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Web-scraped data is considered a promising data source that comes in huge volumes and can be scraped easily and at high frequency. Therefore, if could incorporate web-scraped data into the index generating procedure, then price indices could be generated more effectively and at a higher frequency.

However, web-scraped data do not always come in a way that can be immediately used for price index generation. The category labels for web-scraped prices usually follow the website categorisation that the data are scraped from, which does not necessarily match the categorisation that is used for the national price index generation.

Also, some product information product name, price, etc. Clustering methods are a useful tool for tackling the aforementioned challenges that come with web-scraped data. The problems that we are interested in include both recognising the main clusters of products, given the web-scraped data as well as identifying the incorrectly scraped products. In this project, we will start by exploring the fundamental clustering methods that exist in the literature k-means and spectral clustering methods, in particular.

At a further stage, we will apply these techniques on a web-scraped dataset. The clustering performance evaluation shall be carried out to compare the existing methods and further extensions to the existing techniques shall be explored. Outside of neat theoretical settings, time series are most commonly non-stationary.

Wavelets are a class of oscillatory functions which are well localised in both time and frequency, allowing wavelet-based transforms to capture information in a time series by examining it over a range of time scales.

One prominent method for doing so with non-stationary time series is the locally stationary wavelet LSW model of Nason et al.

Time series in the LSW model are assumed to be zero-mean. In practice, this is rarely the case. Our aim is to explore the behaviour of the model when this assumption is weakened by investigating the effect of different trends on the LSW estimate of the wavelet spectrum. We also plan to examine the treatment of boundary effects that appear in the wavelet coefficients of data near the endpoints of the time series.

The time series are usually assumed to be periodic, however, this too is a poor assumption in most non-zero mean cases. Our project will attempt to analyse the boundary effects caused by a trend and implement methods to reduce them. Changepoint detection relates to the problem of locating abrupt changes in data when the properties of a given time series have changed. This can be extended into finding whether or not a changepoint has actually occurred and if there are multiple changepoints. This area of statistics is hugely important and has many real-world applications such as medical condition monitoring and financial fluctuation detection.

The most studied method for detecting changepoints looks at changes in mean within a time series. This is a popular approach due to the fact that changes like these can be detected by transforming the data and then analysing changes in the mean of the transformed data. Other methods which may prove more accurate at detecting changepoints include looking at changes in variance.

My project aims to analyse various methods of identifying changepoints, whilst studying the advantages and limitations of each approach. This involves the construction and evaluation of numerous algorithms which are used to detect changepoints. Optimisation problems appear in a wide range of applications from investment banking to manufacturing. They involve finding the values of a number of decision variables for example, the number of different products that should be manufactured to maximise or minimise a particular objective function for example, profit , subject to a number of constraints.

In many situations, the value of one or more of the decision variables must be an integer to give a feasible solution. The particular focus of my project is cutting planes. These are inequalities which are satisfied by all the feasible solutions to the MIP but not by all of the solutions that would be feasible if we ignored the integer constraints.

The aim is to investigate different cutting planes in problems where we have fixed charges associated with subsets. In these problems, we have a set of continuous variables whose sum is bounded. We also have subsets of variables defined such that, if any variable in that subset takes a positive value, then a fixed charge is incurred. For example, the variables may represent the amounts of various items to be manufactured and the fixed charges would be start-up costs associated with machines involved in the production of subsets of these items.

Cutting planes can be used to remove infeasible solutions to the MIP to focus on the feasible region and hence the optimal solution to the problem. Many exoplanets are detected via the transit method.

This involves measuring the luminosity of a certain star at regular time intervals to obtain graphs known as light curves. A regular short sharp dip in luminosity could be caused by an exoplanet passing in front of the star. In order to remove some noise caused by phenomena such as sun spots, NASA preprocesses their data to produce a whitened light curve.

However, their current method introduces complications and affects the signature of the transits, which makes the detection of the planets from the whitened data much harder. My project will be focused on modelling the data in such a way as to not distort the transit signals.

So far I have been using R to remove dominant sine waves from the data and will go on to investigate periodicity and autocorrelation within the data. The project I have been assigned is to do with assigning limited assets to a dynamic system. We wish to deploy them so that the reward gained is optimal. The task at hand is finding the optimal policy, where a policy is a mathematical way to decide what decision should be made in the present given the current state of the system.

In railways networks, a single delayed train can delay other trains by getting in their way. This is called reactionary delay and is responsible for over half of all railway delays in the UK. Railway controllers, therefore, have to make decisions in real-time that minimise the amount of reactionary delay. However, the amount of computational time required to run the algorithm, especially on a large network, makes solving these problems in real-time infeasible.

An alternative approach is to use a heuristic, which solves the problem with a lower degree of accuracy but produces an answer in much less time. My project involves developing multiple heuristics, comparing their advantages and limitations and deciding on a final idea.

The standard approach in NLP is to fit the model in a way that avoids relying on features over-represented in the sample known as overfitting. There are two methods: regularization and term-frequency weighting. There is no clear consensus on which method is best. The risk of an accident is an important factor to consider when transporting hazardous materials. There are many situations in which rare and extremely large or small events are of interest.

For example, the focus of my project is the statistical modelling of extreme flood events. Extreme Value Theory is concerned with the modelling of the tails of the distribution and provides a theoretically sound framework for the study of extreme values. In particular, the Generalised Extreme Value distribution is used to model the maxima of a process within blocks of time often a year. Usually, we are mostly interested in estimating the x-year return levels of a distribution, that is, the value we’d expect to be exceeded on average once every x years.

