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Conferences, Lectures, & Seminars
Events for November
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CS Colloquium - Mahendra Shrestha: Wildlife Crime – Threat to Survival of Endangered Species
Fri, Nov 01, 2013 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Mahendra Shrestha, Government of Nepal
Talk Title: Wildlife Crime â Threat to Survival of Endangered Species
Series: CS Colloquium
Abstract: Many rare and endangered species in the wild are at the brink of extinction due to escalated poaching pressure driven by persistent international market demand representing an international crisis. Conservation area management teams on the ground are fighting a war against the relentless threat from poaching with very limited resources putting their own lives at risk. Poaching is no more committed by poor farmers opportunistically to sustain their livelihood. It has expanded to a well-organized business undertaken by transnational criminal syndicates. This transnational wildlife crime generating billions of dollars in illicit revenues pose risk to national and international securities as well as risk of spread of emerging infectious diseases. Most of the reserves, the source of such crime, are quite often limited with low number of frontline staff, their capacity, equipment, and organizational structure. In contrary, the criminals are far ahead on every one of these - they are well equipped with full financial back up and well organized with a good network of trans-national criminal network. This low risk high gain business is attracting international criminals. Continuous decline in population of wildlife species such as tiger, elephants, rhino, apes, and many others clearly indicates that the existing efforts and strategy against wildlife crime is not working well. This generation will witness permanent disappearance of many species from the face of this earth if the management and enforcement strategy is not improved using latest tools and technology and strong commitment from the world leaders. Capacity building of front line conservation practitioners and enforcement agencies on strategic use of available limited resources in making the law enforcement interventions effective in deterring criminals is essential.
Teamcore is pleased to host this upcoming seminar featuring Dr. Mahendra Shrestha. Seminar details are below. Please RSVP by emailing Benjamin Ford at benjamif@usc.edu.
If you would like to meet with Dr. Shrestha, please email Benjamin Ford at benjamif@usc.edu by October 29.
Biography: Mahendra Shrestha has more than 15 years of experience in reserve management and conservation policy making for the Government of Nepal. His PhD research on the Terai Arc Landscape for Tiger Conservation revealed useful information to facilitate the policy decision by the Government of Nepal on the conservation of a landscape extending more than 50,000 sq km in India and Nepal encompassing 12 protected areas. His support and local leadership development has resulted into establishment of new protected areas and restoration of connectivity in this landscape. He works closely with governments and NGOs in 13 tiger range countries in Asia to enhance reserve management for population recovery of wildlife species and local leadership development. He has played an important role in engaging the World Bank to launch the Global Tiger Initiative leading to the Tiger Summit in St. Petersburg, Russia in 2010 that helped build the necessary commitment from the political leaders as well as from the conservation community for tiger conservation. He had led the Save The Tiger Fund program of the National Fish and Wildlife Foundation in Washington, DC to support tiger conservation projects in tiger range countries in the past. Currently, he heads the Tiger Conservation Partnership program at the Smithsonian Conservation Biology Institute in Washington, DC. His program is focused on capacity building in reserve management, institutional capacity building and leadership development.
Host: Milind Tambe
Location: Hedco Neurosciences Building (HNB) - 100
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium - Ashwin Rao (Founder, ZLemma.com)
Thu, Nov 14, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ashwin Rao, Founder, ZLemma.com; Entrepreneur
Talk Title: CS Colloquium - Ashwin Rao
Series: CS Colloquium
Abstract: This talk is for all levels (Undergraduate, Masters and Ph.D.) of students in Computer Science, with the purpose of helping them make sound decisions within the wide array of available job choices, and eventually pick the job/career that is most suited for them. We will particularly focus on jobs for Computer Scientists in the Tech industry (large companies as well as startups) and in the Finance industry (Wall Street as well as hedge funds). We will discuss the different requirements of various jobs, and understand how this work relates to one's academic interests. We will discuss some recent trends in the industry covering Big Data, Functional Programming, Quantitative Modeling, Machine Learning, and Domain-Specific Languages. We will also discuss non-technical aspects of different jobs contrasting between Tech and Finance, between large and small companies, and between 'depth' versus 'breadth' roles. Finally, we will discuss how to prepare a suitable resume and how to prepare for interviews.
Biography: Dr. Ashwin Rao is an entrepreneur based in Palo Alto, California and is the founder of a technology startup - ZLemma.com - that helps students and young professionals identify careers most suited to their talents. Prior to entrepreneurship, Ashwin was a quantitative modeler and trading strategist at Goldman Sachs for ten years in New York, and was subsequently a Managing Director at Morgan Stanley. Ashwin's focus had been on interest rates and mortgage derivatives products. Ashwin holds a Bachelors degree in Computer Science from IIT-Bombay and a Ph.D. in Algorithmic Algebra from University of Southern California. In his personal life, Ashwin is deeply involved in mentoring students at various universities.
