Making AI Impactful in Healthcare

Guest Speaker: Dr. Soroush Saghafian

Moderated by Dr. Reza Rawassizadeh, Associate Prof of CS

Monday, 14th April, 2025

Abstract: There is increasing evidence that Machine Learning and Artificial intelligence algorithms can be used to enhance clinical care. In this talk, I address two critical aspects that can significantly improve the impact of such algorithms in healthcare practices: (1) moving beyond associations and creating algorithms capable of causal reasoning under ambiguity, and (2) a human-algorithm “centaur” model of care and decision-making, in which the power of human intuition is combined with the outstanding capabilities of algorithms. I describe our latest research on these subjects at the Public Impact Analytics Science Lab (PIAS-Lab) at Harvard, and discuss findings based on our various collaborations with the Mayo Clinic, Mass General Hospital, and some other public and private organizations.

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Fantastic Photonic Computing Systems and How to Use Them for AI

Guest Speaker: Dr. Ajay Joshi, Professor, Department of ECE, BU

Moderated by Dr. Avinash Mohan, Assistant Prof of CS

Friday, 28th March, 2025

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Bio: Ajay Joshi received his Ph.D. degree from Georgia Tech in 2006 and then worked as a postdoctoral researcher at MIT. In 2009, he joined the ECE department at Boston University, where he is currently a Professor. He was a Visiting Researcher at Google in 2017-18 and an Architect at Lightmatter Inc. He recently co-founded a company, CipherSonic Labs, which is focused on data privacy. His research is in the areas of computer architecture and digital VLSI with focus on data security and privacy, machine learning and photonic computing. He received the NSF CAREER Award in 2012, Boston University ECE Department’s Award for Excellence in Teaching in 2014, Best Paper Award at ASIACCS 2018 and HOST 2023, and Google Faculty Research Award in 2018 and 2019. He currently serves as the Associate Editor for IEEE Transactions on VLSI Systems.

Understanding LLMs: How They Function and How They Have Changed

Guest Speaker: Dr. Sebastian Ratschka, Staff Research Engineer at Lightning AI

Moderated by Dr. Reza Rawassizadeh, Associate Prof of CS

Monday, 3rd February, 2025

Abstract: This talk will guide the audience through the key stages of developing large language models (LLMs). We’ll start by explaining how these models are built, including the coding of their architectures. Next, we’ll discuss the processes of pretraining and fine-tuning, detailing what these stages involve and why they are important. In this talk, attendees will also learn about recent developments, including how model architectures have evolved from early GPT models to Llama 3.1. Additionally, the talk will provide an overview of the most recent training recipes, which include multi-stage pre-training and post-training.

Bio: Sebastian Raschka, PhD has been working on machine learning and AI for more than a decade. Sebastian joined Lightning AI in 2022, where he is a Staff Research Engineer focusing on AI and LLM research and development. Prior to that, Sebastian worked at the University of Wisconsin-Madison as an assistant professor in the Department of Statistics, focusing on deep learning and machine learning research. He has a strong passion for education and is best known for his bestselling books on machine learning using open-source software. You can find out more about Sebastian’s current projects at https://sebastianraschka.com/.

Combining the Power of Biobanks and System Biology Approaches for Better Infectious Disease Prevention and Treatment

Guest Speaker: Dr. Samira Asgari, Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, NY

Moderated by Dr. Reza Rawassizadeh, Associate Prof of CS

Friday, November 15, 2024 at 10:00 AM EST

Abstract: The clinical outcome of any infectious disease can vary widely, ranging from asymptomatic cases to fatal outcomes, depending on a complex interplay between the pathogen, host, and environment. Traditionally, the study of infectious diseases has focused primarily on the pathogen, which has limited our understanding of the environmental and host-specific factors that influence disease susceptibility and severity. Addressing this gap is crucial for developing effective strategies and treatments for infectious disease control. In this talk, I will present our research on the role of human genetic factors and social determinants of health in infectious disease outcomes. I will also demonstrate how we can harness the power of large-scale biobanks and systems biology approaches to gain deeper insights into the complex mechanisms that determine the clinical outcomes of infectious diseases.

