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Professor Rita Singh

from being mere workhorses to being our friends

Speech Recognition Natural Language Processing

I make machines understand human speech, and the world of sounds around them in general.

It remains unclear to scientists just how the speech signal carries information that is interpreted so effectively by the human brain. Current machine learning techniques for speech make up for this lack of understanding by analyzing large volumes of data and learning surface-level structure from them. Today's best automated speech recognition systems are trained on much more speech than most humans will hear in their lifetime. Yet their performance remains much worse than that of humans. For the most part, my research tries to reduce this gap by building on a better understanding of the speech signal itself.

Bhavana Dalvi

to create a computer system that learns over time to read the web

Information Retrieval Data Mining Machine Learning

During the masters study in India, I got interested in the area of Information Retrieval, Data Mining and Machine Learning. Subsequently, while working at Google, I came across some of the latest advancements in above mentioned fields along with upcoming challenges of harvesting web-scale data.

When I joined PhD program at LTI, I got exposure to entire spectrum of Language technologies and a variety of exciting research problems that researchers here are working on. The question that I find most fascinating is: "Can computers learn to read?"

Thuy Linh Nguyen

different cultures can keep their origins and still able to access to world knowledges

Word Segmentation Morphology Tokenization Machine Translation

I am a PhD candidate at Language Technologies Institute working in word segmentation and morphology tokenization for machine translation.

Languages express meaning differently, for example the translation of a word in some languages might be a long sentence in English. To get a better translation, my research focuses on how computers segment such long foreign words into smaller parts equivalent to English words.

Shilpa Arora

an interactive learning system that learns to identify concepts expressed in text

Question Answering Sentiment Analysis Information Extraction

A lot of text is produced and consumed over internet everyday. It is impossible for us to read everything and find the information we are looking for in real-time. With the advancement in computing, can computers process and understand large volumes of text available on the internet and help us find the information we are looking for, quickly?

Narges Razavian

this field of research is the tip of an iceberg

Computational Biology Structural Biology

I'm a third year PhD student in LTI. My research is about understanding the language of biology. I work to discover the relationship between protein building blocks and how they fold, so that we can design better drugs. I also work on gene expression data, to understand which genes cause specific diseases. Whether it is the language that we speak or the language that our cells speak, LTI has great scientists that can help you understand it better, so I am so happy to be part of LTI. I think this field of research is the tip of an iceberg. The combination of technology, machine learning, and biological data will enable applications that make many people's lives better. I am so passionate about my field and will definitely stay in the area of research, either in academia or industry.

Professor Carolyn Rose

a clear path towards impacting students around the world

CSCLComputer Supported Cooperative Learning Education Machine Learning

Carolyn Penstein Rose is an Assistant Professor with a joint appointment between the Language Technologies Institute and the Human-Computer Interaction Institute. In that role, she leads several research projects in the area of Computer Supported Collaborative Learning and teaches courses in machine learning, discourse analysis, automatic summarization, computer supported collaborative learning, and research communication.

Oznur Tastan

open research areas that LTI could immediately have an effect

Computational Biology Protein Sequence Langauge Applied Machine Learning

My interests are in computational biology and applied machine learning. Computational biology is not one of the main research areas of Language Technologies Institute (LTI) unlike information retrieval or machine translation. LTI research themes cover a broad array of research topics. We are lucky to have open minded and inspirational faculty that allows graduate students to explore different research directions.

Kriti Puniyani

technology that allows us to capture gene expression in live tissue samples

Computational Biology Computer Vision Machine Learning

I work in applying machine learning to computational biology problems. One of the exciting problems that I work on is automatically inferring how genes interact by analyzing microscopic images of their expression. This problem lies at the intersection of computer vision, systems biology and machine learning, and over the next one year, we are also hoping to incorporate genetics knowledge in the analysis!

Meghana Kshirsagar

the level of inter-disciplinary research

Computational Proteomics Machine Learning

My research is in the broad area of Computational Proteomics, with particular emphasis on protein-protein interaction prediction in infectious diseases. I am involved in applying and developing machine learning methods in the study of how diseases work at the level of proteins, which is a part of the bigger puzzle that is the human immune system, and its response to various pathogens.

Derry Wijaya

to help human makes sense or reasons over the wealth of information

Information Extraction Knowledge Discovery

My research area is Information Extraction and Knowledge Discovery. I am involved in the NELL (Never Ending Language Learning) project that aims to build a never-ending machine learning system that acquires the ability to extract structured information from unstructured web pages.

Sunayana Sitaram

Speech and language technologies have matured to a stage where they are actually usable

Assessing Prosody Visualization of Prosody

I am a second year Masters student, currently working on automatically assessing children's oral reading prosody in the context of Project LISTEN's Reading Tutor. Along with assessment, my other topic of research is the visualization of prosody, to enable the Reading Tutor to provide feedback on prosody to children in real time.