Payal Chandak (CC ’21) developed a machine learning model, AwareDX, that helps detect adverse drug effects specific to women patients. AwareDX mitigates sex biases in a drug safety dataset maintained by the FDA.
Below, Chandak talks about how her internship under the guidance of Nicholas Tatonetti, associate professor of biomedical informatics and a member of the Data Science Institute, inspired her to develop a machine learning tool to improve healthcare for women.
How did the project come about? I initiated this project during my internship at the Tatonetti Lab (T-lab) the summer after my first year. T-lab uses data science to study the side effects of drugs. I did some background research and learned that women face a two-fold greater risk of adverse events compared to men. While knowledge of sex differences in drug response is critical to drug prescription, there currently isn’t a comprehensive understanding of these differences. Dr. Tatonetti and I felt that we could use machine learning to tackle this problem and that’s how the project was born.
How many hours did you work on the project? How long did it last? The project lasted about two years. We refined our machine learning (ML) model, AwareDX, over many iterations to make it less susceptible to biases in the data. I probably spent a ridiculous number of hours developing it but the journey has been well worth it.
Were you prepared to work on it or did you learn as the project progressed? As a first-year student, I definitely didn’t know much when I started. Learning on the go became the norm. I understood some things by taking relevant CS classes and through reading Medium blogs and GitHub repositories –– this ability to learn independently might be one of the most valuable skills I have gained. I am very fortunate that Dr. Tatonetti guided me through this process and invested his time in developing my knowledge.
What were the things you already knew and what were the things you had to learn while working on the project? While I was familiar with biology and mathematics, computer science was totally new! In fact, T-Lab launched my journey to exploring computer science. This project exposed me to the great potential of artificial intelligence (AI) for revolutionizing healthcare, which in turn inspired me to explore the discipline academically. I went back and forth between taking classes relevant to my research and applying what I learned in class to my research. As I took increasingly technical classes like ML and probabilistic modelling, I was able to advance my abilities.
Looking back, what were the skills that you wished you had before the project? Having some experience with implementing real-world machine learning projects on giant datasets with millions of observations would have been very valuable.
Was this your first project to collaborate on? How was it? This was my first project and I worked under the guidance of Dr. Tatonetti. I thought it was a wonderful experience – not only has it been extremely rewarding to see my work come to fruition, but the journey itself has been so valuable. And Dr. Tatonetti has been the best mentor that I could have asked for!
Did working on this project make you change your research interests? I actually started off as pre-med. I was fascinated by the idea that “intelligent machines” could be used to improve medicine, and so I joined T-Lab. Over time, I’ve realized that recent advances in machine learning could redefine how doctors interact with their patients. These technologies have an incredible potential to assist with diagnosis, identify medical errors, and even recommend treatments. My perspective on how I could contribute to healthcare shifted completely, and I decided that bioinformatics has more potential to change the practice of medicine than a single doctor will ever have. This is why I’m now hoping to pursue a PhD in Biomedical Informatics.
Do you think your skills were enhanced by working on the project? Both my knowledge of ML and statistics and my ability to implement my ideas have grown immensely as a result of working on this project. Also, I failed about seven times over two years. We were designing the algorithm and it was an iterative process – the initial versions of the algorithm had many flaws and we started from scratch multiple times. The entire process required a lot of patience and persistence since it took over 2 years! So, I guess it has taught me immense patience and persistence.
Why did you decide to intern at the T-Lab? I was curious to learn more about the intersection of artificial intelligence and healthcare. I’m endlessly fascinated by the idea of improving the standards of healthcare by using machine learning models to assist doctors.
Would you recommend volunteering or seeking projects out to other students? Absolutely. I think everyone should explore research. We have incredible labs here at Columbia with the world’s best minds leading them. Research opens the doors to work closely with them. It creates an environment for students to learn about a niche discipline and to apply the knowledge they gain in class.
Almost 400,000 babies were born prematurely—before 37 weeks gestation—in 2018 in the United States. One of the leading causes of newborn deaths and long-term disabilities, preterm birth (PTB) is considered a public health problem with deep emotional and challenging financial consequences to families and society. If doctors were able to use data and artificial intelligence (AI) to predict which pregnant women might be at risk, many of these premature births might be avoided.