Bio: Dr. Asgari is an Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, NY. She completed her M.Sc. in stem cell biology at the University of Tehran, Iran, her Ph.D. in human genomics of infectious diseases at the Lausanne Federal Institute of Technology, Switzerland, and her postdoctoral training in statistical genomics at Brigham and Women’s Hospital, USA. The overarching goal of the Asgari lab is to understand how human genetic, demographic, and environmental diversity translates to phenotypic diversity in the immune system and how these variations impact the clinical outcomes of infectious diseases. The lab achieves this goal by integrating electronic health records, multi-omics datasets, in vitro and in silico experimental models, statistical inference, and machine learning approaches.

Expert-Informed, User-Centric Explanations of Medical Image Classification

Guest Speaker: Dr. Michael Pazzani is the Director of the AI4Health Center and Research Director, Distinguished Principal Scientist, and Senior Supervising Computer Scientist of the Information Sciences Institute at the University of Southern California

Moderated by Dr. Reza Rawassizadeh, Associate Prof of CS

Friday, October 4, 2024 at 4:00 PM EST

Abstract: We argue that the dominant approach to explainable AI for explaining image classification with deep learning– annotating images with heatmaps, provides little value for users unfamiliar with deep learning. Instead, we argue that explainable AI for images should produce output like experts produce when communicating with one another, with apprentices, and with novices. We discuss a bit of the history of interpretable and explainable AI with examples from AI & medicine. A new approach that labels image regions with diagnostic features is proposed and evaluated. We draw on examples from radiology, ophthalmology, dermatology as well as bird classification.

Bio: Michael Pazzani is the director of the AI4Health Center at the University of Southern California. Dr Pazzani received his Ph.D. Computer Science, University of California, Los Angeles, and is a fellow of the Association for the Advancement of Artificial Intelligence. Dr. Pazzani started his career as an assistant, associate, and full professor of Information and Computer Science at the University of California, Irvine, and has served as the Director of the Information and Intelligent Systems Division at the National Science Foundation and as a member of the Board of Regents of the National Library of Medicine at the National Institutes of Health

 

A Unified Framework to Calculate Every Deep Learning Architecture by Hand

Guest Speaker: Dr. Tom Yeh, Associate Professor in the Department of Computer Science and Principal Investigator at the Imagine AI Lab at the University of Colorado Boulder

Moderated by Dr. Reza Rawassizadeh, Associate Professor of CS and Eugene Pinsky, Associate Professor of the Practice, Computer Science; Coordinator, Software Development

Friday, September 13, 2024 at 10:00 AM EST

Abstract: I will present AI by Hand, a new framework I developed to demonstrate the core calculations of a wide range of deep learning architectures by hand, including basic building blocks such as artificial neuros and multi-layer perceptron, well-known architectures such as the transformer,, and state-of-art architectures such as the Diffusion Transformer and the Switch Transformer.

Bio: Prof. Tom Yeh leads the Imagine AI Lab at the University of Colorado Boulder. His research and teaching spans AI, HCI, Education, Ethics, and Neuroscience. He published more than 150 papers. He is the author of the popular AI by Hand ✍️ series, with by more than 70K followers across LinkedIn and X. At his university, Prof. Tom Yeh received several awards, including the Student Affair Faculty of the Year Award.

Using Everyday Routines as a Resource for Understanding Behaviors and Making Recommendations

Guest Speaker: Dr. Anind K. Dey, Professor and Dean of the Information School at the University of Washington.

Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science, Boston University Metropolitan College

Monday, April 15 at 2:30pm EST

Abstract: We live in a world where the promise of ubiquitous computing and the Internet of Things is coming true. We have smart devices that pervade our lives, and that are constantly collecting data about us and mostly discarded as irrelevant. I will demonstrate how researchers can extract relevance from this passively collected data and use it to “image” people’s behaviors. I will describe approaches for extracting behavioral routines from smart devices, and then how these routines can help us better understand individual and group human behaviors, as well as anomalies. Using examples from healthcare, I will describe how we can leverage both routines and anomalies to improve our understanding of health-related behaviors and make recommendations to support behavior change, including substance abuse, depression, and job performance.