However, the point at which we decide to stop sampling and analyse the data is not arbitrary and this choice of stopping point can result in biased return level estimates. After the December floods there was much interest in re-evaluating the return level estimates, as the inclusion of such a large event often led to significant changes in the value of these estimates. We will implement a variety of new estimators developed with the intention to improve upon the existing standard methods.

The internship project will be focused on Time series classification, an area that has applications in various fields. The idea is to build a classifier, which is able to label a time series from a defined list of possibilities. For example if we have heart rate time series for people walking and people running, we have two label: runner or walker. There are two main challenges in classification, firstly a set of labels needs to be chosen and secondly a classifier needs to be built that can label the time series.

The analysis of this data is challenging due to the vast amount of factors influencing such events. Koren and Bagozzi Journal of Peace Research, 54 3 find, for instance, that, in times of war, violence against civilians occurs more frequently in areas with a high percentage of cropland. This result is derived based on a zero-inflated model which accounts for armed conflicts not being present in all areas at all times.

The proposed project considers the publicly available data and aims to slightly extend the model by Koren and Bagozzi , for instance, by accounting for the spatial aspect of the data. In particular, the project can be split over three steps: i Exploratory analysis of the Data, ii Estimation of a similar model which to the one by Koren and Bagozzi and iii Extending the model.

Being able to model spatial extremal behaviour in particular spatial dependence is an important area of Extreme Value Theory and this project will aim to give an introduction into the various methods of trying to capture this behaviour.

The first part of this project will provide a short introduction to univariate extreme value theory and also will look at some methods of spatial statistics – in particular looking at Gaussian Processes, which will be simulated and have interpolation methods performed on them. The second part will introduce the Smith process a particular type of max-stable process and will compare this to using Gaussian Process techniques on data, with the aim of comparing how well the two types of spatial model are able to describe the nature of the data.

Changepoint detection underpins virtually all questions of interest surrounding data analysis in a variety of contexts. Understanding the nature of a change, and when it occurred, is often of vital importance in preventing problems surfacing in the future. With the advent of Big Data, more sophisticated tools are increasingly required to search for changes on datasets of ever-growing size.

Most existing methods for changepoint detection are offline, requiring the collection of an entire dataset prior to analysis, and interest in online techniques, where informed statements regarding changes of the recent past can be made in tandem with data collection, is growing.

This project will examine various existing methodologies which employ an online approach to changepoint detection, both Bayesian and frequentist, and attempt to apply these ideas to real-time datasets for example, share price data for various FTSE companies in order to find the best performing algorithms which can operate most efficiently in the greatest number of contexts. There is also potential scope in helping to pioneer entirely new techniques which can then be tested against some of the existing methods.

When developing a combination therapy, the aim is to produce a synergistic effect while reducing side effects. However, drug development is a long and expensive process which is subject to a considerable amount of uncertainty. Therefore it is important that the decisions made are well informed and are expected to be the most beneficial to both the pharmaceutical company and the patient population. Methods for decision making often require several parameters relating to a drug. We are interested in the estimation of the probability of study success for combination therapies.

Current methods do not allow information to be shared across similar combinations. We believe that incorporating this information in a Bayesian setting will improve the accuracy of our estimates. This will lead to better decision making and improve the outcomes of the development programmes.

In many real world problems, such as complex queueing problems, mathematical models of the system can be too complex to solve analytically.

An alternative way to study stochastic systems is to use a simulation to produce realisations of the system. Simulation can be used to optimise a system by testing alternative settings. The choice of optimisation technique depends heavily on the properties of the problem, such as size of the solution space, how many objectives there are and whether the decision variables are discrete or continuous.

Due to the stochastic nature of the problems, the optimisation is further complicated as the objective must be estimated, rather evaluated exactly. This project will focus on finding simulation optimisation techniques appropriate for the optimal staffing problem for a time dependent queueing system, such as that of an emergency call centre. At the start of the internship time will need to be spent learning about the general offshore maintenance problem and literature associated with it.

This could be simpler sub-problems such as the travelling salesman problem, travelling repairman or scheduling of tasks. The goal of the project is likely to be creating some simple construction heuristics to solve the offshore maintenance routing and scheduling problem.

The performance and results of these heuristics could be compared across several instances. The project will focus on one the main optimisation scheduling problems.

Project planning, it refers to the programming of different activities that need completion for a given project. It is also heavily conditioned by the specifications on the resources and activities, making the problem really interesting for mathematicians.

In this project we will be focussing on understanding the so-called resource-constrained project scheduling problems RCPSP. The generality of the RCPSP allows it to have a wide range of applications where the aim is to schedule some activities or jobs over a period of time such that precedence and resource constraints are satisfied, and a certain objective function is optimised.

Either of these tie in with testing on Python using Gurobi which will be learnt if needed. The linear model is a widely used tool in regression analysis. Linear regression models are most commonly fitted using both conceptually and computationally simple least-squares approaches.

A frequently made assumption in linear least squares regression is that the error terms between the observed responses and the corresponding expected values are independent and identically distributed normal random variables. This assumption greatly simplifies the matter of obtaining confidence intervals for the unknown parameters of our model. However, whether this is a sound assumption depends on the size and nature of the particular dataset under consideration.

