I am Dhaval Adjodah, a machine learning and policy
researcher working on pushing the limits of modern machine learning while maximizing their social good.
Currently, I am a research scientist at the MIT Quest for Intelligence (whose purpose is to make advances towards building machine intelligence) and I do impact-focused work for local communities with the Center of Complex Interventions. I also consult for the World Bank to help build machine learning pipelines to track and implement SDG policy. Previously, I was a research scientist at the MIT Media Lab where I developed new machine learning algorithms using insights from computational social science.
My PhD thesis was in computational social science and reinforcement learning during which I was also a member of the Harvard Berkman Assembly on Ethics and Governance in Artificial Intelligence, and was a fellow at the Dalai Lama Center For Ethics And Transformative Values. Previously, I worked as a data scientist in banking and insurance, consulted with the Veterans Health Administration, and founded two startup incubators. I hold a masters degree from the Technology and Policy Program from the (now) Institute for Data, Systems and Society, and a bachelors in Physics, also from MIT.
In addition to my day-to-day work (machine learning engineering, running live online human experiments and writing papers), I enjoy being of service to the computer science and social science communities, most recently as a member of the organizing committee of the AI for Social Good conference workshop series, and on the program committees of the Black in AI initiative and Theoretical Foundations of Reinforcement Learning workshop.
My email is contact dot dval dot me at gmail dot com. Github here. I do not check LinkedIn often.
I'm also on Twitter:
Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions
A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? We conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using models inspired by Bayesian models of cognition, we show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning.
[Entropy paper link, Code]
Mask mandates, adherence, and attitudes on COVID-19
We investigate an unprecedented breadth and depth of health outcomes, geographical resolutions, types of mask mandates, early versus later waves and controlling for other government interventions, mobility testing rate and weather. We show that mask mandates are associated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13 per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40 days after the introduction of mask mandates both at the state and county level. These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion. We also introduce the novel results that community mask adherence and community attitudes towards masks are associated with a reduction in COVID-19 cases and deaths.
[PLOS One paper link, Code]
Leveraging Communication Topologies Between Deep RL Agents
There has been a lot of recent work showing that sparsity in neural network structure can lead to huge improvements, such as through the Lottery Ticket Hypothesis. Coming from a computational social science background, we know that humans self-organize into sparse social networks. My hypothesis was that organizing the communication topology (social network) between agents might lead to improvements in learning performance. This is especially important because some machine learning paradigms—especially reinforcement learning—are becoming more and more distributed in order to parallelize learning, similar to how human society balances exploration and exploitation. Well, we find huge improvements: 10–798% improvement on the state-of-the-art robotics simulators! [AAMAS abstract link, Code, Full paper]