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Dr. Laura Haas
IBM Fellow and Director, Computer Science |
The Computer Science group at IBM Research - Almaden leads the next generation of research in database management, intelligent information systems, user productivity, healthcare IT and the theoretical foundations of computer science. We invent new algorithms and architectures for finding, integrating, managing, analyzing and protecting information, and explore and prototype new modes of user interaction. We are creating the technical underpinnings for a national healthcare network and do fundamental research into game theory and mechanism design, lattices and the theory of semantic mappings. Our innovations have played a role in creating entirely new disciplines such as relational database management and have also led to novel research areas such as information mining, schema mapping, data disclosure management, and activity management.
We explore these areas from theory to systems, and from the laboratory to practice. We apply our technology to problems faced by companies in a broad range of industries including finance, healthcare, telecommunications, and retail, directly interacting with clients and leveraging IBM's unparalleled technical sales and services teams. We pursue a mix of medium- and long-term research, including high-risk/high-reward projects with the potential for disruptive and revolutionary business impact. We measure our success by our strong publication record and our impact on IBM's business and the world. Our researchers are active and respected members of their scientific communities and collaborate with universities.
More information on computer science research can be found at IBM Research Computer Science.
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CoScripter
![[CoScripter logo]](http://services.alphaworks.ibm.com/images/coscripter_feature.gif) |
CoScripter is a system for recording, automating, and sharing business processes performed in a web browser. CoScripter lets you make a recording as you perform a procedure, play it back automatically in the future, and share it with your co-workers. Read More |
AALIM
![[AALIM logo]](feat_proj_aalim.jpg) |
AALIM (Advanced Analytics for Information Management) is a decision support system for cardiology that exploits the consensus opinions of other physicians who have looked at similar patients to present statistical reports summarizing possible diagnoses. The key idea behind our statistical decision support system is the search for similar patients based on the underlying multimodal data consisting of cardiac echo videos, heart sounds, ECGs, and reports. Read More |
Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization
| This research considers a general framework for iterated decision making under uncertainty. Examples of problems that fall into this framework include online routing in communications networks and ad placement for web search results. For this general framework, we develop a novel algorithm which is both efficient and has provably good performance guarantees, as measured by standard metrics of decision theory. (This work received a 2008 Goldberg Award.) Read More |
- Kenneth L. Clarkson, Kun Liu and Evimaria Terzi.
"Towards Identity Anonymization on Graphs"
Link Mining: Models, Algorithms and Applications.
Ed. Philip S. Yu, Christos Faloutsos, and Jiawei Han. 2009
- F. Wang, T. Syeda-Mahmood, and D. Beymer.
"Information Extraction from Multimodal ECG Documents,"
in Proc. Tenth International Conference on Document Analysis and Recognition (ICDAR), July, 2009.pdf.
- Tyrone Grandison, Daniel Gruhl.
"Turning Web X.0 Data Into Competitive Advantage".
The First Caribbean Conference on Information and Communications Technology (CCICT 2009). Kingston, Jamaica. March 2009.
pdf
- Andrey Balmin, Latha S. Colby, Emiran Curtmola, Quanzhong Li, Fatma Ozcan:
Search Driven Analysis of Heterogenous XML Data.
Conference on Innovative Data Systems Research (CIDR), 2009
- Kevin S. Beyer, Vuk Ercegovac, Rajasekar Krishnamurthy, Sriram Raghavan, Jun Rao, Frederick Reiss, Eugene J. Shekita, David E. Simmen, Sandeep Tata, Shivakumar Vaithyanathan, Huaiyu Zhu:
"Towards a Scalable Enterprise Content Analytics
IEEE Data Eng. Bull. 32(1): 28-35 (2009)
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