This project will investigate the case when the assumption is not satisfied. Various techniques for obtaining confidence sets will be examined and compared to the sets obtained via normal approximation.

The effects of different possible violations of the Gaussian assumption on the constructed confidence sets will be investigated. The simulation uses mathematical modelling in order to mimic real-world systems which cannot be tested in reality; perhaps due to time, cost or safety constraints.

The information gained by running the simulation can then be used to make decisions about the real-world system. For example, retailers want to ensure they have enough servers to prevent customers from having to queue for long periods of time.

A simulation model can be used to understand how the queue behaves and make a decision about how many servers are needed for each shift in order to keep the queue length below a certain level. The inputs in simulation models are usually approximated by observing real-world data; for example, observing the number of customers that are served in a shop over a period of time. Input uncertainty arises from the fact that we only have a finite amount of real-world data, and therefore cannot be certain that the values of the input parameters that are being used to drive the simulation are the true values of the input parameters.

This project aims to quantify the input uncertainty in a queueing simulation model. Each year university league tables are released but many are based on different criteria and have slightly different results.

We are interested in testing the efficiency and productivity of mathematics departments across the country. As we are considering multiple inputs and outputs: student satisfaction, entry requirements, academic and career attainment and the cost of university, etc. We, therefore, need to use a management science method, Data Envelopment Analysis, DEA which can cope with lots of constraints.

What I am finding particularly interesting is the additional questions that arise from examining the data and implementing this approach, for example: Should universities that produce high numbers of good degrees be considered the best? Are some students not reaching their potential and are being let down by their institution, given they entered university with extremely high entry requirements?

Are some universities awarding an unrepresentative number of good degrees considering their place in current league tables, or is the data just extremely biased with a small sample size?

Should all universities be charging the same fees, given their career opportunities after are significantly less? Is university location skewing the career prospects of students, whilst not taking into consideration the living costs and average salary of non-graduates of some locations? As my project advances, I have realised that what seemed like a simple linear programming problem evolves into a complex social and economic issue, which questions the real cost to students when choosing which university is best for them.

The documents detailed widespread accusations of corruption within the sport. The aim of the project is to create simulations of tennis matches and explore sudden changes in performance, which could be linked to match-fixing, using simple change point methods. Features such as dependence and the importance of critical points will also be taken into account to create accurate simulations.

In addition, the current rating system within tennis only takes into consideration the previous year’s results and has no consideration on the strength of opponents. A further aim of the project is to create a rating system based around the ELO system with improvements. In the context of time series, examples of outliers may include the number of complaints received by BT after a power outage, or the increase in supermarket sales during the days leading up to Christmas.

It is important that we are able to detect these outliers as they may have a significant impact on the model selected to fit the data, the parameter estimates for the model, and consequently, on any forecasts made from the model. This project will look into methods and algorithms that are able to automatically detect outliers in time series.

Extreme value theory models the maxima minima of random variables. By their very nature, they occur infrequently and so are hard to model. A robust framework already exists, with block-maxima and threshold-based approaches providing parametric distributions for the maxima. My project looks into the bivariate case, where our variables have extremes that either occur simultaneously Asymptotic Dependence or independently Asymptotic Independence.

There already exist several statistical measures that measure this behaviour however it is hard to obtain reliable estimates of their values. I am looking at developing an alternative method to simultaneously estimate two of these measures, with the hope of finding some synergies. With the advance in technology in education, it is becoming more possible to personalise education software, providing students with questions tailored to their individual learning styles and abilities.

The data gathered from the students’ previous interactions with the education software can be used to simulate students’ responses to future data. This enables us to model student performance. The main aim will be to investigate whether Bayesian methods can provide a more accurate prediction of student performance over frequentist methods. The models will be used to predict whether students would pass an exam of particular questions. Drone technology is fast becoming a vital component of military operations.

Unmanned Aerial Vehicles UAVs , as they are known within the military, can perform a variety of tasks remotely making things both more efficient and safer for military personnel. This project revolves around optimizing the UAV Search Problem by maximising the number of events detected within a given border by a fleet of UAVs equipped with cameras.

The UAVs aim to detect the locations of events of some sort occurring on the border one example may be crossings of the border. Each UAV is to be assigned a specific subsection of the border to patrol, with the assumption being that the larger its subsection is, the less likely it will be to actually detect an event.

Some UAVs may be naturally better at detecting events than others because of better cameras etc. Time series are often grouped in a hierarchical structure. For example, the time series for the total number of tourists visiting a country may be split into more time series according to the purpose of travel, and each of these time series may, in turn, be split into more time series according to the length of stay, thus creating a tree-like hierarchical structure.

The issue of forecasting hierarchical series in a way that allows for a similar hierarchical disaggregation of the forecasts is very important. This project will combine two methods that have recently been proposed, optimal combination and temporal aggregation.

It will then test the accuracy of this new method against that of optimal combination and other standard techniques such as bottom-up and top-down forecasting. Changepoint detection of univariate time series has been widely covered but the increasing availability of multivariate data has motivated the study of multivariate detection methods. Time series data of a multivariate flavour can be found in finance, health monitoring, signal processing, bioinformatics, and detecting credit card fraud.

In my project, I explore a few methods to detect change points in multivariate time series data. I also discuss the drawbacks of these methods and suggest ways in which these drawbacks could be overcome. The volume of telecommunications and social network data has exploded in the last two decades.

