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One example of a capstone project in computer science would be developing a customized medical information system for a clinic or hospital. For a project of this scope and scale, students would work in a team to analyze requirements, design the system architecture, develop the necessary code and applications, implement security features, test all aspects of the system, and deploy it for real-world use at the medical facility.

In the initial phases, the student team would work closely with administrators, doctors, nurses and other medical staff at the facility to understand their detailed workflow processes, data storage and reporting needs, and systems integration requirements. This requirements gathering and analysis phase is crucial to understand all of the features and functionality that must be included in the custom medical information system. The team would document gathered requirements, perform gap analysis on current workflows versus desired future state, and prioritize features to ensure the system addresses top priorities and pain points.

With a comprehensive understanding of requirements in hand, the student team would then begin designing the system architecture. Key consideration would include decisions around database structure and schemas, backend application design using appropriate programming languages and frameworks, front-end user interface designs for various user roles (doctors, nurses, administrators etc.), integration with existing practice management systems or electronic health records if needed. Important non-functional requirements around security, privacy, performance, scalability and maintainability would also influence architectural design decisions.

Detailed documentation of the system architecture design would be created, covering database models, application component diagrams, interface wireframes, infrastructure requirements and more. Students would present and defend their proposed architecture to stakeholders to obtain feedback and approval before moving to implementation.

The implementation phase represents the bulk of effort for the project where students translate designs into working code and applications. Key activities would include:

Building out the backend applications using languages like PHP, Python, Java or .NET to implement the required functionality based on requirements and architectural designs. This includes developing APIs, business logic and integration layers.

Creating a frontend UI using HTML, CSS and JavaScript frameworks like React or Angular that adheres to user experience designs and provides role-based interfaces.

Setting up and configuring a database like MySQL, SQL Server or MongoDB based on the data models and architecting appropriate schemas, indexes, foreign keys etc.

Populating the database with sample test data including demo patient records, appointment schedules, insurance profiles and more to enable thorough testing later.

Integrating the custom system with other existing medical facility systems like practice management software or EHR products through pre-defined APIs.

Implementing security features like multi-factor authentication, authorization controls, encrypted data transfer and storage, input validation etc. based on a thorough security risk assessment.

Developing comprehensive installation, configuration and operation guides for medical staff.

Performing extensive testing of all functionality from different user perspectives to uncover bugs. This includes unit testing code, integration testing, user acceptance testing, load/stress testing and more.

Once development is complete, the student team would help deploy and launch the new medical information system at the partner medical facility. This includes performing the necessary installation and configuration activities, onboarding and training of medical staff, addressing any post-deployment issues, and measuring success based on defined key performance indicators.

Ongoing maintenance and improvements to the system over several months post deployment may also be part of the project scope, requiring the team to monitor system performance, implement requested enhancements, and resolve production issues.

In the concluding project phases, the student team would document the complete system development lifecycle and create a comprehensive final report. An oral presentation would be given to stakeholders highlighting achievements, lessons learned, future roadmap for the system and reflections on career readiness gained through such a hands-on capstone project experience.

An example medical information system capstone project as outlined above covers the full scope from requirements analysis to deployment, addresses real-world problems through technical solutions, and provides students an in-depth industry-aligned experience to showcase their cumulative skills and knowledge gained throughout their computer science education. Completing a complex project of this scale truly allows students to synthesize their learning and strengthens their career preparedness for jobs in both software development and healthcare IT fields.


Mental health is one of the most important fields in healthcare today. There are so many people struggling with various mental illnesses and not getting the help and treatment they need. As a future mental healthcare professional, your capstone project is an important opportunity to explore an area of interest and make a meaningful contribution. Here are some potential capstone project ideas you could pursue:

Development and evaluation of a mental health program for high school students. You could develop a program focused on reducing stigma, increasing mental health literacy, teaching coping skills or supporting students dealing with issues like anxiety, depression or other disorders. Your project would involve designing the specific program elements, getting necessary approvals, implementing the program at a local high school and evaluating its effectiveness through pre/post surveys or focus groups. This type of program could help many youth struggling with their mental health.

Assessment of availability and access to mental healthcare services in rural communities. It’s well known that access to mental healthcare providers and services is often severely lacking in rural and remote areas. For your project, you could research service availability within a certain rural county or region, identify gaps through provider directories or surveying residents, and propose recommendations on how to expand services through telehealth, mobile crisis teams, satellite clinics, incentives for clinicians to practice in underserved areas, etc. Presenting data-driven solutions could help expand access where it’s desperately needed.

