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D&I Initiatives in academia

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Preface: I have never sat on an admissions committee, and my thoughts are based on conversations I have been able to observe, mostly on Twitter. These aren’t necessarily representative of my department, and I can’t speak to any specific university. This is a compilation of overarching themes I’ve observed from a variety of (primarily USA-based) academics. My thoughts are obviously going to be skewed by who I do (and, more importantly, don’t) know.

Diversity and Inclusion. It’s almost as much of a buzzword in Comp Sci as “Machine Learning” right now. Personally, I tend to lump the two together without parsing them as two separate concepts most of the time, so I’d like to take a second to break the two up.

Unsolicited advice on graduate applications

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Graduate school can weirdly isolate graduate students from undergradautes- especially if you’re not a Teaching Assistant (TA). Over the past few years, I’ve been fortunate enough to talk to prospective students through Open House weekends, structured mentoring opportunities, and the occasional cold email. (Totally cool to email me!)

Researchers to follow

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The following is an assortment of young researchers I’ve had the pleasure of interacting with, either personally or by reading their work. If you find my work interesting, you might want to check them out as well. I’ll try to update it as I meet more people in my experience.

NSF GRFP Example

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Here is my research statement that I submitted for the NSF GRFP. Personal statements and reviews are available upon request as well.

portfolio

publications

Egocentric Height Estimation

Published in Winter Conference on Applications in Computer Vision (WACV), 2017

Egocentric, or first-person vision which became popular in recent years with an emerge in wearable technology,is different than exocentric (third-person) vision in some distinguishable ways, one of which being that the camera-wearer is generally not visible in the video frames. Recent work has been done on action and object recognition in egocentric videos, as well as work on biometric extraction from first-person videos. Height estimation can be a useful feature for both soft-biometrics and object tracking. Here, we propose a method of estimating the height of an egocentric camera without any calibration or reference points. We used both traditional computer vision approaches and deep learning in order to determine the visual cues that results in best height estimation. Here, we introduce a framework inspired by two stream networks comprising of two Convolutional Neural Networks, one based on spatial information, and one based on information given by optical flow in a frame. Given an egocentric video as an input to the framework, our model yields a height estimate as an output. We also incorporate late fusion to learn a combination of temporal and spatial cues. Comparing our model with other methods we used as baselines, we achieve height estimates for videos with a Mean Average Error of 14.04 cm over a range of 103 cm of data, and classification accuracy for relative height (tall, medium or short) up to 93.75% where chance level is 33%.

Recommended citation: Jessica Finocchiaro, Aisha Urooj Khan, and Ali Borji. (2017). "Egocentric Height Estimation." IEEE Winter Conference on Applications in Computer Vision. https://arxiv.org/pdf/1610.02714.pdf

Investigating Social Trends in the Iterated Prisoner’s Dilemma

Published in Neural Information Processing Systems, 2017

In ethics, many academics make the assumption that all people want to be good. Evil comes in where there is a conflict of good decisions; where a decision that is good for one person contradicts the good of another. In this case, a person will make a different decision depending on their definition of the good they want to accomplish. In a society that starts with an equal proportion of selfishly good and selfessly good people, we aim to investigate the convergence of behavior through simulating the Iterated Prisoner’s Dilemma over time.

Recommended citation: Jessica Finocchiaro. (2017). "Investigating Social Trends in the Iterated Prisoner's Dilemma." Florida Southern College Honors College Bachelor's Thesis. https://repository.flsouthern.edu/bitstream/handle/11416/315/Finocchiaro_Jessica_S17.pdf?sequence=1

Social Trends in the Iterated Prisoner’s Dilemma (Extended Abstract)

Published in Genetic and Evolutionary Computation Conference (GECCO) 2017, 2017

Abstract: In this paper, we utilize a multi-objective genetic algorithm (GA) to investigate the Iterated Prisoner’s Dilemma problem with a population of players that don’t have uniform objectives. Each of the members of our population has one of four objective pairs. We simulate a tournament similar to those in previous work to investigate patterns of convergence in objective pairs when they are free to change. We also consider the most successful objective pair within a population when members’ objective pairs are fixed.

