I look at how to apply queueing-theoretic analysis to improving the performance of computer systems. These days I am very interested in power management of data centers, and capacity provisioning for data centers, particularly under transient load conditions.

Intra-die process variations in nm technology nodes pose significant challenges to robust design practices. Geometric variations along with random dopant fluctuation effects have had significant impact on Logic/Memory functionality/yield. The inaccuracies in the models and variabilities in the process are more pronounced and key is to understand the variability effects. Innovative analysis techniques as well as methodologies are needed to counteract the variability issues. Our team is heavily engaged in this activity.

Smart statistical and numerical techniques are key for understanding the variability in VLSI circuits. These techniques need to be highly efficient to be used by designers. New algorithms to achieve speed-up in these techniques are essential. Extension of these smart algorithms beyond the VLSI domain would be critical to achieve accurate and computationally practical would benefit such fields (e.g application to medical field). We are exploring new algorithms and their applications to fields other than VLSI.

As the VLSI circuits scale power and performance remain the top issues. Developing efficient circuit techniques based on emerging devices (FinFet derived, nano-devices) needs extra attention.
We are involved in fabrication of such circuits. Also we are engaged in modeling of 3-D structures and the thermal management of multilevel domain.

Scaling and manufacturing of alternate memories such as MRAM and PCM would help to replace DRAM. Variety of activities from modeling to fabrication are explored in our team

All aspects of theory of computation, including crypto, Streaming Algorithms;
Routing and Network Algorithms;
Metric Embeddings;
Search and Classification Problems on High-Dimensional Data.
For more infomration see:

http://www.cs.ucla.edu/~rafail/Rafail-research-statement.pdf

The popularity of web-based mapping services such as Google Earth/Maps
and Microsoft Virtual Earth (Bing), as well as the increasing use of
smartphones, has led to an increasing awareness of the importance of
location data and its incorporation into both web-based search
applications and the databases that support them.

Location data is a subset of the broader area of spatial and
multidimensional data, and my interests lie in developing efficient
representations and algorithms for its use in applications in the
computer graphics, databases, computer vision, geographic information
systems (GIS), image processing, high dimensional data, and search
domains. My work is based on the observation that these
representations invariably reduce to finding ways to sort the data
with the caveat that sorting is a linear process, having a reference
point, which yields an explicit order. The drawback here is that in 2
and higher dimensions, the data must be resorted when the reference
point is changed. This is not the case with methods that use implicit
sorts such as those that sort objects with respect to the space that
they occupy (e.g., quadtrees, octrees, R-trees, etc.). I characterize
the problem as one of “sorting in space”. I am interested in
developing algorithms for problems in the above domains that are based
on the notion of sorting. Some examples include finding nearest
neighbors and shortest paths in spatial networks where the distance is
along the edges of the network rather than as “the crow flies”,
as well as finding similar trajectories.

Although location data is usually specified geometrically, many of
today’s web-based applications increasingly find it being specified
textually as it corresponds to place names. I am interested in
techniques for identifying such data as well as resolving it to the
right geometric location as these specifications are often ambiguous.
I have a number of projects that try to make use of such data for
searching databases such as those in the deep web as well as
spreadsheets, news, tweets, to name a few, and in mobile applications.

Come join a large well-funded research effort in cloud computing! Over 10 Carnegie Mellon University professors and 4 Intel Labs researchers are teaming up to drive a common research agenda in cloud computing. Contrary to the common practice of striving for homogeneous cloud deployments, we are exploring the use of specialization as a primary means for order of magnitude improvements in efficiency (e.g., energy), including the design of new platform configurations based on emerging technologies like non-volatile memory. Other research themes include automation at cloud scale (e.g., resource allocation/scheduling, problem diagnosis), big data analytics beyond search (including over live data feeds), and new paradigms for meshing client devices and cloud. Our research lab is a great place to do research. It combines the resources of a well-funded industrial research lab (e.g., our lab’s cluster has 1500+ cores and 600+ terabytes of storage) with the academic freedom of a university. As for me, I am the Intel PI for the cloud computing efforts. I have over 120 publications (cited 10,000+ times), including co-authoring award-winning papers at ICDE, ISCA(2), NSDI, PLDI, and SIGMOD, as well as 13 other papers that were selected for “best papers” journal issues for their respective conferences (including ICFP, PODC, PODS, SIGCOMM, SPAA, and VLDB). I have served on 50 program committees for international conferences (including program chair, vice-chair, etc), and have helped place junior collaborators on a number of top committees. I am on the editorial board of the Journal of the ACM, and an ACM Fellow.

My research spans networked systems and theory. Ongoing projects include:

- Low-latency networking: Even simple application-level tasks in today’s networks, like retrieving a small web object, take many times longer than the underlying network latency. How can we design the Internet’s protocols to approach speed-of-light responsiveness? We are exploring novel low-latency designs in naming and transport protocols, and in scalable routing on flat names. There is interesting work to be done ranging from theoretical analysis to fundamental protocol design to applications including Content-Centric Networking, Internet architecture, and ad hoc networks.

- Network debugging: We are building a tool, Anteater, that examines data plane state of networks in order to find bugs in a flexible, unified manner. A paper on Anteater will appear in SIGCOMM 2011, and there are very exciting opportunities for further work in this area.

- Data center network topologies: With researchers at UIUC and UC Berkeley, we are studying how to build high-bandwidth data center networks that allow incremental expandability, and are simultaneously more efficient than traditional topologies like fat trees. A short paper on our initial design, Jellyfish, will appear in HotCloud 2011.

- Future Internet Architectures: The Internet has become the basis of most of humanity’s remote communication. Its rapid growth and the fact that we use it in so many unexpected ways attest to the groundbreaking flexibility of its design. Yet because of this growth, the Internet’s architecture has encountered a range of limitations. What would an Internet look like that provides the dependability, security, manageability, and efficiency to enable innovative applications for decades to come? With collaborators at Berkeley, Georgia Tech, MIT, Princeton, and Stanford, we are designing and developing an architecture that answers that question.

- Flexible Networks With Source Control: Giving users some control over packet routing is a promising technique to dramatically improve reliability, efficiency, and flexibility of networks, but it is also a radical shift from today’s Internet. How can a routing and forwarding architecture flexibly support source control, while respecting the interests of network owners? How can users quickly pick reliable, efficient routes that are appropriate for the application, given limited information about the dynamic state of the network? What is the effect of source control on denial of service vulnerability? Answering these questions critically requires tools ranging from planetary-scale systems building to game-theoretic analysis.

