Colloquium talks will be held in the Katharine Reed Cudahy Building on the Marquette University campus. Please address inquiries/suggestions to Dr. Nasim Yahyasoltani.

Fall 2020 - Unless specified, the talks will begin at 1:00pm CT via Microsoft Teams. Contact Dr. Nasim Yahyasoltani to receive an invite to the virtual meeting.

  • September 14: Dr. Jacob Thebault-Spieker (University of Wisconsin-Madison)
    Title: Understanding and Mitigating Biases in Social Computing Systems

    September 21: Dr. Alexandra Papoutsaki (Pomona College)
    Title: Eye tracking tools for research on individuals and teams
    Eye tracking, the process of measuring where a person is looking, can provide great insights into human attention. Although a well-established methodology in the field of Human-Computer Interaction, eye tracking remains largely inaccessible due to its high cost and lack of scalability. In this talk, I will present tools that seek to enable richer and more naturalistic eye tracking studies which can be conducted remotely, at scale, and with minimal cost. One such application, WebGazer, can be added to any website and can seamlessly self-calibrate to predict the gaze of an individual by combining user interactions and common webcams. Another application, SearchGazer, demonstrates how webcam eye tracking can be applied to web search and can lead to similar results with prior research that has been traditionally confined in labs. Going beyond eye tracking studies that focus on a single user, I will introduce my most recent work on building dual eye tracking systems. EyeWrite is one such application that enables research on the effects of gaze sharing during remote collaborative writing. In the context of collaborative problem solving, I will also explore the effect of gaze sharing on the primary medium of communication. Together, these projects show the exciting potential of developing new tools that democratize eye tracking and provide new insights into both individuals and teams.

    October 5: Priyanka Annapureddy (Marquette University)
    Title: Predicting PTSD Severity in Veterans from Self- reports for Early Intervention A Machine Learning Approach
    Early intervention for veterans in crisis represents a crucial area of study to reduce the psychological and health burdens for this population. Traumatic experiences associated with military service are associated with drug and alcohol abuse, suicidality, anger, and disrupted work and family relationships. This project used machine learning (ML) models to integrate data from sociodemographic, self-report baseline symptoms, weekly brief Ecological momentary assessment (EMA) survey of veterans in a community-based 12-week peer support program to predict the discharge PTSD severity level. The ML predictions place the participants into one of the three risk levels: low, medium, and high PCL-5 score. The models were evaluated at different timepoints (weekly intervals) of the program for identifying the earliest week to guide early intervention and reduce veterans’ engagement in risky behaviors.
    Title: Predicting Opioid Overdose Readmission and Opioid Use Disorder with Machine Learning
    Opioid use disorder (OUD) is a medical condition associated with problematic patterns of opioid use that cause interpersonal and social impairment. This research demonstrates how supervised machine learning can be used to predict patients at risk of hospital readmission following opioid overdose, and to predict patients at risk of developing OUD. Two labeled datasets were built and used from deidentified hospital data provided by a Level I Trauma Center Hospital.

    October 19: Dr. Rohit Kate (University of Wisconsin-Milwaukee)
    Title: Automatic Full Conversion of Clinical Terms into SNOMED CT Concepts
    SNOMED CT is the most comprehensive clinical ontology and is also amenable for automated reasoning. However, in order to unleash its full potential for automated reasoning over clinical text, a mechanism to automatically convert clinical terms into SNOMED CT concepts is necessary. In this talk, I will present such a conversion method that we recently developed. The method is also capable of converting clinical terms into concepts which are not already listed in SNOMED CT. The method does not require any additional manual annotations and learns only from existing SNOMED CT terms paired with their concepts. This work is an important step towards enabling automated reasoning over clinical text and also has applications in improving clinical ontologies.

    October 26: Dr. Garvesh Raskutti (University of Wisconsin-Madison)
    Title: High-dimensional tensor modeling through importance sketching
    In this talk, I present a novel procedure for low-rank tensor regression, namely (I)mportance (S)ketching (L)ow-rank (E)stimation for (T)ensors (ISLET). The central idea behind ISLET is importance sketching, i.e., carefully designed sketches based on both the responses and low-dimensional structure of the parameter of interest. We show that the proposed method is sharply minimax optimal in terms of the mean-squared error under low-rank Tucker assumptions and under randomized Gaussian ensemble design. In addition, if a tensor is low-rank with group sparsity, our procedure also achieves minimax optimality. Further, we show through numerical study that ISLET achieves comparable or better mean-squared error performance to existing state-of-the-art methods while having substantial storage and run-time advantages including capabilities for parallel and distributed computing. In particular, our procedure performs reliable estimation with tensors of dimension p=O(108) and is 1 or 2 orders of magnitude faster than baseline methods.

