|October 25, 2019||10:00 AM||Ying Hung: Integration of Models and Data for Inference about Humans and Machines (I)||Konstantin Mischaikow: Analyzing Imprecise Dynamics||Jingjin Yu: Toward Scaleable and Optimal Autonomy||COR-433|
|November 1, 2019||10:00 AM||Rong Chen: Dynamic Systems and Sequential Monte-Carlo||Jason Klusowski: Integration of Models and Data for Inference about Humans and Machines (II)||Cun-Hui Zhang: Statistical Inference with High-Dimensional Data||COR-433|
|November 8, 2019||10:00 AM||Kostas Bekris: Generating Motion for Adaptive Robots||Fred Roberts: Meaningless Statements in Performance Measurement for Intelligent Machines||COR-433|
|November 15, 2019||10:00 AM||Fioralba Cakoni: Inside-Out, Seen and Unseen||Matthew Stone: Colors in Context Inference challenges in Bayesian cognitive science||Wujun Zhang: Numerical approximation of optimal transport problem||COR-433|
|February 7, 2020||10:00 AM||
Patrick Shafto (Mathematics and Computer Science; Rutgers University)
Title: Cooperation in Humans and Machines
Abstract: Cooperation, specifically cooperative information sharing, is a basic principle of human intelligence. Machine learning, in contrast, focuses on learning from randomly sampled data, which neither leverages others’ cooperation nor prioritizes the ability to communicate what has been learned. I will discuss ways in which our understanding of human learning may be leveraged to develop new machine learning, and form a foundation for improved integration of machine learning into human society.
|May 8, 2020||10:00 AM||
Rene Vidal (Biomedical Engineering; Johns Hopinks University)
Title: From Optimization Algorithms to Dynamical Systems and Back
Abstract: Recent work has shown that tools from dynamical systems can be used to analyze accelerated optimization algorithms. For example, it has been shown that the continuous limit of Nesterov’s accelerated gradient (NAG) gives an ODE whose convergence rate matches that of NAG for convex, unconstrained, and smooth problems. Conversely, it has been shown that NAG can be obtained as the discretization of an ODE, however since different discretizations lead to different algorithms, the choice of the discretization becomes important. The first part of this talk will extend this type of analysis to convex, constrained and non-smooth problems by using Lyapunov stability theory to analyze continuous limits of the Alternating Direction Method of Multipliers (ADMM). The second part of this talk will show that many existing and new optimization algorithms can be obtained by suitably discretizing a dissipative Hamiltonian. As an example, we will present a new method called Relativistic Gradient Descent (RGD), which empirically outperforms momentum, RMSprop, Adam and AdaGrad on several non-convex problems. This is joint work with Guilherme Franca, Daniel Robinson and Jeremias Sulam.
|June 5, 2020||12:00 PM||
Lydia Chilton (Computer Science; Columbia University)
Title: AI Tools for Creative Work
|June 16, 2020||12:00 PM||
Mykhaylo Tyomkyn (Applied Mathematics; Charles University)
Title: Many Disjoint Triangles in Co-triangle-free Graphs
|June 22, 2020||12:00 PM||
Lenka Zdeborova (Institute of Theoretical Physics; French National Centre for Scientific Research)
Title: Understanding Machine Learning with Statistical Physics
|June 30, 2020||12:00 PM||
Rebecca Wright (Computational Science Center; Barnard College)
Title: Privacy in Today’s World
|July 7, 2020||12:00 PM||
Vivek Singh, (Behavioral Informatics Lab; Rutgers University)
Title: Algorithmic Fairness
Abstract: Today Artificial Intelligence (AI) algorithms are used to make multiple decisions affecting human lives and many such algorithms have been reported to be biased. This includes parole decisions, search results, and product recommendation, among others. Using multiple examples of recent efforts from my lab, I will discuss how such bias can be systematically measured and how the underlying algorithms can be made less biased. More details available at: http://wp.comminfo.rutgers.edu/vsingh/algorithmic-bias/
|July 17, 2020||10:00 AM||
Cynthia Rudin (Prediction Analysis Lab; Duke University)
Title: Interpretability vs. Explainability in Machine Learning
Abstract: With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. Explanations for black box models are not reliable, and can be misleading. If we use interpretable machine learning models, they come with their own explanations, which are faithful to what the model actually computes.
In this talk, I will discuss some of the reasons that black boxes with explanations can go wrong, whereas using inherently interpretable models would not have these same problems. I will give an example of where an explanation of a black box model went wrong, namely, I will discuss ProPublica’s analysis of the COMPAS model used in the criminal justice system: ProPublica’s explanation of the black box model COMPAS was flawed because it relied on wrong assumptions to identify the race variable as being important. Luckily in recidivism prediction applications, black box models are not needed because inherently interpretable models exist that are just as accurate as COMPAS.
I will also give examples of interpretable models in healthcare. One of these models, the 2HELPS2B score, is actually used in intensive care units in hospitals; most machine learning models cannot be used when the stakes are so high.
Finally, I will discuss two long-term projects my lab is working on, namely optimal sparse decision trees and interpretable neural networks.
|July 21, 2020||12:00 PM||
Peter Winkler (Math and Computer Science; Dartmouth)
Title: Cooperative Puzzles
|September 11, 2020||10:00 AM||
Mauro Maggioni (Data Intensive Computation; Johns Hopkins)
Title: Learning Interaction laws in particle- and agent-based systems
Abstract: Interacting agent-based systems are ubiquitous in science, from modeling of particles in Physics to prey-predator and colony models in Biology, to opinion dynamics in economics and social sciences. Oftentimes the laws of interactions between the agents are quite simple, for example they depend only on pairwise interactions, and only on pairwise distance in each interaction. We consider the following inference problem for a system of interacting particles or agents: given only observed trajectories of the agents in the system, can we learn what the laws of interactions are? We would like to do this without assuming any particular form for the interaction laws, i.e. they might be “any” function of pairwise distances. We consider this problem both the mean-field limit (i.e. the number of particles going to infinity) and in the case of a finite number of agents, with an increasing number of observations, albeit in this talk we will mostly focus on the latter case. We cast this as an inverse problem, and study it in the case where the interaction is governed by an (unknown) function of pairwise distances. We discuss when this problem is well-posed, and we construct estimators for the interaction kernels with provably good statistically and computational properties. We measure their performance on various examples, that include extensions to agent systems with different types of agents, second-order systems, and families of systems with parametric interaction kernels. We also conduct numerical experiments to test the large time behavior of these systems, especially in the cases where they exhibit emergent behavior. This is joint work with F. Lu, J.Miller, S. Tang and M. Zhong.
|October 23, 2020||10:00 AM||
Jason Hartline (Computer Science; Northwestern University)
Title: Mechanism Design and Data Science
Abstract: Computer systems have become the primary mediator of social and economic interactions. A defining aspect of such systems is that the participants have preferences over system outcomes and will manipulate their behavior to obtain outcomes they prefer. Such manipulation interferes with data-driven methods for designing and testing system improvements. A standard approach to resolve this interference is to infer preferences from behavioral data and employ the inferred preferences to evaluate novel system designs.