Gaining a statistical understanding of the processes generating and maintaining network structure can be used to make confident statements about properties of a network, detect anomalous behaviour or target adverts.

In recent years more data has been collected alongside the network. Can such covariate information improve inference for network structure compared to network data alone? Many have attempted to model how networks grow, however, most models have poor statistical properties.

This project will investigate approaches for combining statistical methodology from static modelling techniques with methods for analysing data indexed through time. The prevalence of extra-tropical cyclones in the mid-latitudes is a dominant feature of the weather landscape affecting the United Kingdom.

The UK has come to expect a consistent annual pattern of temperate summers and mild winters. However, in recent years it has been a focus of extreme weather events, for example, major floods and damaging windstorms. Accurate modelling and forecasting of extreme weather events are essential to protect human life, minimise potential damage and economic losses, and to aid the design of appropriate defence mechanisms.

In this context, an extreme event is one that is very rare, with the consequence that datasets of extreme observations are small. The statistical field of extreme value theory is focused on modelling such rare events, with the ideology of extrapolating physical processes from the observed data to unobserved levels.

This project will focus on applying extreme value methods to remote sites in the North Atlantic and European domain. Sequences arise naturally in linguistics with the number of occurrences of a linguistically salient feature changing over time as language attitudes evolve.

While not as widespread nowadays, flat adverbs were commonly used during Authors of this period used flat adverb forms and were publicly criticised for doing so. This project will introduce a Bayesian statistical framework to investigate whether the rate of flat adverb use changed significantly after an author’s writing had been subjected to such criticism.

This will focus on the detection of changes in a sequence of data points using a Bayesian approach, specifically, we will be interested in quantifying in a precise way whether or not a change in the sequence has occurred at some point.

Cluster analysis is the process of partitioning a set of data vectors into disjoint groups clusters such that elements within the same cluster are more similar to each other than elements in different clusters. There are three main categories of algorithms which can be applied in order to find solutions to data clustering problems: hierarchical, partitioning and density-based.

The main focus of this project is to explore density-based clustering methods and to compare the performance of these algorithms via simulation studies. The aim of a classification model is to predict the class label of a new observation using only historical observations.

Traditional classification approaches assume this historical dataset is a fixed size and is drawn from some fixed probability distribution s. However, in recent years a new paradigm of data stream classification has emerged.

In this setting, the observations arrive in rapid succession, with classifiers capable of being trained sequentially, and an adaptable underlying probability distribution. These classifiers have applications in areas as diverse as spam email filtering, analysing the sentiment of tweets and high-frequency finance. This project will investigate how models can be used to produce streaming versions of classifiers. A time series is a sequence of data points measured at equally spaced time intervals.

Often we assume that such series are second-order stationary. In other words, the statistical properties of the time series remain constant over time, e. However, the reality is that many time series are not second-order stationary and therefore it is not appropriate to model them using such methods. Instead, we must consider time-varying equivalents of the autocorrelation or autocovariance. One method that analysts use to adapt the regular autocorrelation function to be a time-varying quantity, is applying rolling windows of the data.

Unfortunately, this can present quite different answers for segments of different lengths based on segment length choice and location of the time series sample. This project will explore alternative methods of estimating a time-varying auto-correlation function in order to overcome these problems. The underlying strategy for most statistical modelling is to find parameter values that best describe the fit of the model to the data.

This requires optimising an objective function while minimising the difference between the model and the observations. When analytical solutions to the optimisations are unavailable, statisticians often rely on numerical optimisation routines to perform this fit, trusting that this will produce stable estimates of the parameters.

Firstly, some issues may arise in the choice of the best algorithm given the characteristics of the problem at hand. Secondly, the algorithm considered may not actually perform well and needs to be understood and adapted to work better on the model considered. This project will investigate different numerical optimisation algorithms used in statistical inference and curve fitting, and how to overcome some of the problems associated with these types of algorithms.

In medical research, in both pre-clinical and clinical trials, the objective is to learn about the behaviour and effect of potential new drugs in the body. This breaks down into two categories- how the drug affects the body Pharmacodynamics and how the body affects the drug Pharmacokinetics. This application-driven project focuses on pharmacokinetic modelling, which involves modelling the concentration of a compound in the blood over time.

The aim of the project is to apply statistical modelling techniques to real data in order to obtain an understanding of the role of pharmacokinetics in the drug development process. The field of statistics focusing on models incorporating spatial information is called Spatial Statistics.

Spatial statistics generally distinguishes between three types of data: geostatistical data, lattice data and spatial point patterns. This project will focus on lattice data, where the number of sites at which observations are recorded is finite, for example, the population in each county of the UK or the results of the last general election per district.

Spatial statistical methods for lattice data are often applied in epidemiology to model the occurrence of a disease in a region depending on covariates.

This is known as Disease Mapping, with models aiming to predict the occurrence rate or the number of cases of a particular disease. This project will investigate the basic methods used in Disease Mapping and apply them to economic data. Car-sharing is a new concept that enables the general public to access a fleet of vehicles for short rental periods.

These systems have several benefits including environmental, energy and societal considerations. For the customer, the one-way system is generally preferred however one of the difficulties in implementing a one-way system is managing the relocation of vehicles and personnel. This project will develop and implement models for improving relocation operations for the one-way car-sharing problem.

Offshore structures such as oil platforms and vessels must be designed to have very low probabilities of failure due to extreme weather conditions.

