Every two years, IEEE Intelligent Systems acknowledges and celebrates 10 young stars in the field of AI as “AI’s 10 to Watch.” These accomplished researchers have all completed their doctoral work in the past five years. Despite being relatively junior in their career, each one has made impressive research contributions and had an impact in the literature—and in some cases, in real-world applications as well.
Nominations in all subfields of AI were sought from a wide range of senior AI researchers. A short list of top candidates was voted on by the award committee, and then the decisions were finalized with the entire advisory and editorial boards of IEEE Intelligent Systems. I would like to take this opportunity to thank two past editors-in-chief of IEEE Intelligent Systems, Jim Hendler and Fei-Yue Wang, who served as the co-chairs of the AI’s 10 to Watch award committee and did a great job managing the nomination and selection process.
The group nominated this year was particularly strong. It has been a struggle to choose the best of the best. In the end, the top 10 surfaced with unanimous support from the advisory and editorial boards. We’re particularly pleased about the diversity of the winning group. It’s safe to say that everyone involved in the selection process has been very proud of these young stars’ contributions, of what AI as a community can offer, and how bright the future of AI can be. We’re sure that young AI students and researchers will find inspiration from these young stars, and that the AI community will look forward to their continued excellence and sustained impact.
Congratulations again to our young colleagues for winning this special recognition!
— Daniel Zeng
Automatic Multirobot Coordination
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Multirobot systems have been applied successfully in manufacturing and warehousing applications, but they require task-specific code that isn’t transferable to other applications, amenable to dynamic conditions, or accessible to nonexperts. To realize the full potential of multirobot systems in areas such as transportation, logistics, agriculture, and disaster response, we must enable them to autonomously collaborate and interact in dynamic, resource-constrained environments with humans and other agents. The critical need for end-to-end solutions for multirobot autonomy—starting with high-level specifications all the way to delivering code for individual robots—built on distributed multirobot planning and control foundations is my work’s underlying theme.
Imagine deploying hundreds of mobile sensors to monitor an underground pipeline without so much as a keystroke. Abstractions let you decouple high-level behaviors from a distributed system’s complex, low-level control, so users can specify high-level actions and behaviors for the team without worrying about instructions for individual robots. I’m currently using these abstractions to create a simplified interface via an iPad app that, with only simple multitouch gestures, generates code to navigate and control large teams of robots.
To enable high-level specification, I’m developing safe, provably correct, and automatically synthesized control policies, which in turn must address three subproblems of multiagent coordination: task assignment, planning, and feedback control. Each of these subproblems individually presents a significant challenge, but distilling them into a single turnkey solution for distributed multiagent problems is a necessity for user-friendly systems.
It isn’t enough, however, to operate safely in controlled environments. Autonomous systems must interact in the dynamic world, so they must be equipped with flexible control policies that can adapt and react to changing conditions. As humans, we use local information to make critical decisions without the need for central oversight, so I’m currently working on algorithms that allow autonomous, distributed systems to make decisions based on the wealth of contextual information that humans use.
Bringing the human in the loop can help create a data-driven approach to group control, as well as inspire new capabilities for distributed autonomous multiagent systems. At MIT, we’re analyzing data from experiments involving large crowds of people, forming patterns with limited instruction and feedback, limited to local interaction. A surprising lesson we’ve learned so far is that distributed localization is quite difficult for humans!
Within the next few decades, autonomous multiagent systems will play an integral and visible role in our daily lives. From transporting people and packages to monitoring environment and infrastructure to distributing resources such as energy and water, their ubiquity will present significant usability challenges, necessitating the development of end-to-end solutions that start with high-level specifications and deliver specialized code for the entire system. My research is in developing the technologies that lower the barrier to entry for multirobot systems.
Defining Diseases with Data
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Combined with the big data revolution in medicine, latent variable models promise to help us redefine disease along lines that ultimately matter for treatment. Traditional disease definitions are based on expert-defined diagnostic criteria. When a patient presents with a set of symptoms, the physician uses these criteria to match the patient to some “hidden” underlying disease. However, as more clinical data becomes available in electronic form, we can now turn this around and ask, “What sets of hidden diseases best match the symptoms that we see in our clinical population?” Data-driven approaches to defining disease represent a fundamental shift in medicine to more evidence-based diagnosis and treatment.
