http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&feed=atom&action=historyDocument:MIT-MFin-essay - Revision history2024-03-28T11:15:59ZRevision history for this page on the wikiMediaWiki 1.22.1http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=864&oldid=prevChangtau2005 at 13:03, 4 January 20162016-01-04T13:03:27Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research consisted of self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method <del class="diffchange diffchange-inline">proposed </del>originally, and then <del class="diffchange diffchange-inline">suggest </del>and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research consisted of self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method <ins class="diffchange diffchange-inline">suggested </ins>originally, and then <ins class="diffchange diffchange-inline">propose </ins>and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=863&oldid=prevChangtau2005 at 12:58, 4 January 20162016-01-04T12:58:17Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research consisted of self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method proposed originally, and then <del class="diffchange diffchange-inline">propose </del>and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research consisted of self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method proposed originally, and then <ins class="diffchange diffchange-inline">suggest </ins>and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=862&oldid=prevChangtau2005 at 10:46, 4 January 20162016-01-04T10:46:38Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>From the perspective of an engineer, I could qualitatively appreciate the simpler or more mechanical ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. However, the important issues being debated, like Professor Lo presenting evidence against the random walk hypothesis, or Mandelbrot criticizing the assumptions of portfolio theory - these are presented in literature beyond my current mathematical reach. Hence my first reason for applying to the MFin programme: I wish to learn about core concepts in modern finance, the current models in use, and their limitations, so that I can reason about them. Secondly, during my time here, I want to develop my understanding of nonparametric methods in modelling time-series data. I'm particularly interested in Markov models and neural nets, due to the ubiquity of their applications in general data science and their lack of assumptions beyond their structure. Perhaps such an approach could offer novel solutions, or complement conventional models when the usual assumptions break down.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>From the perspective of an engineer, I could qualitatively appreciate the simpler or more mechanical ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. However, the important issues being debated, like Professor Lo presenting evidence against the random walk hypothesis, or Mandelbrot criticizing the assumptions of portfolio theory - these are presented in literature beyond my current mathematical reach. Hence my first reason for applying to the MFin programme: I wish to learn about core concepts in modern finance, the current models in use, and their limitations, so that I can reason about them. Secondly, during my time here, I want to develop my understanding of nonparametric methods in modelling time-series data <ins class="diffchange diffchange-inline">and general random processes</ins>. I'm particularly interested in Markov models and neural nets, due to the ubiquity of their applications in general data science and their lack of assumptions beyond their structure. Perhaps such an approach could offer novel solutions, or complement conventional models when the usual assumptions break down.</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=861&oldid=prevChangtau2005 at 10:41, 4 January 20162016-01-04T10:41:31Z<p></p>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Finance permeates everything in modern society (just like computers); having a sound understanding of its principles, limitations, and potential, is paramount, as individual and organizations stand to gain and lose a great deal, sometimes not even from their own mismanagement. To me, finance embodies rigorous treatment of risk and payoff under uncertainty, through applying advanced statistical methods on lots and lots of data. Having just finished my MEng Computer Science with a focus on machine learning, most of my experience is with (un)supervised learning, data mining, and working with various forms of data, such as search or advertising data, ontologies, images, DNA sequences etc. My background, as well as my personal interest in becoming an educated participant in the open market, makes this a very appealing field of study.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Finance permeates everything in modern society (just like computers); having a sound understanding of its principles, limitations, and potential, is paramount, as individual and organizations stand to gain and lose a great deal, sometimes not even from their own mismanagement. To me, finance embodies rigorous treatment of risk and payoff under uncertainty, through applying advanced statistical methods on lots and lots of data. Having just finished my MEng <ins class="diffchange diffchange-inline">in </ins>Computer Science with a focus on machine learning, most of my experience is with (un)supervised learning, data mining, and working with various forms of data, such as search or advertising data, ontologies, images, DNA sequences etc. My background, as well as my personal interest in becoming an educated participant in the open market, makes this a very appealing field of study.