Analysis of the mental health impacts of the COVID-19 pandemic. The pandemic has taken an immense toll on people’s mental wellbeing through isolation, job losses, health fears and other stressors. Your capstone could analyze survey data, clinical observations or published research on the rise of depression, anxiety, PTSD, substance use and other issues linked to the pandemic. You could also explore effective coping strategies, telehealth programs or community supports implemented to assist those struggling during this difficult time. Highlighting the mental health consequences of such a crisis could help guide future disaster responses.

Evaluation of mental health courts or forensic diversion programs. For individuals with mental illnesses who come into contact with the criminal justice system, specialized mental health courts and diversion programs aim to provide treatment and services as alternatives to incarceration where appropriate. Your project could study the outcomes and cost-effectiveness of such programs in a specific jurisdiction to determine if they are successfully linking participants to ongoing care and reducing recidivism rates compared to traditional criminal case processing. Presenting an analysis could help show the benefits to policymakers considering implementing similar initiatives.

Exploring mental health and wellness among diverse populations. Issues like cultural stigma, lack of inclusiveness, poor linguistic access and Provider bias can negatively impact mental healthcare for many minority groups. You could focus your capstone on the unique needs and experiences of a specific population like LGBTQ youth, veterans, Native American communities, immigrant families, etc. Through community surveys, focus groups and provider interviews, develop a deeper understanding of the challenges faced and culturally-sensitive recommendations for improving outreach, engagement and effective care. Highlighting the mental health disparities and resilience within underserved groups is an important area worthy of dedicated research.

Comparing the effectiveness of different therapeutic approaches. As the field of psychology and counseling expands, new therapies are regularly being developed and evaluated. Your capstone could assess different therapeutic models for a specific disorder or issue like depression, trauma, addiction, etc. For example, compare outcomes of cognitive behavioral therapy versus dialectical behavior therapy for clients with borderline personality disorder receiving outpatient treatment over 6 months. Another option would be to analyze published clinical trials of emerging therapies like EMDR, art therapy or equine therapy to determine the strength of evidence and appropriate applications. Providing an impartial review of treatment options could help inform clinical decision making.

So The options for a meaningful mental health capstone project are endless. Choosing a topic that investigates an important issue, assesses available services or programs, explores the experiences of underserved groups, compares therapeutic models or makes recommendations to address gaps in care will allow you to apply research skills, contribute new perspectives and lay the groundwork for directly helping those affected by mental health challenges. With careful design and presentation of reliable findings, your capstone has great potential to create positive change and serve as the culminating demonstration of your education.


The Human Genome Project was one of the earliest and most important high-performance computing projects that had a massive impact on the field of computer science as well as biology and medicine. The goal of the project was to sequence the entire human genome and identify all the approximately 20,000-25,000 genes in human DNA. This required analyzing the 3 billion base pairs that make up human DNA. Sequence data was generated at multiple laboratories and bioinformatics centers worldwide, which produced enormous amounts of data that needed to be stored, analyzed and compared using supercomputers. It would have been impossible to accomplish this monumental task without the use of high-performance computing systems that could process petabytes of data in parallel. The Human Genome Project spanned over a decade from 1990-2003 and its success demonstrated the power of HPC in solving complex biological problems at an unprecedented scale.

The Distributed Fast Multipole Method (DFMM) is an HPC algorithm that is very widely used for the fast evaluation of potentials in large particle systems. It has applications in the fields of computational physics and engineering for simulations involving electromagnetic, gravitational or fluid interactions between particles. The key idea behind the DFMM algorithm is that it can simulate interactions between particles with good accuracy while greatly reducing the calculation time from O(N^2) to O(N) using a particle clustering and multipole expansion approach. This makes it perfect for very large particle systems that can number in the billions. Several HPC projects have focused on implementing efficient parallel versions of the DFMM algorithm and applying it to cutting edge simulations. For example, researchers at ORNL implemented a massively parallel DFMM code that has been used on their supercomputers to simulate astrophysical problems with up to a trillion particles.