Latent variable models in machine learning are generally trained to make good predictions. Hidden states—in our case, diseases—are inferred because they provide predictive information about observed sets of symptoms, rather than being defined by existing standards of practice. This data-driven approach to defining disease has two key benefits over traditional approaches: defining disease subtypes based on predictive criteria lets us use these subtypes to more accurately make predictions based on a patient’s characteristics, and data-driven disease definitions can drive new scientific hypotheses about the true underlying causes of related diseases.
My current work focuses on deriving data-driven phenotypes for three complex, heterogeneous diseases—autism spectrum disorder, type 2 diabetes, and inflammatory bowel disease. Patients with these diseases don’t fall neatly into traditional classification criteria, and our goal is to define a patient’s subtype based on empirical criteria such as diagnostic codes and laboratory tests. For example, our clustering analyses have revealed subgroups within autism at high risk for major psychiatric disorders, as well as those with increased rates of gastrointestinal disorders. The discovery of the first subgroup prompted additional research to identify high-risk patients, while the second led to a study to identify common genetic causes of inflammatory bowel disease and autism.
Central to all of these approaches is the use of latent variables. I use Bayesian nonparametric approaches to flexibly model the number of latent variables in a dataset, because they scale the model’s sophistication depending on the complexity of the observations. More generally, the language of Bayesian graphical models lets me integrate information across a variety of (extremely) noisy and incomplete clinical databases, expert-curated medical ontologies, and popular text sources. While models have been developed for specific clinical data sources, a core technical component of my research involves developing novel models and inference techniques to capture structure from large, truly heterogeneous data.
My doctoral work in Bayesian nonparametric statistics—in which I showed how these techniques can produce state-of-the-art results in general sequential decision-making problems—and my current position at a medical school put me in a unique position to drive new science based on these sophisticated machine-learning techniques. Of course, redefining disease is only the first step: with better disease models, we can start the journey toward more personalized, evidenced-based treatment.
Extracting Information from Heterogeneous Data
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Recent years have witnessed a big data boom that includes a wide spectrum of heterogeneous data types, from image, speech, and multimedia signals to text documents and labels. Much of this information is encoded in natural language, which makes it accessible to some people—for example, those who can read that particular language—but much less amenable to computer processing beyond a simple keyword search.
My chosen research area, cross-source information extraction (IE) on a massive scale, aims to create the next generation of information access in which humans can communicate with computers in natural languages beyond keyword search, and computers can discover the accurate, concise, and trustable information embedded in big data from heterogeneous sources.
IE works by identifying facts, such as a person’s publicly accessible job history or location, merger and acquisition activity from news coverage, disease outbreaks from medical reports, and experiment chains from scientific papers. Traditional IE techniques pull this information from individual documents in isolation, but users might need to gather information that’s scattered among a variety of sources (for example, in multiple languages, documents, genres, and data modalities). Complicating matters, these facts might be redundant, complementary, incorrect, or ambiguously worded; the extracted information might also need to augment an existing knowledge base, which requires the ability to link events, entities, and associated relations to Knowledge Base entries.
In my research, I aim to define several new extensions to the state-of-the-art IE paradigm beyond “slot filling,” getting to the point where we systematically develop the foundation, methodologies, algorithms, and implementations needed for more accurate, coherent, complete, concise, and most importantly, dynamic and resilient extraction capabilities.
More specifically, my research aims to answer the following questions:
• How can we ensure global coherence and conduct inferences to reduce uncertainty? (My research is developing novel inference frameworks to identify and resolve morphed and implicit information.)
• How can we accurately translate the extracted facts into another language? (My research involves information-aware machine translation.)
• How can we adapt methods from one genre to another, from one domain to another? (My research combines natural language processing and social cognitive theories.)
• How will we discover and fuse information from noisy data in multiple data modalities such as text, speech, image, and video? (My research involves developing new representation and methodology for multimedia information networks.)
Big data offers an explosive amount of material to mine, and IE techniques will help all of us make sense of it.