- In addition to the above topics, my past work deals with randomized algorithms, peer-to-peer systems, incentive compatibility, distributed systems, and more; I maintain my interest in these and other areas of networked systems and am open to collaborations on various topics.

A particular feature of my research is to integrate practical network systems design with theory. This approach leads to systems built on firm foundations with guarantees on their behavior, as well as interesting and difficult theoretical problems that can expose fundamental principles. As such, I’m actively seeking fellows who are system builders, theoreticians, or interested in bridging the two areas.

Theory and algorithms for machine learning. Topics include:
- Design and analysis of algorithms for clustering data streams
- Climate Informatics: a new subfield on machine learning for climate science
- Online learning, and learning from data streams (including in semi- or unsupervised settings)
- Privacy-preserving machine learning
- Active learning

I am interested in algorithmic economics and game theory. I am particularly interested in understanding dynamic behavior, and in identifying settings which are well-behaved, e.g. markets which converge toward price equilibria. I am also interested in mechanism design.

Analysis of Boolean functions, Approximability of optimization problems,
Learning theory, Complexity theory, Property Testing, Probability

My research focuses on the development of mathematical models and algorithms for estimating evolutionary history in Biology and Historical Linguistics. The main objective is to develop methods that produce much more accurate estimations of evolutionary history than can be obtained using existing tools. Our group is distinguished from many other groups in computational biology due to our focus on ultra-large datasets, with up to 500,000 sequences. We use real data and perform simulations to evaluate the performance of methods that we develop. This research area inolves mathematics, probability, statistics, computer science, and intensive collaborations with domain specialists. My current research is funded by two grants from the National Science Foundation, one an Assembling the Tree of Life (ATOL) grant for simultaneous estimation of alignments and trees, and another for estimating species trees from gene trees. I also have an active interest in metagenomic analysis. No background in Biology is needed. However, good mathematical intuition, and skill in algorithm and software development are important.

I am interested in shape / data analysis using geometric and topological methods, with applications ranging from graphics, visualization, bio-informatics, and manifold learning. In particular, my research aims to integrate geometric and topological ideas to develop both theoretical foundations and practical algorithms for information estimation, feature identification and abstraction, shape characterization, matching, and shape deformation. I am also interested in exploring how structures and results developed in computational geometry and topology can be used and extended to complement / augment statistical data analysis methods.

I work mostly in two areas Computational Geometry and Computational Biology.

Over the last three decades Prof. Crutchfield has worked in the areas of nonlinear dynamics, solid-state physics, astrophysics, fluid mechanics, critical phenomena and phase transitions, chaos, and pattern formation. His current research interests center on computational mechanics, the physics of complexity, statistical inference for nonlinear processes, genetic algorithms, evolutionary theory, machine learning, quantum dynamics, and distributed intelligence. He has published over 130 papers in these areas, including the following recent, related publications. Most are available from his website: http://csc.ucdavis.edu/~chaos/.

I’m applying my bioinformatics research to solve problems in systems biology with implications to medical conditions such as cancers and addictions. My interest include cancer genetic and neurogenetic discovery based on SNPs, gene expression microarrays, protemics data, and biomedical ontologies. Specifically, I utilize machine learning tools, information theory, and holistic statistical constructs such as Bayesian Networks. My research involves both leveraging well established methodologies and developing novel algorithms to push the limits of biomedical informatics.

Ginkgo BioWorks is a young company out of MIT with the mission of making biology easier to engineer. We engineer organisms to solve challenges across a range of industries from fuels to pharmaceutical production. We aren’t trying to study biology, we are trying to build it – constructing, editing, and redesigning the living world. Our bioengineers make use of an in-house pipeline of synthetic biology technologies to design and build new organisms.

We are looking for a computing postdoc who is passionate about developing CAD tools for the engineering of organisms. You probably have a background in comparative genomics, computational biology, metagenomics, or other similar areas. You have likely been building computational tools to study natural biology — but at Ginkgo you would have the opportunity to apply your skills and tools to the challenge of engineering new organisms.

At Ginkgo, our organism engineers have immediate demands for expanded tools to support organism design. We believe that we provide a unique environment for someone to cut their teeth on real CAD problems in biological engineering. Lastly, we’d be happy to serve as mentors for CIFellows if you are pursuing that fellowship.

Our group at MIT aims to further our understanding of the human genome by computational integration of large-scale functional and comparative genomics datasets. We develop algorithmic, statistical, and machine learning methods to interpret the functional elements encoded in the human genome, reconstruct the regulatory circuits they define, and understand their evolutionary mechanisms.

(1) We use comparative genomics of multiple related species to recognize evolutionary signatures of protein-coding genes, RNA structures, microRNAs, regulatory motifs, and individual regulatory elements.

(2) We use combinations of epigenetic modifications to define chromatin states associated with distinct functions, including promoter, enhancer, transcribed, and repressed regions, each with distinct functional properties.

(3) We use dynamics of functional elements across many cell types to link regulatory regions to their target genes, predict activators and repressors, and cell type specific regulatory action.

(4) We combine these evolutionary, chromatin, and activity signatures to dramatically expand the annotation of the non-coding genome, elucidate the regulatory circuitry of the human and fly genomes, and to revisit previously uncharacterized disease-associated variants, providing mechanistic insights into their likely molecular roles.

Large complex systems require real time operations management. Some example include domains such as managing transportation networks, managing large computer systems and massive data centers, managing electric grids, etc.

Since the underlying mathematical models for these complex systems are intractable, real time management of such systems require us to build systems that can process large amount of data and take decisions in real time. The system will need to be able to efficiently process stored and streaming data, structured and unstructured data, and event and sensor data.

Given the problem above, we are interested in building new algorithms and techniques in areas such as complex event processing, data mining and machine learning, information management. Besides the algorithmic challenge we are also interested in building, designing and optimizing a large scale system that can implement these algorithms and techniques for various industry verticals such as healthcare, education, energy, etc.