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Empowering Engagement: Data-Driven Strategies in Digital Health and Wellbeing

Guest Speaker: Dr. Uichin Lee, Professor in the School of Computing, Korea Advanced Institute of Science and Technology (KAIST)

Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science, Boston University Metropolitan College

Friday, March 1 at 10:00 AM EST

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Computational Behavior Modeling for Personalized Health

Guest Speaker: Dr. Afsaneh Doryab, Assistant Professor of Computer Science and Systems Engineering at the University of Virginia

Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science, Boston University Metropolitan College

Friday, February 2 at 10:00 AM EST

Abstract: Humans interact extensively with a wide range of computing devices, generating data streams that can be analyzed to extract cues about their physical and mental states. This latent information can be used to create more intelligent systems that can anticipate users’ needs and provide personalized services and interventions. However, this capability also poses new technical challenges. In this talk, I will present my research in modeling human behavior from multimodal data streams to address some of those challenges. I will showcase how we integrate behavior models into music melodies and social robots for personalized intervention.

Speaker Bio: Afsaneh Doryab is an Assistant Professor of Computer Science and Systems Engineering at the University of Virginia. Prior to joining UVA, she was a Systems Scientist at Carnegie Mellon University’s School of Computer Science. Her research lies at the intersection of Ubiquitous Computing, Artificial Intelligence, Human-Computer Interaction, and Digital Health. Dr. Doryab’s work revolves around creating computational models of human behavior using data streams collected through mobile, wearable, and embedded sensors. She applies her research to various domains, including health, social, and economic sectors. Her work has received funding from the National Science Foundation and the National Institutes of Health and has been published in top-tier conferences and journals such as IMWUT, CHI, CSCW, and JMIR.

SPLICE: Securing the lifecycle of Smart Homes

Guest Speaker: Dr. David Kotz, the Provost, and Pat and John Rosenwald Professor in the Department of Computer Science at Dartmouth College

Moderated by Dr. Farshid Alizadeh-Shabdiz  , Associate Professor of the Professional Practice, Boston University Metropolitan College

Friday, November 17 at 2:30 PM EST

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Speaker Bio: David Kotz is the Provost, and the Pat and John Rosenwald Professor in the Department of Computer Science, at Dartmouth College. He previously served as Associate Dean of the Faculty for the Sciences, as a Core Director at the Center for Technology and Behavioral Health, and as the Executive Director of the Institute for Security Technology Studies. His current research involves security and privacy in smart homes, and wireless networks. He has published over 250 refereed papers, obtained $89m in grant funding, given over 200 invited lectures, and mentored over 100 research students and postdocs. He is an ACM Fellow, an IEEE Fellow, a 2008 Fulbright Fellow to India, a 2019 Visiting Professor at ETH Zürich, and an elected member of Phi Beta Kappa. He received his AB in Computer Science and Physics from Dartmouth in 1986, and his PhD in Computer Science from Duke University in 1991.

 

Machine Learning and the Data Center: A Dangerous Dead End 

Guest Speaker: Dr. Nicholas D. Lane, Professor of Computer Science and Technology at the University of Cambridge and a Fellow of St. John’s College

Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science, Boston University Metropolitan College

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Foresight: A Game-Theory Hybrid Algorithm with Reinforcement-Learning for Predictive Analytics

September 29, 2023

Guest Speaker: Dr. Shahram Rahimi, Gloria & Douglas Marchant Endowed Chair, Professor and Head of the Department of Computer Science and Engineering at Mississippi State University

Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science, Boston University Metropolitan College

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Multimodal Machine Learning and Human Centered Computing for Health and Wellbeing

April 21, 2023

Guest Speaker: Dr. Akane Sano, Assistant Professor of Electrical and Computer Engineering, Rice University

Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science, Boston University Metropolitan College

Abstract: What if we can design data driven + human centered personalized feedback loop systems for patients, clinical stakeholders, and healthy people for processing and modeling multimodal clinical and moment-to-moment data and managing and improving health?

First, I will introduce potential and challenges in designing such a system by combining multimodal measurements such as electric health records, fMRI, and clinical assessment with field measurements via mobile and remote sensing.

Second, I will introduce a series of studies, algorithms, and systems we have developed for addressing these issues and measuring, predicting, and supporting personalized health and wellbeing.

