Next Monday's speaker is Professor William Goetzmann, from the Yale School of Management.
William N. Goetzmann is the Edwin J. Beinecke Professor of Finance and Management Studies, Faculty Director of the Yale International Center for Finance, and Faculty Director of Executive MBA in Asset Management at the Yale School of Management. He is an expert on a diverse range of investments. His past work includes studies of stock market predictability, hedge funds and survival biases in performance measurement. His current research focuses on alternative investing, factor investing, behavioral finance and the art market.He will be speaking about arbitrage pricing theory. He recommends the wiki article on APT as a good place to start. Also recommended but not required is Chapteer VI: The Arbitrage Pricing Theory from his book on investment theory, as well as his paper: Pairs Trading: Performance of a Relative-Value Arbitrage Rule.Professor Goetzmann has written and co-authored a number of books, including Modern Portfolio Theory and Investment Analysis (Wiley, 2014), The Origins of Value: The Financial Innovations that Created Modern Capital Markets (Oxford, 2005), The Great Mirror of Folly: Finance, Culture and the Crash of 1720 (Yale, 2013) and most recently, Money Changes Everything: How Finance Made Civilization Possible (Princeton, 2016). He teaches portfolio management, alternative investments, real estate, and financial history at the Yale School of Management, and is Executive Editor of the Financial Analyst Journal.
He will be in person and we will take him out to dinner afterward. We will invite up to four students. Lottery in class today.
Thursday, March 10, 2022
4:00 -5:00 pmMatt Golub “Reverse Engineering Computation in the Brain”
Zoom link: https://yale.zoom.us/j/96682905133
Abstract: Behavior and cognition are driven by the coordinated activity of populations of neurons in the brain. A major challenge in systems neuroscience is to infer the computational principles underlying the activity of these neural populations. What are the algorithms implemented by neural populations? How can we design experiments and analyses with hypotheses about computation in mind? My research is aimed at developing the theory, modeling, and machine learning techniques needed to realize this vision of reverse engineering computation in the brain. Progress in this research could lead to new treatments for neurological injuries and disorders, new paradigms for optimizing our behavior and cognition, and new approaches to generating artificial intelligence. In this talk, I will present lines of previous, ongoing, and proposed research that highlight the potential of this vision. First, I will present a line of brain-computer interface experiments and modeling that revealed principles guiding neural populations as they reorganize during learning. Here, we discovered constraints faced by neural populations, which predicted empirically observed limitations to behavioral improvement with learning. Second, I will present a framework of recurrent neural network (RNN) modeling for identifying the computations performed by a population of recorded neurons. Here, we train RNN-based sequential variational autoencoders to recapitulate single trial neural population activity, and then we decompose the RNN into a collection of interpretable dynamical motifs that reveal the computation performed by the neural population. Finally, I will propose future plans for leveraging these tools and insights toward i) dissecting the interplay between attention and decision making, ii) optimizing stimulation of neural population dynamics, and iii) accelerating learning in the brain.
Check out sarcastic-explanation. Some of the natural language
generation code has gotten rusty.
Expert systems - an overview. The rules help provide an explanation.
See ExpertSystems.html MYCIN and friends.