Event Archive

Deformable image registration is fundamental and critical to both diagnostics and therapeutics in precision and personalized medicine. Existing software packages for deformable registrations either provide only a forward deformation vector field (DVF), or render both forward and backward DVFs with time-intensive computation. The latter has multiple advantages in medical image analysis and processing. However, its latency, which is substantially longer than clinical time windows, hinders the transition of its benefits to clinical applications.

Deformable image registration is fundamental and critical to both diagnostics and therapeutics in precision and personalized medicine. Existing software packages for deformable registrations either provide only a forward deformation vector field (DVF), or render both forward and backward DVFs with time-intensive computation. The latter has multiple advantages in medical image analysis and processing. However, its latency, which is substantially longer than clinical time windows, hinders the transition of its benefits to clinical applications.

Jaundice, the yellow discoloration of the skin and sclera due to the buildup of bilirubin in the blood, is a symptom associated with medical conditions involving the liver and pancreas (e.g., alcoholism, pancreatitis, pancreatic cancer). Jaundice is only recognizable to the naked eye in severe stages, but a ubiquitous test using computer vision and machine learning can detect milder forms of jaundice.

Many real-world networks such as the ones arising out of facebook and twitter, webpages and hyperlinks etc. evolve with the passage of time. This motivates the study of dynamic graph algorithms, where we have to maintain the solution to a given optimization problem when the input graph keeps changing via a sequence of updates (edge insertions/deletions). The goal is to design algorithms whose update times (time taken to handle an edge insertion/deletion) are significantly faster than recomputing the solution from scratch after each update in the input graph.

Video cameras have become increasingly prevalent, making human motion analysis an important field. This thesis explores the level of detail necessary to distinguish human activities for tasks of regression and classification.

A fundamental computational challenge that arises in the operation of online systems, services, and platforms is that of resource allocation. Broadly defined, a resource allocation problem is one where a set of users generate demands, which then have to be satisfied using a limited set of resources. In many of these scenarios, demands arrive in an online sequence, meaning they arrive over time and must be irrevocably satisfied or assigned without knowledge of future requests.

In today's age, huge data sets are becoming ubiquitous. In addition to their size, most of these data sets are often noisy, have outliers, and are incomplete. Hence, analyzing such data is challenging. We look at applying geometric techniques to tackle some of these challenges, with an emphasis on designing provably efficient algorithms.

In many surveillance or monitoring applications, one or more cameras view several people that move in an environment. Multi-person tracking amounts to using the videos from these cameras to determine who is where at all times. The problem is very challenging both computationally and conceptually. On one hand the amount of videos to process in enormous while near real-time performance is desired. On the other hand people's varying appearance due to lighting, occlusions, viewpoint changes, and unpredictable motion in blind spots make person re-identification challenging.

I will describe a novel framework that aims to create bridges between the computational social choice and the database management communities. This framework enriches the tasks currently supported in computational social choice with a relational database context, thus making it possible to formulate sophisticated queries about voting rules, candidates, voters, issues, and positions. At the conceptual level, we give rigorous semantics to queries in this framework by introducing the notions of necessary answers and possible answers to queries.

This dissertation presents several novel architectures for DNA computing from different perspectives including analog DNA circuits, polymerase-based DNA logic circuits, and localized DNA-based biomolecular reaction networks on cancer cell membranes.

Companies such as Google or Facebook collect a substantial amount of data about their users to provide useful services. The release of these datasets for general use can enable numerous innovative applications and research. However, such data contains sensitive information about users, and simple anonymization techniques have been shown to be ineffective to ensure users’ privacy. These privacy concerns have motivated many leading technology companies and researchers to develop algorithms that share data with provable privacy guarantees including differential privacy.

Algorithms play an increasing role in informing human decisions. We consider two classes of problems where this is the case. First, we discuss the design of algorithms for shared ownership. We extend the standard fair division framework to allow resources to be public, meaning that multiple agents can benefit from them simultaneously, and problems to be online, rather than one-shot. Second, we consider the problem of eliciting information for probabilistic forecasting.

For decades, Moore’s Law and its partner Dennard Scaling have driven technology trends that have enabled exponential performance improvements in computer systems at manageable power dissipation.  With the slowing of Moore/Dennard improvements, designers have turned to a range of approaches for extending scaling of computer systems performance and power efficiency.  Unfortunately, these scaling gains come at the expense of degraded hardware-software abstraction layers, increased complexity at the hardware-software interface, and increased challenges for software relia

Reasoning with equations is a central part of mathematics. Typically we think of solving equations but another role they play is to define algebraic structures like groups or vector spaces. Equational logic was formalized and developed by Birkhoff in the 1930s and led to a subject called universal algebra. Universal algebra was used in formalizing concepts of data types in computer science. In this talk I will present a quantitative analogue of equational logic: we write expressions like s =_ε t with the intended interpretation "s is within ε of t".

Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe.

The social sciences are crucial for deciding billions in spending, and yet are often starved for data and badly underserved by modern computational tools. Building data-intensive systems for social science workloads holds the promise of enabling exciting discoveries in both computational and domain-specific fields, while also making an outsized real-world impact.

Deep learning has had enormous success on perceptual tasks but still struggles in providing a model for inference. To address this gap, we have been developing Memory-Attention-Composition networks (MACnets). The MACnet design provides a strong prior for explicitly iterative reasoning, enabling it to support explainable, structured learning, as well as good generalization from a modest amount of data. The model builds on the great success of existing recurrent cells such as LSTMs: A MacNet is a sequence of a single recurrent Memory, Attention, and Composition (MAC) cell.

Data Centers have emerged as one of the dominant forms of cloud computing. However, there are several interesting (new) questions related to scheduling that arise. In this survey talk, we discuss several problems related to scheduling in data centers. The talk covers job scheduling problems related to utilization efficiency of VMs, along with questions dealing with basic communication issues that arise when multiple competing applications are running. We will also briefly discuss questions related to scheduling on multiple-data centers.