My laboratory is interested in key challenges in Systems Biology and Medicine. Our larger objective is to decipher biological mechanisms, reconstruct networks, predict phenotypes and build quantitative systems models. Towards this end we continue to develop computational and some experimental methods for integrative analysis of multiple types of biological data and for reconstruction of biochemical networks. It is increasingly become clear that time series data in cellular biology and longitudinal data in mammalian in vivo biology are essential for understanding complex phenotypes and we are developing methods to analyze these data. And most importantly, I subscribe to the view that context-free biology is content-free biology!

My major research interests are in the design and analysis of efficient algorithms. A major focus of my recent research has been on theory and algorithms for multicore computing. Multicore computing is a new and important area, and there are many important and challenging topics for research.

I will be happy to serve as a mentor for research in theoretical topics in multicore computing.
I continue to be interested in traditional theoretical study of algorithms and data structures, especially relating to graph algorithms, and I will be happy to serve as a mentor for research in these areas as well.

I work to understand motion and change in video — separating changes in seasons from changes over a day in outdoor time-lapse, capturing the motion patterns in an MRI video of a heart, or parsing the surveillance video of a intersection into traffic cycles. I mostly work on data-driven methods that could scale to work with (for example) all the webcams connected to the web, or within large scale, image based Citizen Science applications.

I am working on exciting computational biology projects related to synthetic biology, designing algorithms and tools that enable life scientists to create synthetic genomes. Working with virologists, we have already designed and synthesized virus genome sequences to serve as vaccines. Our work was reported in Science and Nature Biotechnology. We expect the next generation of tools to help design artificial chromosomes and cells performing unique functions, using existing genes, transcription factors and pathways as optimized design components.

I am involved in a number of other bioinformatics projects, including genomic sequence classification using machine learning, repeat mapping from sparse data, and algorithms for optimizing protein encodings adhering to a variety of constraints, including secondary structure, codon bias and other factors. I am also exploring the area of copy number variation detection from fragmented genomic sequence data, as well as using grid computing for time/space consuming applications, such as short read genome assembly.

I also have an active collaboration in the field of video streaming and video pattern matching. We are exploring efficient algorithms for simultaneously streaming compressed video through fixed bandwidth channels. We are also working in identifying patterns in the information content of video streams.

My research is in mobile networking and distributed real-time algorithms based on data mining, with a specific application area in Intelligent Transportation Systems (ITS). Currently in Anchorage, many vehicles are transmitting data gathered from the vehicle’s OBD port back to a central server hosted at the University. This data includes over 200 vehicular parameters. Gathering data from individual vehicles instead of from hardware installed in the roadways at discrete locations allows applications that require much more precise and granular data to be developed. To gather data from individual vehicles requires specialized architectures, such as vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V). The applications that can be developed are endless, with a few examples being fastest paths, traffic congestion improvement, incident identification, roadway slippage determination, and emergency vehicle routing, among others too numerous to include. My research spans the areas of software and network architectures, mobile networking, and distributed real-time algorithms, so an emphasis in any of those areas is acceptable.

I work mostly in database theory. In recent years, I have focused on data exchange, although I am happy to work on other aspects of database theory. I still have a deep fondness for finite model theory.

My current research interests lie in energy intelligent computing. This involves designing energy-efficient protocols and algorithms for different wireless technologies like Wireless LANs, RFID Systems, Wireless Sensor Networks, and Wireless Mesh Networks. I also work on emerging topics like sustainable computing, and designing communication architectures for smart electric grids.

My work centers around the study of machine learning and inference methods to facilitate natural language understanding.

We are pursuing both fundamental questions in learning and inference and how they interact — most recently: Structure Learning, Constraints Driven Learning, Integer Linear Programming formulations for NLP, Constrained Conditional Models — and a range of natural language processing (NLP) problems — moving in the direction of semantics and natural language understanding, as well as ESL and Psycholinguistically motivated language acquisition.

In robotics we are interested in motion planning with emphasis on high-dimensional systems and kinodynamic planning, planning from high-level temporal goals, assembly planning, reasoning with sensing and control uncertainty, flexible object manipulation, physical modeling, probabilistic methods in robotics, the geometry of motion, and the use of new enabling technologies. We also develop educational modules and distribute software for teaching motion planning.

In computational structural biology and bioinformatics we develop tools on high-performance systems to model protein structure and function, understand biomolecular interactions and help analyze, in the long run, the molecular machinery of the cell. We integrate sequence information with three-dimensional structural information to analyze and represent molecular flexibility and motion. Our work in computer-assisted drug design has also led us to investigations in systems biology.

I am interested in a broad range of questions in machine learning, applications and connections to cognition and other areas of science.

I am particularly interested in understanding and modeling non-linear structure of data in high dimensions, its applications to algorithms design and in the theoretical analysis and limits of such algorithms.

My background is in theoretical computer science, especially algorithms. My current research interests are in several areas of computational biology and bioinformatics. I am especially interested in genomics and the analysis and manipulation of genomic sequences. An example is the study of genomic signatures that can be used to identify which organism a “new” sequence comes from. Another example is an advanced kind of genome compression, supporting both efficient storage and transmission as well as efficient information retrieval and manipulation. Also, I am interested in analyzing or mining genomic sequences to identify evolutionary phenomena such as convergent evolution, gene conversion, and retrogenes.

Finally, I am contributing my expertise to the analysis of a metagenomics project.

The following NSF grant was recently awarded:

http://nsf.gov/awardsearch/showAward.do?AwardNumber=1062472

My current research interests are in incremental analysis and modular analysis for concurrent programs.

In Incremental analysis the differences between closely related program (system) versions serve as the basis for reducing the cost of checking functional correctness and achieving desired coverage of the program. Analyses based on program differences are attractive and have considerable potential benefits since most systems are developed follow an evolutionary process. Functional requirements of a system evolve and change over time. The program sources are changed to match the change in the functional elements. The program is also changed when a fault (bug) is detected and fixed in the program. The challenge with effectively using incremental analysis, however, lies in determining precisely which program execution behaviors are affected by the program changes. We are looking at using data and control flow analysis to conservatively estimate the impact of the change and use symbolic execution to precisely generate program behaviors “affected” by the change.

information integration, query-evaluation algorithms, temporal reasoning,
automata-theoretic algorithms, firmware validation, protocol synthesis, constraint satisfaction, discrete techniques in robotic motion planning

Broad research interest in information theory and systems and the theory biological networks. Specifically, in the area of information theory and systems, exploring data representation schemes for multi-level flash memory as well as for novel substrates. In the area of biological networks, studying the computational power of stochastic chemical reaction networks and how they relate to more conventional models of computation. In addition, developing mathematical abstractions to enable the analysis of stochastic biological networks; specifically, generalizing the notion of logic design to probabilistic logic design we consider relay circuits where deterministic switches are replaced by probabilistic switches.