More specifically, I will talk about (1) leveraging unlabeled data to design robust but interpretable models, (2) approaches that positively transfer knowledge from multiple modalities to fewer modalities in model deployment in the real world, and (3) balancing bias and performance in health prediction machine learning models and collecting diverse data samples in mobile health systems.

I will also discuss learned lessons and potential future directions in health and wellbeing research.

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Data-Efficient Deep Learning using Physics-Informed Neural Networks

Guest Speaker: Dr. Maziar Raissi, Assistant Professor of Applied Mathematics, University of Colorado Boulder
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science, Boston University Metropolitan College

Mar 24, 2023

Abstract: A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviours expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions:

(1) designing data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and

(2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations.

Guest Speaker Bio: I am currently an Assistant Professor of Applied Mathematics at the University of Colorado Boulder (CU Boulder). I received my Ph.D. in Applied Mathematics & Statistics, and Scientific Computations from University of Maryland College Park. I then moved to Brown University to carry out my postdoctoral research in the Division of Applied Mathematics. I then worked at NVIDIA in Silicon Valley for a little more than one year as a Senior Software Engineer before moving to Boulder. My expertise lies at the intersection of Probabilistic Machine Learning, Deep Learning, and Data Driven Scientific Computing. I have been actively involved in the design of learning machines that leverage the underlying physical laws and/or governing equations to extract patterns from high-dimensional data generated from experiments.

Systems Research Towards Solving Societal Problems
February 27, 2023

Abstract: Much of systems and networking research focuses on improving performance, often defined in terms of throughput, latency, and robustness. Our lab have been focusing on systems research that also has societal impact. I will describe two such research threads: one on sustainable NLP and the other on accessibility

In the first part of the talk, I will describe systems optimizations we have developed that significantly reduce the compute, memory, and energy requirement of large NLP models. The optimizations we developed can be applied broadly and results in over 10x reduction in energy. Beyond optimizations, we have been working on accurate energy prediction of large NLP models. Existing energy prediction approaches are not accurate, making it difficult for developers and practitioners to reason about their models in terms of energy. I will describe our work on developing an accurate and interpretable energy model for NLP applications.

In the second part of the talk, I will describe our work on improving the accessibility of smartphone applications for users with disabilities. Blind users face several challenges when interacting with applications starting from fast-draining battery to having to use difficult touchscreen gestures. I will describe our work that builds on virtualization techniques to make smartphone interactions significantly more accessible.

Speaker Bio: Aruna Balasubramanian is an Associate Professor at Stony Brook University. She received her Ph.D from the University of Massachusetts Amherst, where her dissertation won the UMass outstanding dissertation award and was the Sigcomm dissertation award runner up. She works in the area of networked systems. Her current work consists of three threads: (1) significantly improving Quality of Experience of Internet applications, and (2) improving the usability, accessibility, and privacy of mobile systems, and (3) sustainable NLP.

She is the recipient of the SIGMobile Rockstar award, a Ubicomp best paper award, a Computing Innovation Fellowship, a VMWare Early Career award, several Google research awards, and the Applied Networking Research Prize. She is passionate about improving the diversity in Computer Science and leads the diversity committee in the department, is the faculty advisor for the WiCS and WPhD groups at Stony Brook, and is an active member of the N2Women group.

MaRz: Machine Learning on the Fly
January 9, 2023

Abstract: This research concerns machine learning on the fly, for problems where learning data changes continually, or where waiting for the system to learn is impractical. The principle behind MaRz (Machine Learning in Real Time by Fuzzification) is a fuzzy interpretation of data. This is a quantitative expression of the fact that each datum need not be taken literally. For example, a particular student with SAT score 1300 and college GPA of 3.5, among hundreds of thousands, can be just as appropriately thought of as having approximately 1300 and 3.5 scores respectively. MaRz expresses this via fuzzy values centered on traditional “crisp” values. We show results on increasingly complex data sets and discuss the technical issues involved.

Speaker Bio: Eric Braude has a Ph.D. in mathematics from Columbia University and master’s degree in computer science. He taught at CUNY and Penn State, followed by 12 years in government and industry as a software engineer, scientist, and manager. He is an associate professor of computer science at Boston University’s Metropolitan College. Eric has written, cowritten, or edited six books. His research concerns machine learning and program design.

