REHS Project Descriptions

Capture summer fun at UCSD with photos and videos! Help us add to our photo gallery! This internship gives you the opportunity to be as creative as you wish well helping us to archive our summer 2025 outreach programs.

Stays until they ask a visit each of our summer programs during the week. Students will then create a small photo gallery for each workshop that we can not only share online, but also share with parents.

Students will be asked to help us with our social media presence, such as assistance with Instagram and  Blue Sky.

Students must be proficient in Canva and be highly skilled in social media outfit

This fun and interesting summer opportunity allows students to interact with summer camp attendees, their parent and instructors. Schedules for students in grades 6–12, this summer students will have a chance to explore beginning robotics, advanced robotics and programming! Some students may also experience our new ARE internship, which offers exposure to research areas such as computer science, medical engineering and data science

Student interns will act as teaching assistants for the above mentioned camps and workshops. Students must possess excellent communication skills and be able to interact easily with students in middle and high school. In addition to helping students better understand the materials being taught, students will help with daily check in and check out and assist instructor instructors with classroom and computer set up.

PLEASE NOTE – This is a five week opportunity only. Students will be asked to intern 20 hours per week. Camps and workshops will end on Friday, July 18, 2025.

Cancer is the second leading cause of death in the United States, and responsible for 580,350 deaths in 2013, it has become ever more imperative that researchers focus their attention in this field. Ingenuity Integrated Pathway Analysis (IPA) program is used to build and explore transcriptional networks, microRNA-mRNA target networks, phosphorylation cascades, and Protein-Protein or Protein-DNA interactions. We try to identify regulatory effects that lead from signaling events to transcriptional events for causal network analysis and to construct a comprehensive chart of diseases and functions associated with target genes so as to elucidate gene functionality and possible points of drug administration.

The Science Gateways Center of Excellence (SGX3) is looking for talented communicators to explain cyber infrastructures and the research they are conducting to the community. To do this, the communicator will conduct research about these cyber infrastructures – which we call science gateways, understand each science gateways’ research applications to societal challenges, and document how they are solving research challenges or instructing the next generation. Tasks will include conducting your own research about multiple science gateway projects, writing down findings in a digital document to be shared with your mentor, and writing a story about the science gateway. During the summer you will publish several stories about these science gateways with support of your mentor. You will also work with the SGX3 team to publish these stories in monthly newsletters and on the sciencegateways.org website. The successful student candidate(s) will meet regularly with their mentor to review tasks and provide suggestions and feedback. The student candidate(s) will also have access to other communication channels to talk to other members of the SGX3 team via Slack and email.

This project involves running Jupyter Notebooks on the NSF funded Expanse high-performance (HPC) system [1] and testing software to be used on the NSF funded ICICLE AI project [3]. Expanse is SDSC’s newest supercomputer. The result of a $10M National Science Foundation (NSF) award, Expanse delivers over 5,2 peak petaFlOps of computing power to scientists, engineers, and researchers all around the world [2]. Expanse provides three kinds of HPC/CI resources: General Computing Nodes, NVIDIA GPU Nodes, and the petascale Luster filesystem. Thousands of users have accessed these high-performance computing (HPC) resources via traditional runs from the command line and using batch queuing systems.

The National Science Foundation funded AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) [3] will build the next generation of Cyberinfrastructure to render Artificial Intelligence (AI) more accessible to everyone and drive its further democratization in the larger society. ICICLE will develop intelligent cyberinfrastructure with transparent and high-performance execution on diverse and heterogeneous environments. It will advance plug-and-play AI that is easy to use by scientists across a wide range of domains, promoting the democratization of AI.

Scientists using HPC Systems working with interactive HPC tools such as Jupyter notebooks to implement computational and data analysis functions and workflows [5]. Jupyter notebooks are web applications that allow you to create and share documents that contain live code, equations, visualizations and narrative text. These notebooks part of a general trend in research computing away from command-line style interfaces and towards browser-based and graphical interfaces. Jupyter notebooks are especially useful for interactivity: the development, testing, and exploration of data sets or as an instructional resource [6]. Users working interactively expect a timely response, both for initial application startup and during the course of a session.

The goals of this research project are:

1. To test and develop Jupyter Notebooks that run software components developed by the ICICLE project.
2. To learn the basics of  Jupyter notebooks, AI, and Knowledge Graphs.