It is now widely recognized that current commercial many-core systems are simply not good enough: most programmers can’t handle them. Therefore, alternatives must be developed. Anticipating this problem over a decade ago, the Explicit Multi-Threading (XMT) framework has been under development at the University of Maryland. XMT is a general-purpose many-core computing platform with the vision of a 1000-core chip that is easy to program but does not compromise on performance.

XMT is built to support the PRAM theory of parallel algorithm, which is second in its wealth only to the serial algorithms. Since four decades of parallel computing research provided no real alternative to the PRAM, the XMT project sought to draft specifications for the general-purpose many-core desktop of the future, by first inventing hardware and software support for the abstractions developed by PRAM algorithmics — a task deemed impossible by architecture researchers prior to the accomplishments of the XMT project.

A 2010 status report of XMT appears in U. Vishkin, Using simple abstraction for reinventing computing for parallelism, CACM, January 2011. Order of magnitude speedups, dramatic advantages on teachability from middle school to graduate courses have been demonstrated. And favorable student ranking for achieving speedups relative to standard platforms have been demonstrated.

So far, XMT has spanned applications, parallel algorithms, compilers, HW/SW and education of parallelism. Research opportunities building on this promising foundation include now also CS education, bioinformatics, machine learning and other applications, security, OS, and SW architectures.

There is so much more to the potential of many-core parallel computing than the horizons of commercial hardware offer!

My research and educational career straddles the border between computational theory and mechatronic engineering. Motivated by applications in confined spaces, my group pursues a comprehensive program in mechanism design, path planning, motion planning, and estimation. These research topics are important because once the robot is built (design), it must decide where to go (path planning), determine how to get there (motion planning), and use feedback to close the loop (estimation). Already, we have directly applied this body of work to challenging and strategically significant problems in diverse areas such as surgery, manufacturing, infrastructure inspection, and search and rescue.

Many of the research fundamentals support the development of snake robots, highly articulated mechanisms that can thread through tightly packed spaces reaching locations that people and conventional machinery otherwise cannot. We have developed snake robots for minimally invasive cardiac surgery; recently, we completed our first in human procedure. Current work includes further mechanism development for natural orifice surgery, and prescribing estimation/filtering approaches, based on Kalman and Bayes filtering, to map the internals of the body, i.e., it is SLAM on the inside.

We are also addressing the motion planning of snake robots and all underactuated systems. Our approach takes recourse to the fundamentals, drawing from advanced concepts in differential geometry to prescribe gaits. Examples of results in this work are applying Stokes Theorem to the local form of the connection on shape spaces to efficiently design gaits. Recently, we have shown that many biological systems, including fish and lizards, can be modeled this way.

By taking recourse to the fundamentals, we have been able to address other problems such as multi-agent planning. Recently, we have developed an efficient provably complete optimal multi-agent path planner that can plan paths for 40+ robots in large spaces (note that the size of the configuration space makes it impossible for A* to even search one step, let alone complete a path). We have also developed an algorithm for multi-agent manipulation. Current work includes applying these techniques to distributed manufacturing. We are also working with a biologist to use these concepts to model swarms.

It is the excitement of working with students that continues to draw me to academia. I am certain that a casual tour of my lab reveals a feeling of energy and productivity. My students, both graduate and undergraduate, work hard to provide fresh new insights within the framework of mathematical and experimental rigor endowed by my research program.

Current interests include:
– 3D shape and motion capture and reconstruction
– joint analysis of large corpora of images. shapes, or motions
– large-scale virtual content creation
– qualitative analysis of stochastic dynamical systems
– lightweight fusion of distributed signals
– mobility patterns and their influence in mobile network communication
– inference on rankings and other discrete structures

Information assurance, cybersecurity, social networking

My group focuses on verification of embedded and cyber-physical systems, studying the question “how can we build computerized controllers for physical systems that are guaranteed to meet their design goals?” These questions are of crucial importance in many areas, including automotive, aeronautics, railway, mobile robotics, factory automation, and medical devices. After all, our society cannot afford to have these systems malfunction. In our research, we have developed powerful logic-based verification techniques that help producing reliable complex systems, e.g., in aeronautical, railway, and automotive applications. We have developed KeYmaera, the first theorem prover for hybrid systems. We also developed the first verification technique for distributed hybrid systems, which combine the challenges of hybrid systems with those of distributed systems. We have further developed the first logic and compositional verification technique for stochastic hybrid systems. My grou p also works on statistical model checking techniques. There are plenty of exciting challenges ahead in these and related directions of research.

Professor Xu’s research interest lies in the development of machine learning models and optimization algorithms for the problems in the field of Computational Biology and Bioinformatics. He is currently working on the following topics: protein sequence/structure alignment, homology detection, protein structure prediction, protein side-chain packing, protein-protein interaction prediction, and biological network analysis. Professor Xu has developed a popular protein structure prediction program RAPTOR, which performed very well in several CASP (Critical Assessment of Structure Prediction) events. His tree-decomposition algorithm for protein side-chain packing now is a major technique underlying the new version of SCWRL, the most widely-used side-chain packing program.

Video processing, image processing, filter banks and wavelets, image analysis and understanding, 3D – multiview signal processing, machine learning, signal processing for medical devices, 3D video processing and communications.

The Lydia news/blog analysis project seeks to build a relational model of people, places, and things through natural language processing of news sources and the statistical analysis of entity frequencies and co-locations. Our analysis is quite different from Google News. We track the temporal and spatial distribution of the entities in the news: who is being talked about, by whom, when, and where? Please visit our website (http://www.textmap.org) to see our analysis of news obtained from over 500 daily online news sources.

Now is a particularly exciting time to join the Lydia project ! We have begun active collaborations with political scientists, sociologists, and finance professionals to apply our news analysis to their fields. This opens up Computer Science research opportunities in data mining, machine learning, network analysis, and visualization, We easily keep up with all world’s news feeds plus one terabyte corpus of historical news, thanks to our 28-node cluster and Hadoop-based distributed processing. Our current focus is on NLP, particularly improving our entity recognition and sentiment analysis, in English and other languages.