</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=860&oldid=prevChangtau2005 at 10:33, 4 January 20162016-01-04T10:33:24Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>I share MIT's belief in lifelong learning, and I firmly believe that having an understanding <del class="diffchange diffchange-inline">in </del>multiple disciplines provides the necessary insight and expertise to comprehend and ultimately solve today's most difficult problems. I seek out challenging environments and new perspectives to change the way I think; that is when I make the most important mistakes, learn the most useful lessons, and develop fastest both personally and professionally, and so I actively work towards putting myself in such situations every opportunity I get. I think the combination of both MIT and the Master of Finance programme is the best fit at this stage in my career, and I look forward to learning and contributing with the very best this summer.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>I share MIT's belief in lifelong learning, and I firmly believe that having an understanding <ins class="diffchange diffchange-inline">across </ins>multiple disciplines provides the necessary insight and expertise to comprehend and ultimately solve today's most difficult problems. I seek out challenging environments and new perspectives to change the way I think; that is when I make the most important mistakes, learn the most useful lessons, and develop fastest both personally and professionally, and so I actively work towards putting myself in such situations every opportunity I get. I think the combination of both MIT and the Master of Finance programme is the best fit at this stage in my career, and I look forward to learning and contributing with the very best this summer.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div><noinclude>[[Category:Documents]]</noinclude></div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div><noinclude>[[Category:Documents]]</noinclude></div></td></tr>
</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=859&oldid=prevChangtau2005 at 23:07, 3 January 20162016-01-03T23:07:17Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>From the perspective of an engineer, I could qualitatively appreciate the simpler or more mechanical ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. However, the important issues being debated, like Professor Lo presenting evidence against the random walk hypothesis, or Mandelbrot criticizing the assumptions of portfolio theory - these are presented in literature beyond my current mathematical reach. Hence my first reason for applying to the MFin programme: I wish to learn about core concepts in modern finance, the current models in use, and their limitations, so that I can reason about them. Secondly, during my time here, I want to develop my understanding <del class="diffchange diffchange-inline">of the applications </del>of nonparametric methods in modelling time-series data. I'm particularly interested in Markov models and neural nets, due to the ubiquity of their applications in general data science and their lack of assumptions beyond their structure. Perhaps such an approach could offer novel solutions, or complement conventional models when the usual assumptions break down.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>From the perspective of an engineer, I could qualitatively appreciate the simpler or more mechanical ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. However, the important issues being debated, like Professor Lo presenting evidence against the random walk hypothesis, or Mandelbrot criticizing the assumptions of portfolio theory - these are presented in literature beyond my current mathematical reach. Hence my first reason for applying to the MFin programme: I wish to learn about core concepts in modern finance, the current models in use, and their limitations, so that I can reason about them. Secondly, during my time here, I want to develop my understanding of nonparametric methods in modelling time-series data. I'm particularly interested in Markov models and neural nets, due to the ubiquity of their applications in general data science and their lack of assumptions beyond their structure. Perhaps such an approach could offer novel solutions, or complement conventional models when the usual assumptions break down.