Recommended citation: Jessica Finocchiaro, H. David Mathias. (2017). "Social Trends in the Iterated Prisoner's Dilemma." Genetic and Evolutionary Computation Conference (GECCO) 2017. https://dl.acm.org/citation.cfm?id=3082037

Convex Elicitation of Continuous Properties

Published in Neural Information Processing Systems, 2018

Abstract: A property or statistic of a distribution is said to be elicitable if it can be expressed as the minimizer of some loss function in expectation. Recent work shows that continuous real-valued properties are elicitable if and only if they are identifiable, meaning the set of distributions with the same property value can be described by linear constraints. From a practical standpoint, one may ask for which such properties do there exist convex loss functions. In this paper, in a finite-outcome setting, we show that in fact every elicitable real-valued property can be elicited by a convex loss function. Our proof is constructive, and leads to convex loss functions for new properties.

Recommended citation: Jessica Finocchiaro and Rafael Frongillo. (2018). "Convex Elicitation of Continuous Properties." Neural Information Processing Systems. https://papers.nips.cc/paper/8241-convex-elicitation-of-continuous-properties

An Embedding Framework for Consistent Polyhedral Surrogates

Published in Neural Information Processing Systems (NeurIPS) 2019, 2019

Abstract: We formalize and study the natural approach of designing convex surrogate loss functions via embeddings for problems such as classification or ranking. In this approach, one embeds each of the finitely many predictions (e.g. classes) as a point in Rd, assigns the original loss values to these points, and convexifies the loss in between to obtain a surrogate. We prove that this approach is equivalent, in a strong sense, to working with polyhedral (piecewise linear convex) losses. Moreover, given any polyhedral loss L, we give a construction of a link function through which L is a consistent surrogate for the loss it embeds. We go on to illustrate the power of this embedding framework with succinct proofs of consistency or inconsistency of various polyhedral surrogates in the literature.

Recommended citation: Jessica Finocchiaro, Rafael Frongillo, and Bo Waggoner. (2019). "An Embedding Framework for Consistent Polyhedral Surrogates" https://arxiv.org/abs/1907.07330

Embedding Dimension of Polyhedral Losses

Published in Conference on Learning Theory (COLT) 2020, 2020

Abstract: A common technique in supervised learning with discrete losses, such as 0-1 loss, is to optimize a convex surrogate loss over R^d, calibrated with respect to the original loss. In particular, recent work has investigated embedding the original predictions (e.g. labels) as points in R^d, showing an equivalence to using polyhedral surrogates. In this work, we study the notion of the embedding dimension of a given discrete loss: the minimum dimension d such that an embedding exists. We characterize d-embeddability for all d, with a particularly tight characterization for d=1 (embedding into the real line), and useful necessary conditions for d>1 in the form of a quadratic feasibility program. We illustrate our results with novel lower bounds for abstain loss.

Recommended citation: Jessica Finocchiaro, Rafael Frongillo, and Bo Waggoner. (2020). "Embedding Dimension of Polyhedral Losses"

Fairness and Discrimination in Mechanism Design and Machine Learning

Published in AI for Social Good (AI4SG) Workshop at Harvard CRCS 2020, 2020

As fairness and discrimination concerns permeate the design of both machine learning algorithms and mechanism design problems, we discuss differences in approaches between these two fields. We aim to bridge these two communities into a cohesive narrative that encompasses both the large-scale capabilities of machine learning and group-focused fairness as well as the strategic incentives and utility-based notions of fairness from mechanism de-sign, showing their necessity in designing a fair pipeline.

Recommended citation: Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtis, "Fairness and Discrimination in Mechanism Design and Machine Learning" (2020.)