I also have an exciting bioinformatics project in synthetic biology. Working with virologists, we design virus genome sequences to serve as vaccines, then synthesize our designs to see how they grow! Our work has been in Science and Nature Biotechnology, and is now being applied to several human and animal pathogens.

Electrical-engineering (ee) degrees. Learning in fields such as art/architecture.
Widely published in archival journals. Three edited books. Authored encyclopedia articles.
Doctorate: dissertation in stochastic control (ee); mathematics and statistics minors; reading knowledge of Russian and French. Now use Google-translate to write Russian.
Rand Corporation employment and UCLA projects on applied problems ranging from operations research to biomedical computing.

Emeritus Professor since 1994.

VLSI CAD for More Moore (nanolithography beyond 22nm) and More-than-Moore (3D-IC, nanophotonics, etc.);
Physical design and technology co-optimization;
Computational lithography;
Vertical integration of architecture/CAD/circuit/technology;
Design/automation of emerging technologies

Random walks, Percolation, Mixing times of Markov chains, graph partitioning, Fractals and Hausdorff dimension, Phase transitions, Game theory, probabilistic combinatorics, martingales and concentration inequalities

I am currently interested in distributed algorithms deployed on a complex network of coupled processors. Other interests include using parallel architectures such as GPU to speed up image processing operations.

My group has developed middleware, FG (http://www.cs.dartmouth.edu/FG/), which helps to mitigate latency in data-intensive programs. With FG, the programmer structures the computation as one or more pipelines (not constrained to be linear pipelines), where stages work on buffers asynchronously. This work came out of real implementations of out-of-core algorithms for the Parallel Disk Model, but it applies to much more than the PDM. I’m looking for a creative researcher to help us extend what FG can do and to help us incorporate FG into real applications.

I am also interested in developing and implementing algorithms for reducing the number of high-latency operations, on models such as the PDM. Because implementing such algorithms is as important as designing them, this project would be a good opportunity for a CIFellow who is interested in algorithm engineering.

GUI for molecular modeling software and highly parallel computation with GPUs for materials modeling

My research focuses on machine learning with emphasis on developing fast scalable algorithms for massive data analysis. Towards this end our lab develops optimization algorithms and focuses on graphical models, structured prediction, and kernel methods. Application areas include computer vision, social network analysis. I am open to explore any area that is fun and is sufficiently challenging!

I’m interested in how one does data mining when the underlying space of data is non-Euclidean: maybe data lies on a surface in Euclidean space, or on a general Riemannian manifold. There are numerous applications where the underlying data exhibits such characteristics, and in order to solve such problems, we need a wholesale reevaluation of the tools of high dimensional approximate computational geometry. I’m also interested in a variety of large-data/streaming problems, especially those arising from data integration problems.

Anything that involves transforming images into descriptions (or vice versa)

Our group develops algorithms for analysis and interpretation of genome sequences. Current projects include the study of structural variation in human and cancer genomes using next-generation DNA sequencing technologies, network analysis of somatic mutations in cancer, and evolution of the human genome. Further details available at the group website.

My primary research interests are in understanding the approximability thresholds of NP-hard optimization problems. I am also interested in some aspects of algorithmic game theory.

My research interests are in the design and analysis of approximation algorithms for NP-Hard problems as well as online algorithms for scheduling problems. Research touches upon various aspects of discrete and combinatorial optimization, mathematical programming techniques, and graph theory. Specific topics that I have recently worked on include:

* Network Design
* Routing in Graphs and Networks
* Submodular function maximization and related topics
* Broadcast scheduling

* Streaming algorithms and data mining
* Internet Ad Exchanges: Mechanisms, Dynamics, Optimizations, Data analysis. Economics and Game Theory.
* Online Approximation for Internet ad systems.
* Databases: Probabilistic and Stochastic databases.

I am interested in extraction of information from scientific documents to help improve recommendation systems, search engines, etc. We have ongoing projects in table, figure, and algorithm metadata extraction from the CiteSeerX digital library. We are also working on a citation recommendation system for CiteSeerX. As part of a second project, ChemXSeer, which aims to build cyberinfrastructure for chemistry, I am investigating indexes for graph databases. I am also interested in text mining and exploring techniques to extract entities and their relationships in vertical search engines.

matrix theory and computation, large-scale scientific simulations, image data analysis, image reconstruction, algorithm-architecture co-design, mathematical software

A range of parallel computing: applications to large scientific problems (such as climate
modeling and high-energy physics), performance analysis, and parallel algorithms
for physically realizable models such as mesh-connected computers.

Resource allocation in wireless networks, quality of information, mobility models, network security

measurement-driven networking research; dynamics of and over Internet-related connectivity structures (e.g., router- and AS-level topology, P2P systems and Online Social Networks); multi-resolution analysis for the Internet

Communication Networks, Network Economics, Game Theory, Statistical Learning, Networked Control Systems

The computational aspects of the research include signal analysis, modeling and optimization of procedures, improved methods of modeling to provide robust conclusions from experimental data.
The lab’s experimental research interests focus on the structural biology of protein domains in intracellular signal transduction, including SH2, SH3, kinase, phosphatase, PH domains, and many others and how natural ligands interact with them. An additional area of interest is the development of NMR and related methods for structural biology. Projects include segmental labeling, other novel isotopic labeling methods, and detection of protein-protein interaction surfaces and dynamics.

The main research theme of my group is to understand the relationship between protein sequence, structure, and function, which is of fundamental importance in life science and the health care industry. We address important questions and problems in proteomics, structural biology, and biomolecular dynamics, using computational biology and bioinformatics approaches. In particular, we develop methodologies and techniques for massively parallel supercomputers such as the IBM Blue Gene, and other high performance computing (HPC) platforms such as grid computing and cloud computing. The group pursues a broad but well grounded approach to leverage and expand upon our existing techniques for scientific and technological advancement.

The current research projects in our group include: (1). Development of novel methods for parallel multi-scale modeling of complex biological systems. (2). Free energy perturbation (FEP) methods for large scale protein-protein, protein-ligand binding affinity predictions. (3) Influenza modeling with both statistical models using bioinformatics tools and physics-based models using molecular dynamics simulations. (4) Protein folding, misfolding, and aggregation with massively parallel molecular dynamics simulations. (5) Interactions of nanoparticles with biological systems and the related mechanism of nanotoxicity. The group is also actively engaged in research projects on modeling of GPCR, and proteomics/biomarkers.