In this talk Prof. Hartline will describe a method for estimating and comparing the performance of novel systems directly from behavioral data from the original system. This approach skips the step of estimating preferences and is more accurate. Estimation accuracy can be further improved by augmenting the original system; its accuracy then compares favorably with ideal controlled experiments, a.k.a., A/B testing, which are often infeasible. A motivating example will be the paradigmatic problem of designing an auction for the sale of advertisements on an Internet search engine.
|October 27, 2020||10:00 AM||
Woojin Jung, (School of Social Science; Rutgers University)
Title: Using satellite imagery and deep learning to target aid in data-sparse contexts
Abstract: Aid policy has the potential to alleviate global poverty by targeting areas of concentrated need. A critical question remains, however, over whether aid is reaching the areas of most need. Often little ground-truth poverty data is available at a granular level (e.g., village) where aid interventions take place. This research explores remote sensing techniques to measure poverty and target aid in data-sparse contexts. Our study of Myanmar examines i) the performance of different methods of poverty estimation and ii) the extent to which poverty and other development characteristics explain community aid distribution. This study draws from the following sources of data: georeferenced community-driven development projects (n=12,504), daytime and nighttime satellite imagery, the Demographic and Health Survey, and conflict data. We first compare the accuracy of four poverty measures in predicting ground-truth survey data. Using the best poverty estimation in the first step, we investigate the association between village characteristics and aid per capita per village. Our results show that daytime features perform the best in predicting poverty as compared to the analysis of RSG color distribution, Kriging, and nighttime-based measures. We use a Convolutional Neural Network, pre-trained on ImageNet, to extract features from the satellite images in our best model. These features are then trained on the DHS wealth data to predict the DHS wealth index/poverty for villages receiving aid. The linear and non-linear estimator indicate that development assistance flows to low-asset villages, but only marginally. Aid is more likely to be disbursed to those villages that are less populous and farther away from fatal conflicts. Our study concludes that the nuances captured in satellite-based models can be used to target aid to impoverished communities.
|November 13, 2020||10:00 AM||
Vivek Singh, (Behavioral Informatics Lab; Rutgers University)
Title: Auditing and Controlling Algorithmic Bias
Abstract: Today Artificial Intelligence algorithms are used to make multiple decisions affecting human lives, and many such algorithms, such as those used in parole decisions, have been reported to be biased. In this talk, I will share some recent work from our lab on auditing algorithms for bias, designing ways to reduce bias, and expanding the definition of bias. This includes applications such as image search, health information dissemination, and cyberbullying detection. The results will cover a range of data modalities, (e.g., visual, textual, and social) as well as techniques such as fair adversarial networks, flexible fair regression, and fairness-aware fusion.
|December 4, 2020||10:00 AM||
Magnus Egerstedt (Electrical and Computer Engineering; Georgia Institute of Technology)
Title: Long Duration Autonomy With Applications to Persistent Environmental Monitoring
Abstract: When robots are to be deployed over long time scales, optimality should take a backseat to “survivability”, i.e., it is more important that the robots do not break or completely deplete their energy sources than that they perform certain tasks as effectively as possible. For example, in the context of multi-agent robotics, we have a fairly good understanding of how to design coordinated control strategies for making teams of mobile robots achieve geometric objectives, such as assembling shapes or covering areas. But, what happens when these geometric objectives no longer matter all that much? In this talk, we consider this question of long duration autonomy for teams of robots that are deployed in an environment over a sustained period of time and that can be recruited to perform a number of different tasks in a distributed, safe, and provably correct manner. This development will involve the composition of multiple barrier certificates for encoding tasks and safety constraints through the development of non-smooth barrier functions, as well as a detour into ecology as a way of understanding how persistent environmental monitoring can be achieved by studying animals with low-energy life-styles, such as the three-toed sloth.
Bio: Magnus Egerstedt is a Professor and School Chair in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, where he also holds secondary faculty appointments in Mechanical Engineering, Aerospace Engineering, and Interactive Computing. Prior to becoming School Chair, he served as the director for Georgia Tech’s multidisciplinary Institute for Robotics and Intelligent Machines. A native of Sweden, Dr. Egerstedt was born, raised, and educated in Stockholm. He received a B.A. degree in Philosophy from Stockholm University, and M.S. and Ph.D. degrees in Engineering Physics and Applied Mathematics, respectively, from the Royal Institute of Technology. He subsequently was a Postdoctoral Scholar at Harvard University. Dr. Egerstedt conducts research in the areas of control theory and robotics, with particular focus on control and coordination of complex networks, such as multi-robot systems, mobile sensor networks, and cyber-physical systems. He is a Fellow of both the IEEE and IFAC, and is a foreign member of the Royal Swedish Academy of Engineering Sciences. He has received a number of teaching and research awards for his work, including the John. R. Ragazzini Award from the American Automatic Control Council, the O. Hugo Schuck Best Paper Award from the American Control Conference, and the Best Multi-Robot Paper Award from the IEEE International Conference on Robotics and Automation.
|December 18, 2020||10:00 AM||
Tanya Berger-Wolf (Computer Science and Engineering; Ohio State University)
Title: Artificial Intelligence for Wildlife Conservation: AI and Humans Combating Extinction Together
Abstract: Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. I will show how fundamental data science and machine learning methods can be used to turn massive collections of images into high resolution information database, enabling scientific inquiry, conservation, and policy decisions. I will demonstrate how computational data science methods are used to collect images from online social media, detect various species of animals and even identify individuals. I will present data science methods to infer and counter biases in the ad-hoc data to provide accurate estimates of population sizes from those image data. I will also point out the risks that AI poses to endangered species data.