Learning through Human Interaction
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To serve us well, robots and other agents must understand both what we want and how to achieve it. To this end, my research aims to create robots that empower humans by interactively learning from them. Conventional robot learning algorithms are rigid—optimizing a task objective predefined by a practitioner of AI—and are often slow to learn. Interactive learning methods address both of these limitations by enabling technically unskilled end-users to designate correct behavior and communicate their task knowledge.
My research on interactive learning has focused on algorithms that facilitate teaching by signals of approval and disapproval from a live human trainer. Operationalizing these signals as numeric reward in a reinforcement learning framework, I ask: Given the reward that technically unskilled users actually provide, how should a robot learn from these signals to behave as desired by the trainer?
In one line of this research, I consider how to learn exclusively from these signals. To this end, I developed the Training an Agent Manually via Evaluative Reinforcement (TAMER) framework. TAMER is a myopic (that is, valuing near-term reward while ignoring long-term reward) and model-based reinforcement learning approach that has been successfully implemented to teach a number of tasks, including Tetris and interactive navigational behaviors on a physical robot. More recently, I examined the impact of the chosen reinforcement learning objective on task performance, focusing on the rate at which the robot discounts future reward and whether the robot experiences separate episodes of learning. This investigation led to the first successful training of robots that maximize human-generated reward non-myopically. Such non-myopic learning promises to shift the burden from users to the robot, presenting new algorithmic challenges.
I also considered how robots can learn from both human reward and a predefined evaluation function (that is, a reward function from a Markov Decision Process), combining TAMER with more conventional learning. In this setting, the evaluation function is given authority to determine correct behavior—the robot’s performance is judged solely by the evaluation function’s output—and the trainer’s feedback provides guidance. Compared to the learning speed and final performance of reinforcement learning without human training, this research produced considerable improvements.
In the near future, I hope to confront some of the new challenges raised by this work, including scaling up non-myopic learning to complex, real-world applications; combining these algorithms with demonstration-based teaching methods; and learning from implicit human communication such as smiling or giving attention. In my postdoctoral research, I continue to work on learning from human reward while also developing a robotic reading companion for young children, trained by interactive demonstrations from parents.
Through this research into interactive robot learning, my goal is to connect robots to our expertise and desires. With interactive learning, humans are more than passive beneficiaries of fully autonomous robots. Instead, they actively understand and exert control over the behavior of robots, progressing towards a human-centered artificial intelligence.
Learning Representations from Data
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Machine learning has successfully tackled many problems related to real-world applications in artificial intelligence, but the ultimate performance of machine learning systems critically relies on the quality of features. To date, most state-of-the-art features are hand-crafted by domain experts who put a great deal of efforts into domain-specific knowledge. However, hand-crafted features can’t capture high-level semantics or be adapted to training data, which often yields suboptimal performance. Further, in some problem domains, such hand-designed features may not be readily available. Therefore, the problem of feature construction is a fundamental challenge in machine learning.
To address this challenge, my research focuses on developing algorithms for learning useful feature representations automatically from data—a concept broadly called representation learning. My research’s central theme is to develop generative and discriminative learning models by combining properties such as distributed representation, sparsity, hierarchical structures, invariance, and scalability to high dimensionality.
Over the years, I’ve made important contributions to this rapidly growing field. For example, I developed some of the key algorithms for learning sparse representations—specifically, through one of the fastest sparse coding algorithms currently in use and a sparsity regularizer for learning restricted Boltzmann machines, deep belief networks, and autoencoders. I also developed convolutional deep generative learning algorithms that can effectively learn compositional feature hierarchies from high-dimensional data such as images, videos, and audio.
More recently, I’ve been working on models that combine representation learning with structured priors. Specifically, I’ve demonstrated how to integrate conditional random fields (which can enforce local consistencies in output space) with a Boltzmann machine prior (which can enforce global consistencies in output space) for structured output prediction. This hybrid graphical model produces both qualitatively and quantitatively superior results in image segmentation and labeling. In addition, I’ve demonstrated that nonparametric Bayesian priors can be incorporated into hierarchical distributed representations, which allows for learning mid-level, attribute-like features in a weakly supervised setting.
