Projects

ICSI hosts basic, pre-competitive research of fundamental importance to computer science and engineering. Projects are chosen based on the interests of the Institute’s principal investigators and the strengths of its researchers and affiliated UC Berkeley faculty.

Recent projects are listed below; the full list of each group's projects is accessible via the links listed in the sidebar.

Representation Learning

We study learning mid-level representation from natural non-curated data to achieve efficient and generalizing performance on downstream visual tasks such as recognition, segmentation, and detection.  We exploit instance discrimination, instance grouping, model bias and variance analysis, pixel-to-segment contrastive learning, and visual memory to handle open-set recognition, long-tail distribution, open compound domain adaptation, unsupervised or weakly supervised recognition and segmentation.

Vision
Complex-valued Deep Learning

Complex-valued data is ubiquitous in physics and signal processing applications, and complex-valued representations in deep learning have appealing theoretical properties. While these aspects have long been recognized, complex-valued deep learning lags far behind its real-valued counterpart.  Existing methods ignore the rich geometry of complex-valued data, instead opting to use the same techniques and architectures as real-valued data, with undesirable consequences such as decreased robustness, larger model sizes, and poor generalization.

Vision
Sound and Vision Integration

Sound carries complementary information to vision and can help scene understanding and navigation. We train a model to tell individual sounds apart without using labels, which can be used to accelerate subsequent training on supervised sound event classification, and to explain how song birds such as zebra finch can develop communication without any external supervision.  We also demonstrate with a low-cost real system that learns echolocation and generates depth images only from sound.

Vision
Implicit Deep Learning

Most modern neural networks are defined explicitly as a sequence of layers with various connections.  Any desired property such as translational equivariance needs to be hard-coded into the architecture, which is inflexible and restrictive.  In contrast, implicit models are defined as a set of constraints to satisfy or criteria to optimize at the test time.  This framework can help express a large class of operations such as test-time optimization, planning, dynamics, constraints, and feedback.  Our research explores implicit models to integrate invariance and equivariance constraints in co

Vision
Sketch Recognition and Photo Synthesis

Sketches are rapidly executed freehand drawings that make an intuitive and powerful visual expression.  While they lack visual details and have spatial/geometrical distortions, humans can effortlessly envision objects from sketches.  We study translations between sketches, photos, and 3D models at both the object level and the scene level.

Vision
3D Point Cloud Parsing

Deep neural networks are widely used for understanding 3D point clouds. At each point convolution layer, features are computed from local neighborhoods of 3D points and combined for subsequent processing in order to extract semantic information. We study a novel approach to learn different non-rigid transformations of the input point cloud so that optimal local neighborhoods can be adopted at each layer. 

Vision
Model Learning and Compression

Traditional deep neural network learning seeks an optimal model in a large model space.  However, the optimal model ends up with a lot of redundancy which is then removed during model compression.  We seek an optimal model in a reduced model space without jeopardizing optimality.  We study several techniques such as tied block convolution (TBC), light-cost regularizer (OCNN), and recurrent parameter generator (RPG) where smaller and leaner models are optimized and can be deployed directly with more robustness and generalizability.

Vision
Vision-Based Reinforcement Learning

Vision-based reinforcement learning (RL) is successful, but how to generalize it to unknown test environments remains challenging.  It not only needs to process high-dimensional visual inputs, but it is also required to deal with significant variations in new test scenarios, e.g. color/texture changes or moving distractors.

Vision
Robotic Manipulation and Locomotion

Existing methods for robotic manipulation and locomotion overlook real world constraints such as data availability, data efficiency, and data quality.  We explore novel approaches that incorporate curriculum learning, latent space information extraction, and invariant states to improve the generalizability of learned control policies against environmental and robot configurational changes. 

Vision
Machine Learning for Medical Applications

We explore unsupervised representation learning to not only reduce labeling bias inherent in supervised image-based medical diagnosis, but also allow data-driven discovery of novel pathological, physiological, and camera-related domains.  We also explore machine learning to generate realistic healthy and tumor medical scans to study human visual perception in radiology. 

Vision
Machine Learning in the Hyperbolic Space

We study how hyperbolic space can be used to facilitate the formation of hierarchical representations from natural data without any supervision.  We demonstrate that hyperbolic neural networks outperform standard  Euclidean counterparts when their optimization process is improved with a restricted feature space, resulting in higher classification performance, more adversarial robustness, and better out-of-distribution detection capability.

Vision
Adversarial Robustness

We study how to increase both classification accuracy and robustness with image-wise and pixel-wise representation learning, where perceptual organization is incorporated.

Vision
Agent Based Modeling at the Boundary of Law and Software

ICSI researchers are studying how agent-based models (ABMs) of social contexts can improve the design and regulation of accountable software systems. Agent-based modeling is a social scientific research method that involves bottom-up modeling of complex systems and computationally determining their emergent properties by running simulations. The ICSI researchers are using ABMs to model elements of the social and regulatory environment in which a software system operates.

Networking and Security
Scalable linear algebra and neural network theory

While deep learning methods have in no doubt transformed certain applications of machine learning (ML) such as Computer Vision (CV) and Natural Language Processing (NLP), its promised impact on many other areas has yet to be seen. The reason for this is the flip side of why it has been successful where it has.

Big Data
Scalable Second-order Methods for Training, Designing, and Deploying Machine Learning Models

Scalable algorithms that can handle the large-scale nature of modern datasets are an integral part of many applications of machine learning (ML). Among these, efficient optimization algorithms, as the bread and butter of many ML methods, hold a special place. Optimization methods that use only first derivative information, i.e., first-order methods, are the most common tools used in training ML models. This is despite the fact that many of these methods come with inherent disadvantages such as slow convergence, poor communication, and the need for laborious hyper-parameter tuning.

Big Data
Towards an Extensible Internet

This research is in collaboaration with scientists at University of Washington, NYU, and Mount Holyoke College.

Extensible Internet, Research Initiatives
Foregrounding Bystanders as Stakeholders in Smart Home Product Design

As computing advances, we are faced with tough decisions like how to balance individual privacy with the potential for innovation. People are often uncomfortable with how data is collected and used, yet we continue to see new data-driven technologies deployed. The oft-touted approach of transparency and control has not been an effective solution to individual privacy. People are ill-equipped to decipher how systems work, so cannot effectively use tools intended to put them in control. And as technology expands beyond devices for individuals, privacy expands beyond individual choice.

Usable Security and Privacy
Narrowing The Gap Between Privacy Expectations and Reality in Mobile Health

ICSI and St. Mary's College are collaborating on an NSF-funded project that seeks to answer important questions about privacy and security practices in mobile health technologies (mHealth), such as health apps.

Usable Security and Privacy
Pangeo

The Pangeo project is a community platform for Big Data geoscience. 2i2c collaborates with Pangeo by developing and running JupyterHub infrastructure for the Pangeo Hubs. This work focuses around building collaborative data science platforms that can draw from large cloud datasets, as well as integrating JupyterHub with scalable computing in the cloud via Dask Gateway.

This research is funded by the Moore Foundation and is a collaboration with Columbia University.

2i2c
Backdoor Detection via Eigenvalues, Hessians, Internal Behaviors, and Robust Statistics

Although Deep Neural Networks (DNNs) have achieved impressive performance in several applications, there are several by now well-known sensitivities that they exhibit. Perhaps the most prominent of these is sensitivity in various types of adversarial environments. As an example of this, recall that it is common in practice to outsource the training of a model (which is known as Machine Learning as a Service, MLaaS) or to use third-party pre-trained networks (and then perform fine-tuning or transfer learning).

Big Data

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