Projects
My group works at the interface of game theory, optimization, (machine/statistical) learning, and
artificial intelligence.
Motivated by societal-scale
systems (e.g., intelligent infrastructure systems, platform based markets
supporting e-commerce/healthcare/transportation/logistics, etc.), our
research seeks algorithms and methodological innovations that have broad applicability.
Learning-based markets and other forms of automated/algorithmic decision
making are now commonplace in a variety of critical areas of our economy. As
an example, it is now common to interact with predictions produced by algorithms that learn from
offline data such as in search/recommender systems, and similarly, we experience
the output of computer vision and
natural language processing algorithms on devices in our homes, vehicles, and
beyond. Indeed, algorithms are now often incorporated into complex systems where they must learn in the presence
of competition and execute sequences of decisions in the face of scarcity of
resources and under uncertainties that influence the ability to robustly draw future
inferences and satisfy downstream objectives. The research in my group seeks to
develop algorithms for everything from inference to influence in non-stationary,
uncertain, and competitive environments. Below are short descriptions of some
the projects that align with this theme.
A common thread across core research projects is the development of
performance guarantees for coupled
decision-making agents and/or learning
algorithms and seeking such guarantees even in non-stationary environments.
Applications Areas: human-machine
interaction & autonomy, online platforms/data markets, societal-scale
systems (e.g., intelligent transportation systems).
Funding
for this Research
This work is largely funded by the National Science Foundation ((CRII) CNS-1656873, (SCC) CNS-1736582, (CPS) CNS-1836819, (CPS) CNS-1931718,
(RI)
IIS/CSE-1907907,
(CAREER) CNS-1844729, SCC Track
1 CNS-1952011), and the
Office of Naval Research (ONR YIP)
Past funding: (CPS) CNS-1634136,
(US-Ignite) CNS-1646912
Awards & Recognition
for this Research
- 2020 ONR Young Investigator Award
- 2019 National Academy of Engineering, Invited Speaker for the China-America Frontiers of Engineering Symposium
- 2019 National Science Foundation CAREER Award
- 2017 National Science Foundation CISE Research Initiation Initiative Award
- 2009 National Science Foundation Graduate Research Fellowship
Certifiable Reinforcement Learning |
| Many of the same kinds of
uncertainties (particularly, those arising from human
decision-makers) arise in a number of applications where research
into the efficacy of reinforcement learning approaches to control
and automation are being explored. For example, in (semi-)autonomous driving and robotic surgery applications humans are directly coupled with another autonomous agent and real-time decisions are being made often over short horizons where there is little time for cogitation and risk or reference points (e.g., past experiences) are the basis for decisions. We are developing algorithms for
reinforcement learning with certificates of performance with assessment on
benchmark problems. Applications include intelligent infrastructure
(societal-scale CPS) and human-in-the-loop systems. [joint with Sam
Burden (UW); Sam Coogan (GaTech)] |
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Recent Papers
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Adaptive Mechanism Design and Multi-Sided Markets |
| In situations of asymmetric information, we are designing incentive or information shaping schemes for changing user behavior via algorithmic mechanisms, with provable guarantees on performance, that leverage behavioral models capturing salient features of the human decision-making process. We take motivation from a number of applications including peer review (bidding and reviewer-paper matching), online labor markets (proposals and reverse selection), and incentive/information design in mobility applications including on-street parking, micro-vehicle sharing, concierge service companies (e.g., delivery on-demand), etc. [collaborators: K. Jamieson, UW; L. Jain, UW; Nihar Shah, CMU] |
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Recent Papers:
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Algorithmic Competition & Cooperation |
| A number of machine learning problems are begin cast as games in order to learn more robust, better performing algorithms. On the other hand, the rise of multi-agent learning (e.g., AlphaGo) in non-stationary environments as well as competition between algorithms (e.g., in financial markets or online marketplaces) exposes the need for better analysis tools for the interaction between learning agents/algorithms. With these two observations in hand, our goal is to derive performance guarantees for classes of (competitive and cooperative) multi-agent learning algorithms in non-stationary environments. Such tools are useful for both analysis of the behavior of coupled learning agents as well as synthesizing mechanisms for shaping agent behavior. In addition, we seek to draw connections between dynamical systems theory (in particular, types of limiting behavior beyond equilibria) and learning dynamics. |
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Recent Papers:
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Applications |
| Urban Mobility |
| A major source of motivation is problems in urban mobility. The emergence of novel digitally mediated markets for movement of goods and people as well as on-demand door-to-door services has lead to a number of interesting challenges requiring the careful design of market mechanisms that do not introduce new unintended outcomes such as unfair treatment of socioeconomic groups or increased congestion. Furthermore, a number of classical problems in transportation such as curbside management are revealing themselves to be very interesting problems to revisit with a modern perspective due to the availability of data and the new transit mechanisms such as ride-sharing, concierge services, and micro-vehicles (e.g., scooters, bikes, etc.). |
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Recent Papers:
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| Human-Machine Interaction & Behavior Models of Humans |
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Recent Papers:
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| Online Platforms |
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Recent Papers:
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