The 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) fosters the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects.
The annual meeting is one of the premier gatherings in artificial intelligence and machine learning that featured talks, demos from industry partners as well as tutorials. Professor Vishal Misra, with colleagues from the Massachusetts Institute of Technology (MIT), held a tutorial on synthetic control.
At this year’s NeurIPS, 21 papers from the department were accepted to the conference. Computer science professors and students worked with researchers from the statistics department and the Data Science Institute.
Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender) and is usually done under the assumption that the sensitive feature available in a training sample is perfectly reliable.
This assumption may be violated in many real-world cases: for example, respondents to a survey may choose to conceal or obfuscate their group identity out of fear of potential discrimination. In the paper, the researchers show that fair classifiers can still be used given noisy sensitive features by simply changing the desired fairness-tolerance. Their procedure is empirically effective on two relevant real-world case-studies involving sensitive feature censoring.
This paper address causal inference in the presence of unobserved confounder when proxy is available for the confounders in the form of a network connecting the units. For example, the link structure of friendships in a social network reveals information about the latent preferences of people in that network. The researchers show how modern network embedding methods can be exploited to harness the network estimation for efficient causal adjustment.
The paper characterizes the theoretical properties of a popular machine learning algorithm, variational Bayes (VB). The researchers studied the VB under model misspecification, which is the setting that is most aligned with the practice, and show that the VB posterior is asymptotically normal and centers at the value that minimizes the Kullback-Leibler (KL) divergence to the true data-generating distribution.
As a consequence, they found that the model misspecification error dominates the variational approximation error in VB posterior predictive distributions. In other words, VB pays a negligible price in producing posterior predictive distributions. It explains the widely observed phenomenon that VB achieves comparable predictive accuracy with MCMC even though VB uses an approximating family.
The paper introduces a model that captures a min-max competition over complex error landscapes and shows that even a simplified model can provably replicate some of the most commonly reported failure modes of GANs (non-convergence, deadlock in suboptimal states, etc).
Moreover, the researchers were able to understand the hidden structure in these systems — the min-max competition can lead to system behavior that is similar to that of energy preserving systems in physics (e.g. connected pendulums, many-body problems, etc). This makes it easier to understand why these systems can fail and gives new tools in the design of algorithms for training GANs.
Dynamic Treatment Regimes (DTRs) are particularly effective for managing chronic disorders and is arguably one of the key aspects towards more personalized decision-making. The researchers developed the first adaptive algorithm that achieves near-optimal regret in DTRs in online settings, while leveraging the abundant, yet imperfect confounded observations. Applications are given to personalized medicine and treatment recommendation in clinical decision support.
The paper proposes a latent bag of words model for differentiable content planning and surface realization in text generation. This model generates paraphrases with clear steps, adding interpretability and controllability of existing neural text generation models.
This paper addresses how to design neural networks to get very accurate estimates of causal effects from observational data. The researchers propose two methods based on insights from the statistical literature on the estimation of treatment effects.
The first is a new architecture, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment. The second is a regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal asymptotic properties “out-of-the-box”. Studies on benchmark datasets for causal inference show these adaptations outperform existing methods.
The researchers prove that properly tailored zero-order methods are as effective as their first-order counterparts. This analysis requires a combination of tools from optimization theory, probability theory and dynamical systems to show that even without perfect knowledge of the shape of the error landscape, effective optimization is possible.
Metric Learning for Adversarial Robustness Chengzhi Mao Columbia University, Ziyuan Zhong Columbia University, Junfeng Yang Columbia University, Carl Vondrick Columbia University, Baishakhi Ray Columbia University
Deep networks are well-known to be fragile to adversarial attacks. The paper introduces a novel Triplet Loss Adversarial (TLA) regulation that is the first method that leverages metric learning to improve the robustness of deep networks. This method is inspired by the evidence that deep networks suffer from distorted feature space under adversarial attacks. The method increases the model robustness and efficiency for the detection of adversarial attacks significantly.