Securing the Next Billion Consumer Devices on the Edge
December 9, 2022

Guest Speaker: Dr. Hamed Haddadi, Reader in Human-Centered Systems, Department of Computing, Imperial College London
Moderated by: Dr. Reza Rawassizadeh, Associate Professor of Computer Science

Abstract: In the next 5 years, I aim to address a major challenge in the adoption of user-centered privacy-enhancing technologies: Can we leverage novel architectures to provide private, trusted, personalized, and dynamically- configurable models on consumer devices to cater for heterogenous environments and user requirements? Importantly, such properties must provide assurances for the data integrity and model authenticity/trustworthiness, while respecting the privacy of the individuals taking part in training and improving such models , in particular when dealing with sensitive models and data from the consumer Internet of Things (IoT) devices. I will discuss some of the use cases in the space of IoT, edge computing, and browsers.

Speaker Bio: Hamed is a Reader in Human-Centered Systems at the Department of Computing at Imperial College London. He also serves as a Security Science Fellow of the Institute for Security Science and Technology. In his industrial role, he is the Chief Scientist at Brave Software where he works on developing privacy-preserving analytics protocols. He is interested in User-Centered Systems, IoT, Applied Machine Learning, and Data Security & Privacy. He enjoys designing and building systems that enable better use of our digital footprint, while respecting users’ privacy.

Dual-purpose Interaction: Designing interactive systems interwoven with what people already do
November 15, 2022
Guest Speaker: Dr. Koji Yatani, Associate Professor, School of Engineering, University of Tokyo
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science

Abstract: Interweaving technology into our daily life is a dream of ubiquitous computing. Such integration not only achieves indistinguishability of technology, but also creates additional values and benefits in existing user interaction. In this talk, I am sharing our recent work that adds new benefits to existing user interaction and behavior. I present three projects that exploit existing user interaction in usable security and interactive machine teaching. I also share our envision on how HCI research can enrich people’s life through interface design with multiple purposes.

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The Internet of Materials: Rethinking the future of computing
October 3,2022
Guest Speaker: Dr. Gregory D. Abowd, Dean of the College of Engineering, Northeastern University
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science

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Abowd is a Fellow of the ACM and an elected member of the ACM SIGCHI Academy. He was a 2009 recipient of the ACM Eugene Lawler Humanitarian Contributions within Computer Science and Informatics. He earned his Bachelor of Science in Honors Mathematics (summa cum laude) from the University of Notre Dame in 1986 as well as a Master of Science (1987) and Doctor of Philosophy (1991) in Computation from the University of Oxford, where he attended as a Rhodes Scholar.

Ultra-low-power Acoustic Perception in IoT
April 8, 2022
Guest Speaker: Dr. Nirupam Roy, Assistant Professor in Computer Science, University of Maryland, College Park
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science

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Speaker Bio: Nirupam Roy is an Assistant Professor in Computer Science at the University of Maryland, College Park (UMD). He received his Ph.D. from the University of Illinois, Urbana-Champaign (UIUC) in 2018. His research interests are in wireless networking, mobile computing, and embedded systems with applications to IoT, cyber-physical systems, and security. His doctoral thesis was selected for the 2019 CSL Ph.D. thesis award at UIUC. Prof. Roy is the recipient of the Valkenburg graduate research award, the Lalit Bahl fellowship, and the outstanding thesis awards from both his Bachelor’s and Master’s institutes. His research received the MobiSys best paper award and was selected for the ACM SIGMOBILE research highlights. Many of his research projects have been featured in news media such as the MIT Technology Review, The Telegraph, and The Huffington Post.

Dense and Cyclic Gray Codes
March 3, 2022
Guest Speaker: Dr. Thomas H. Cormen, Emeritus Professor, Dartmouth College
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science

Abstract: The binary reflected Gray code, patented by Frank Gray in 1953, is a permutation of the sequence ⟨0, 1, …, n-1⟩, where n is a power of 2, with the property that the binary representation of each number in the sequence differs from that of the preceding number in exactly one bit. The binary reflected Gray code is also cyclic, in that the last and first numbers also differ in only one bit.

