Dr. Rose's research group works towards advancing language technologies for use in supporting online collaboration. One of the major cross-cutting themes of her work is identification of conversational constructs that predict important group learning outcomes such as learning, identification, motivation and relationship formation. Several projects focus on development of technology for automatic analysis of conversational data as well as development of tool benches that put this technology in the hands of other researchers. One of the most exciting developments has been the effective adaptation of tutorial dialogue technology for the purpose of improving online collaboration. Results from numerous studies in a variety of learning domains show statistically significant improvements in student learning, exchange of help, productivity in brainstorming, and improvement of social climate using this technology.

As a faculty member here, she is strongly interdisciplinary and actively involved in an international network of researchers in the fields of Language Technologies, Sociolinguistics, Education, Health, and Psychology. It is her strong desire to see her research in computer supported collaborative learning make an impact in the world. To this end she is involved in efforts that have a clear path towards impacting students around the world and transforming how they learn on-line through discussion, including partnerships with The Math Forum, a major university based math service reaching millions of students each year, and dissemination through Worth Publishing's Psychology Portal, which is packaged with our country's most popular undergraduate psychology textbook.

Beyond her research, Dr. Rose is pursuing her mission through spearheading The Internship Program for Technology Supported Education, which is a partnership between faculty at the International Institute for Information Technology in Hyderabad (IIIT-H) and the School of Computer Science at Carnegie Mellon University (CMU). The goal is to reach out particularly to 3rd year undergraduates at Indian institutions, to expose them to research, and to start mentoring relationships with the students, and ultimately research partnerships with faculty at their home institutions.

In my case, as a PhD student I began conducting research in an NSF funded project titled 'The Biological Language Modeling project'. The idea which initiated this project was based on the assumption that biological sequences such as protein sequences from different organisms can be viewed as text written in different languages. Therefore techniques and tools that are applied and developed for analyzing languages can be utilized in analyzing biological sequences. In general, once you abstract the computational problems in a domain, it very often maps to a more general computer science or mathematical problem, therefore algorithms are often transferable across different domains. I worked for a while in protein sequence language. Later on I started working on the virus-host protein-protein interaction networks, which turned out to be the topic of my PhD thesis. I should mention that LTI is in close relationship with other departments such as Machine Learning Department of SCS or Biological departments both within CMU and UPITT, which allowed me to get access to different disciplines and collaborate with researchers from very diverse backgrounds.

The advent of the sequencing of the human genome has marked a start of a revolution in biology. The advances in high-throughput techniques in molecular biology continue to produce diverse experimental data e.g. genomic sequences, gene expression, and proteomics data. This biological dataset opens a window into how the cell works and what goes wrong when the cell malfunctions and provides a fragmentary, but a complementary view of the cell at different levels of resolution. The availability of such data sets exiting challenges and opportunities for computer scientists. In order to realize the full potential of this diverse and large-scale data, algorithms are needed to integrate diverse datasets and to gain insight in the molecular underpinnings of the diseases such as cancer or AIDS. Furthermore, the translation of the knowledge to the clinics, such as personalized therapies, requires accurate and robust computational and statistical algorithms.

LTI is already a place where a state of the art research is conducted for many areas of language technologies including nature language processing, machine translation and speech recognition. This makes me proud. I am most eager to see LTI deeping its expertise in biology. There are several open research areas, that LTI could immediately have an effect. For instance building ontologies to represent different biological information such as protein interactions, diseases is one area that could make the data scientists life easier. Similarly extracting biological information from unstructured text in an automated fashion is an area which LTI is expert of and can help biologist to leverage the literature in a more efficient way.

I have already graduated. I am currently a post-doctoral researcher in Microsoft Research (MSR) New England Lab. I like to continue to be a researcher either in industry or in academia.

The most exciting development in my field has been the development of imaging technology that allows us to capture gene expression in live tissue samples. This allows researchers to track how genes interact in different parts of the tissue at different times, and will allow us to better understand the causes of various diseases. While current studies have been limited to fruit-flies and worms, I hope that over the next few years, we will develop new algorithms to allow similar studies to be possible in humans.

In the future, I want to continue working on problems in computational biology, as I find this field very challenging and it concerns problems which have a broader impact on the society.

According to me, the best part of LTI is the level of inter-disciplinary research that happens here and I wish that it continues to create new inroads on this front.

With the onset of the internet, more information is made available online. However, most of the information is in unstructured form, making it almost impossible (especially manually) to do further processing, analysis or reasoning over the billions of information that is available online. The goal of NELL project is to automatically read the abundant information on the web, extract and store it in a structured form that will make it easier for human or machine alike to browse or reason over it. There has been many development in the field of information extraction in the past 10 years. Most of the early systems were using human-constructed rules and patterns to extract information from unstructured text. Recent approaches use machine learning to automatically learns these patterns to extract information. NELL project is unique in that not only it attempts to learn these patterns automatically, it is also built to continuously improve its "reading competence" so that tomorrow it can extract more information from the web more accurately. In the future, it will be exciting to see the development of a machine that can read (extract information) as well as human and perhaps more: to help human makes sense or reasons over the wealth of information there is in the web.

One of the most exciting developments in my field, in my opinion is that speech and language technologies have matured to a stage where they are actually usable and are being integrated into devices we use daily. I am eager to see more open source software come out of the LTI, so that people all over the world can build systems for their own languages. It is my dream to build products using these technologies that can be useful to people, especially in developing countries, and I hope to go back to industry and work on this after I graduate.