I will show how it all can come together to a deployed system, Wildbook, a project of tech for conservation non-profit Wild Me, with species including whales (flukebook.org), sharks (whaleshark.org), giraffes (giraffespotter.org), and many more. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy’s zebra) census using photographs taken by ordinary citizens in Kenya.The resulting numbers are now the official species census used by IUCN Red List and we repeated the effort in 2018, becoming the first certified census from an outside organization accepted by the Kenyan government. The 2020 event has just concluded on January 25-26. Wildbook is becoming the data foundation for wildlife science, conservation, and policy. Read more: https://www.nationalgeographic.com/animals/2018/11/artificial-intelligence-counts-wild-animals/
Bio: Dr. Tanya Berger-Wolf is a Professor of Computer Science Engineering, Electrical and Computer Engineering, and Evolution, Ecology, and Organismal Biology at the Ohio State University, where she is also the Director of the Translational Data Analytics Institute. As a computational ecologist, her research is at the unique intersection of computer science, wildlife biology, and social sciences. She creates computational solutions to address questions such as how environmental factors affect the behavior of social animals (humans included). Berger-Wolf is also a director and co-founder of the conservation software non-profit Wild Me, home of the Wildbook project, which enabled the first ever full census of the entire species, the endangered Grevy’s zebra in Kenya, using photographs from ordinary citizens. Wildbook has been featured in media, including The New York Times, CNN, and National Geographic.
Prior to coming to OSU in January 2020, Berger-Wolf was at the University of Illinois at Chicago. Berger-Wolf holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. She has received numerous awards for her research and mentoring, including University of Illinois Scholar, UIC Distinguished Researcher of the Year, US National Science Foundation CAREER, Association for Women in Science Chicago Innovator, and the UIC Mentor of the Year.
|February 19, 2021||10:00 AM||
Dan Halperin (Computer Science; Tel Aviv University)
Title: Throwing a Sofa Through the Window
Abstract: Planning motion for robots and other artifacts toward desired goal positions while avoiding obstacles on the way becomes harder when the environment is tight or densely cluttered. Indeed, prevalent motion-planning techniques often fail in such settings. The talk centers on recently-developed efficient algorithms to cope with motion in tight quarters.
We study several variants of the problem of moving a convex polytope in three dimensions through a rectangular (and sometimes more general) window. Specifically, we study variants in which the motion is restricted to translations only, discuss situations in which such a motion can be reduced to sliding (translation in a fixed direction) and present efficient algorithms for those variants. We show cases where sliding is insufficient but purely transnational motion works, or where purely transnational motion is insufficient and rotation must be included. Finally, we explore the general setup, where we want to plan a general motion (with all six degrees of freedom) for the polytope through the window and present an efficient algorithm for this problem, with running time close to O(n^4), where n is the number of edges of the polytope. (Joint work with Micha Sharir and Itay Yehuda.)
As time permits I will present additional recent results for motion in tight settings in assembly planning, fixture design, and casting and molding.
Bio: Dan Halperin is a professor of Computer Science at Tel Aviv University. His main field of research is Computational Geometry and Its Applications. A major focus of his work has been in research and development of robust geometric algorithms, principally as part of the CGAL project and library. The application areas he is interested in include robotics, automated manufacturing, algorithmic motion planning and 3D printing. Halperin is an IEEE Fellow and an ACM Fellow. http://acg.cs.tau.ac.il/danhalperin
|February 24, 2021||11:45 AM||
Yang Ning (Department of Statistics and Data Science Cornell University)
Title: Adaptive Estimation in Multivariate Response Regression with Hidden Variables
Abstract: A prominent concern of scientific investigators is the presence of unobserved hidden variables in association analysis. Ignoring hidden variables often yields biased statistical results and misleading scientific conclusions. Motivated by this practical issue, this paper studies the multivariate response regression with hidden variables, $Y = (\Ps)^TX + (B^*)^TZ + E$, where $Y \in \RR^m$ is the response vector, $X\in \RR^p$ is the observable feature, $Z\in \RR^K$ represents the vector of unobserved hidden variables, possibly correlated with $X$, and $E$ is an independent error. The number of hidden variables $K$ is unknown and both $m$ and $p$ are allowed, but not required, to grow with the sample size $n$.
Bio: Dr. Ning is an assistant professor in the Department of Statistics and Data Science at Cornell University. Prior to joining into the Cornell University, he was a post-doc at Princeton University. He received his Ph.D in Biostatistics from the Johns Hopkins University. His research interests focus on the high-dimensional statistics and causal inference with applications to biology, medicine and public health.
|February 26, 2021||10:00 AM||
Hossein Khiabanian (Cancer Institute of New Jersey)
Title: Integrated inference analyses to dissect tumor mutational profiles
Abstract: Recent advances in the use of clinical sequencing platforms in precision oncology settings have resulted in unprecedented access to the genomes of individual tumors. These assays aim to reliably identify and annotate somatic alterations specific to cancer cells for accurate diagnosis and treatment. However, due to the common lack of patient-matched controls, there is a need for a systematic effort to interpret detected variants in tumor-only sequencing data and to accurately describe the genomic landscape of a single tumor. In this talk, I will present a set of integrated, information-theoretic approaches that permit selecting the most consistent mutational model, distinguishing alterations in the tumor from those present in all cells (germline), while accounting for biases inherent to DNA sequencing and sample purity estimation. Using simulations and large, independent clinical datasets, we demonstrate the accuracy and precision of our methods. We will also discuss cases for which these analyses provide a model for tumor evolution, demonstrating that additional inference of mutational signatures and dissection of heterogeneity in tumor microenvironment can generate diagnostic hypotheses that may lead to improved prognostication and treatment design.
