Data Analytics Techniques Algorithms (DATA) Lab

Our group is interested in

• understanding, modeling, and leveraging data,
• that is large-scale, streaming, multi-modal, and/or noisy,
• toward solving real-world problems,
• through effective and scalable computational methods,
• with implications on policy and decision making.

We specifically focus on identifying trends and patterns and in turn rare, anomalous, fraudulent, and/or emerging events.

Our current projects include:

• Formalizing anomaly mining problems for complex data
• Anomaly detection in large multi-modal and relational data
• Fraud detection (medical fraud, ad fraud, intrusion, opinion spam, credit card fraud, etc.)
• Understanding and designing neural network models
• Analysis and algorithm design for complex graphs
• Vulnerability and resilience in large time-varying graphs

Funding

We are thankful to following funding agencies for their support to our research.

Ongoing projects of our group include:

• NSF: Toward Fairer Outlier Detection
• IARPA: Hidden ActivitY Signal and Trajectory Anomaly Characterization (HAYSTAC)
• PwC (Risk and Regulatory Services Innovation Center)

               
In the past, our research was also funded by:

• NSF CAREER 1452425 [Read abstract]

  Anomaly mining is the task of finding irregularities in the data. It finds applications in a plethora of domains, such as security, finance, astronomy, and medicine. Despite its immense popularity, however, it remains an extremely challenging task for many real world applications. For many practitioners, the task is poorly defined and under-specified as existing definitions and solutions are often too simplistic and do not directly correspond to the needs of modern applications. This project takes the essential steps to bridge the gap between research and practice to dramatically improve the usability, effectiveness, and interpretability of anomaly mining techniques, and to ultimately mature the field into a more valuable contributor to the larger world. It promises significant impact on many concrete problems, such as insider threat, tax evasion, and health-care fraud detection, important for the government, industry, and the society. Collaborations with industry and hospital partners aim to shepherd innovations into deployed technology, with tangible impact on security and healthcare.

  
  

  The primary agenda to achieve these goals involves developing a new framework for anomaly mining that utilizes multiple heterogeneous data sources and techniques in a corroborative fashion to fundamentally reframe our understanding and ability to define, detect, and describe real-world anomalies. The project formalizes novel definitions of complex anomalies that fuse multiple data sources, and invents complex anomaly detection algorithms that further present descriptions that provide rationale for the detected anomalies. Research also explores and models anomaly ensembles that systematically harness evidences from multiple detection techniques. Ultimately, this project strives to push the boundaries of anomaly mining as a field through this quest for principled foundations and practices.

• Adobe (University Marketing Research Grant)
• NSF IIS 1408287 [Read abstract]

  Given user reviews on Web sites such as Yelp, Amazon, and TripAdvisor, which ones should one trust? Online reviews have become an important resource for public opinion sharing. They influence our decisions over an extremely wide spectrum of daily and professional activities: e.g., where to eat, where to stay, which products to purchase, which doctors to see, which books to read, which universities to attend, and so on. However, the credibility and trustworthiness of online reviews are at stake. It is well known that a large body of reviews is fabricated -- either by owners, competitors, or entities paid by those -- to create false perception on the actual quality of the products and services. What is more, opinion fraud is prevalent; while credit card fraud is as rare as 0.2% or less, it is estimated that 20-30% of the reviews on well-known service sites could be fake. This poses a serious risk to businesses and the public, from investing on a low-quality product to consulting an incompetent doctor for diagnosis and treatment. Like other kinds of fraud, opinion fraud is a serious legal offense. In fact, it is currently being recognized as a serious issue in law enforcement by policymakers. Thus solving this problem is of great importance to businesses and the general public alike. Accurately spotting opinion fraud will enable site owners to provide trustworthy content, maintain the integrity of their service, and protect the online citizens from unfair (or potentially harmful) products and services. Businesses will also benefit from reviews with reliable feedback. Honest businesses will be indirectly rewarded, as it will no longer be easy for unscrupulous businesses to benefit from fake reviews. The research outcomes will thus contribute significantly to the healthy growth of the Internet commerce. Educational activities include incorporating research findings in graduate level courses, educating public on fraudulent behavior and misinformation, and providing publicly available educational materials including lectures and manuscripts.

  
  

  Given the critical issues of opinion fraud in online communities, how can one identify fake reviews and attribute responsible culprits behind them? By conjoining expertise of the PIs over various modalities of deception footprints ranging over language, user behavior, and relational information, this project presents a research program that will result in much needed solutions to this emergent, prevalent, and socially impactful problem. The ultimate goal is to create a unified detection framework via synergistic integration of multiple information sources; from linguistics, user behavior, and network effects, to obtain the best of all worlds. The main idea is to formulate the problem as a relational inference task on composite heterogeneous networks, providing a principled, extensible approach that can blend and reinforce all the above cues towards effective and robust detection of fraud. From a scientific point of view, the research brings together three disciplines: natural language analysis, behavioral modeling, and graph mining. The outcome is a suite of novel, principled, and scalable techniques and models that will enhance our understanding of the creation and dissemination of opinion fraud and misinformation in general at a large scale. The PIs will collaborate with industry partners such as Yelp, Google, and Amazon, directly solicit online fake reviews, and conduct well-designed user studies for testing and validation of their techniques.