The researchers studied the least-squares linear regression over $N$ uncorrelated Gaussian features that are selected in order of decreasing variance with the number of selected features $p$ can be either smaller or greater than the sample size $n$. And give an average-case analysis of the out-of-sample prediction error as $p,n,N \to \infty$ with $p/N \to \alpha$ and $n/N \to \beta$, for some constants $\alpha \in [0,1]$ and $\beta \in (0,1)$. In this average-case setting, the prediction error exhibits a “double descent” shape as a function of $p$. This also establishes conditions under which the minimum risk is achieved in the interpolating ($p>n$) regime.
The paper investigates the adaptive influence maximization problem and provides upper and lower bounds for the adaptivity gaps under myopic feedback model. The results confirm a long standing open conjecture by Golovin and Krause (2011).
The researchers studied low-rank matrix approximation with general loss function and showed that if the loss function has several good properties, then there is an efficient way to compute a good low-rank approximation. Otherwise, it could be hard to compute a good low-rank approximation efficiently.
The researchers studied how to compute an l1-norm loss low-rank matrix approximation to a given matrix. And showed that if the given matrix can be decomposed into a low-rank matrix and a noise matrix with a mild distributional assumption, we can obtain a (1+eps) approximation to the optimal solution.
The researchers developed a surrogate distribution for the Dirichlet that offers explicit, tractable reparameterization, the ability to capture sparsity, and has barycentric symmetry properties (i.e. exchangeability) equivalent to the Dirichlet. Previous works have used the Kumaraswamy distribution in a stick-breaking process to create a non-exchangeable distribution on the simplex. The method was improved by restoring exchangeability and demonstrating that approximate exchangeability is efficiently achievable. Lastly, the method was showcased in a variety of VAE semi-supervised learning tasks.
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. The researchers extend normalizing flows to discrete events, using a simple change-of-variables formula not requiring log-determinant-Jacobian computations. Empirically, they find that discrete flows obtain competitive performance with or outperform autoregressive baselines on various tasks, including addition, Potts models, and language models.
This work is all about learning causal relationships – the classic aim of which is to characterize all possible sets that could produce the observed data. In the paper, the researchers provide a complete characterization of all possible causal graphs with observational and interventional data involving so-called ‘soft interventions’ on variables when the targets of soft interventions are known.
This work potentially could lead to discovery of other novel learning algorithms that are both sound and complete.
Causal identification is the problem of deciding whether a causal distribution is computable from a combination of qualitative knowledge about the underlying data-generating process, which is usually encoded in the form of a causal graph, and an observational distribution. Despite the obvious need for identifying causal effects throughout the data-driven sciences, in practice, finding the causal graph is a notoriously challenging task.
In this work, the researchers provide a relaxation of the requirement of having to specify the causal graph (based on substantive knowledge) and allow the input of the inference to be an equivalence class of causal graphs, which can be inferred from data. Specifically, they propose the first general algorithm to learn conditional causal effects entirely from data. This result is particularly useful for evaluating the impact of conditional plans and stochastic policies, which appear both in AI (in the context of reinforcement learning) and in the data-driven sciences.
Regression analysis is one of the most common tools used in modern data science. While there is a great understanding and powerful technology to perform regression analysis in high dimensional spaces, the output of such a method is purely associational and devoid of any causal interpretation.
The researchers studied the problem of identification of structural (causal) coefficients in linear systems (deciding whether regression coefficients are amenable to causal interpretation, etc). Building on a technique called instrumental variables, they developed a new method called Instrumental Cutset, which partitions the systems into tractable components such that identification can be decided more efficiently. The resulting algorithm was efficient and strictly more powerful than the current state-of-the-art methods.
The Columbia Engineering community has come together to combat the coronavirus pandemic on multiple fronts. In close collabo-ration with the Columbia University Irving Medical Center, we’re leveraging our expertise and innovation to address short term medical needs and long term societal impacts.
Dean Boyce's statement on amicus brief filed by President Bollinger
President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”
This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.
I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.
Mary C. Boyce
Dean of Engineering
Morris A. and Alma Schapiro Professor