Inadequate design can lead to structural damage, lost revenue, danger to operating staff and environmental pollution. Design codes demand that all offshore structures exceed specific levels of reliability, most commonly expressed in terms of an annual probability of failure or return period. Hence, interest lies in environmental phenomena that occur extremely rarely, and we want to estimate the rate and size of future occurrences.

The aim of this project is to gain a deep understanding of extreme value theory in the application of ocean environments. A common scheduling problem in industrial settings is concerned with scheduling jobs on identical machines with the objective of minimizing the total active time. The problem finds important applications in the field of energy-aware scheduling, especially in applications relating to optimal network design.

The aim of this project is to investigate the performance of some natural heuristics proposed for finding near-optimal solutions to these computationally hard problems. This will involve learning about integer and linear programming formulation-based methods and using computer programming to implement algorithms and solve linear programs.

Seasonal adjustment involves estimating and removing a seasonal component from a time series. This project aims to develop and test a method for the automatic detection of changes in the seasonal pattern of time series by comparing alternative methods and assessing the impact on the estimation of seasonal factors for series that do and do not present changes in the seasonal pattern. Intelligence is information regarding threats to national security and potentially hostile forces.

After raw intelligence data is collected it must be processed and screened, often in time-critical situations. Only relevant information is then passed on for further analysis. With huge amounts of intelligence data collected daily, potentially relevant information can be missed. Given a set of intercepted communications, how should we process the communications to maximise the amount of relevant information passed on for analysis?

This project will develop a model for processing intercepted information and explore how to overcome problems associated with this type of model. Changepoints are a widely studied area of statistics with applications including, but not restricted to, finance; detecting changes in volatility, computer science; detecting instant messaging worms and viruses and environmental such as oceanography and climatology.

Changepoints are considered to be the points in a time-series where we experience a change in some statistical property, for example, a change in mean or a change in variance. There are many different approaches to changepoint analysis however current methods have the trade-off of being fast but approximate or exact but slow. The aim of this project is to develop an understanding of changepoint detection methods and in particular explore ways in which we can assess the performance of different detection methods.

In many applications, there is some indicator that is constantly monitored as new data are collected, for example in an industrial setting, the number of faults recorded on a large network per week.

Typically at a managerial level interest lies in the total number of faults over the entire network and patterns or changes that may occur. One important change in this indicator is a spike outlier where suddenly there is a large increase in the number of faults over the entire network.

Understanding why these sudden increases occur is important so they can be prevented from happening again. This project will investigate methods for detecting outliers in large time-series datasets. For many complex datasets, one feature is that the likelihood of the statistical model is intractable, in the sense that it is difficult to evaluate the likelihood values of the observations, and standard inference methods for unknown parameters, like Maximum Likelihood Estimation and Monte Carlo Markov Chain, do not work.

For intractable problems in which sampling from the likelihood given parameter values is easy, Approximate Bayesian Computation ABC is a useful Bayesian inference method using Monte Carlo simulations. The project will investigate the impact of the tolerance level, a core parameter of the ABC algorithm, in various situations and try to design an automatic algorithm to select the tolerance level.

Scheduling problems can be found in many industrial settings. The complexity of scheduling problems is often such that optimal solutions cannot be guaranteed to be found in a short computational time. However, many companies need to produce schedules on a daily basis, so they need a computationally fast way of implementing this. A well-known example of a difficult to solve scheduling problem is the travelling salesman problem TSP which is concerned with finding the shortest route which visits each of a number of locations exactly once.

If every location can be travelled to directly from every other location, then the number of possible solutions increases very quickly as more locations are added to the problem. For example, one veteran contacted our office with a simple request to check the status of his eligibility for a war medal.

After thirty minutes of talking with this constituent, I came to realize that the medal he was referring to was the Purple Heart. During his time in Vietnam, this constituent had shrapnel from a grenade imbedded in his arms. However, since he did not want to be separated from his platoon, he told the medical examiner that his wounds were mosquito bites. Now more than 40 years later, this veteran is working with Congresswoman DeLauro, the Department of Veterans Affairs, and the National Personnel Records Center to receive the medal he earned fighting in Vietnam.

I encountered a great deal of inspiring stories from the veterans of my community and was thrilled to be able to assist them in receiving their honors and remembering the sacrifices they made for our Country.

This project had two goals. Second, it was meant to provide a forum for veterans to communicate and connect with one another. Although I completed my internship before the videotaping process began, I am positive that it will be a major success seeing as there was immense support for the project from the veteran community. My internship with Congresswoman Rosa DeLauro also brought me to many interesting events and ceremonies. The most memorable ceremony I attended was a dedication service for the State Veterans Cemetery.

This ceremony held great value because it was an extremely emotional event that had terrific support from the entire community. I will never forget the tears of pride and joy I saw running down the faces of the veterans who attended the ceremony and stood in the rain to hold up the American Flag during the opening remarks.

At that moment, it became clear that the work I did during my internship with Congresswoman Rosa DeLauro had a significant impact on the lives of the veterans I assisted.

What I viewed as a simple letter of correspondence to help a veteran receive care from the VA hospital or obtain a military medal held significant meaning in the lives of these veterans. I am extremely grateful to have had the opportunity to intern with Congresswoman Rosa DeLauro, which was made possible through the stipend offered by the Amherst College Internship Fund. In my sophomore summer, I chose to intern as a volunteer at Bellevue Hospital Center.