What if n is not a power of 2? How can we create a dense Gray code: a permutation of ⟨0, 1, …, n-1⟩ with the Gray-code property for any integer n > 1? The answer turns out to be surprisingly simple, but not obvious at first why it should work. When can we create a dense Gray code with the cyclic Gray-code property, and how do we do it? The answer here is a bit more obvious—once you have already seen it.

Time-permitting, I will also touch on Gray codes for radices other than 2 and for mixed radices. All results are joint work with my senior thesis students, Jessica Fan ’17 and Devina Kumar ’18.

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Integrating Interactive Devices with the User’s Body
February 1, 2022
Guest Speaker: Pedro Lopes, Assistant Professor in Computer Science, University of Chicago
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science

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Speaker Bio: Pedro Lopes is an Assistant Professor in Computer Science at the University of Chicago. Pedro focuses on integrating computer interfaces with the human body—exploring the interface paradigm that supersedes wearable computing. Some of these new integrated-devices include: muscle stimulation wearable that allows users to manipulate tools they have never seen before or that accelerate their reaction time, or a device that leverages the sense of smell to create an illusion of temperature. Pedro’s work has received a number of academic awards, such as four ACM CHI/UIST Best Papers. It also captured the interest of the media, such as New York Times or Wired and was exhibited at Ars Electronica & World Economic Forum. (More: https://lab.plopes.org)

Developing a Framework for Natural Language Processing of Clinical Documents
January 25, 2022
Guest Speaker: Dr. Frank Meng, Assistant Professor of Medicine in General Internal Medicine, Boston University School of Medicine
Moderated by Dr. Guanglan Zhang, Associate Professor of Computer Science

Abstract: The capturing of increasingly more and more patient data by healthcare systems has enabled much innovative advancements in medical science and patient care through the application of artificial intelligence (AI) and other data-driven technologies. However, much useful information continues to be locked within free text documents, limiting the usefulness of the data to the elements that are already being captured in structured format. In this talk, I will present use cases for implementing natural language processing (NLP) systems that automate the extraction of information from clinical documents with the goal of illustrating the unique challenges of working within the healthcare domain. In addition, I will also provide an overview of the Department of Veterans Affairs (VA) Healthcare System and the VA’s large-scale patient data repository.

Speaker Bio: Frank Meng, PhD is an Assistant Professor of Medicine in General Internal Medicine at the Boston University School of Medicine and the Associate Director of Clinical Informatics at the VA Boston Healthcare System. Before entering academia, Dr. Meng worked as a software engineer and a project leader at companies like IBM and The Aerospace Corporation. He began working in the medical field in 2013 when he first taught a course called “Introduction to medical informatics seminar” at UCLA. Since then he has published papers and given lectures, but his technological background is apparent in his writing, which includes big data and informatics as they related to the medical field.

How to Make Your Own StatQuest!!!
December 6, 2021
Guest Speaker: Dr. Josh Starmer, StatQuest
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science
Abstract: Hi! I’m Josh Starmer and I make YouTube videos that clearly explain Data Science, Statistics and Machine Learning Topics. In this talk I’ll describe the methods I use to research and master new material as well as the tips, tricks and design principles that I use to create presentations that allow the audience to focus on the main ideas.

Speaker Bio: Josh Starmer is the person behind the popular YouTube channel, “StatQuest with Josh Starmer.” Since 2016, Josh has used an innovative and unique visual style to clearly explain Statistics, Data Science and Machine Learning concepts and algorithms to curious people all over the world. Rather than dumb down the material, Josh brings people up with simple examples worked through, step-by-step, using pictures to make sure every main idea is easy to understand and remember. Josh is called the “Patron Saint of Silicon Valley” because people binge watch StatQuest videos before job interviews, the “Bill Nye of Statistics” by making the topic fun and exciting and the “Bob Ross of Data” by cutting through the hype and helping people relax with silly songs.

Extrapolating the Research Timeline of HCI: Cyber Meets Physical
November 17, 2021
Speaker:Dr. Max Mühlhäuser, Professor, University of Darmstadt
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science

Abstract:We witness the era of digital transformation: ever more areas of our business and everyday lives are pervaded by information and communication technologies. However, the majority of *interactions* between humans and this “new digital world” still happens through traditional computer-based means. This implies interaction via screens, which by their very nature *separate* the physical world—and thus people—from the digital world.