Bio: Hossein Khiabanian is an Associate Professor of Pathology in Medical Informatics at Rutgers Cancer Institute of New Jersey. He trained in physics and systems biology, and has developed statistical approaches for analyzing high-throughput data to study hematologic and solid tumors. At Rutgers, he has focused on problems in computational biology and cancer genomics, based on the idea that studying complexity, dynamics, and stochastic patterns in biological data is critical for understanding how disease states initiate and evolve.
|March 5, 2021||10:00 AM||
Moshe Y. Vardi (Computer Science; Rice University)
Title: Ethics Washing in AI
Abstract: Over the past decade Artificial Intelligence, in general, and Machine Learning, in particular, have made impressive advancements, in image recognition, game playing, natural-language understanding and more. But there were also several instances where we saw the harm that these technologies can cause when they are deployed too hastily. A Tesla crashed on Autopilot, killing the driver; a self-driving Uber crashed, killing a pedestrian; and commercial face-recognition systems performed terribly in audits on dark-skinned people. In response to that, there has been much recent talk of AI ethics. Many organizations produced AI-ethics guidelines and companies publicize their newly established responsible-AI teams. But talk is cheap. “Ethics washing” — also called “ethics theater” — is the practice of fabricating or exaggerating a company’s interest in equitable AI systems that work for everyone. An example is when a company promotes “AI for good” initiatives with one hand, while selling surveillance tech to governments and corporate customers with the other. I will argue that the ethical lens is too narrow. The real issue is how to deal with technology’s impact on society. Technology is driving the future, but who is doing the steering?
Bio: Moshe Vardi is a Professor of Computer Science at Rice University, where he also holds the titles of University Professor, the Karen Ostrum George Professor in Computational Engineering and Distinguished Service Professor. He also directs the Ken Kennedy Institute for Information Technology. Prior to joining Rice in 1993, he was managing a research department at the IBM Almaden Research Center. Dr Vardi received his Ph.D. from the Hebrew University of Jerusalem in 1981. His interests focus on applications of logic to computer science and teaching logic across the curriculum. He is an expert in model checking, constraint satisfaction and database theory, common knowledge (logic), and theoretical computer science. Vardi is the recipient of multiple awards and distinctions, including 3 IBM Outstanding Innovation Awards, co-winner of the 2000 Gödel Prize, co-winner of the 2005 ACM Paris Kanellakis Theory and Practice Award, co-winner of the LICS 2006 Test-of-Time Award, the 2008 and 2017 ACM Presidential Award, the 2008 Blaise Pascal Medal in computational science by the European Academy of Sciences, and others. He holds honorary doctorates from eight universities. He is a Guggenheim Fellow, as well as a Fellow of ACM, AAAS and AAAI. He is a member of the US National Academy of Engineering, the National Academy of Sciences, the European Academy of Sciences, and the Academia Europaea. Professor Vardi is an editor of several international journals and the president of the International Federation of Computational Logicians. He is Senior Editor of Communications of the ACM, after serving as its Editor-in-Chief for a decade.
|March 5, 2021||2:00 PM||
Uli Bauer (TU Munich)
Title: Persistent matchmaking
|Workshop on Topology: Identifying Order in Complex Systems|
|March 12, 2021||10:00 AM||
YingLi Tian (Electrical Engineering; The City College of New York)
Title: Learning Sign Language with AI Driven Grammar Checking
Abstract: American Sign Language (ASL) is a primary means of communication for over 500,000 people in the US, and a distinct language from English, conveyed through hands, facial expressions, and body movements. Most prior work on ASL recognition has focused on identifying a small set of simple signs performed, but current technology is not sufficiently accurate on continuous signing of sentences with an unrestricted vocabulary. In this talk, I will share our research of AI driven ASL learning tools to assist ASL students by enabling them to review and assess their signing skills through immediate, automatic, outside-of-classroom feedback. Our system can identify linguistic/performance attributes of ASL without necessarily identifying the entire sequence of signs and automatically determine if a performance contains
Bio: Dr. YingLi Tian is a CUNY Distinguished Professor in Electrical Engineering Department at the City College of New York (CCNY) and Computer Science Department at Graduate Center of the City University of New York (CUNY). She is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), as well as a Fellow of International Association of Pattern Recognition (IAPR). She received her PhD from the Department of Electronic Engineering at the Chinese University of Hong Kong in 1996. Her research interests include computer vision, machine learning, artificial intelligence, assistive technology, medical imaging analysis, and remote sensing. She has published more than 200 peer-reviewed papers in journals and conferences in these areas with 21,500+ citations, and holds 29 issued patents. She is a pioneer in automatic facial expression analysis, human activity understanding, and assistive technology. Dr. Tian’s research on automatic facial expression analysis and database development while working at the Robotics Institute at Carnegie Mellon University has made significant impact in the research community and received the “Test of Time Award” at IEEE International Conference on Automatic Face and Gesture Recognition in 2019. Before joining CCNY, Dr. Tian was a research staff member at IBM T. J. Watson Research Center and led the video analytics team. She received the IBM Outstanding Innovation Achievement Award in 2007 and the IBM Invention achievement Awards every year from 2002 to 2007. Since Dr. Tian joined CCNY in Fall 2008, she has been focusing on assistive technology by applying computer vision and machine learning technologies to help people with special needs including the blind and visually impaired, deaf and hard-of-hearing, and the elderly. She serves as associate editors for IEEE Trans. on Multimedia (TMM), Computer Vision and Image Understanding (CVIU), Journal of Visual Communication and Image Representation (JVCI), and Machine Vision and Applications (MVAP).
|March 19, 2021||2:00 PM||
Henrik Ronellenfitsch (Williams College)
Title: Physics of Functional Networks
Abstract: We are surrounded by functional networks, from fluid transport in plants and animals to macroscopic elastic scaffoldings and microscopic crystals and materials, and engineered power grids. Often, such networks can be seen as optimized for their function, either through evolution and natural selection or by human design. In this presentation, we investigate a number of functional networks from biology and engineering and show how optimization shapes their weighted topology in similar ways despite their different functional goals.
|Workshop on Topology: Identifying Order in Complex Systems|
|March 24, 2021||11:45 AM||
Colin Fogarty (Sloan School of Management, MIT)
Title: Prepivoting in Finite Population Causal Inference
Abstract: In finite population causal inference exact randomization tests can be constructed for sharp null hypotheses, hypotheses which fully impute the missing potential outcomes. Oftentimes inference is instead desired for the weak null that the sample average of the treatment effects takes on a particular value while leaving the subject-specific treatment effects unspecified. Without proper care, tests valid for sharp null hypotheses may be anti-conservative even asymptotically should only the weak null hold, creating the risk of misinterpretation when randomization tests are deployed in practice. We develop a general framework for unifying modes of inference for sharp and weak nulls, wherein a single procedure simultaneously delivers exact inference for sharp nulls and asymptotically valid inference for weak nulls. To do this, we employ randomization tests based upon prepivoted test statistics, wherein a test statistic is first transformed by a suitably constructed cumulative distribution function and its randomization distribution assuming the sharp null is then enumerated. For a large class of test statistics common in practice, we show that prepivoting may be accomplished by employing a sample-based Gaussian measure governed by a suitably constructed covariance estimator. In essence, the approach enumerates the randomization distribution (assuming the sharp null) of a p-value for a large-sample test known to be valid under the weak null, and uses the resulting randomization distribution to perform inference. The versatility of the method is demonstrated through various examples, including inference for rerandomized experiments.