Host: CS PhD Committee
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Vincent Conitzer (Duke University) - Tearing Down the Wall Between Mechanism Design With and Without Money
Mon, Nov 18, 2013 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Vincent Conitzer, Duke University
Talk Title: Tearing Down the Wall Between Mechanism Design With and Without Money
Series: CS Colloquium
Abstract: Many mechanism designers (algorithmic or other) draw a sharp line between mechanism design with money (auctions, exchanges, ...) and without money (social choice, matching, ...). I will discuss two papers that indicate that this line is blurrier than it seems. In the first, we study generalizations of the Vickrey auction to settings where a single agent wins, but with an arbitrary contract instead of a simple payment. In the second, we study repeated allocation of a good without payments. Here, we can create a type of artificial currency that affects future assignment of the good and that allows us to use modified versions of existing mechanisms with payments to reach provably approximately optimal solutions.
Based on:
B. Paul Harrenstein, Mathijs M. de Weerdt, and Vincent Conitzer.
Strategy-Proof Contract Auctions and the Role of Ties. To appear in Games and Economic Behavior.
Mingyu Guo, Vincent Conitzer, and Daniel Reeves. Competitive Repeated Allocation Without Payments. Short version in the Workshop on Internet and Network Economics.
Biography: Vincent Conitzer is the Sally Dalton Robinson Professor of Computer Science and Professor of Economics at Duke University. He received Ph.D. (2006) and M.S. (2003) degrees in Computer Science from Carnegie Mellon University, and an A.B. (2001) degree in Applied Mathematics from Harvard University. His research focuses on computational aspects of microeconomics, in particular game theory, mechanism design, voting/social choice, and auctions. This work uses techniques from, and includes applications to, artificial intelligence and multiagent systems. Conitzer has received the Social Choice and Welfare Prize (2014), a Presidential Early Career Award for Scientists and Engineers (PECASE), the IJCAI Computers and Thought Award, an NSF CAREER award, the inaugural Victor Lesser dissertation award, an honorable mention for the ACM dissertation award, and several awards for papers and service at the AAAI and AAMAS conferences. He has also been named a Kavli Fellow, a Bass Fellow, a Sloan Fellow, and one of AI's Ten to Watch. Conitzer and Preston McAfee are the founding Editors-in-Chief of the ACM Transactions on Economics and Computation (TEAC).
Host: Milind Tambe
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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PhD Student Colloquium
Tue, Nov 19, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Presenters / Abstracts In Announcement Body, USC
Talk Title: PhD Student Colloquium
Series: CS Colloquium
Abstract: Soravit Changpinyo and Kuan Liu
Title: Similarity Component Analysis
Abstract: Measuring similarity is crucial to many learning tasks. To this end, metric learning has been the dominant paradigm. However, similarity is a richer and broader notion than what metrics entail. For example, similarity can arise from the process of aggregating the decisions of multiple latent components, where each latent component compares data in its own way by focusing on a different subset of features. We propose Similarity Component Analysis (SCA), a probabilistic graphical model that discovers those latent components from data. In SCA, a latent component generates a local similarity value, computed with its own metric, independently of other components. The final similarity measure is then obtained by combining the local similarity values with a (noisy-) OR gate. We derive an EM-based algorithm for fitting the model parameters with similarity-annotated data from pairwise comparisons. We validate the SCA model on synthetic datasets where SCA discovers the ground-truth about the latent components. We also apply SCA to a multiway classification task and a link prediction task. For both tasks, SCA attains significantly better prediction accuracies than competing methods. Moreover, we show how SCA can be instrumental in exploratory analysis of data, where we gain insights about the data by examining patterns hidden in its latent components’ local similarity values.
Boqing Gong
Title: Reshaping Visual Datasets for Domain Adaptation
Abstract: In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (lighting, pose, background, resolution, etc.) We propose an approach to automatically discover latent domains in image or video datasets. Our formulation imposes two key properties on domains: maximum distinctiveness and maximum learnability. By maximum distinctiveness, we require the underlying distributions of the identified domains to be different from each other to the maximum extent; by maximum learnability, we ensure that a strong discriminative model can be learned from the domain. We devise a nonparametric formulation and efficient optimization procedure that can successfully discover domains among both training and test data. We extensively evaluate our approach on object recognition and human activity recognition tasks.
Mrinal Kalakrishnan
Title: Learning Objective Functions for Autonomous Locomotion and Manipulation
Abstract: Efforts on learning from demonstration in robotics have largely been focused on reproducing behavior similar in appearance to the provided demonstrations, loosely classified as Direct Policy Learning. An alternative approach, known as Inverse Reinforcement Learning (IRL), is to learn the objective function that the demonstrations are assumed to be optimal under. With the help of a planner or trajectory optimizer, such an approach allows the system to synthesize novel behavior in situations that were not experienced in the demonstrations. We present new algorithms for IRL that have successfully been applied in two real-world, competitive robotics settings: (1) In the domain of rough terrain quadruped locomotion, we present an algorithm that learns an objective function for foothold selection based on "terrain templates". The learner automatically generates and selects the appropriate features which form the objective function, which reduces the need for feature engineering while attaining a high level of generalization. (2) For the domain of autonomous manipulation, we present a local sampling-based path integral IRL approach to deal with the high dimensional space of trajectories. We apply this method to two problems in robotic manipulation: redundancy resolution in inverse kinematics, and trajectory optimization for grasping and manipulation. Both methods have proven themselves as part of larger integrated systems in competitive settings against other teams, where testing was conducted by an independent test team in situations that were not seen during training.
Host: PhD Committee
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