Molecular dynamics simulations are another area that has greatly benefited from advances in high-performance computing. They can model atomic interactions in large biomolecular and material systems over nanosecond to microsecond timescales. This provides a way to study complex dynamic processes like protein folding at an atomistic level. Examples of landmark HPC projects involving molecular dynamics include simulating the folding of complete HIV viral capsids and studying the assembly of microtubules with hundreds of millions of atoms on supercomputers. Recent HPC projects by groups like Folding@Home also use distributed computing approaches to crowdsource massive molecular simulations and contribute to research on diseases. The high fidelity models enabled by ever increasing computation power are providing new biological insights that would otherwise not be possible through experimental means alone.

HPC has also transformed various fields within computer science itself through major simulation and modeling initiatives. Notable examples include simulating the behavior of parallel and distributed systems, development of new parallel algorithms, design and optimization of chip architectures, optimizing compilers for supercomputers and studying quantum computing architectures. For instance, major hardware vendors routinely simulate future processors containing billions of transistors before physically fabrication them to save development time and costs. Similarly, studying algorithms for exascale architectures requires first prototyping them on petascale machines through simulation. HPC is thus an enabler for exploring new computational frontiers through in silico experimentation even before the actual implementations are realized.

Some other critical high-performance computing application areas in computer science research that leverage massive computational resources include:

Big data analytics: Projects involving analyzing massive datasets from genomics, web search, social networks etc. on HPC clusters and using techniques like MapReduce. Examples include analyzing NASA’s satellite data or commercial applications by companies like Facebook, Google.

Artificial intelligence: Training very large deep neural networks on datasets containing millions or billions of images/records requires HPC resources with GPUs. Self-driving car simulations, protein structure predictions using deep learning are examples.

Cosmology simulations: Modeling the evolution of the universe and formation of galaxies using computational cosmology on some of the largest supercomputers. Insights into dark matter distribution, properties of the early universe.

Climate modeling: Running global climate models with unprecedented resolution to study changes, make predictions. Projects like CMIP, analyzing petascale climate data.

Cybersecurity: Simulating network traffic, studying botnet behavior, malware analysis, encrypted traffic analysis require high performance systems.

High-performance computing has been instrumental in solving some of the biggest challenges in computer science as well as enabling discovery across a wide breadth of scientific domains by providing massively parallel computational capabilities that were previously unimaginable. It will continue powering innovations in exascale simulations, artificial intelligence, and many emerging areas in the foreseeable future.


Customer churn prediction model: A telecommunications company wants to identify customers who are most likely to cancel their subscription. You could build a predictive model using historical customer data like age, subscription length, monthly spend, service issues etc. to classify customers into high, medium and low churn risk. This would help the company focus its retention programs. You would need to clean, explore and preprocess the customer data, engineer relevant features, select and train different classification algorithms (logistic regression, random forests, neural networks etc.), perform model evaluation, fine-tuning and deployment.

Market basket analysis for retail store: A large retailer wants insights into purchasing patterns and item associations among its vast product catalog. You could apply market basket analysis or association rule mining on the retailer’s transactional data over time to find statistically significant rules like “customers who buy product A also tend to buy product B and C together 80% of the time”. Such insights could help with cross-selling, planograms, targeted promotions and inventory management. The project would involve data wrangling, exploratory analysis, algorithm selection (apriori, eclat), results interpretation and presentation of key findings.

Customer segmentation for banking clients: A bank has various types of customers from different age groups, locations having different needs. The bank wants to better understand its customer base to design tailored products and services. You could build an unsupervised learning model to automatically segment the bank’s customer data into meaningful subgroups based on similarities. Variables could include transactions, balances, demographics, product holdings etc. Commonly used techniques are K-means clustering, hierarchical clustering etc. The segments can then be profiled and characterized to aid marketing strategy.

predicting taxi fare amounts: A ride-hailing company wants to optimize its dynamic pricing strategy. You could collect trip data like pickup/drop location, time of day, trip distance etc and build regression models to forecast fare amounts for new rides. Linear regression, gradient boosting machines, neural networks etc. could be tested. Insights from the analysis into factors affecting fares can help set intelligent default and surge pricing. Model performance on test data needs to be evaluated.

Predicting housing prices: A property investment group is interested in automated home valuation. You could obtain datasets on past property sales along with attributes like location, size, age, amenities etc and develop regression algorithms to predict current market values. Both linear regression and more advanced techniques like XGBoost could be implemented. Non-linear relationships and feature interactions need to be captured. The fitted models would allow estimate prices for new listings without an appraisal.