• ONR SBIR grant (Contract No. N00014-14-P-1155)
• Stony Brook University Office of Vice President for Research
• Northrop Grumman Aerospace Systems (NGAS)
• Facebook (faculty gift)
• ARO Young Investigator Program (Contract No. W911NF-14-1-0029)
• PNC (Financial Services Innovation Center)
• DARPA TC Project (Contract No. FA8650-15-C-7561)

            

              

Student collaborators

I have had the opportunity to work with the following MS/PhD students and post docs:

at CMU:

• Haomin Wen,  Post-doc (May 2024 - )
• Shurui Cao,  PhD student (co-advisor, F 2023 - )
• Xueying Ding,  PhD student (advisor, F 2021 - )
• Lingxiao Zhao,  PhD student (advisor, F 2018 - )


• Konstantinos Sotiropoulos,  Post-doc (Jul 2023 - Dec 2023)
• Yue Zhao,  PhD student (advisor, F 2019 - 2023)
• Shubhranshu Shekhar,  PhD student (advisor, F 2017 - 2023)
• Saurabh Sawlani,  Post-doc (Jul 2020 - Aug 2021)
• Dimitris Berberidis,  Post-doc (Jul 2019 - Jul 2020)
• Hung Nguyen,  Post-doc (Aug 2018 - Aug 2019)
• Xuejian Wang,  PhD student (advisor, F 2018 - F 2019)
• Sachin Grover,  PhD student (advisor, F 2018 - F 2019)
• Jing Mao,  MS student, ECE (research advisor, Summer 2019)
• Tuan Le,  Post-doc (F 2017 - F 2018)
• Meghanath Macha,  PhD student (advisor, F 2016 - F 2018)
• Hemank Lamba,  PhD student (advisor, F 2017 - S 2018)
• Emaad Ahmed Manzoor,  PhD student (advisor, F 2016 - S 2018)
• Lingxiao Zhao,  MS student, ECE (research advisor, F 2017 - S 2018)
• Xuan Wu,  MS student, SCS (research advisor, S 2017 - S 2018)
• Abhinav Maurya,  PhD student (Summer 2017)
• Runshan Fu,  PhD student (S 2017)

at Stony Brook:

• Shebuti Rayana,  PhD student (Thesis advisor, 2013 - 2017)
• Emaad Ahmed Manzoor,  PhD student (advisor, 2015 - 2016)
• Aria Rezaei,  PhD student (advisor, 2015 - 2016)
• Junting Ye,  PhD student (advisor, 2014 - 2016)
• Hau Chan,  PhD student (co advisor, 2010 - 2015)
• Bryan Perozzi,  PhD student (2011 - 2016)
• Mirza Basim Baig,  PhD student (advisor, 2014 - 2015 )
• Shuchu Han,  PhD student (2013 - 2014)
• Santhosh K. M. Lakshminarayanan,  MSc advanced project (2015 - 2016)
• Abhinav Mishra,  MSc advanced project (2015 - 2016)
• Heeyoung Kwon,  MSc Thesis (2014 - 2015)
• Vasudev Bhat,  MSc student (2013 - 2014)
• Jagat Sastry Pudipeddi,  MS student (2012 - 2013)
• Vivek Kulkarni,  MSc student (2012 - 2013)
• Tiantian Gao,  MSc student (2012 - 2013)
• Junhee Park,  BSc student (2012 - 2013)

See our group page for more information.
You can also subscribe to our email list for our reading seminar meetings here.

Prospective students

I am looking for PhD students!    Criteria: (self-) motivated, hardworking, curious, background in stats/computer science/math.
Those interested in joining DATA Lab and/or working with my research group:

• If you are not yet at CMU:
  
  
  • Apply (by early January) to Heinz College's Ph.D. program in Information Systems and Management (ISM) here.
  • Information about Ph.D. in ISM can be found here.
  • You can earn a joint Ph.D. degree in Machine Learning and Public Policy after being admitted to the Heinz College as a Ph.D. student, by completing a short list of additional requirements. You can find details here (scroll down to PhD in Machine Learning and Public Policy), as well as here.
• If you are at CMU: please directly email me with your CV and a brief statement about your research interests.

Note: I do NOT hire MS students unless they have taken some subset of the following courses at CMU and shown excellence (A, A+): Machine Learning (10601, 10701), Data Mining (36462), Probability and Mathematical Statistics (36700), Convex Optimization (10725), NLP (11611), etc.