Own Your Experience

This is a computer-generated message from the Campus Navigation Portal (CNAV), which can be accessed via the URL: Campus Navigation Portal (CNAV). It was sent to you to inform you of a significant event. I received this email when I was a young, nervous First Year student. I took advantage of the clean slate I got from attending a new school and was scrolling through the Digest in search of a new identity. Maybe I could be one of those quirky unicycle riding, juggling, circus kids—it was all up in the air. I wasn’t going to let the past hold me back anyway. Then I read this: You have been added to the group, IRC_Asian_Students You do not have the ability to unsubscribe yourself from this group. [excerpt

A Meal for the Man on the Redline

These words will bite, Acid bubbling At the pit of your bowels Vowels volatile won’t Be easy to swallow. [excerpt

Precious Knowledge

An essay about my name and its true meaning..

Are Under- and Over-reaction the Same Matter? A Price Inertia based Account

Theories on under- and over-reaction in asset prices fall into three types: (1) they are respectively driven by different psychological factors; (2) they are driven by different types of investors; and (3) they reflect un-modeled risk. We design an asset market where information arrives sequentially over time and is revealed asymmetrically to investors. None of the three hypotheses is supported by our data: (1) Investors do not respond differently to public information and private information, and they do not behave in ways that are claimed by multiple psychological models; (2) no groups of investors are identified to drive under- or over-reaction in particular; (3) price deviation from expected payoff cannot be justified by risk metrics. We find that prices react insufficiently to news surprises, possibly because of cautious conservatism on the part of investors and under-reacting drifts outnumber overreacting reversals substantially. Contrary to common beliefs, we find that over-reaction is caused by slow adjustment of prices to surprises, similar to the cause of under-reaction. It is the degree of price inertia that drives the relative frequencies of under- and over-reaction. We propose a simple price inertia theory of under- and over-reaction: when information arrives sequentially over time, the market is characterized by a slow convergence toward intrinsic value; when news surprises are of the same signs, prices falls behind newly updated intrinsic values, manifesting under-reacting drifts; when news surprises change signs, prices again do not adjust quick enough to catch up with the new intrinsic values, manifesting a temporal pattern of overreacting reversals.Experimental finance, under-reaction, overreaction, behavior, price inertia, risk aversion

Stop Ducking

When I joined the Phi Kappa Psi fraternity, a small voice in my head kept saying that it was a bad idea. “Don’t become part of the system, Stephen.” But I defended my decision and believed in the idea of Phi Kappa Psi returning to campus with a clean slate. The possibilities far outweighed the cons. I dreamt of the potential of what Phi Psi could become and how we would stand above the traditional expectations of Greek organizations. I wanted to tell everyone about this dream and I couldn’t wait to find like-minded people. I felt inspired by how we might define ourselves. [excerpt

DCTM: Discrete-Continuous Transformation Matching for Semantic Flow

Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there lack practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space. To address this problem, we present a discrete-continuous transformation matching (DCTM) framework where dense affine transformation fields are inferred through a discrete label optimization in which the labels are iteratively updated via continuous regularization. In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor. Experimental results show that this model outperforms the state-of-the-art methods for dense semantic correspondence on various benchmarks