Fraud detection at an e-commerce website: Online transactions are vulnerable to fraudulent activities like payment processing and identity theft. You could collect data on past orders with labels indicating genuine or fraudulent class and build supervised classification models using machine learning algorithms like random forest, logistic regression, neural networks etc. Features could include payment details, device specs, order metadata, shipping addresses etc. The trained models can then evaluate new transactions in real-time and flag potentially fraudulent activities for manual review. Model performance, limitations and scope for improvements need documentation.

These are some examples of data-driven projects a student could undertake as part of their capstone coursework. As you can see, they involve applying the data analytics workflow – from problem definition, data collection/generation, wrangling, exploratory analysis, algorithm selection, model building, evaluation and reporting insights. Real-world problems from diverse domains have been considered to showcase the versatility of data skills. The key aspects covered are – clearly stating the business objective, selecting relevant datasets, preprocessing data, feature engineering, algorithm selection basis problem type, model building and tuning, performance evaluation, presenting results and scope for improvement. Such applied, end-to-end projects allow students to gain hands-on experience in operationalizing data analytics and communicate findings to stakeholders, thereby preparing them for analytics roles in the industry.


EY is a professional services firm that provides assurance, tax, transaction and advisory services. As digital transformation becomes increasingly important for businesses, EY has undertaken several initiatives to help clients navigate this change. Some notable examples include:

CXO Dialogues – EY hosts regular “CXO Dialogues” that bring together C-level executives from various industries to discuss challenges and opportunities around digital transformation. Through these events, EY helps organizations gain insights on emerging technologies, strategies used by innovative companies, and lessons learned from digital leaders. This helps clients understand how to effectively transform their own businesses.

EY Analytics Sandbox – The EY Analytics Sandbox is a collaborative environment that allows companies to experiment with different data sets and analytics tools to identify new insights, opportunities and solutions. Clients have access to a range of datasets and tools for data management, visualization, advanced and predictive analytics. EY consultants work with clients in the sandbox to help unlock the power of data and analytics to enable digital transformation. This hands-on approach helps organizations become more data-driven.

Alliance partnerships – EY has formed strategic alliances with technology companies like SAP, Microsoft and IBM to provide clients with integrated solutions for digital transformation. Through partnerships, EY combines its advisory and industry expertise with emerging technologies from these firms. For example, the EY and SAP alliance helps clients leverage SAP S/4HANA, SAP Cloud Platform, SAP Leonardo and other SAP technologies as part of their digital journeys in areas such as finance transformation, supply chain optimization and customer experience improvement.

Digital Acceleration Platform – EY’s Digital Acceleration Platform (DAP) is designed to help clients achieve their digital goals in an integrated, scalable way. DAP brings together EY services and resources with those of strategic technology partners. It includes assets, accelerators and a governance model to help organizations address challenges like legacy modernization, workforce transition and change management. DAP helps clients kickstart their digital journeys and rapidly start generating business value through transformation initiatives.

EY Studios – EY has launched Studios in various cities that act as innovation hubs. The Studios bring together cross-industry experts, clients, startups and technology firms to co-create solutions for digital challenges. Clients can access emerging technologies like AI, IoT, blockchain through “co-innovation programs” at EY Studios to help solve strategic business problems. EY consultants work with clients in rapid prototyping sessions to build and test digital capabilities. This ecosystem approach fosters innovation and provides a sandbox to experiment with new business models.

HorizonScanning – EY regularly conducts HorizonScanning exercises to identify emerging technologies, trends, risks and opportunities that could impact various industries in the future. The insights from these scans help shape EY’s insights offerings and solution frameworks. Clients leverage HorizonScanning reports to understand potential digital disruptions and develop future-ready strategies. This helps them stay ahead of the curve in continually transforming their business models.

Digital Accelerators – EY has developed a series of Digital Accelerators that help clients tackle common transformation challenges through reusable frameworks, assets and solutions. These accelerators address areas such as finance transformation, supply chain digitization, tax technology migrations and customer experience reinvention. By addressing cross-industry pain points, accelerators help organizations quickly realize the benefits of emerging technologies and digital business models.

Through initiatives like CXO dialogues, analytics sandbox, strategic alliances, digital platforms, innovation studios, horizon scanning and digital accelerators – EY is effectively helping organizations across industries embark upon and achieve their unique digital journeys. EY combines deep expertise with emerging technologies to address both common and industry-specific transformation needs of clients.