To make representation learning more robust and scalable, some of my recent work addresses the following questions: How can we learn from scratch by jointly learning and selecting relevant features from noisy data? How can we tease out factors of variations from data with deep generative models? How can we learn invariant representations with the notion of transformations? How can we learn better features from multimodal data? How can we incrementally control the capacity of a feature-learning algorithm from a large stream of online data? How can we develop a hyperparameter-free feature-learning algorithm by exploiting theoretical connections between unsupervised learning models?
Overall, representation learning has shown promise in many areas, with potentially transformative impacts on computer vision, speech/audio recognition, information retrieval, robotics, and natural language processing. I look forward to many more technical breakthroughs and exciting applications in the near future.
Optimization, Social Choice, and Game Theory
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Social factors are an increasingly important component in solving real-world optimization problems in logistics, traffic control, online services, and many other domains. Classical optimization theory is often concerned with finding the best strategy for a set of agents, where agents can be, for example, software programs, companies, or individuals. One underlying assumption in many optimization problems is that agents have common goals, but in practice, that’s not always true. Moreover, competition between agents introduces a game theoretic component in solving optimization problems, as agents can behave strategically.
In my research, I focus on building interfaces between social choice and optimization theory. In particular, together with my colleagues from the ADT group at NICTA and the University of New South Wales led by my ex-supervisor Toby Walsh, I investigate efficient techniques for solving user preference-oriented optimization problems by leveraging ideas from the domains of optimization, social choice, and game theory. One interesting problem at the boundary of optimization and social choice theories is that of user preference elicitation. For example, if a user wants to buy a car, she can easily answer whether a given car configuration is acceptable. However, it might be difficult for her to write a formal model to find valid configurations. With my collaborators, we contributed to this problem by investigating how computationally hard it is to help the user define a problem by asking a set of simple questions about preferences. In particular, we’ve investigated different ways of eliciting user constraints by asking queries about partial solutions.
Another interesting research question arises when performing preference aggregation over multiple agents: Given a set of individual preferences, how do we take all these preferences into account in building a socially optimal preference that best represents the interests of all individuals? A natural and generic mechanism to combine such preferences is voting. Here, we’re looking at strategic behavior in a set of agents, such as misreporting preferences, bribery, and control.
Together with my colleagues, I’m also working on the resource allocation problem: Given a set of indivisible resources or items and a set of agents that have their own preferences over these items, how do you efficiently allocate? We might have several CPUs available in the cloud and a set of users who want to access these resources. One general mechanism is to let agents select items following a picking order, such as you get to go first and pick the item that you most prefer, and then I get to pick the item left that I most prefer, and so on. This mechanism is one of the oldest and well-known sharing procedures that many people have used at least once in their life. We proved that the best mechanism is to let agents alternate picking items in turns. This solves a long-standing open question and, for the first time, provides justification for the mechanism found on school playgrounds all over the world. We also investigated strategic aspects of this problem and designed an efficient algorithm for constructing a picking order that makes strategic behavior unnecessary.
AI and Economics: The Dynamic Duo
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The interaction between computer science and economics has created a fast-growing research area that makes up in dynamism and inspiration for what it lacks in having a proper name. From the computational side, AI plays an increasingly significant and visible role; the synergies between AI and economics are one of the themes that drive my own work.
It’s perhaps not surprising that AI techniques facilitate economic approaches. For example, kidney exchanges offer patients with kidney failure the option to swap willing but incompatible donors with other patients, thereby enabling more transplants from living donors. Although economics offers insights into the design of efficient kidney exchanges, challenges arise in optimizing the number of swaps, especially in light of the uncertainty caused by patients and donors dynamically entering and leaving the exchange pool. We’ve developed practical stochastic optimization algorithms that leverage AI parameter-tuning methods to “look into the future” and refrain from swapping now if it’s anticipated that the same donors can enable more swaps later; these results can potentially save lives.
But AI is also contributing to economics in subtler ways, as researchers are becoming aware of the role that AI paradigms can play in reshaping economic theory. For example, work in social choice theory, which studies topics such as voting, typically assumes static preferences. In contrast, our work draws on AI research on symmetries in Markov decision processes to construct a model of social choice with dynamically evolving preferences.