My best known early result is the tight deterministic time hierarchy. It says that as soon as we allow a k-tape Turing machine to run a little longer, it can tackle a more di?cult problem. The classical hierarchy result has only guaranteed such a performance increase, when the time was multiplied by a factor of log n.

In Approximation Algorithms, my best results are on “Approximating Maximum Independent Set in Bounded Degree Graphs” with Piotr Berman and “Approximation of k-Set Cover by Semi-Local Optimization” with Rong-chii Duh. With Shiva Kasiviswanathan, I have the fastest approximation algorithm for the permanent of random 0-1 matrices.

On the graph isomorphism problem, I have obtained “Normal Forms for Trivalent Graphs and Graphs of Bounded Valence” with Walter Schnyder and Ernst Specker, I have shown how to do “Graph Isomorphism Testing without Numerics for Graphs of Bounded
Eigenvalue Multiplicity” and most importantly, I have shown the limits of combinatorial methods in “An Optimal Lower Bound on the Number of Variables for Graph Identification” with Jin-Yi Cai and Neil Immerman. On the other hand, I have shown that combinatorial invariants are at least as powerful as any spectral invariants.

On fixed parameter tractable problems, I have by far the fastest algorithm for computing the characteristic polynomial of bounded degree graphs. I also have results on moderately exponential time algorithms for NP-hard problems.

In the area of algebraic algorithms, I have found the fastest integer multiplication algorithm. Still, I would like to further improve its asymptotic running time.

I am interested cryptography and data privacy, and their connections to diverse fields such as information theory, combinatorics, quantum mechanics and statistics. Recently, I’ve focused on a few specific topics:

* Privacy-preserving publication of statistical data
* Cryptography based on noisy secrets (”fuzzy cryptography”)
* Efficient two- and multi-party protocols for tasks such as agreement, commitment and secure function evaluation.
* Quantum cryptography, and quantum information theory

IT economics, strategy, and policy issues, including economics of network architectures (e.g., incentive-centered design, industry structure, competition and innovation policy, clean-slate design); economics of information security (e.g., rational behavior, interdependent security, insurance versus protection, risk management); peer-to-peer (p2p) incentive mechanisms and business models; information and communication technologies and development (ICTD).

I am interested in extending methods from mathematical analysis (such as measure theory, fractal dimensions, ergodic theory, and Fourier analysis) to make them into suitable tools for attacking problems in the theory of computing, especially structure of complexity classes, algorithmic information theory, data compression, prediction, algorithmic self-assembly, and other inherently spatial models of computation.

I work on the design and analysis of algorithms and data structures for geometric problems, especially those that arise in modeling molecules or terrain. Working in collaboration with biochemists or geographers not only gives practical application of theoretical research, but also gives new theoretical questions. And you constantly learn as you see from the eyes of another discipline.

Machine Learning, theory and applications: Interactive learning, causal filtering, unsupervised learning, cluster analysis. Information theory. Foundations of learning theory and statistical mechanics. Quantitative finance (econophysics). Robotics. Neuroinformatics. Analysis of animal vocalizations.

My primary interests are in the relations between cryptographic primitives and assumptions. I also have interests in new types of attacks the present themselves due to the changing nature of ubiquitous computing, and its infrastructure. Finally, I am interested in the use of cryptography (specifically applied forms of secure multi-party computing) in security heath care records and wearable sensors.

Cloud Resource Management and Virtualization; P2P and Peer-Assisted Content Distribution; Economics-Inspired and Game-Theoretic Approaches to Resource Management in Distributed Systems and Networks; Formal Specification and Verification of Cyber-Physical Systems; Compile-Time and Run-Time Support for Embedded Real-Time Systems.

In Machine Learning, I have been working on developing theory and algorithms for understanding and exploiting new types of learning protocols that have become crucially important for modern machine learning applications. These include the ability to use unlabeled data in the learning process (i.e., semi-supervised learning), clustering, interactive learning, learning with kernel and similarity functions, multi-agent learning or learning in the presence of other learners.

A second major focus of my research has been on Algorithmic Game Theory. My work here includes the use of machine learning techniques to automate aspects of Mechanism Design and formally address the problem of market analysis, the development of pricing algorithms with improved guarantees over previous methods, and the development of techniques for understanding the overall behavior of complex multi-agent systems in which selfish entities are optimizing for themselves.

A third major focus of my research has been on developing fundamental connections between Machine Learning
and other areas of Theoretical Computer Science including Game Theory and Combinatorial Optimization. These connections are enabling both to advance Machine Learning and to solve important problems in Theoretical Computer Science via a fresh set of ideas and techniques.

I am broadly interested in formal, automated methods for program verification and synthesis, in particular abstract interpretation and model checking. I am also broadly interested in languages, models, and systems for parallel programming.

I am seeking a postdoctoral researcher to work with me on Cauchy, a long-term project aiming to build an “analytical calculus of computation”: a system of mechanized reasoning that can verify whether a program satisfies “analytic” properties like continuity and smoothness, and compute derivatives, discontinuities, limits, and other analytic attributes of programs. The practical motivation of the project is to develop a new class of program analyses for an era where computing is intertwined with sensor-derived perceptions of the physical world, and correctness is a continuum rather than a boolean fact. For example, we may now require that the program be “robust” to small amounts of uncertainty in its inputs—i.e., that small perturbations to an input state only lead to small changes to the output state. A way to formalize this statement would be to define a metric space over the states of the program, and ask that the program encode a continuous function over this space. Alternately, we may consider the Lipschitz constant of the program as a measure of its robustness. To tune program parameters, we may want to use classical root-finding techniques, which may only work on an analytic approximation of the program. To argue that a program converges, we may want to compute limits on its outputs as time elapses to infinity. Common to the above scenarios is the need for some form of analytic reasoning about programs.

The scope of Cauchy includes the theoretical foundations of such reasoning, ways to automate it using state-of-the-art constraint-solvers and numerical optimizers, and its applications in program verification, synthesis, optimization, and approximation. From evidence so far, Cauchy opens a new playground for research on reasoning about programs. It also raises the possibility of a fruitful union of program semantics and verification with control theory, numerical analysis, machine learning, and computer algebra.