Bio: Colin Fogarty is the Sarofim Family Career Development Professor and an Assistant Professor of Operations Research and Statistics at the MIT Sloan School of Management. His research interests lie in the design and analysis both of randomized experiments, and of observational studies while assessing the robustness of a study’s findings to hidden biases. Much of his work explores the extent to which classical randomization-based approaches for inference in experiments and observational studies extend to circumstances where heterogeneous treatment effects are suspected. His work also illustrates tangible benefits for many quasi-experimental devices in terms of improved robustness to hidden bias in observational studies. Before joining MIT he completed his Ph.D. in Statistics at the Wharton School of the University of Pennsylvania, where he was advised by Professor Dylan Small.
|March 31, 2021||11:45 AM||
Ya’acov Ritov (Department of Statistics, University of Michigan, Ann Arbor)
Title: The partial linear model (PLM) from semiparametric to modern ramifications.
Abstract: Engle, Granger, Rice, and Weiss (1986) suggested the partially linear model to deal with regression, which has both linear and nonparametric components. Its modern equivalent, inference in the ultra high dimensional regression, was analyzed by Zhang and Zhang (2014) and others. We consider different variations on this theme, including inference without assuming compatibility, design of experiments with unlabeled data, single-index models, and regression discontinuity designs.
Bio: PhD from the Hebrew University of Jerusalem, where he was a professor until 2015. Thereafter, a professor of statistics at the University of Michigan, Ann Arbor.
|April 2, 2021||2:00 PM||
Vidit Nanda (Oxford University)
Title: The missing link
Abstract: Links of strata in singular spaces are fundamental invariants which govern the topology of small neighbourhoods around points in those strata. This talk will focus on inferring links of strata from incomplete information in three completely different contexts. In each case, there are exciting consequences of learning the structure of such links.
|Workshop on Topology: Identifying Order in Complex Systems|
|April 7, 2021||11:45 AM||
Xiaofeng Shao (Department of Statistics, Univ. of Illinois at Urbana-Champaign)
Title: Change-point detection for COVID-19 time series via self-normalization
Abstract: This talk consists of two parts. In the first part, I will review some basic
Bio: Xiaofeng Shao is currently a professor at University of Illinois at Urbana-Champaign.
|April 16, 2021||2:00 PM||
Paul Bendich (Duke)
Title: From Geometry to Topology: Inverse Theorems for Distributed Persistence
Abstract: What is the “right” topological invariant of a large point cloud X? Prior research has focused on estimating the full persistence diagram of X, a quantity that is very expensive to compute, unstable to outliers, and far from a sufficient statistic. We therefore propose that the correct invariant is not the persistence diagram of X, but rather the collection of persistence diagrams of many small subsets. This invariant, which we call “distributed persistence,” is trivially parallelizable, more stable to outliers, and has a rich inverse theory. The map from the space of point clouds (with the quasi-isometry metric) to the space of distributed persistence invariants (with the Hausdorff-Bottleneck distance) is a global quasi-isometry. This is a much stronger property than simply being injective, as it implies that the inverse of a small neighborhood is a small neighborhood, and is to our knowledge the only result of its kind in the TDA literature. Moreover, the quasi-isometry bounds depend on the size of the subsets taken, so that as the size of these subsets goes from small to large, the invariant interpolates between a purely geometric one and a topological one. Finally, we note that our inverse results do not actually require considering all subsets of a fixed size (an enormous collection), but a relatively small collection satisfying certain covering properties that arise with high probability when randomly sampling subsets. These theoretical results are complemented by two synthetic experiments demonstrating the use of distributed persistence in practice. This is joint work with Elchanan Solomon and Alexander Wagner
|Workshop on Topology: Identifying Order in Complex Systems|
|April 30, 2021||2:00 PM||
Sabetta Matsumoto (Georgia Tech)
Title: Twisted topological tangles or: the knot theory of knitting
Abstract: Imagine a 1D curve, then use it to fill a 2D manifold that covers an arbitrary 3D object – this computationally intensive materials challenge has been realized in the ancient technology known as knitting. This process for making functional materials 2D materials from 1D portable cloth dates back to prehistory, with the oldest known examples dating from the 11th century CE. Knitted textiles are ubiquitous as they are easy and cheap to create, lightweight, portable, flexible and stretchy. As with many functional materials, the key to knitting’s extraordinary properties lies in its microstructure.
At the 1D level, knits are composed of an interlocking series of slip knots. At the most basic level there is only one manipulation that creates a knitted stitch – pulling a loop of yarn through another loop. However, there exist hundreds of books with thousands of patterns of stitches with seemingly unbounded complexity.
The topology of knitted stitches has a profound impact on the geometry and elasticity of the resulting fabric. This puts a new spin on additive manufacturing – not only can stitch pattern control the local and global geometry of a textile, but the creation process encodes mechanical properties within the material itself. Unlike standard additive manufacturing techniques, the innate properties of the yarn and the stitch microstructure has a direct effect on the global geometric and mechanical outcome of knitted fabrics.
|Workshop on Topology: Identifying Order in Complex Systems|
|May 21, 2021||10:00 AM||
Haotian Wang (Computer Science; Rutgers University)
Title: Co-evolution of Opinion and Social Tie Dynamics Towards Structural Balance
Abstract: In the natural network structure, especially in the social networks, community structures are one of the prominent properties. An extreme case of that is when the network is partitioned into two camps with opposing relationships. In this talk, I will introduce our co-evolution model for both dynamics of opinions (people’s views on a variety of topics) and dynamics of social appraisals (the approval or disapproval towards each other). It leads to the formation of communities in the networks. The opinion of an individual is updated by the weighted average of opinions from neighbors. And the tie appraisal of two nodes is updated with a margin proportional to the agreement of their opinions.