The research components are:

1. Contribute to the body of knowledge needed for hosting live, dynamic, interactive services that interface to HPC systems
2. Learn how to develop interactive notebooks to be used for education and training of the users of these systems

Autistic Spectrum Disorders (ASD):
ASD is a neurodevelopmental disorder that includes social impairments, restrictive and repetitive behavioral patterns, and communication difficulties. Scientists have not found the cause for autism, however ASD is associated with both genetic and environmental factors. Autism is predicted to be caused by genetic mutations in critical neurological areas of the genome due to its etiological heterogeneity and likelihood in siblings. Specific genes and genomic loci have been located and implicated in ASD development. We study these gene interactions with the transcription factors that activate them and to find the spots of possible drug administration, molecular modeling and molecular dynamics simulations.

Alzheimer’s:
Alzheimer’s Disease is a neurodegenerative disorder that affects a variety of the brain’s functions, starting with short term memory loss, and eventually progressing to mood swings, disorientation, depression, and even loss of bodily functions. It is responsible for 60-70% of all dementia cases, and is the sixth leading cause of death in America. It is characterized by a buildup of β-amyloid plaques in the brain. There is no known cure for the disorder today, but recently, the amyloid hypothesis has been heavily researched as the mechanism of the disease. The amyloid precursor protein (APP) is cut by two enzymes, β-secretase and γ-secretase, and β-amyloid, the toxic form of the protein, is one of the segments that remains. Since the marker of Alzheimer’s is the buildup of this plaque in the brain, there is potential for slowing the progression of the disease if production of this protein is inhibited. We study existing drugs inactions with the target proteins and try to find methods to improve their action.

Cancer:
Cancer is the second leading cause of death in the United States, and responsible for 580,350 deaths in 2013, it has become ever more imperative that researchers focus their attention in this field. Ingenuity Integrated Pathway Analysis (IPA) program is used to build and explore transcriptional networks, microRNA-mRNA target networks, phosphorylation cascades, and Protein-Protein or Protein-DNA interactions. We try to identify regulatory effects that lead from signaling events to transcriptional events for causal network analysis and to construct a comprehensive chart of diseases and functions associated with target genes so as to elucidate gene functionality and possible points of drug administration.

The EarthCube Office strives to assist geoscientists and the wider research community through tools, methods, standards, architectures, and community connections. Our group seeks high school students that are interested in writing pieces related to EarthCube projects from start to finish. As a result of this internship, students will come away with an ability to reduce technical jargon and other barriers for the creation of easy-to-understand written pieces. Students will have the opportunity to choose a geoscience domain that is of interest to them and create both written and video pieces regarding that topic.

The Neuroscience Gateway (NSG – https://www.nsgportal.org) provides access to supercomputing resources for computational and cognitive neuroscientists. Through a simple web-based portal, the NSG provides a user-friendly environment for uploading models or data, specifying supercomputing job parameters, querying running job status, receiving job completion notices, and storing and retrieving output data. The NSG distributes user jobs to appropriate supercomputing resources. REHS students will learn about supercomputing and NSG during the initial period of the internship. Followed by that REHS students will be involved in either computational modeling of neurons using the NEURON software or processing of Electroencephalography (EEG) data using the EEGLAB software. In computational modeling scientists build models of neurons or network of neurons that perform various functions in our brain. NEURON is a widely used software for such modeling of neurons. Students will learn about a modeling project as part of their internship. EEG project involves recording of brain’s electrical activities tied to some function a subject is performing, and processing the data using software (such as the EEGLAB) and computing resources to see connection between brain signals and the function. Students will record and process EEG data as a part of their internship.

We are interested in students who have some background and interest in programming and scripting and are interested in neuroscience, computer science, data processing and modeling. Some experience with programming languages and exposure to Linux systems are preferred.

Our group seeks high school students who are interested in journalism with a focus on learning more about effectively communicating the results and societal benefit of science, engineering, and technological research projects. Candidates should have a strong desire to learn more about how to communicate technological research — with a keen eye for making the work both engaging and easily comprehended by a general audience. At the same time, students should be mindful of not diminishing the technical work of the research team in any way. Sound challenging?

As a result of this internship, students will come away with an ability to reduce technical jargon and other barriers for the creation of easy-to-understand written pieces. Students will have the opportunity to choose a science, engineering, and/or technological domain that is of interest to them and create written pieces regarding that topic as related to supercomputing.

Students will be assisted with both the science writing process as well as the final editing and completion of their articles with accompanying images, captions, and credits. 