I sincerely believe healthcare is a right to everyone. Because this patient population poses the added challenges of barriers to communication, through Project Healthcare PHC , the volunteer program, I was able to practice cultural competence while learning of the different medical fields and what fosters a positive health care culture.

Also, I assisted the emergency room doctors, nurses, social workers, and administrators by making up stretchers, stocking supplies, transporting patients, and conducting clinical research. Many of the patients are unable to afford primary care or a visit to a clinic; they rely on Bellevue for almost all of their medical needs.

As such, I had some assumptions about the patient before even dealing with her. Hence, there was a challenge to let go of stereotypes and to focus on providing whomever with the best possible support. During their tumultuous transitional period, I aimed to give patients a voice and my complete care.

My interactions with various patients have had a lasting effect. I remember distinctly my Comprehensive Psychiatric Emergency Program shift. I sat on an interview with a nurse practitioner of a lawyer. A white male, Jim not his real name , felt he was being held against his will. He had tried to commit suicide because of societal pressure and constant altercations with his boss.

In his interview, he spoke of his sanity, intelligence, and his boss. He seemed presentable, as society would say, yet unfocused. I refuse to be here, and I just want to go back out there. You can see that I’m not trying to commit suicide right now, and I promise I won’t if you release me. I’m different from all these people! Jim was irascible, and the interviewer had pushed him when he made Jim realize the mistakes he made.

I learned from that one interview that if we place our worth in our occupation, then our self-worth becomes dependent on something as fickle as the New England weather.

Our occupation does not determine our sanity; psychiatrists do not either. Our body and our mind’s impulses do. Where there is suffering, there is a lack of self-care. When I was in the first Egyptian revolution, I nearly snapped from the heat, lost my voice, and was bombarded with tear gas bombs. I chose to gain my strength from others because my own had faltered. I, however, will say that in certain situations, this is difficult — if not impossible.

However, I am more interested in my failures — about the moments I forgot to be kind. Frankie was a Spanish-speaking man of maybe forty years old.

I was working in Urgent Care and had been asked to escort him. He said, I think, he wanted a metro card from Social Work, but they were unavailable. He continued to move his hands trying to communicate to me what he wanted. Frustrated, I said in a loud and angry tone, „What do you want?! Talk to him slowly and calmly. I apologized to Frankie. I told him I would bring someone who speaks Spanish to help him and that he should wait for me near the door.

I went into the ED, found someone who spoke Spanish, and when I was walking towards the outside door, I noticed that Frankie was gone. I took a moment to reflect on this failure. I took a moment to remind myself why I loved being a volunteer: I loved helping people and making their day better. For a moment, I forgot my passion and drove a then-homeless, Spanish-speaking man into the cruel city of New York. These experiences are but a shallow overview of what I was affected by.

I believe there is a fundamental connection between personal illness and the societal, political, and cultural determinants of health; hence, through this opportunity, I wanted to raise awareness and to educate the community about the patterns that underlie health disparities whatever my research topic is. I was assigned Prostate Cancer.

Honestly, I did not know much about the topic but after intense research I learned about that African-Americans were affected to a greater extent by it. After the health fair, interns were to address either the topic of smoking or diet in a multimedia project. A volunteer and I chose to address the topic of smoking using video. However, it could very well be your last one.

Essentially, that was the message of the video. In my opinion, our way of addressing the topic of smoking was effective because it was short and laconic. As part of the program, interns were also required to attend a weekly team meeting and a public health course taught by one of the attending physicians. Through these opportunities, interns investigated the practical problems that hospitals, patients, and medical staff encounter in the healthcare system as well as explored how culture and health intersect.

From my experiences at Cairo Medical Center and knowledge of the Egyptian healthcare system, I brought an international awareness of healthcare to these meetings to make discussions more rewarding. I initiated dialogue around health disparities resulting from social injustices and socioeconomic differences when I had the chance. Ultimately, Bellevue continues to provide humane and equitable health services to those who speak a different language or are from a different culture, which is a mission in line with my values as an aspiring healthcare provider.

I had initially felt like I was working hard without recompense, but as the summer progressed, I began to understand that the hard work intrinsically offered its own compensation.

I grew to love the hospital, where nervous patients, whom I am meant to soothe, taught me compassion. I have helped people in the most vulnerable moments in their lives and have been humbled by them allowing me to do so. I recognized the immense value of people and volunteers using their unique skill sets to pursue their passions in dedication to servicing the common good of their fellow humans. In the process, I discovered that the social facet of medicine presents a challenge matching the inherent complexity of the body.

For these reasons, I aspire to one day utter the vow of doing no harm and to become a great doctor. This community based group works against issues of Domestic Violence and Sexual Assault in the interest of helping women build and maintain healthy relationships. Through education and prevention outreach, The Advocates aims to inform local youth about forming healthy relationships and the warning signs of danger in relationships. Staff members visit elementary, middle school, and high school classes throughout the year and lead workshops surrounding themes of dating violence, healthy friendships, and bullying prevention, catered to each age group.

The Shelter welcomes mothers, children, women with pets, and those alike. Finally, the survivor support is at the hands of an amazing team of case managers and client advocates who work with survivors one to one with a myriad of things. My day to day was comprised of anything someone on staff needed me to do. It was a general role that spanned across all departments, especially across the administrative staff and the Shelter staff. I also created the comprehensive excel workup of all of the data needed for the annual report.