Bio: Haotian Wang is a Ph.D. candidate in the department of computer science at Rutgers University. His research interests include: computational geometry, algorithm design, and networking application.
|May 28, 2021||10:00 AM||
Kai Gao (Computer Science; Rutgers University)
Title: On Minimizing the Number of Running Buffers for Tabletop Rearrangement
Abstract: For tabletop rearrangement problems with overhand grasps, storage space outside the tabletop workspace, or buffers, can temporarily hold objects which greatly facilitates the resolution of a given rearrangement task. This brings forth the natural question of how many running buffers are required so that certain classes of tabletop rearrangement problems are feasible. In this work, we examine the problem for both the labeled (where each object has a specific goal pose) and the unlabeled (where goal poses of objects are interchangeable) settings. On the structural side, we observe that finding the minimum number of running buffers (MRB) can be carried out on a dependency graph abstracted from a problem instance, and show that computing MRB on dependency graphs is NP-hard. We then prove that under both labeled and unlabeled settings, even for uniform cylindrical objects, the number of required running buffers may grow unbounded as the number of objects to be rearranged increases; we further show that the bound for the unlabeled case is tight. On the algorithmic side, we develop highly effective algorithms for finding MRB for both labeled and unlabeled tabletop rearrangement problems, scalable to over a hundred objects under very high object density. Employing these algorithms, empirical evaluations show that random labeled and unlabeled instances, which more closely mimics real-world setups, have much smaller MRBs.
Bio: Kai Gao is a second-year doctoral student in Robotics at Rutgers, the State University of New Jersey, working with Professor Jingjin Yu. Currently, his research focuses on resolving combinatorial challenges in robot tasks and motion planning. Before arriving at Rutgers, he received a Bachelor’s degree in Mathematics from the University of Science and Technology of China in 2019.
Rui Wang (Computer Science; Rutgers University)
Abstract: Picking an item in the presence of other objects can be challenging as it involves occlusions and partial views. Given object models, one approach is to perform object pose estimation and use the most likely candidate pose per object to pick the target without collisions. This approach, however, ignores the uncertainty of the perception process both regarding the target’s and the surrounding objects’ poses. This work proposes first a perception process for 6D pose estimation, which returns a discrete distribution of object poses in a scene. Then, an open-loop planning pipeline is proposed to return safe and effective solutions for moving a robotic arm to pick, which (a) minimizes the probability of collision with the obstructing objects; and (b) maximizes the probability of reaching the target item. The planning framework models the challenge as a stochastic variant of the Minimum Constraint Removal (MCR) problem. The effectiveness of the methodology is verified given both simulated and real data in different scenarios. The experiments demonstrate the importance of considering the uncertainty of the perception process in terms of safe execution. The results also show that the methodology is more effective than conservative MCR approaches, which avoid all possible object poses regardless of the reported uncertainty.
Bio: Rui Wang is a Ph.D. candidate in the department of Computer Science at Rutgers University, supervised by Professor Kostas Bekris. His research lies in task and motion planning on robot manipulation, specifically with failure-explanation planning approaches which reason about the failure of finding a valid plan and use the explanation for further guidance. Prior to his Ph.D. in Rutgers, he received his Master degree in Mechanical Engineering from Columbia University and his Bachelor degree in Vehicle Engineering from Nanjing University of Aeronautics and Astronautics, China.
|October 1, 2021||10:00 AM||
Cameron Thieme, (DIMACS; Rutgers University)
Title: Attractors of Nonsmooth and Multivalued Dynamical Systems
Abstract: Over the past few decades, piecewise-continuous differential equations have become increasingly popular in scientific models. In particular, conceptual climate models often take this form. These nonsmooth systems are typically reframed as Filippov systems, a special type of multivalued dynamical system. Some qualitative properties of these inclusions have been studied over the last few decades, primarily in the context of control systems. Our interest in these systems is in understanding what behavior identified in the nonsmooth model may be continued to families of smooth differential equations which limit to the Filippov system; determining this information is particularly important in this context because the piecewise-continuous model is frequently considered to be a heuristically understandable approximation of a more realistic smooth system. In this talk we will examine how Conley index theory may be applied to the study of differential inclusions in order to address this goal. In particular, we will discuss how attractor-repeller pairs identified in a Filippov system continue to nearby smooth systems.
Bio: Cameron Thieme is a postdoctoral researcher at DIMACS associated with the DATA-INSPIRE Institute. His research focuses on the use of topological methods in dynamical systems. In particular, he is interested in how classical methods developed for single-valued dynamical systems (flows, maps) may be generalized to set-valued ones; these modern, multivalued dynamical systems have applications in conceptual modeling and data analysis. He received his PhD in Mathematics at the University of Minnesota under the supervision of Richard McGehee in 2021.
|October 15, 2021||4:00 PM||
Ronitt Rubinfeld, (MIT)
Title: Locality in Computation
Abstract: Consider a setting in which inputs to and outputs from a computational problem are so large, that there is not time to read them in their entirety. However, if one is only interested in small parts of the output at any given time, is it really necessary to solve the entire computational problem? Is it even necessary to view the whole input? We survey recent work in the model of “local computation algorithms” which for a given input, supports queries by a user to values of specified bits of a legal output. The goal is to design local computation algorithms in such a way that very little of the input needs to be seen in order to determine the value of any single bit of the output. Though this model describes sequential computations, techniques from local distributed algorithms have been extremely important in designing efficient local computation algorithms. In this talk, we describe results on a variety of problems for which sublinear time and space local computation algorithms have been developed — we will give special focus to finding maximal independent sets and generating random objects.