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research <del class="diffchange diffchange-inline">were </del>self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method proposed <del class="diffchange diffchange-inline">in the original grant</del>, and then propose and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research <ins class="diffchange diffchange-inline">consisted of </ins>self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method proposed <ins class="diffchange diffchange-inline">originally</ins>, and then propose and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=854&oldid=prevChangtau2005 at 20:20, 3 January 20162016-01-03T20:20:54Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research were self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method proposed in the original grant, and then <del class="diffchange diffchange-inline">I had to </del>propose and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research were self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method proposed in the original grant, and then propose and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=853&oldid=prevChangtau2005 at 20:18, 3 January 20162016-01-03T20:18:43Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research were self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule <del class="diffchange diffchange-inline">- which I did</del>. I had to first convince the project stakeholders to forgo the overly optimistic method proposed in the original grant, and then I had to propose and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, as well as industrial and academic research. As a developer, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research were self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule. I had to first convince the project stakeholders to forgo the overly optimistic method proposed in the original grant, and then I had to propose and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=852&oldid=prevChangtau2005 at 20:11, 3 January 20162016-01-03T20:11:06Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">For decades, proponents of </del>the <del class="diffchange diffchange-inline">random walk hypothesis have asserted that the market as unpredictable, while Black attributed price movements to random noise trades and informed trades which move market price towards the unobservable intrinsic "shadow" price. More recently, evidence was presented (including by Professor Lo) that goes against the random walk hypothesis. Meanwhile, Mandelbrot has criticized the assumptions </del>of <del class="diffchange diffchange-inline">portfolio theory</del>, <del class="diffchange diffchange-inline">and proposed using fractals for modelling instead. Considering the implications, one would do well to heed the debate being waged in the literature, but they are largely out of my current mathematical reach, although </del>I <del class="diffchange diffchange-inline">do have a qualitative appreciation for </del>the simpler ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. <del class="diffchange diffchange-inline">To properly reason about financial theory and methods</del>, <del class="diffchange diffchange-inline">I must first establish a mathematical understanding for them</del>, <del class="diffchange diffchange-inline">and that is what </del>the <del class="diffchange diffchange-inline">core </del>of the MFin programme <del class="diffchange diffchange-inline">is </del>about<del class="diffchange diffchange-inline">. The study of non-independent time series data</del>, <del class="diffchange diffchange-inline">general random processes</del>, and <del class="diffchange diffchange-inline">the statistical methods involved</del>, <del class="diffchange diffchange-inline">would also be very useful even in other areas of data science</del>. <del class="diffchange diffchange-inline">My current academic goal is </del>to <del class="diffchange diffchange-inline">investigate </del>the applications of nonparametric methods in modelling <del class="diffchange diffchange-inline">financial </del>data. <del class="diffchange diffchange-inline">In particular, </del>neural nets <del class="diffchange diffchange-inline">are interesting because </del>the assumptions <del class="diffchange diffchange-inline">they make is largely only based on </del>their <del class="diffchange diffchange-inline">own </del>structure <del class="diffchange diffchange-inline">- perhaps they </del>could offer novel solutions, or complement conventional models <del class="diffchange diffchange-inline">in situations </del>when the usual assumptions break down.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">From </ins>the <ins class="diffchange diffchange-inline">perspective </ins>of <ins class="diffchange diffchange-inline">an engineer</ins>, I <ins class="diffchange diffchange-inline">could qualitatively appreciate </ins>the simpler <ins class="diffchange diffchange-inline">or more mechanical </ins>ideas. For instance, Markowitz elegantly reduced the selection of portfolios of risky assets to a pareto-optimization problem, the computation of which Sharpe simplified (approximately) by computing a common index, which I liken to how log ratios of gene expression in microarrays are compared among multiple samples. <ins class="diffchange diffchange-inline">However</ins>, <ins class="diffchange diffchange-inline">the important issues being debated</ins>, <ins class="diffchange diffchange-inline">like Professor Lo presenting evidence against </ins>the <ins class="diffchange diffchange-inline">random walk hypothesis, or Mandelbrot criticizing the assumptions </ins>of <ins class="diffchange diffchange-inline">portfolio theory - these are presented in literature beyond my current mathematical reach. Hence my first reason for applying to </ins>the MFin programme<ins class="diffchange diffchange-inline">: I wish to learn </ins>about <ins class="diffchange diffchange-inline">core concepts in modern finance</ins>, <ins class="diffchange diffchange-inline">the current models in use</ins>, and <ins class="diffchange diffchange-inline">their limitations</ins>, <ins class="diffchange diffchange-inline">so that I can reason about them</ins>. <ins class="diffchange diffchange-inline">Secondly, during my time here, I want </ins>to <ins class="diffchange diffchange-inline">develop my understanding of </ins>the applications of nonparametric methods in modelling <ins class="diffchange diffchange-inline">time-series </ins>data. <ins class="diffchange diffchange-inline">I'm particularly interested in Markov models and </ins>neural nets<ins class="diffchange diffchange-inline">, due to </ins>the <ins class="diffchange diffchange-inline">ubiquity of their applications in general data science and their lack of </ins>assumptions <ins class="diffchange diffchange-inline">beyond </ins>their structure<ins class="diffchange diffchange-inline">. Perhaps such an approach </ins>could offer novel solutions, or complement conventional models when the usual assumptions break down.