

UNDERGRAD DEGREE: Bachelor of Engineering, Computer Science, California Polytechnic State University-San Luis Obispo, 2026

WHY ROCHESTER: I chose the University of Rochester because of its extensive biomedical research opportunities and close connection with its Medical Center, where I hope to translate genomic findings to clinical solutions. The GIDS-REM program will help me grow as a genomic scientist through collaboration with a community of fellows and mentorship from program faculty.

PREVIOUS EXPERIENCE: During undergrad, I engaged in multiple bioinformatics research opportunities such as analyzing methylation data from a lung cancer cohort for Protean BioDiagnostics, building a machine learning model to predict DNA melting temperature at UC Riverside, and developing a phylogenetics pipeline for protein subclassification under Cal Poly's Bioinformatics Research Group (BiRG).

SELECTED MENTORS:

FUTURE PLANS: My career goal is to become an R&D Bioinformatician in cancer genomics and biomarker discovery, helping develop novel treatment options and guide clinical decision-making.

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Caden Gunnarson

HOMETOWN: Rogers, Minnesota

UNDERGRADUATE DEGREE: BA, Biochemistry and Molecular Biology, Gustavus Adolphus College, 2023

WHY ROCHESTER: The GIDS-REM program is a great opportunity to build expertise, engage with researchers, and develop mentorship relationships in the field of genomics. This program provides many opportunities for research through the Medical Center. In addition, Rochester is much like my home state of Minnesota, with all four seasons and beautiful scenery, with plenty to do in only a short drive.

PREVIOUS EXPERIENCE: As an undergraduate, I did research in the field of analytical chemistry, working on simulations and building tools for liquid chromatography. After graduation, I worked for two years at Oncodea Corporation, a cancer screening startup, where I combined spectroscopy and machine learning to build detection models. That experience taught me the importance (and difficulty) of collecting high-quality, diverse data for training models.

SELECTED MENTORS:

FUTURE PLANS: Develop a career in biotechnology combining machine learning and biology to understand and solve problems. I look forward to developing my specific interests and learning where new developments in the field could come from.

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Sofia Heferle

HOMETOWN: Buffalo, NY

UNDERGRAD DEGREE: BS Biomedical Engineering with a concentration in biosignals and biosystems; minor in computational biology, University of Rochester, 2026

WHY ROCHESTER: I enjoyed my undergraduate experience at the University of Rochester, so being able to continue my education here was a clear-cut choice. As someone coming from an engineering background, I appreciated how the master’s program was welcoming to people with different backgrounds wanting to gain experience in Data science and genomics. I also loved the opportunity to make lasting connections through the mentorship program of the GIDS-REM fellowship.

PREVIOUS EXPERIENCE: During my undergraduate education, I gained competencies in various biomedical engineering techniques. For my senior project, my team designed an anatomical model to simulate a prostatectomy for promotion of LSI Solutions®’s PROSTAGRIP™ . Before beginning my master’s at the University of Rochester I joined the Choe lab as a research assistant where I am currently assisting in the research and validation of a semi-automated process for analyzing large scale EEG data. This information is used for neurological examinations of ECMO patients to monitor for encephalopathy.

SELECTED MENTORS:

FUTURE PLANS: With the attainment of this master’s degree, I plan to seek a position as a bioinformatician in industry. I am passionate about gynecology and oncology, so I am interested in research focused on the heritability of gynecological cancers. I am mainly focused on the usage of bioinformatic tools to better screen for these cancers and improve women’s health.

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Angelina John

HOMETOWN: Houston, Texas

UNDERGRAD DEGREE: BS, Data Science, University of Texas at Dallas, Magna Cum Laude, 2026

WHY ROCHESTER: I always knew I wanted to use my technical Data Science knowledge in fields like Bioinformatics, Healthcare, and Genomics. The GIDS-REM Fellowship and the genomics track were exactly the kind of Master's program I was looking for. I chose Rochester to build upon what I learned in my undergraduate degree and to specialize further into a field I'm very interested in. I also greatly value the mentorship, support, and resources that will help me achieve my goals. The environment and community here were another reason too; I knew there would be a lot of opportunities for research and growth.

PREVIOUS EXPERIENCE: My undergraduate degree gave me a good foundation in Data Science with course work in Computer Science, Statistics, and Math. During my time there, I got introduced to genomics when I joined a neuroscience lab at UT Dallas that was studying the neurobiology of the basal ganglia. Here, I got to analyze single cell RNA-seq data and gain insights on the neural populations relevant to Parkinson's disease, autism, and compulsive disorders.

SELECTED MENTORS:

FUTURE PLANS: My end goal is to get a job as a Data Scientist, Data Analyst, Bioinformatician, or similar position working in Biotech or Healthcare industries. I plan on using the knowledge and skills gained in this program to secure an industry position in the intersection of Data Science, AI, and Medicine/Bioinformatics.

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Paulete Perez-Haiek

HOMETOWN: Chicago, IL

UNDERGRAD DEGREE: BS, Computational Biology, University of Rochester, 2026

WHY ROCHESTER: The University of Rochester offers a unique combination of data‑science training and active research in genomics, which aligns directly with my academic background in Computational Biology and my long‑term goals in translational data science. Its collaborative environment, supportive mentors, and strong connection with the University of Rochester Medical Center provide the opportunities and guidance I need to pursue oncology and immune‑genomics work in industry.

PREVIOUS EXPERIENCE: I have experience in comparative transcriptomics, where I worked with cross‑species retinal bulk RNA‑seq datasets and GWAS integration to study conserved, disease‑relevant pathways. I completed a two‑semester method‑development project in the Larracuente Lab, optimizing a PCR protocol using Drosophila melanogaster embryos for future genotyping workflows. I also have teaching experience as a TA for Computational Biology and a Python workshop leader. Currently, I'm working on clinical oncology machine‑learning research at UR Health Lab, focusing on CAR‑T patient outcomes.

SELECTED MENTORS:

FUTURE PLANS: After graduating, I plan to build a career at the intersection of oncology, immune genomics, and clinical machine learning. In industry, I hope to work on data‑science and ML teams developing tools for precision oncology, biomarker discovery, and immunotherapy response prediction. Long‑term, I am committed to applying these approaches in combination with population‑genomics analyses to help reduce inequities in health care and improve outcomes for underserved patient populations.

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