News featuring Elaheh Raisi

DAC students use summer months to broaden knowledge at tech-related jobs across the U.S.

Michelle Dowling, DAC Ph.D. student in computer science, teaching at her alma mater, Grand Valley State University.

Students at the Discovery Analytics Center have headed off to summer jobs and internships from coast to coast. Following is a good example of the kind of real world experience they are getting.

Payel Bandyopadhyay, a Ph.D. student in computer science, is working on data visualization at UPS Advanced Technology Group, Atlanta, Georgia, where she is helping redesign the UPS parcel tracker website. Her advisor is Chris North.

Jinwoo Choi, a Ph.D. student in electrical and computer engineering, is a computer vision researcher at NEC Labs America, Cupertino, California, working in the area of video understanding/action recognition. Choi’s advisor is Jia-Bin Huang.

Michelle Dowling, a Ph.D. student in computer science, is an instructor at her alma mater, Grand Valley State University in Allendale, Michigan.  She is co-teaching an introductory computer science course with Professor Roger Ferguson. Dowling’s advisor is Chris North.

Shuangfei Fan, a Ph.D. student in computer science, is a software engineer at Instagram in New York City. Her advisor is Bert Huang.

Abhinav Kumar, a master’s student in computer science, is an intern at PayPal in San Jose, California, where he is working on credit risk centric problems. His advisor is Edward Fox.

Tianyi Li, a Ph.D. student in computer science, is a software engineer at Cloudera, in Palo Alto, California, working on visual analytics for interpreting and better training machine learning models. Her advisor is Chris North.

Yufeng Ma, a Ph.D. student in computer science, is a research scientist at Yahoo! Research, in Sunnyvale, California, where he will apply deep learning techniques to data with both images and text. Ma’s advisor is Weiguo (Patrick) Fan and his co-advisor is Edward Fox.

Elaheh Raisi, a Ph.D. student in computer science is a data scientist on the Global Risk and Data Sciences team at PayPal in San Jose, California. This team is responsible for developing and enhancing machine learning and data mining capabilities, which are key to PayPal’s top-of-the-line data-driven decisions. Raisi’s advisor is Bert Huang.

John Wenskovitch, a Ph.D. student in computer science, is visualizing sequential content in multimodal documents/reports in team collaboration settings at FXPAL in Palo Alto, California. His advisor is Chris North.

Sirui Yao, a Ph.D. student in computer science, is a research scientist at Walmart in Bentonville, Arkansas.  She is working on a project that uses machine learning to build a hiring tool, an intelligent system that assists Human Resources in selecting candidate resumes. She will also study related issues such as fairness and security. Yao’s advisor is Bert Huang.

Xuchao Zhang, a Ph.D. student in computer science, will be researching argumentative zoning and note-taking behavior during document authoring at Microsoft Research AI, in Redmond, Washington.  Zhang’s advisor is Chang-Tien Lu.

Yuliang Zou, a Ph.D. student in electrical and computer engineering, will be a researcher at Adobe Research, San Jose, California. His advisor is Jia-Bin Huang.

Sneha Mehta, a Ph.D. student in computer science, is at the Netflix headquarters in Los Gatos, California, working on the open-ended problem of using Natural Language Processing (NLP) techniques to tangibly improve the quality of machine translated subtitles. Her advisor is Naren Ramakrishnan.

“Our DAC students greatly benefit from being out in the workforce during the summer months,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering in the Department of Computer Science and director of the Discovery Analytics Center. “In addition to contributing their skills to problems faced by companies, what they learn from these opportunities is invaluable and an important part of their graduate education.”


DAC Student Spotlight: Elaheh Raisi

DAC Ph.D. student, Elaheh Raisi

Elaheh Raisi’s enthusiasm for math dates back to high school. So it was not surprising when Raisi chose applied mathematics as her major at the Amirkabir University of Technology -Tehran Polytechnic.

“I realized early on that mathematics is essential for many practical sciences,” said Raisi. “My aim was to gain a strong knowledge of mathematics that I could use in problem solving.”

During her freshman year Raisi concentrated on mathematics and programming-related courses but after taking some computer science classes, she developed an interest in artificial intelligence. She earned a master’s degree in artificial intelligence at the Science and Research branch of the Islamic Azad University.

Raisi chose to pursue a Ph.D. in computer science at Virginia Tech because of “the abundance of advanced research resources and facilities in the field of data mining, a supporting environment at a prestigious university, and outstanding professors.” Now, a fifth-year Ph.D. candidate, Raisi is advised by Professor Bert Huang and works on cyberbullying detection on social media in his Machine Learning Laboratory.

To address the computational challenges associated with designing automated, data-driven machine learning approaches for harassment-based cyberbullying detection Raisi and Huang have developed a weakly supervised framework, which is specialized for cyberbullying detection,” she said.

The framework consists of two learning algorithms to improve predictive performance by taking into account not only language, but also social structures. One learner identifies bullying incidents by examining the language content in the message; another learner considers social structure to discover bullying. Intuitively, each learner is using different body of information. The learning algorithm tries to make them eventually agree whether social interactions are bullying.

“Our research is geared toward a very important topic in any online automated harassment detection: fairness against particular targeted groups including race, gender, religion, and sexual orientations,” said Raisi. “Our goal is to decrease the sensitivity of models to language describing particular social groups.”

For their research, Raisi and Huang won a best paper award at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2017.

They also won a best paper award at the Learning with Limited Labeled data (LLD) workshop at NIPS, 2017 for including deep learning methodologies (word and node embedding) into their framework.


NIPS Conference 2017 showcases work from DAC Ph.D. students

 

 

 

 

 

 

 

 

 

 

 

 

A group of Ph.D. students from the Discovery Analytics Center headed with their faculty advisors to Long Beach, California, last week to present papers and posters at the 2017 Conference on Neural Information Processing Systems (NIPS). One of the workshop papers was distinguished with a Best Paper Award and two of the students received NIPS Travel Awards.

2017 marks the 31st year for the international multi-track machine learning and computational neuroscience conference includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers, and workshops.

The Women in Machine Learning Workshop was held in conjunction with this year’s NIPS conference and DAC students presented during that event as well.

At the main conference, Sirui Yao presented “Beyond Parity: Fairness Objectives for Collaborative Filtering” (Yao and Bert Huang, assistant professor of computer science). An overview video for the paper can be viewed on YouTube.

DAC faculty Jia-Bin Huang, assistant professor of electrical and computer engineering collaborated on two papers which were also presented at the main NIPS conference: “Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks” (with the University of California MERCED); and “MaskRNN: Instance Level Video Object Segmentation” (with the University of Illinois in Urbana-Champaign).

Yuliang Zou presented “Label-Efficient Learning of Transferable Representations across Domains and Tasks” (Zou collaborating with Stanford University and the University of California, Berkeley).

Both Yao and Zou received NIPS Travel Awards.

A Best Paper award went to “Co-trained Ensemble Models for Weakly Supervised Cyberbullying Detection” (Elaheh Raisi and Bert Huang), presented by Raisi during the conference workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond.

“Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight” (Jia-Bin Huang and Yen-Chen Lin, visiting student, collaborating with Nvidia Research and the National Tsing Hua University in Taiwan) was presented by Lin at the conference workshop on Machine Deception.

Raisi and Yao presented posters at the Women in Machine Learning Workshop. Raisi presented “A Weakly Supervised Deep Model for Cyberbullying Detection” (Elaheh Raisi, Bert Huang); and Yao presented “Fairness and Accuracy in Recommendation with Imbalanced Data Sparsity” (Sirui Yao, Bert Huang).