The Mathematical Sciences Department at the IBM T.J. Watson Research Center is engaged in basic and applied research in several areas of scientific computing, high-performance computing, algorithms, and optimization. We are seeking postdoctoral researchers in the areas of highly parallel graph algorithms, preconditioners, and other core methods to aid the development of massively parallel scalable solvers for large sparse systems of linear equations.

Solving large sparse systems of linear equations is the core computation in a large number of applications in science, engineering, and optimization. With the rapidly increasing number and complexity of scientific applications that model time dependent physical phenomena, there is an urgent need for robust, high-performance, highly-parallel, practical general purpose sparse linear solvers because of the limitations of the current iterative solver software libraries and the asymptotically superlinear time and memory demands of direct solvers. The development of such solvers requires advances in some key enabling algorithms and preconditioning techniques that are self adaptable to a variety of sparse linear systems and machine architectures. In this context, some of the topics that we plan to explore in the near future include parallel approximate and exact algorithms for graph matching (particularly, maximum edge-weight matchings for general and bipartite graphs), parallel gra ph partitioning and mesh distribution, use of machine learning for preconditioner tuning, self adapting preconditioners, preconditioners based on random sampling, composite preconditioners and solvers, etc.

Prof. Vitter’s research interests include the design and mathematical analysis of algorithms, especially dealing with massive data. He has worked extensively in data compression, string algorithms, external memory algorithms and I/O efficiency, computational geometry, caching and prefetching, machine learning, incremental algorithms, and order statistics. His work on the analysis of algorithms deals with the precise study of the performance of algorithms and data structures under various models. His work on I/O-efficient methods for solving problems involving massive data sets has helped shape the subfield of external memory algorithms. He is actively involved in developing efficient indexing methods for text and techniques for space-efficient processing of massive data. Other work includes sorting, information storage and retrieval, geographic information systems and spatial databases, clustering and geometric optimization, random sampling, and random variate generation.

My main research focus is in coding theory, especially on list decoding. I am also interested in coding questions in the sublinear algorithms framework. Recently, I have been doing some work at the intersection of coding theory, data stream algorithms and compressed sensing.

Titles of current projects:
* Robustness of Simulations in Model Based Design Environments
* Robust Testing for System Validation
* Temporal and Modal Logics for Dynamical and Hybrid Systems
* Hybrid System Synthesis from High Level Specifications
* Task and Motion Planning for Mobile Robots
* Natural Language Interfaces for Robotics

geometric and topological reconstruction from samples, detection of symmetries and repeated patterns in data, sense making from distributed sensor signals, structure-preseving summaries, information discovery and brokerage in sensor networks, mobility analysis and prediction, routing to mobile users

A CI Fellow will be part of the Cryptography Research Group at the IBM T.J.Watson Research Lab. Other group members are: Craig Gentry, Shai Halevi, Charanjit Jutla, Hugo Krawczyk and Tal Rabin. We are involved in a variety of research projects: from the theoretical foundations of cryptography to the design and implementation of cryptographic protocols. We have ongoing research projects in Homomorphich Encryption, Cloud Computing Security, Cryptographic Hash Design, Leakage-Resilient Cryptography, Key Exchange Protocols and Secure Protocols for Mobile Ad-Hoc Networks. Visit the group page at www.research.ibm.com/security for more information.

Develop natural language processing (computational linguistic) methods for analyzing clinical free-text, emphasis on neuro-psychiatric text.

public-key cryptography; design and analysis of cryptographic protocols

My research interests lie in quantum computation and in quantum information, i.e., in quantum information science (QIS). I have published extensively in this field, including three books, with a fourth to appear this summer. I have written papers on such research topics as for example, quantum algorithms, quantum entanglement, quantum cryptography, quantum knots. I am especially interested in interdisciplinary research at the interface between QIS, mathematics, and physics. For more information, please refer to my webpage: www.csee.umbc.edu/~lomonaco

1. Designing efficient algorithms for networking, security, and database applications. My current focus is on efficient packet processing algorithms used on core Internet devices such as routers and IDS/IPSes.

2. Designing efficient privacy preserving protocols for practical problems. My current focus is on privacy and integrity preserving queries.

More information is on my homepage http://www.cse.msu.edu/~alexliu/

My research interests lie at the intersection of statistical signal processing and machine learning. I am interested in developing principled approaches, particularly for semi-supervised and active learning, that adaptively learn and harness the dependency structure between variables to enable efficient inference. Application areas of interest to me include networked systems and bioinformatics.

My interests are in model checking programs, probabilistic systems, and hybrid systems. I am also interested in the core areas of logic and automata theory.

performance modeling of computer systems; queueing theory; stochastic processes; stochastic/Markov modeling; stochastic scheduling in queues; SRPT; FB; resource allocation/ capacity planning; task assignment in server farms; power management; scheduling for supercomputing centers

Research at University of Dayton is aimed at signal/image/video processing and sensor designs that enable new capabilities in computer vision, camera designs, and surveillance. We emphasize on working from first principles, and there is a strong emphasis on math and statistics driven research. In particular, if you have keen interests in rigorous wavelets and/or Bayesian analysis, you will find many opportunities here.

Please check my webpages for details:
– academic.udayton.edu/hirakawa
– www.accidentalmark.com/research

One more point… Our research lab enjoys unusually high visibility among camera companies. Our work related to next generation camera processing pipelines have gained much recognition.

My broad research interests are in theory and algorithms with strong interests in distributed algorithms, game theory, security, anti-censorship, fault-tolerant computation. A current interest is determining how large groups can function effectively when there is no leader.

Algorithms, especially algorithms that involve real numbers.

I study interesting things like quantum computation (especially post-quantum cryptography and the possibility of algorithms for Graph Isomorphism), phase transitions in NP-complete problems (e.g. the colorability of random graphs, or the satisfiability of random formulas) and social networks (in particular, automated techniques for identifying important structural features of large networks).

Applying algebraic geometry to computational complexity (e.g. holographic algorithms). Algebraic statistics, algebraic methods in machine learning.

My primary research area is the foundations of cryptography. Topics I am interested in include (but are not limited to) the following: cryptographic complexity, secure computation, black box reductions and separations, strong public key encryption, privacy preserving data mining, secure search, security against hardware attacks, provable security for practical systems, connections of cryptography, differential privacy and learning theory.