In order to overcome this barrier, two ‘movements’ must be pursued in HCI research and development: in one direction, the digital world and corresponding interaction must become tightly integrated with the physical world and with the way we perceive and interact with it; in the other direction, physical objects must become ‘naturally’ digitally interactive in the sense of Human Cyber-Physical Interaction (“HCI” revisited).

The talk will first explain the interaction obstacle mentioned in the first paragraph from a historic perspective of HCI. Thereafter, the two required grand directions of HCI R&D will be discussed. A number of recent research advancements will be mentioned as examples, and showcased via video. The talk will end with a (not so serious) sneak preview into the newest interaction modality.

Speaker Bio: Max Mühlhäuser is a full professor at Technical University of Darmstadt and head of the Telecooperation Lab. He holds key positions in several large collaborative research centers and is leading the Doctoral School on Privacy and Trust for Mobile Users. He and his lab members conduct research on Human Computer Interaction, the future Internet, Intelligent Systems, and Cybersecurity including Privacy & Trust. Max founded and managed industrial research centers, and worked as either professor or visiting professor at universities in Germany, the US, Canada, Australia, France, and Austria. He is a member of acatech, the German Academy of the Technical Sciences. He was and is active in numerous conference program committees, as organizer of several annual conferences, and as a member of editorial boards or a Guest Editor for journals such as ACM IMWUT, ACM ToIT, Pervasive Computing, ACM Multimedia, and Pervasive and Mobile Computing.

Vulnerability Relationship between Feature Vector Scale and Convolutional Neural Networks with Adversarial Examples

October 21, 2021
Guest Speaker: Dr. Sang-Woong Lee, Professor, Gachon University
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science

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Speaker Bio: Sang-Woong Lee received the B.S. degree in electronics and computer engineering and the M.S. and Ph.D. degrees in computer science and engineering from Korea University, Seoul, South Korea, in 1996, 2001, and 2006, respectively. From June 2006 to May 2007, he was a Visiting Scholar with the Robotics Institute, Carnegie Mellon University. From September 2007 to February 2017, he was a Professor with the Department of Computer Engineering, Chosun University, Gwangju, South Korea. He is currently a Professor with the Department of Software, Gachon University. His current research interests include face recognition, computational aesthetics, machine learning, and medical imaging analysis.

Passive Sensing Analytics and Mobile Health
September 23, 2021
Speaker: Dr. Nabil Alshurafa, Assistant Professor of Preventive Medicine, Northwestern University
Moderated by Dr. Reza Rawassizadeh, Associate Professor of Computer Science

Abstract: Researchers seek to understand human behaviors in their natural setting so they can design interventions that help manage symptoms, prevent illness, and improve health and wellbeing. Wearables (with embedded sensors) combined with machine learning algorithms are increasingly being adopted to understand human behavior. Through analysis of continuous streams of data provided by these sensors, machine learning and analytics pipelines are used to understand a person’s moment-to-moment behavior, psychological state and environmental contexts in which the behavior occurs. This is allowing researchers to understand the interplay between behavior, physiological states and environmental influences along with individual’s physical and mental health. One important goal is to be able to use these novel methods to detect and predict appropriate times to apply interventions that improve health and well-being.

In this talk I hope to go through an overview of the end-to-end process needed for analyzing passive sensing data and inferring human behavior using wearables (with examples in eating and stress). We will go through an example passive sensing data analytic chain (PASDAC), which enables users to clean, curate, segment, classify and evaluate the signals generated from wearable sensors using signal processing and machine learning. We will also touch on current challenges and opportunities for research at the intersection of passive sensing data analytics and mobile health.