    November 9: Dr. Christine Cheng (University of Wisconsin-Milwaukee)
    Title: Stable Matchings with Restricted Preferences
    In the ​stable marriage problem​ (SM), there are ​n​ men and n​ ​ women each of whom has a preference list that ranks the opposite gender in a linear order. A m​ atching​ is a set of n​ disjoint man-woman pairs; it is s​ table​ if there is no man-woman pair who prefer each other over their partners in the matching. The goal of the problem is to find a stable matching if one exists. A seminal result of Gale and Shapley in the 1960s states that e​ very​ SM instance has a stable matching that can be computed in​ O(n2​) ​​time. Today, centralized stable matching algorithms are used to match medical residents to hospitals and students to schools.
    In this talk, we will focus on SM instances whose preferences model situations that arise in practice - the ​k​-bounded, k​ -​ attribute, and (​ k,∞)-​ list models. We show that, for some constant ​k,​ they are structurally as complex as general SM instances. Consequently, some problems that are computationally hard for general SM instances are also hard for these models.

    November 23: Dr. Dong Hye Ye (Marquette University)
    Title: Machine Learning for Image Processing
    In recent years, it has become increasingly easy to gather large quantities of images. Processing these large image databases is key to unlocking a wealth of information with the potential to be used. However, both interpretation of that big data and connecting it to downstream image processing is still challenging. To tackle this challenge, I unlock the valuable prior knowledge from large image databases via machine learning techniques and use it to improve image processing. In this talk, I will present how machine learning can help image processing such as CT Metal Artifact Reduction/ Reconstruction, microscopic imaging, and UAV detection/tracking.

Spring 2020

January 14: Dr. Maia Jacobs (Center for Research on Computation and Society, Harvard) "One Size Doesn’t Fit Anyone: Tailoring Digital Tools for Personal Health Journeys"

January 15: Saeed Tizpaz Niari (CS Department, CU Boulder) "Differential Debugging for Side Channel Vulnerabilities"

January 16: Dr. Kevin Moran (Computer Science, William & Mary) "Toward Practical Automation for Software Engineering"

January 21: Hao Wang (PhD candidate, University of Toronto) "Optimizing Distributed Machine Learning with Reinforcement Learning"

January 24: Xiaowei Jia (PhD candidate, University of Minnesota). "Physics Guided Machine Learning: A New Paradigm for Scientific Knowledge Discovery"

January 28: Sabirat Rubya (PhD candidate, Computer Science & Engineering Department at University of Minnesota) "Facilitating access to peer support through technology for recovery from substance use disorders"

April 27: Dr. Serdar Bozdag (Marquette University) "Integrating high-dimensional biological datasets to uncover disease-associated genes and gene networks"


April 28: Dr. Praveen Madiraju (Marquette University) "Predicting Post-traumatic Stress Disorder Risk in Veterans"

April 29: Dr. Keke Chen (Wright State University) "Mining Social Signals to Enhance Privacy Protection and Wisdom of Crowds"

April 30: Dr. Bill Chen (AIWAYS AUTO) "Deep Learning for Big Data"

Fall 2019

September 9 - Mike Slattery, Department of Computer Science, Marquette University, "Quantum Computing for Beginners, or, How I Spent My Summer Part I"

September 16 - Mike Slattery, Department of Computer Science, Marquette University. "Quantum Computing for Beginners, or, How I Spent My Summer Part II"

October 7 - Arjun Krishnan, Department of Computational Mathematics, Science and Engineering, Michigan State University, "Hurdles in the marathon of data-driven biology"

October 11 - Blair Taylor, Department of Computer and Information Sciences, Towson University

October 14 - Stevie Chancellor, Department of Computer Science, Northwestern University. "Human-Centered Machine Learning for High-Risk Health Behaviors"

October 21 - Marina Kogan, School of Computing, University of Utah, "Human-Centered Data Science for Crisis Informatics"                  

October 30 - Sameer Patil, School of Informatics, Computing and Engineering, Indiana University, Bloomington, “Mental Models and User Experiences of the Tor Browser”

November 4 - Bilge Mutlu, Department of Computer Sciences, University of Wisconsin Madison, “Designing Robots for Human Interaction”

November 18 - Elena Zheleva, Department of Computer Science, University of Illinois, Chicago, “Causal inference and counterfactual learning from relational data”

November 25 - Mustafa Bilgic, Department of Computer Science, Illinois Institute of Technology, “Active and Interpretable Classification.”


Spring 2019

Colloquium dates and speakers for Spring 2019 - Unless specified, the talks will begin at 2:00pm CT in Room 401 at Cudahy Hall.

Fall 2018

Spring 2018

Fall 2017

Spring 2017