Lastly, I consulted with the CEO on her role in raising funds for the organization and wrote a few grants myself. On the Shelter side, I met with clients as an advocate and case manager on a daily basis. I led morning meetings and taught morning yoga as a positive activity for the shelter residents to being their day.

I gained a first-hand understanding of the complex network of individuals, immense time, and meticulous attention necessary to maintain a well-run shelter. The more independent and creative component of my internship consisted of my own study and photographic exploration of these issues that the Advocates works against. I had a two-pronged approach that on one end developed promotional and campaign material for the organization to use as material in their own interests.

The overall goal in complementing my internship with this project was to relate the work of the Advocates in the small community of Hailey Idaho back to the Amherst college community. I hoped to show how interwoven these issues are across contrasting communities, essentially examining the question: How can Amherst College as a community integrate and reflect the vital work of the Advocates in the context of a broader fight against power based violence and sexual assault?

The importance of this study and issue is actually reflected in the question at hand. My aim was to bring that consciousness back to Amherst. I experimented with the archetypal campaign photography often seen in PETA or other nonprofit promotions. Writing on naked bodies often female sexualized bodies has become a canvas that draws in the spectator with the shocking image of a hyper sexual naked woman.

In my study, I found this to be problematic and actually counter intuitive for organizations that employ such imagery in the interest of fighting against the same kind of exploitation.

When constructing campaign and promotional material for The Advocates, the team I met with helped me think about imagery that still grabs the attention of a viewer, but holds less controversy. They each held up signs that were used in the Soiree that displayed a different service that The Advocates provides to show the breadth of influence this one organization covers. This final work shed light on the faces behind the actual work who make a difference every day.

Rather than an artificial and sexualized image of a woman with writing all over her naked body, this project reimagined the face of a cause as the actual agents who initiate change. In maintaining the creative and artistic elements, the message remained true and evident without the sexualized and mediatized tropes of some non-profit photography.

Investigation into an ongoing, breathing issue is never limited and should be as constant and ongoing as the fight for healthy relationships and safe lives. I would be curious to see a similar campaign through the lens of the Amherst community and the agents of change that exist below the surface of our campus.

Who are the faces of at Amherst College that would hold a sign as they fight to implement change? One day, I hope it would be everybody. This summer I worked as a policy intern with the Council for Court Excellence in Washington, DC, as small non-profit focused on improving access to justice for the DC community. It primarily focuses on local issues, including criminal justice reform, juvenile justice, and policy reforms in local courts. The CCE is a rare nonprofit that frequently works closely with the institutions it influences and has ever since its advent in the early s by leaders of the DC legal community.

Over the summer I was able to attend DC council hearings at which CCE employees and board members testified in favor of bills to reduce juvenile jail and prison placement in adult facilities and bolster language accessibility.

A challenge during this process was establishing consistent thresholds for evaluating the presence and robustness of appeals processes, disciplinary hearings, adequate and accessible notifications, additional provisions for students with disabilities, and language accessibility, among others. With this data we constructed a memo that detailed our methodology and conclusions, as well as a presentation that we delivered to a DC educational authority agency at the end of our internship.

Our primary conclusion was that while charter schools as a whole improved considerably in some aspects, there was still much reform work to be done in areas like language accessibility for student handbooks and zero-tolerance policies, which can have especially harmful effects on the lives of young children.

We even found charter schools for preschoolers that included zero-tolerance provisions. Challenges I addressed during this project included reverse-engineering the statistics published in the previous report in order to align our methodologies for calculating updated versions for While doing this I identified a critical error that led the previous intern team to over-report the percentage of schools with zero-tolerance policies by a significant margin.

The individual project I got the most from over the summer was researching post-conviction civil rights restoration policies by state, specifically the right for people previously convicted of felonies to serve on juries. This specific area of criminal justice had little attention.

I identified a few important scholarly papers on the subject and compiled data that I used to construct a memo to the executive director and board of directors.

I confirmed the data by researching jury exclusion policies in for all 50 states. It was sometimes a challenge to find current versions of state laws, which change frequently, and comprehend them in the context I needed to, as this was my first extended exposure to reading and analyzing statues themselves. In constructing my memo, I learned about writing to professional audience and also got to discover some striking trends in jury service rights throughout the United States.

Because of widespread restrictions on felons from serving on juries, over one-third of African American men in some counties in states like Georgia are unable to serve on juries. This policy deprives the justice system of a critically valuable perspective needed on its juries — the perspective of having been through a process similar to the one the defendant they observe is. The papers I identified also featured a widespread and fervent rejection of the justification used almost exclusively for these laws — that previously incarcerated persons hold biases against the court system and prosecutors, and will disproportionately side with defendants in criminal cases.

Research done on the subject suggests that this population actually has less bias in either direction than people who normally serve on juries, and that their perspective and knowledge of the criminal justice process would be valuable to have on juries. The court has experienced numerous growing pains since its advent in the early s, many of which the CCE addressed in a comprehensive report which it presented to the Office at the end of the summer.

I also constructed data visualizations of key statistics, highlighted quotes for interviews the CCE conducted with OAH litigants, lawyers, represented agencies, and judges, and reviewed and made legal footnotes.

It also gave me an important perspective on graduate school and the career opportunities afforded by different post-undergrad paths.