Bio: Ronitt Rubinfeld is the Edwin Sibley Webster Professor in MIT’s Electrical Engineering and Computer Science department, where she has been on the faculty since 2004. She has held faculty positions at Cornell University and Tel Aviv University, and has been a member of the research staff at NEC Research Institute.
|November 12, 2021||10:00 AM||
Zhigang Zhu, (Computer Science, Grove School of Engineering, The City College and Graduate Center / CUNY)
Title: SAT-Hub: Smart and Accessible Transportation Hub for Assistive Navigation and Facility Management
Abstract: SAT-Hub aims to provide better location-aware services to traveling public, especially for underserved populations including those with visual impairment, Autism Spectrum Disorder (ASD), or simply navigation challenges, with minimal infrastructure changes. The SAT-Hub project has the following three main technical components: (1). A SAT multilayer live facility model, with a building feature layer, a space information layer, a crowd dynamic layer, and a service information layer. (2). SAT hybrid mobile localization algorithms, using beacons, 2D/3D cameras and onboard sensors, integrated with the information from the multilayer model. (3). SAT multimodal human-centered interfaces, with both the the layered model and the localization algorithms as the drivers for users with disabilities and/or travel challenges to better perform their traveling tasks. This talk will provide an overview of the project, with a number of sample results on various aspects of the cyber-physical-human ecosystem in research, development and commercialization. The research is a collaboration among CUNY, Rutgers, Lighthouse Guild and Bentley Systems, Inc., and is supported by the DHS Summer Research Team (SRT) Program, the NSF Smart and Connected Community Program, the NSF Partnerships of Innovation Program, and the Bentley Research Collaboration Program.
Bio: Dr. Zhigang Zhu is currently Herbert G. Kayser Professor of Computer Science, at The City College and The Graduate Center, The City University of New York. He is Director of the City College Visual Computing Laboratory (CCVCL), and Co-Director of the Master’s Program in Data Science and Engineering at CCNY. Previously he was Associate Professor at Tsinghua University, Beijing and a Senior Research Fellow at the University of Massachusetts, Amherst. Dr. Zhu obtained his BS, MS and PhD degrees, all in Computer Science from Tsinghua University. His research interests include computer vision, multimodal sensing, human-computer interaction, and various applications in assistive technology, robotics, surveillance and transportation. Among other honors, he is a recipient of the President’s Award for Excellence at CCNY in 2013, and in 1999 his PhD thesis was selected into the Hundred National Excellent Doctoral Theses in China. He is an Associate Editor of Machine Vision Applications, Springer
|November 19, 2021||10:00 AM||
Chinwe Ekenna, (University at Albany, SUNY)
Title: Motion Planning Advancements and Applications in Computational Biology: An Algebraic Topology Perspective
Abstract: Techniques for motion planning have advanced to address high-dimensional and complex environments. Understanding the approximations utilized in generating various robot configurations, as well as how much sampling is required to ensure that a path is constructed if one exists, is still a challenge. My talk will highlight advances in the topological representation of planning spaces for robots, as well as topological tools I developed to help explore, measure, and provide an upper-bound on the amount of sampling required in a given environment. This method is used to study protein-protein interactions in computational biology. The identification of biomolecular structures, functions, and interactions is aided by geometric properties of protein surfaces. These characteristics have proven to be significant in predicting protein-ligand or protein-protein interactions. I’ll show how to extract significant geometric information from the protein surface using an algorithm that uses simplicial complexes and discrete Morse theory. We offer the probable intermediate conformations of the biomolecule around the protein surface as it travels to the binding site using the retrieved geometric information.
Bio: Chinwe Ekenna is an Assistant Professor in the Department of Computer Science at the University at Albany, State University of New York who got her PhD from Texas A&M University with Dr. Nancy Amato as her advisor. Chinwe’s research centers on intelligent motion planning applied to robotics and proteins. She has explored intelligent adaptation of robotic motion planning to improve planning time and topological data analysis methods to capture important features of robot planning spaces. Her research interest includes Machine learning, computational geometry, and computational biology. Chinwe is a recipient of the NSF-CRII award on “Topology aware configuration spaces” and has gone on to publish several works in ICRA and IROS on this subject. She is currently an Associate Editor for IEEE-RAL and has served on several program committees for the ICRA, IROS and WAFR conferences. She is a committee member of the IEEE RAS Committee to Explore Synergies in Automation and Robotics (CESAR), which comprises top researchers in the field of automation and robotics.
|March 2, 2022||11:45 AM||
Fei Xue, (Department of Statistics; Purdue University)
Title: Statistical Inference for High-dimensional Block-wise Missing Data
Abstract: For multi-source data, blocks of variable information from certain sources are likely missing. Most existing methods for handling missing data do not take structures of block-wise missing data into consideration. In this talk, I will describe a Multiple Block-wise Imputation (MBI) approach, which incorporates imputations based on both complete and incomplete observations. Specifically, for a given missing pattern group, the imputations in MBI incorporate more samples from groups with fewer observed variables in addition to the group with complete observations. We propose to construct estimating equations based on all available information, and integrate all estimating functions to achieve efficient estimators. In addition, we propose a nearly unbiased estimator for each individual regression coefficient, which is asymptotically normally distributed under mild conditions. Based on these debiased estimators, asymptotically valid confidence intervals and statistical tests about each regression coefficient are constructed. Numerical studies and ADNI data application confirm that the proposed method outperforms existing methods under various missing mechanisms.
Bio: Fei is an Assistant Professor of Statistics at Purdue University. She got her PhD from UIUC and was a postdoc at University of Pennsylvania. Her research interests are data integration, missing data, mediation analysis, machine learning, and statistical genetics.
|March 23, 2022||11:45 AM||
Russell Shinohara (University of Pennsylvania)
Title: Statistical Methods for Harmonizing Multi-scanner Neuroimaging
Abstract: While magnetic resonance imaging (MRI) studies are critical for the diagnosis, monitoring, and study for a wide variety of diseases, their use in quantitative analysis can be complex. An increasingly recognized issue involves the differences between MRI scanners that are used in large multi-center studies. To address this, the current state of the art is to “regress out” or “adjust for” scanner differences. Our group has found these methods to be insufficient, and have advocated for the adaptation of methods pioneered in genomics to help mitigate inter-scanner differences which can vary across the brain and result in both mean and variance shifts. We further study the implications of differences in correlation structures across and between images, and how this affects downstream inference.
Bio: Taki Shinohara is an Associate Professor of Biostatistics at the University of Pennsylvania. He directs the Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), a Center of Excellence focusing on imaging statistics at the Perelman School of Medicine. His laboratory focuses on statistical methods and applications for neuroimaging data, with particular emphasis on multiple sclerosis research and neurodevelopmental studies.
|March 30, 2022||11:45 AM||
Peng Wang (University of Cincinnati)
Title: Repro Sampling Method for Statistical Inference of High Dimensional Linear Models
Abstract: This paper proposes a new and effective simulation-based approach, called the Repro Sampling method, to conduct statistical inference in high dimensional linear models. The Repro method creates and studies the performance of artificial samples (referred to as Repro samples) that are generated by mimicking the sampling mechanism that generated the true observed sample. By doing so, this method provides a new way to quantify model and parameter uncertainty and provide confidence sets with guaranteed coverage rates on a wide range of problems. A general theoretical framework and an effective Monte-Carlo algorithm, with supporting theories, are developed for high dimensional linear models. This method is used to create confidence sets for both the selected models and model coefficients, with both exact and asymptotic inferences, are included. It also provides theoretical development to support computational efficiency. The development provides a simple and effective solution for the difficult post-selection inference problems.