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Dynamic functional brain network analysis using hidden semi-Markov models
Speaker: Dr. Heather Shappell, Assistant Professor of Biostatistics and Data Science, Wake Forest University School of Medicine
Moderated by Kia Teymourian
April 22, 2021

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The Smart Hospital Living Lab
Speaker: Vasillis Kostakos, Professor of Human Computer Interaction, University of Melbourne
Moderated by Professor Reza Rawassizadeh
March 25, 2021

Abstract: In this talk I will present a range of projects we have recently begun under the “Smart Hospital Living Lab” in Melbourne. The work is still ongoing, so I will take this opportunity to received feedback from the audience regarding the direction of our work and our future plans. In this presentation I will describe how we have approached our collaboration with hospitals in Melbourne, and will discuss how we use our expertise in Ubiquitous Computing and user-centered design to solve problems that the hospitals have. The presentation will give a summary of how a living lab approach to research works, and how it can be used to identify problems and solutions by involving multiple stakeholders in the process. The ongoing projects I will present include patient & staff tracking, the smart pillbox, the IV detection project, and handwashing detection.

Speaker Bio: Prof. Vassilis Kostakos is Professor of Human Computer Interaction in the School of Computing and Information Systems at the University of Melbourne. He was previously a Professor at the University of Oulu (Finland). He earned his B.Sc. and Ph.D. in Computer Science from the University of Bath, UK. He is a computer scientist who works in the fields of human-computer interaction and Ubiquitous Computing. His research focuses on how to model user behaviour based on sensor data, and how to develop technologies that better understand and better respond to humans. His work combines user-centered design, machine learning, and mobile & smartphone-based technologies. He has previously led projects on pedestrian mobility at a city-scale, smartphone software for sensing users’ lifestyles, and large-scale analysis of social media. His main expertise is in smartphone sensing, context-aware computing, and behaviour modelling.

Striim, A Next Generation Distributed Streaming Platform

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Guest Speaker Bio: Alok Pareek is Founder and head of engineering at Striim, a Palo Alto based software startup. Alok started his career as a developer in the Oracle kernel development team where he contributed to core redo generation algorithms, point in time media recovery, and high-speed data movement algorithms for ten years. Subsequently, Alok served as CTO and software architect at GoldenGate software, the industry leader in heterogeneous database replication. GoldenGate was acquired by Oracle in 2009. Post-acquisition, Alok led the product strategy for Oracle’s data integration software product portfolio including GoldenGate. Alok also led the engineering and performance teams that collaborated with strategic customers on architecture, and real-world implementations. He holds multiple patents in data management and has presented at numerous academic and industry conferences. Alok holds a graduate degree in Computer Science from Stanford University.

Application of Machine Learning to Construct Investment Portfolios
Speaker: Dr. Eugene Pinsky, Associate Professor of the Practice
Moderated by: Kia Teymourian, Assistant Professor of Computer Science
January 28, 2021

Abstract: One of the primary applications of machine learning methods is to uncover patterns from historical relationships and trends in the data. In unsupervised learning, we apply these methods and let the system itself find structure in the data. The most widely used algorithm in such learning is kmeans clustering that looks for patterns in data and partitions the data points into k groups (“clusters”). The key to using such a method is the existence of similarity (distance) measure with resulting clusters containing “close” points. With clustering, we discover patterns in data and classify data according to cluster membership. This provides a way to describe even the large data sets in much simpler terms.

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The proposed method can be used in many applications where we need to analyze and visualize evolution of large multi-dimensional data patterns over time.

Delete: The Forgotten Operator
Speaker: Dr. Mathos Athanassoulis, Assistant Professor
Moderated by: Kia Teymourian, Assistant Professor of Computer Science
December 11, 2020

Abstract: When building new data systems, and their underlying storage managers, typically we optimize performance (access time or throughput), however, as data management tasks are increasingly moving to the cloud and refer to massive data sets a set of other metrics are being considered, including the cost (space amplification, monetary, energy) and the deployment time. In this work we focus on deletion, and we discuss that the typical deletion-through-invalidation approach comes at a cost with respect to space amplification and potentially of the privacy of the users. We focus on the concept of out-of-place deletes which are frequently employed by storage engines that use the log-structure merge (LSM) tree as their backend.

LSM-Trees support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as a second-class citizen. A delete inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion. Further, LSM engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written. In this talk we present how LSM-Trees work and present a family of new techniques that allow for timely deletion without compromising privacy or space amplification, leading in small read performance benefits and near-zero increase in the amortized write amplification.

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