At the same time, the models and tools of economics are increasingly being applied to problems in AI and in computer science more broadly. Applications of fair division theory are particularly promising. Indeed, economists have devised techniques to allocate divisible goods in ways that guarantee formal notions of fairness; some of these techniques can be adapted to address modern technological challenges such as the allocation of multiple computational resources in cluster computing environments and multiagent systems. Our theoretical and experimental work attempts to bridge the gap between theory and reality by tackling dynamic settings in which users can arrive and depart, and brings us one step closer to creating fair and practical resource allocation algorithms.
I’m equally excited about applying social choice theory to human computation systems, which combine human and machine intelligence by employing humans to solve problems that are difficult for computers. These systems often use voting methods to aggregate opinions, but in a naïve way. We’ve established that a principled approach to voting, based on the literature on voting rules as maximum likelihood estimators, can significantly increase the efficiency of human computation systems.
In light of this lively interaction between AI and economics—and the special role played by dynamic environments—I like to think of AI and economics as the “dynamic duo.” In the tradition of another famous dynamic duo—Batman and Robin, not the Korean hip hop duo—the partnership between the two fields promises both groundbreaking technology and societal impact.
Talking to Robots
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In the home, in the factory, and in the field, robots have been deployed for tasks such as vacuuming, assembling cars, and disarming explosives. As robots become more powerful and more autonomous, it’s crucial to develop ways for people to communicate with them. Natural language is an intuitive and flexible way of enabling this type of communication. The aim of my research program is to construct robots that use language to seamlessly meet people’s needs.
To understand language, a robot must be able to map between words in language and aspects of the external world. At MIT, my collaborators and I developed the Generalized Grounding Graph framework to address this problem. Our framework creates a probabilistic model according to the structure of language, defined by cognitive semantics. We train the model using large datasets collected from human annotators via crowdsourcing. By using data collected from many different people, our system learns robust models for the meanings of a wide variety of words.
Robots must establish common ground with their human partners to accurately follow instructions. One way to establish common ground is for a person to tell the robot about the world, in the person’s own terms. I’m developing interfaces to enable a robot to understand a person’s descriptions of the world and integrate them with its own representations. The resulting semantic map enables the robot to more accurately follow directions because its mental model of the world more closely matches the person’s.
However, no matter how much training data a robot has, there will always be failures to understand. To address this problem, I’m developing ways for robots to recover from failures by applying the same strategy a person does: asking a question. Methods based on information-theoretic human-robot dialog enable a robot to use ordinary language to explain what it needs to an untrained person. The human provides help that enables the robot to recover from its failure and continue operating autonomously.
Language-based interfaces will make robots accessible and intuitive for a wide variety of applications. Household robots will engage in tasks in the home such as cooking and cleaning. A robot that understands human language will enable untrained users to express complex requirements, ranging from what to make for dinner to where to put away the socks. In the factory floor, or in search and rescue tasks, humans will supervise heterogeneous teams of robots to assemble products or search for survivors after an explosive event. Using language, people can quickly deploy a robot team where it’s most needed, and robots can communicate with the human supervisor about what they’ve discovered and where they need help.
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The world is an uncertain place because of physical randomness, incomplete knowledge, ambiguities, and contradictions. Drawing inference from noisy or ambiguous data is an important part of intelligent systems, where Bayesian theory serves as a principled framework of combining prior knowledge and empirical evidence. The past 20 years have seen tremendous progress in developing both Bayesian and nonparametric Bayesian methods for resolving model complexity and adapting to stochastic and changing environments with data-driven learning algorithms. However, conventional Bayesian inference is facing great challenges in dealing with large-scale complex data, arising from unstructured, noisy, and dynamic environments such as the Web, which records massive digital traces of human activities.