In this project, we will explore how modern machine learning approaches can be employed in computational chemistry. As a background, the work in our lab encompasses computer simulations based on quantum chemistry and molecular dynamics. We develop numerical models and software for atomistic simulations of condensed phase systems and apply these to solve problems in the chemical sciences, ranging from atmospheric chemistry to biochemistry and drug design. Our focus has been on multiscale methods that couple computationally demanding quantum descriptions of the electronic structure with computationally simpler classical interaction potentials in so-called QM/MM methods. This enables numerical simulations of realistic models that are intractable with pure QM methods. The importance of such methods has been recognized with the award of the 2013 Nobel Prize in chemistry for “the development of multiscale models of complex chemical systems”. While machine learning approaches have a long history in computational chemistry, recent advances in data storage and compute capabilities have ushered in a new era of data-driven approaches, which we seek to explore and combine with existing QM/MM models. We are involved in the development of several software packages that are used by many research groups in academia and industry. This includes the quantum chemistry software packages ADF and QUICK and the molecular dynamics simulations package AMBER. Several other major codes that are deployed on major national computer resources are also an integral part of our work.

In this REHS project, the students will explore machine learning approaches that may improve the speed or enhance the accuracy of current state-of-the-art QM/MM simulation approaches. We will jointly work through online course material that introduces machine learning concepts from simple classifications to deep learning, culminating in applications that predict chemical properties. We will make use of Jupyter notebooks, the Python programming language, and widely used machine learning software frameworks. We will explore the performance of the software on CPU and GPU hardware including the Expanse supercomputer at SDSC. Time permitting, we will generate training data using quantum chemistry software and develop models that can predict new data without having to run the expensive QM simulations in the first place. The interns will thus have the possibility to acquire a wide range of skills in data science, computational chemistry, and high-performance computing. As an essential part of the internship the students will learn how to document their research, prepare research reports and present their results to their peers – skills that are very important for a successful research and engineering career.

Aggregation of specific proteins is associated with a number of diseases including Parkinson’s (PD), Alzheimer’s (AD), and other neurodegenerative diseases. Type II diabetes (T2D) is also linked to aggregation of Islet amyloid polypeptides (IAPP) IAPP is found to be significantly accumulated as extracellular amyloid deposits in the pancreas of T2D patients; it is shown that early oligomers are significantly cytotoxic species that lead to beta-cell apoptosis. The mechanisms through which IAPP leads to T2D are unclear. Various lines of evidence appeared to support the contention that IAPP oligomers built in the early stages of aggregation form annular protofibrils and consequently pores in the membranes the same way as amyloid-beta and alpha-synuclein correspondingly create pores in the cell membrane in AD and PD. Increase of calcium influx to the cells can lead to apoptosis in case of all these diseases.

The specific tasks of the project are using computer modeling and simulations to accomplish the following goals: (1) To characterize the interactions between IAPP, amyloid beta, and alpha-synuclein molecules in oligomers, and interactions between molecules and the membranes. (2) To validate a concept of self-assembly of these molecules, using a set of mutants known to enhance or inhibit IAPP aggregation process with identifiable structural properties.

The task will include extensive analysis of research articles in the field, related to modeling and simulations above mentioned proteins and their interactions with the membrane. Computational molecular modeling and molecular dynamics simulations.

The Open Science Chain (OSC – http://www.opensciencechain.org) project uses blockchain technologies to protect the integrity and provenance of research artifacts. The project is looking for summer interns who are interested in working with the OSC project. Interns will have the opportunity to engage in various tasks, including developing blockchain applications, revamping the web portal, or assisting in unit test development, depending on their interests and expertise levels.  Programming experience (Go, Java etc) and familiarity with Linux and container technology are preferred. This is a completely virtual internship. 

The MLPerf Inference Benchmarks are designed to measure and evaluate the performance of artificial intelligence (AI) and machine learning (ML) inference workloads on different computer hardware and software platforms. The benchmarks include problems in computer vision, natural language processing, and generative AI.

The fundamental aim of this research project is to develop a continuous integration and continuous deployment (CI/CD) pipeline to automate the process of building, testing, deploying, and running a subset of the MLPerf Inference Benchmarks on Expanse – a high-performance computing (HPC) system designed to support a wide range of scientific research.

During the course of this project, you will learn how to:

• run compute jobs on a supercomputer like Expanse via the Slurm workload manager;

• setup a secure, end-to-end CI/CD pipeline using GitHub Actions; and

• evaluate the performance of AI/ML inference workloads.