Bio: Dr. Peng Wang is an Associate Professor of Business Analytics in Lindner College of Business at the University of Cincinnati. Prior to joining the College, Dr. Wang obtained his Ph.D. degree in statistics from the University of Illinois at Urbana -Champaign and worked as an Assistant Professor at Bowling Green State University. Dr. Wang’s research interests include longitudinal data analysis, high dimensional inference, basics of statistical inference, and applied statistical learning.
|April 6, 2022||11:45 AM||
Andrew Nobel (University of North Carolina, Chapel Hill)
Title: Stationary Optimal Transport with Applications to Graph Alignment
Abstract: Optimal transport seeks to find couplings of two given distributions with minimum expected cost. This talk considers the setting in which the distributions of interest are stationary stochastic processes, and the cost function depends only on a finite number of coordinates. In this setting, I will argue that it is appropriate, and desirable, to restrict attention to stationary couplings, also known as joinings. The first part of the talk will address estimation of optimal joinings from observations of two ergodic processes. I will then consider optimal transport for Markov chains via transition couplings, beginning with fast computation based on techniques from reinforcement learning. As an illustration, I will show how optimal joinings of Markov chains can be used to effectively compare two weighted graphs with potentially different node sets. This approach yields interpretable alignments of nodes and edges, has a desirable edge-preserving property, and implicitly account for graph factors when these exist.
Bio: Andrew Nobel is the Robert Paul Ziff Distinguished Professor of Statistics and Operations Research at UNC Chapel Hill. His research interests include optimal transport, dynamical systems, and statistical genomics. His research encompasses mathematical foundations and methodological development, as well as real-world applications. His work has addressed an array of problems, including uniform ergodic theorems for VC-classes, matrix reconstruction in Gaussian noise, analysis and implementation of biclustering procedures for large average submatrices, community detection in weighted networks, and analysis of joint and individual variation in multi-view genomic data. Nobel is a fellow of the IMS, and is currently an Associate Editor at JRSS-B.
|April 15, 2022||4:00 PM||
Bin Yu (UC Berkeley, Statistics, EECS, CCB)
Title: Predictability, stability, and causality with a case study to find genetic drivers of a heart disease
Bio: Bin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and EECS at UC Berkeley. She leads the Yu Group which consists of students and postdocs from Statistics and EECS. She was formally trained as a statistician, but her research extends beyond the realm of statistics. Together with her group, her work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics and precision medicine. She and her team develop relevant theory to understand random forests and deep learning for insight into and guidance for practice.
Link to video: https://youtu.be/mY1dDreIG9w
|April 22, 2022||4:00 PM||
Kevin Jamieson, University of Washington
Title: Instance Dependent Sample Complexity Bounds for Interactive Learning
Abstract: The sample complexity of an interactive learning problem, such as multi-armed bandits or reinforcement learning, is the number of interactions with nature required to output an answer (e.g., a recommended arm or policy) that is approximately close to optimal with high probability. While minimax guarantees can be useful rules of thumb to gauge the difficulty of a problem class, algorithms optimized for this worst-case metric often fail to adapt to “easy” instances where fewer samples suffice. In this talk, I will highlight some my group’s work on algorithms that obtain optimal, finite time, instance dependent sample complexities that scale with the true difficulty of the particular instance, versus just the worst-case. In particular, I will describe a unifying experimental design based approach used to obtain such algorithms for best-arm identification for linear bandits, contextual bandits with arbitrary policy classes, and smooth losses for linear dynamical systems.
Bio: Kevin Jamieson is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington and is the Guestrin Endowed Professor in Artificial Intelligence and Machine Learning. He received his B.S. in 2009 from the University of Washington, his M.S. in 2010 from Columbia University, and his Ph.D. in 2015 from the University of Wisconsin – Madison under the advisement of Robert Nowak, all in electrical engineering. He returned to the University of Washington as faculty in 2017 after a postdoc with Benjamin Recht at the University of California, Berkeley. Jamieson’s work has been recognized by an NSF CAREER award and Amazon Faculty Research award. His research explores how to leverage already-collected data to inform what future measurements to make next, in a closed loop. The work ranges from theory to practical algorithms with guarantees to open-source machine learning systems and has been adopted in a range of applications, including measuring human perception in psychology studies, adaptive A/B/n testing in dynamic web-environments, numerical optimization, and efficient tuning of hyperparameters for deep neural networks.
Link to video: https://youtu.be/BLpT6QSfb9Y
|April 29, 2022||4:00 PM||
Adnan Darwiche, (University of California, Los Angeles)
Title: Explaining the Decisions of AI Systems
Abstract: I will present a theory for reasoning about the decisions made by AI systems, particularly classifiers such as decision trees, random forests, Bayesian networks and some limited types of neural networks. The theory is based on “compiling” the input-output behavior of classifiers into discrete functions in the form of tractable circuits. At the heart of the theory is the notion of “complete reason” behind a decision which is extracted from a circuit-instance pair and can be used to answer many queries about the decision, including ones pertaining to explainability, robustness and bias. I will also overview developments on tractable circuits which provide the computational arm for employing this theory in practice and will briefly overview recent results on quantified Boolean logic which provide classifier-independent semantics of this theory that further broadens its applicability.
Bio: Adnan Darwiche is a professor and former chairman of the computer science department at UCLA. He directs the Automated Reasoning Group, which focuses on symbolic reasoning, probabilistic reasoning and their applications to machine learning. Professor Darwiche is Fellow of AAAI and ACM and recipient of the Lockheed Martin Excellence in Teaching Award. He is a former editor-in-chief of the Journal of Artificial Intelligence Research (JAIR) and author of “Modeling and Reasoning with Bayesian Networks,” by Cambridge University Press.
Dana Randall (Computer Science; Georgia Institute of Technology)