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, <del class="diffchange diffchange-inline">while on the </del>research <del class="diffchange diffchange-inline">side</del>, I have <del class="diffchange diffchange-inline">been solving problems specific to the tech industry. I have mostly </del>worked in teams of 2 to 6 <del class="diffchange diffchange-inline">on the development front</del>, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research were self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>While I am keeping an open view about what I intend to do until I get a better feel for the nature of the work, I tend to gravitate towards either academic or industrial research. Up to now, I have been involved in small-scale software development and the occasional hackathon or two, <ins class="diffchange diffchange-inline">as well as industrial and academic </ins>research<ins class="diffchange diffchange-inline">. As a developer</ins>, I have worked in teams of 2 to 6, often with flexible team dynamics as required by agile development methods. Small teams afforded flexibility, but also demanded self-sufficiency and proficiency in multiple roles, so it was a great learning opportunity. The hackathon format was essentially the same, other than the intense time pressure and severe trade-offs one has to consider to be successful. My research were self-directed projects at least 2 to 3 months long, with input from my colleagues or supervisors. Decisions had to be defended on a regular basis, especially in industrial settings where the end-goals change and the deadlines are tight<ins class="diffchange diffchange-inline">. For example, I was hired by DigitalMR to salvage a grant-funded feasibility study running behind schedule - which I did. I had to first convince the project stakeholders to forgo the overly optimistic method proposed in the original grant, and then I had to propose and follow through with a new method which would realistically yield the most useful results in the time remaining (it did), by leveraging two relatively new datasets and by modifying existing methods in recent literature</ins>. These experiences, as well as my familiarity with experimental overhead like cleaning data, collecting ground truth labels, or computing inter-rater agreement, gives me the practical experience and confidence to tackle multifaceted problems.</div></td></tr>
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</table>Changtau2005http://lqkhoo.com/wiki/index.php?title=Document:MIT-MFin-essay&diff=843&oldid=prevChangtau2005 at 23:56, 2 January 20162016-01-02T23:56:27Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{CVContent|</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>{{CVContent|</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Finance permeates everything in <del class="diffchange diffchange-inline">a </del>modern society (just like computers); having a sound understanding of its principles, limitations, and potential, is paramount, as individual and organizations stand to gain and lose a great deal, sometimes not even from their own mismanagement. To me, finance embodies rigorous treatment of risk and payoff under uncertainty, through applying advanced statistical methods on lots and lots of data. Having just finished my MEng Computer Science with a focus on machine learning, most of my experience is with (un)supervised learning, data mining, and working with various forms of data, such as search or advertising data, ontologies, images, DNA sequences etc. My background, as well as my personal interest in becoming an educated participant in the open market, makes this a very appealing field of study.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Finance permeates everything in modern society (just like computers); having a sound understanding of its principles, limitations, and potential, is paramount, as individual and organizations stand to gain and lose a great deal, sometimes not even from their own mismanagement. To me, finance embodies rigorous treatment of risk and payoff under uncertainty, through applying advanced statistical methods on lots and lots of data. Having just finished my MEng Computer Science with a focus on machine learning, most of my experience is with (un)supervised learning, data mining, and working with various forms of data, such as search or advertising data, ontologies, images, DNA sequences etc. My background, as well as my personal interest in becoming an educated participant in the open market, makes this a very appealing field of study.</div></td></tr>
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</table>Changtau2005