“John,” she said, “does it make every one — unhappy when they study and learn lots of things?”

He paused and smiled. “I am afraid it does,” he said.

“And, John, are you glad you studied?”


Hi, I'm


“Yes,” came the answer, slowly but positively.

She watched the flickering lights upon the sea, and said thoughtfully, “I wish I was unhappy,—and—and,” putting both arms about his neck, “I think I am, a little, John.”



Coursework

I study Applied Math/Economics/CS at Harvard - here are the classes I've taken so far!


Research

I'm particularly curious about CS and economics, with future plans of getting a PhD. Feel free to check out some of my research experiences thus far, most of which have been focused on the intersection of the two fields.


Experiences / Projects

Outside of research, I've also worked as a course assistant for some Harvard classes and made some cool projects with friends! Feel free to check them out here!


About Me

“All men have the stars”, he answered, “but they are not the same things for different people. For some, who are travelers, the stars are guides. For others they are no more than little lights in the sky. For others, who are scholars, they are problems.

For my businessman they were wealth. But all these stars are silent. You – you alone – will have the stars as no one else has them – ”


My Coursework

“In one of the stars I shall be living. In one of them I shall be laughing. And so it will be as if all the stars were laughing, when you look at the sky at night...

You – only you – will have stars that can laugh!”




Computer Science
  • CS 50 (Intro to CS)
  • CS 51 (Functional Programming)
  • CS 61 (Systems Programming & Machine Organization)
  • CS 91R (Economics & Computation II)
  • CS 121 (Theoretical Computer Science)
  • CS 124 (Data Structures & Algorithms)
  • CS 136 (Economics & Computation)
  • CS 181 (Machine Learning)
  • CS 223 (Probabilistic Analysis & Algorithms)
  • CS 229BR (Foundations of Deep Learning)
  • CS 238 (Optimized Democracy)
Math
  • Math 25A/B (Theoretical Linear Algebra/Real Analysis)
  • Math 114 (Measure Theory and Functional Analysis)
  • Math 116 (Convexity and Optimization)
  • Math 122 (Algebra I: Theory of Groups and Vector Spaces)
Economics & Statistics
  • Econ 1011A/B (Intermediate Microeconomics/Macroeconomics)
  • Econ 2070 (A Computer Science Toolbox for Modern Economic Theory)
  • Econ 2120/2140 (Econometrics)
  • Stat 111 (Statistical Inference)
  • Stat 185 (Unsupervised Learning)
  • Stat 210 (Probability I)
Other
  • HUM 10A/B (A Humanities Colloquium)

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About Me


My Research


I am afraid. Not of life, or death, or nothingness, but of wasting it as if I had never been.



Publications/Preprints


Strategic Recommendation: Revenue Optimal Matching for Online Platforms (AAAI Student Abstract & Poster Program 2024, Oral Presentation)

with Gary Qiurui Ma and David Parkes


American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers (NeurIPS 2023 Datasets & Benchmarks Track)

with Melissa Dell, Jacob Carlson, Tom Bryan, Emily Silcock, Abhishek Arora, Zejiang Shen, Quan Le, Pablo Querubin, and Leander Heldring


Noise Robust De-Duplication at Scale (ICLR 2023)

with Emily Silcock, Jinglin Yang, and Melissa Dell


Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization (Under Review)

with Hugh Zhang, Marc Lanctot, and David Parkes


Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks (Under Review)

with Lukas Fesser and Richard Qiu


Experiences


Research with Professors David Parkes and Yannai Gonczarowski (May 2023 - Present)

I am working on modeling the incentives that face online platforms when recommending products to buyers.


Research Assistant for Professor David Parkes (June 2022 - June 2023)

I worked to develop new algorithms for multi-agent reinforcement learning, unifying work on single-player and multi-agent settings into a single general-purpose algorithm.


Research Assistant for Professor Ed Glaeser (November 2021 - August 2022)

I worked on an upcoming book written by Ed Glaeser, Lawrence Summers, and Ben Austin on prime-aged male joblessness in America. My tasks include conducting literature reviews as well as gathering, cleaning, and analyzing data (in R) to better understand the potential effects of policy reforms targeted to deal with the problem of joblessness.