This past summer I interned in the health industry at ServiceNet Pathways. The Pathways program is a milieu therapy program for children ages who have been referred by the Department of Mental Health as being a good fit for the program. Over the course of my work with the children I realized that they are heavily stigmatized and I strive to know why people stigmatize children with mental illnesses and how we can put an end to this.

When I first discovered that I had gotten the internship I was very excited and began to share the news with friends and family. This thought had never crossed my mind; I was too occupied with the idea of having the opportunity to help children. But why did it cross theirs? How did children go from being cute and innocent to crazy and violent with the simple addition of two words: mentally ill?

To my relief, the children were just that- children. I never once felt threatened or endangered by any of them. I found them to be sweet and kind, despite their previous hospitalizations and rough backgrounds.

I connected, to varying degrees, with all of the children and am happy to say that I met each and every one of them. They were not defined by their illnesses as some would think. With the combination of medication and therapy, they were just like anyone else. As an intern, my main job was to learn. My other responsibilities consisted of opening up in the mornings, helping to make lunches and researching projects and activities the children would enjoy.

I also helped to create bulletin boards and materials for activities. More than anything though, I gave my undivided attention to the children and tried to make it clear that I was someone that they could talk to. They continually impressed me and I learned just as much from them as they did from me. It deeply saddens me that people assumed that these children would be violent and were beyond my ability to help them. A mental illness is just like any other illness and should be treated so.

People should not be judged for saying they have depression any more than saying they have a cold. Because of this stigmatization people are afraid to come out about their mental illness which can make matters even worse.

If only everyone could see what I saw, which is bright, creative and talented children who simply need guidance to work through their difficult upbringing. This past summer, I had the privilege of working in the Washington, D. Congressman McGovern represents the second district of Massachusetts, which includes both my hometown of Leicester and Amherst. It was exciting to see the ways in which our office addressed issues pertinent to the two places I call home and to take part in that process.

My hands on work in such an influential office gave me better insight into the inner workings of our political process and comprehensive knowledge of the communication between the electorate and their public officials. As an English major hoping to pursue a career in journalism, or a similar communications based field, working firsthand to communicate directly with constituents was one of the most interesting aspects of my work. I found that the communication channels between our office and the voting public were ample, giving constituents myriad opportunity to be involved in the political process and voice their opinions.

As an intern in the Washington, D. The majority of my work centered on communicating with constituents about information directly related to the legislative process. Despite this standard balance of responsibility, I was pleased to have opportunities to truly and directly serve and inform my community. The information I provided helped lead to a more informed electorate and to uphold the standards of democracy that allow our government to thrive.

One of the most interesting ways that I interacted with constituents was through my work giving tours of the Capitol building. Community members from our district are able to book a special tour of the Capitol building through our office, providing them with an exclusive experience and giving them a chance to interact with a representative from our office.

This experience has given me the confidence to step into any newsroom. Thank you, CSJ. I was allotted creative freedom, which allowed me to produce quality graphics, videos, web design and photography for the CSJ.

I was the point of contact for project partners, which helped me expand my leadership abilities. I started my first position about a month into the first semester of freshman year, and I stayed until I graduated. My positions here shaped me into the professional I am today. Working here allowed me to be an integral part of an amazing team, and I love that I was able to build my skillset to have an impact on the organization. It has allowed me to use my skillset to find various photographers across the world.

I have learned how to manage a social media account, which will be beneficial for my career in the future. Working with the CSJ team in a virtual setting has been wonderful because everyone is so helpful. Marketing research, communications and management were skills that I was able to hone from this internship.

I am thankful for the real-world experiences and projects I have worked on, and I am forever grateful for having this opportunity. Overall, working here has been a great experience. I look forward to the next chapter in my life! I was able to carve a path toward my career goals and overcome any obstacles that came my way. I thank Youth Today for allowing me to work alongside people who not only wanted to see me succeed, but also lend a hand in the next step in my career. Prior to working here, I always found it to be kind of awkward emailing and calling people I have never met, but now I feel much more comfortable.

In addition to that, managing a customer base on this level has helped me with my own pool of customers as well — setting in place certain processes to ensure nobody slips through the cracks, etc. I learned about library database acquisitions, which I plan to incorporate into other business acquisitions in the future. Working with Chelsey and John was a pleasure and the office loves to celebrate one another. As the digital platform host for the Bokeh Focus event I was able to interact with many of the student photographers and CSJ supporters.

I truly valued the partnership with the Maya Heritage Community Project and I hope that we all continue to work together in the future! This experience has pushed me to become a better reporter, digital producer, and writer. I started knowing very little about reporting and what digital careers were available to me in journalism.

Through my time at Fresh Take, I have reported hyperlocal stories and gathered a plethora of digital media skills that now have equipped me for any newsroom. I can confidently say that being a reporter at Fresh Take Georgia has given me the zeal and confidence I needed to see myself as a multimedia journalist.

I can apply the knowledge in interviews, and future employers are always very impressed with my experience. It has been a great transition from a classic college job in the next chapter of my life. I have accepted a job offer with Granite Telecommunications. Thanks to understanding and hard-working mentors, I feel prepared to take on the world in my career and am more aware of my skill set and how to use it. Because of my time at the CSJ, I was able to land an important internship with Comcast that I believe has the potential to become a full-time job.

I appreciate the independence I was given and the team that I got to work with. My perceptions of nonprofits and grant writing have changed for the better. It is truly an honor to be immersed in such an amazing environment and to be surrounded by such hard-working people.

 
 

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