To address these challenges, my research consists of developing Bayesian inference methods and scalable algorithms to address important problems in scientific and engineering domains. Through my work, I’ve developed regularized Bayesian inference, or RegBayes, a computational mechanism allowing Bayesian inference to directly control the properties of posterior distributions by imposing posterior regularization, which provides a significant source of extra flexibility to incorporate domain knowledge in rich forms such as those represented as a logical knowledge base and those derived from some learning principle. RegBayes has rich connections with information theory, optimization theory, and statistical learning theory. When the posterior regularization is derived from the discriminative max-margin principle, RegBayes sets up a bridge between Bayesian nonparametrics and max-margin learning, two important subfields in machine learning that have taken largely disjoint paths over the past 20 years. In addition, I address the fundamental computational challenges of scaling up Bayesian methods to huge and high-dimensional data. I developed highly scalable inference algorithms for RegBayes by exploring problem structures and utilizing recent advances in Markov chain Monte Carlo methods and variational methods.
I’ve worked with social scientists, computer vision researchers, and biologists to develop hierarchical Bayesian models to understand how social links are created and how to predict new links; how natural scene images are composed with objects, and how to categorize natural scenes at a near-human level; and how to incorporate biological domain knowledge to understand the relationship between genomic variations among population and complex diseases. Answers to such questions are vital to a range of important applications for public good. My long-term research goal is to develop AI systems that can effectively incorporate rich domain knowledge, cope with various sources of uncertainty, discover latent structures of complex data, and adapt to dynamic environments. To achieve this goal, it’s important to perform interdisciplinary research—to that end, I’m collaborating with neural scientists on developing Bayesian AI algorithms with strong neural and biological evidence.
Incentives in Multiagent Systems
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Designers of multiagent systems set rules by defining their underlying protocols that define both the basic language of interaction and the behavior that agents should adopt. For example, TCP defines the format of messages that are sent but also asks communicating parties to lower the rate of data transmission if too many packets are lost (this is done to keep the network uncongested). Due to the system’s distributed nature and each participant’s autonomy, there’s no way to strictly enforce such behavior. Agents that gain from deviating from the prescribed behavior can do so, and the system behaves quite differently than initially expected. In the case of TCP congestion control, deviating from the protocol can cause a congestion collapse that drastically slows down all traffic passing through the network.
My research is aimed at analyzing the incentives of agents in such systems using tools from AI, game theory, and economics. I seek to create protocols in which the recommended behavior is also the best course of action for each participant without compromising other properties such as system efficiency or robustness.
Along with my various collaborators, I’ve explored a wide range of computational systems including core Internet communication protocols such as Border Gateway Protocol (BGP) and TCP, where communicating parties might attempt to gain more bandwidth or a more desirable path through the network. These foundational protocols, while imperfect, have interesting incentive structures that help explain their adoption.
In other systems such as peer-to-peer file sharing (where participants typically lack the incentives to upload files to others), incentive issues hinder wider adoption. My work has focused on the partial improvements of current protocols such as BitTorrent and on exploring the market-like ad hoc solutions that file-sharing communities have adopted.
Finally, novel distributed open systems such as the crypto-currency Bitcoin continue to emerge, bringing with them new challenges. One of Bitcoin’s main strengths is in its incentives for nodes that authorize transactions. The transaction fees awarded to these nodes have attracted many to join Bitcoin’s network and to invest their computing resources in securing it. On the other hand, competition for these very fees, which is expected to increase in the future, could cause profit-maximizing nodes to behave in ways that damage the system. I continue to work on ways to improve the protocol before such problems are encountered. The Internet’s rise and the prevalence of computing devices promise that innovative and exciting multiagent systems will continue to appear and that multiagent systems research will continue to flourish.
Nora Ayanian is a postdoctoral associate in the Distributed Robotics Lab at the Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology. She will join the University of Southern California as a WiSE Gabilan Assistant Professor of Computer Science in 2013. Ayanian has a PhD in mechanical engineering from the University of Pennsylvania. She received a graduate fellowship from the National Science Foundation (NSF) in 2005 and won Best Student Paper in the International Conference of Robotics and Automation in 2008. Contact her at firstname.lastname@example.org.
Finale Doshi-Velez is an NSF postdoctoral fellow at the Center for Biomedical Informatics at Harvard Medical School. Her research focuses on developing novel probabilistic modeling techniques to create clinical diagnostic tools and generate scientific research hypotheses in medicine. Doshi-Velez has a PhD in computer science from MIT. She is a 2007 Marshall Scholar and was a Trinity Prince of Wales Research Student at the University of Cambridge. Contact her at email@example.com.