Evolutionary Optimization of Cooperative Strategies for the Iterated Prisoner’s Dilemma

Published in IEEE Transactions on Games, 2020

Abstract: Abstract—The Iterated Prisoner’s Dilemma (IPD) has been studied in fields as diverse as economics, computer science, psychology, politics, and environmental studies. This is due, in part, to the intriguing property that its Nash Equilibrium is not globally optimal. Typically treated as a single-objective problem, a player’s goal is to maximize their own score. In some work, minimizing the opponent’s score is an additional objective. Here, we explore the role of explicitly optimizing for mutual cooperation in IPD player performance. We implement a genetic algorithm in which each member of the population evolves using one of four multi-objective fitness functions: selfish, communal, cooperative, and selfless, the last three of which use a cooperative

Recommended citation: Jessica Finocchiaro, H. David Mathias. (2020). "Evolutionary Optimization of Cooperative Strategies for the Iterated Prisoner’s Dilemma"

talks

teaching

CSCI 2270 (Data Structures)

Undergraduate course, University of Colorado, Department of Computer Science, 2018

Teaching Assistant for Data Structures in Summer 2018.
We taught introductory Data Students concepts using C++.

ucdblog

Introductory post

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Introduction


Hi, I’m Jessie. My pronouns are she/her and I’m starting my 3rd year in the CS Theory group.

Week 1 Summary

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This week, I got to work with a handful of people, including (but not limited to… I didn’t catch all the names) Vinitha, Lucas, Isabella, and Adam.

Project cultural probe practice

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Cultural probe design practice


For our project, we are interested in designing something to make sorting waste easier and more accurate. One major thing we need to understand with this is how people interact with their waste.

For a cultural probe related to our project, we can give volunteers recycling and compost bins to use in their house, observing where they place these bins (in relation to the landfill bin they already presumably have) and noting what waste items, if any, make their way into the recycling and compost bins.

Interview practice

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Interview practice


For this activity, Lucy interviewed me on activities I like to do. Being me, I talked about playing soccer and running, and most of her questions were directed at understanding how often I played and what the culture of my team was like. She asked how long I had played soccer and directed and adjusted her questions towards understanding the context in which I played soccer in order to understand why I love it.

Sketching practice

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Sketching practice


The first practice sketch we had was for an App to share your location with friends. alt text

The second object we practiced sketching was another version of the good ole’ Apple TV remote from day 1. alt text

Storyboarding practice

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Project storyboard practice


Here is a practice storyboard of someone interacting with our project design. alt text

Sketching practice

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Study buddy prototype


The study buddy app paper prototype we did in class. alt text

Usability heuristics with the inbox

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Usability heuristics: email inbox


In class, we talked about what some common heuristics for an email inbox were. Among many things, we discussed the organization of mail into folders, usuall managed on the left side. In order to start writing a new email, there is usually a bigger “compose” button that is differentiated by color.

Moreover, once you’re typing the email, the design of the email box is such that one works from top to bottom, starting with addressing the letter, having a greeting or subject line, then proceeding with the body of the message. Once all that is done, you can send the email by hitting the send button below all these entries.

Gesture design

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Gesture design


In class, we discussed what makes gestures useful and easy to execute, and were tasked with designing our own gesture for a task. In cars, my table discussed gestures that could be associated with driving. My favorite alternative gesture was flicking someone off could be an alternative way to blare your horn at them. Alternatively, we could have taps on the back of the steering wheel with the right hand skip forward to the next song, if applicable, so that driver hands do not need to leave the wheel to navigate music.

KLM model testing

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KLM Model


In class, we discussed the KLM (Keystroke-level model) way of estimating performance of different interfaces depending on the number of clicks, points, mental preparation, drawing points, and hand relocations required. In class, I faced off against Chance to complete a form on Shaun’s test code and got destroyed. I think he took 14 seconds, where I took 21 seconds to fill out the form, which was closer to what our classmates calculated to be an expected completion time.