Anura Jayasumana is a Professor of electrical and computer engineering at Colorado State University, where he also holds a joint appointment as Professor of computer science. He directs the Computer Networking Research Laboratory at CSU, and is a member of NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere. At CSU, he has supervised over 15 Ph.D. and 45 M.S. theses, and taught courses ranging from freshmen undergraduate courses to specialized graduate courses in Electrical and Computer Engineering. He has published over 200 research papers and a book. He has served as a consultant to numerous companies ranging from start-ups to Fortune 100 companies.

My research interests include cryptography, lattices, coding theory, algorithms, and computational complexity. A particular focus is on designing cryptographic schemes whose security can be based on the apparent intractability of lattice problems.

Dr. Ferris’ research is concerned with algorithmic and interface
development for large scale problems in mathematical programming,
including links to the GAMS and AMPL modeling languages, and general
purpose software such as PATH, NLPEC and FATCOP. He has worked on
several applications of both optimization and complementarity,
including cancer treatment plan development, radiation therapy,
video-on-demand data delivery, economic and traffic equilibria,
structural and mechanical engineering.

I work in the intersection of optimization and machine learning, designing efficient algorithms for basic theoretical problems in online learning. Examples of problems i’ve worked on include the multi-armed bandit problem, the “experts” problem, or the “online investing” problem.

N-body solvers, which calculate pairwise interactions among a large set of particles, are a vital tool in many simulations of physical phenomena. There is an opportunity to significantly increase the power and the applicability of this technology through the development of algorithms and software of unprecedented simplicity, efficiency, and generality. The basis for such an advance is a relatively obscure approach known as the multilevel summation method (MSM), which is s flexible unified methodology based on a hierarchy of grids (with the possibility of finer grids being localized) and well suited for modern computer architectures. Indeed, we expect multilevel summation to perform an order of magnitude better than other methods for important classes of problems, such as simulations of macromolecules, and expect it to be a formidable competitor in other situations.

The calculation of pairwise interactions and the solution of discrete elliptic equations are the time-limiting steps of applications that consume vast amounts of CPU cycles. Molecular dynamics, in particular, can require months of computer time. The project envisioned here is motivated by problems in computational molecular biophysics, which is being transformed by increasing computing power into a quantitative science with predictive value. And though the MSM will benefit many applications, there is a particular application that makes development of the MSM truly compelling: the use of the spherical and the generalized solvent boundary potential methods for modeling very large systems for which full atomic detail is not needed at a long distance from the active site. Such boundary potentials are implemented in molecular simulators but their use is limited due to the high cost of using standard methods for nonperiodic boundaries. This application is of great interest for very large-scale simulations on proteins that play a crucial role in human disease.

There are several key parts to this work: One is to achieve higher accuracy, using techniques inspired by those that are employed in competing fast N-body solvers. The second is to achieve accelerated performance on current and emerging computer architectures, e.g, using OpenMP or OpenCL (for multicore, multiprocessor CPUs and GPGPUs) and/or MPI (for clusters) for ensembles of smaller particle systems and/or for large particle systems. A third important goal is an adaptive version, e.g. using a pruned oct-tree, for problems with nonuniform particle distributions.

Data anonymization techniques are needed to allow data owners to safely share their data without compromising the privacy of the individuals that the data concerns. Since this process is primarily concerned with introducing uncertainty into the resulting anonymized data, it is instructive to view this process through the lens of uncertain data analysis. Recent work has developed frameworks for modeling and processing uncertain data, but new work is needed to “bridge the gap” between anonymization and uncertainty.

I am particularly interested in working with researchers with expertise in either anonymization or uncertain data management in how tools and models from one can be applied to the other, as well as extending their existing research directions. This is a very open ended area, and can lead to research over a wide variety of settings (to be defined by the postdoc), developing many kinds of techniques.

The postdoc can be hosted at either AT&T Labs (Florham Park NJ) or the DIMACS/DyDAn center at Rutgers University (Piscataway, NJ). Interaction with other experts at both centers will be encouraged. These are also many other academics based at universities close by in the New York/New Jersey area (Princeton, NYU, Columbia, U Penn).

Research in network science. Focus areas include: (1) development of a network measurement science for communication networks and social networks with a particular emphasis on techniques for characterizing graphs from measurements, (2) development of simple network resource allocation mechanisms robust to faults and workload changes, (3) security in wireless networks, (4) modeling and understanding dynamic networks including mobile networks and social networks.

Statistical Properties of Natural Images:

A “prior” probability model for visual images (i.e., a specification of the likelihood that any given image would be seen) provides a powerful constraint for many applications in image processing, computer vision, and computer graphics. I’ve studied statistical image properties, developed developed parametric models for these properties, and used these models in a variety of applications in image processing, computer vision, and digital image forensics. In building such statistical models for natural images, we rely on machine learning methods to facilitate efficient computation, and theoretical and practical aspects of probabilistic graphical models are also my research interest.

I have just been awarded for an NSF CAREER award for 2010-2015.

Theory and algorithms for machine learning. Past and ongoing topics include online learning (including in active and unsupervised settings), active learning, and privacy-preserving machine learning. We are also working on new applications of machine learning, for example, to climate science.

We are looking for postdocs in machine learning, theory, Climate Informatics, privacy/security, or related areas.

My group is interested in how to program the behavior of information-based molecular systems. We focus on theory and lab experiments involving synthetic DNA devices that self-assemble, compute, and move. We are interested in models of computation appropriate for molecular systems; molecular programs for tasks such as how to grow a complex molecular structure or how to detect a complex molecular environment; compilers for automatically producing low-level specifications (such as DNA sequences) from high level descriptions of the desired system behavior; and theoretical analysis of resource complexity and fault-tolerance in molecular programs.

Our research interests are in the design and implementation of rigorous algorithms, and their dissemination as useful software tools, for fundamental problems in computational biology. Our current research is applying techniques from string algorithms, graph algorithms, combinatorial optimization, and machine learning to biological sequence analysis. Topics of interest include multiple sequence alignment, genome alignment, next generation sequencing, approximate repeat finding, motif discovery, RNA simultaneous folding and alignment, protein secondary structure prediction, and inverse parametric optimization. Recent work has lead to the release of the software tools AlignAlign, Opal, and IPA for multiple sequence alignment and inverse parametric alignment.