Research Assistant for Professor Melissa Dell (June 2021 - Present)

I am working on a project to leverage deep learning methods to better understand the role of newspapers and the press in influencing public opinion in America. I helped to design and implement a clustering pipeline to detect networks of reprinted news content across time and space (arXiv).



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About Me


My Projects


Intelligence is one of the greatest human gifts. But all too often a search for knowledge drives out the search for love. This is something else I've discovered for myself very recently. I present it to you as a hypothesis: Intelligence without the ability to give and receive affection leads to mental and moral breakdown, to neurosis, and possibly even psychosis. And I say that the mind absorbed in and involved in itself as a self-centered end, to the exclusion of human relationships, can only lead to violence and pain.



CA for CS136 (Economics and Computation, Fall 2023)

I hosted sections, prepared materials, and hosted office hours for Harvard's course on economics and computation, taught by David Parkes.


Head CA for Econ 1011A (Intermediate Microeconomics, Fall 2021-2023)

I prepared review materials, graded problem sets, and hosted office hours and review sessions for Harvard's Intermediate Microeconomics course, taught by Ed Glaeser.


Teaching Fellow for CS50 (Intro to CS, Fall 2021)

I prepared materials for my own section of ~20 students, graded and left feedback on homework and projects, and hosted office hours for Harvard's introductory CS course, taught by David Malan.


The Almighty Adversary in Combinatorial Posted Price Auctions (Econ 2070 Final Project)

For our Econ 2070 final project, we wrote a paper analyzing the robustness of posted-price combinatorial auctions.


A New Approach to Kidney Matching (CS136 Final Project)

For our CS136 final project, my friends and I wrote a paper proposing and examining a failure-robust longevity-aware (FRLA) kidney matching algorithm designed to minimize the number of deaths in a kidney exchange.


WiCS Spring Workshop

I help design and organize a semi-annual workshop for Harvard WiCS (Women in Computer Science) that offers classes and other resources to young women interested in computer science. I helped create our website, created some puzzles, and plan to continue hosting more workshops like this one in the future!



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About Me


It's our only hope even if it fails us, although it fails us, because it fails us. Am I losing precision? What I'm searching for is the right comparison. Love and truth, yes, that's the prime connection. We all know objective truth is not obtainable, that when some event occurs we shall have a multiplicity of subjective truths which we assess and then fabulate into history, into some God-eyed version of what 'really' happened. This God-eyed version is a fake - a charming, impossible fake, like those medieval paintings which show all the stages of Christ's Passion happening simultaneously in different parts of the picture. But while we know this, we must still believe that objective truth is obtainable; or we must believe that it is 99 percent obtainable; or if we can't believe this we must believe that 43 per cent objective truth is better than 41 per cent. We must do so, because if we don't we're lost, we fail into beguiling relativity, we value one liar's version as much as another liar's, we throw up our hands at the puzzle of it all, we admit that the victor has the right not just to the spoils but also to the truth.


About Me


And so it is with love. We must believe in it, or we're lost. We may not obtain it, or we may obtain it and find it renders us unhappy; we must still believe in it. If we don't, then we merely surrender to the history of the world and to someone else's truth.

It will go wrong, this love; it probably will. That contorted organ, like the lump of ox meat, is devious and encoded. Our current model for the universe is entropy, which at the daily level translates as: things fuck up. But when love fails us, we must still go on believing in it. Is it encoded in every molecule that things fuck up, that love will fail? Perhaps it is. Still we must believe in love, just as we must believe in free will and objective truth. And when love fails, we should blame the history of the world. If only it had left us alone, we could have been happy, we could have gone on being happy. Our love has gone, and it is the fault of the history of the world.




Hi! I'm Luca, a senior at Harvard originally from Lubbock, Texas - home to Texas Tech and acres worth of cotton and dirt. And the occasional haboob (Google it if you don't know what it is!)

Outside CS and economics, some of my hobbies include reading, playing tennis, ping pong, soccer, or any other sports you can think of, and spending way too much time scrolling on Reddit. I'm a huge Roger Federer fan and will never be convinced that he's not the greatest of all time (no matter how many slams Novak ends up with).

In Cambridge, you'll probably find me sleeping or eating way too much pasta, hot pot, and gelato :). See you around!

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