Heng Ji is an associate professor in the computer science department at Queens College and a doctoral faculty member in the Departments of Computer Science and Linguistics at the Graduate Center of City University of New York. In Fall 2013 she is joining Rensselaer Polytechnic Institute as an associate professor and the Edward G. Hamilton Development Chair in Computer Science. Her research interests focus on natural language processing, especially on cross-source information extraction and knowledge base population. Ji has a PhD in computer science from New York University. She received a Google Research Award in 2009, an NSF CAREER award in 2010, and the Sloan Junior Faculty award and IBM Watson Faculty award in 2012. Contact her at firstname.lastname@example.org.
Brad Knox is a postdoctoral researcher in the MIT Media Lab at the Massachusetts Institute of Technology. His research interests span machine learning, human-robot interaction, and psychology, especially machine-learning algorithms that learn through human interaction. Knox has a PhD in computer science from the University of Texas at Austin. He won the best student paper award at AAMAS in 2010, and his dissertation, “Learning from Human-Generated Reward,” received the Bert Kay Dissertation Award from his doctoral department and was runner-up for the Victor Lesser Distinguished Dissertation Award. Contact him at email@example.com.
Honglak Lee is an assistant professor of computer science and engineering at the University of Michigan, Ann Arbor. His research interests lie in machine learning, which spans representation learning, unsupervised and semisupervised learning, transfer learning, graphical models, and optimization. Lee has a PhD in computer science from Stanford. He received best paper awards at ICML and CEAS, and the Google Faculty Research Award. Lee has served as an area chair for ICML 2013 and as a guest editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence special issue on learning deep architectures. Contact him at firstname.lastname@example.org.
Nina Narodytska is a postdoctoral research fellow at the University of Toronto, Canada. She’s also a visiting researcher in the School of Computer Science and Engineering at the University of New South Wales. Narodytska has a PhD in computer science from the University of New South Wales and NICTA. She received an Outstanding Paper Award for AAAI 2011 and an outstanding program committee member award at the Australasian Joint Conference on Artificial Intelligence 2012. Contact her at email@example.com.
Ariel Procaccia is an assistant professor in the computer science department at Carnegie Mellon University. Procaccia has a PhD in computer science from the Hebrew University of Jerusalem, and was subsequently a postdoc at Microsoft and Harvard. He is a recipient of the Victor Lesser Distinguished Dissertation Award (2009), a Rothschild postdoctoral fellowship (2009), an inaugural Yahoo Academic Career Enhancement Award (2011), and a TARK best paper award (2011). He is currently the editor of ACM SIGecom Exchanges and an associate editor of the Journal of AI Research (JAIR) and Autonomous Agents and Multi-Agent Systems (JAAMAS). Contact him at firstname.lastname@example.org.
Stefanie Tellex will join the computer science department at Brown University as an assistant professor in September 2013. She is currently a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory. Her research interests include probabilistic graphical models, human-robot interaction, and grounded language understanding. Tellex has a PhD from the MIT Media Lab for her work on models for the meanings of spatial prepositions and motion verbs. Contact her at email@example.com.
Jun Zhu is an associate professor in the Department of Computer Science and Technology at Tsinghua University. His research focuses on developing machine-learning methods to understand complex scientific and engineering data. Jun has a PhD in computer science from Tsinghua University. He’s an active member of the research community, serving as an area chair for Advances in Neural Information Processing Systems 2013 and a local chair for International Conference on Machine Learning 2014. His dissertation received the China Computer Federation Distinguished Dissertation Award, which recognizes the best PhD in China in computer science. Contact him at firstname.lastname@example.org.
Aviv Zohar is a senior lecturer at the School of Engineering and Computer Science in the Hebrew University of Jerusalem and he is a Golda Meir Fellow. His postdoctoral work was at Microsoft Research. Zohar has a PhD in computer science from the Hebrew University. His other honors include an award of excellence from the Israeli Knesset and the committee of university heads, a Leibniz scholarship during his PhD studies, and a scholarship from the Wolf Foundation during his MSc. Contact him at email@example.com.