August 26, 2024: Shriram Krishnamurthi, Brown University.
Title: Building Programming Media for Novices
Abstract: Many programming media take a maximalist perspective: the more language features, tools, IDE buttons, the better. Building effective media for novices demands attention to different details. For nearly three decades I've been involved in building variants of conventional programming media that create a pleasant experience for beginners. This has involved attention to everything from the surface language to presentation of output to error messages to the run-time system. This talk will describe some of these experiences and lessons, focusing on Racket, WeScheme, and Pyret.
Bio Sketch: Shriram is the Vice President for Programming Languages at Brown University in Providence, RI, USA. He's not, really, but that's what it says on his business card. At heart, he's a person of ill-repute: a Schemer, Racketeer, and Pyreteer. He believes tropical fruit are superior to all other kinds. He is terrified of success, because he may be forced to buy a suit. He is known to interrogate his audiences to ensure they're paying attention. So, be alert. You can read email later.
Format: Zoom presentation. Link available via email announcement.
Jointly presented with New Mexico Tech: https://www.cs.nmt.edu/~jeffery/courses/585/
September 9, 2024: Terence Soule, University of Idaho.
Title: Evolutionary Computation: Mechanisms and Applications
Abstract: Join us for a seminar on evolutionary computation, a fascinating field that mimics natural selection to solve complex problems. This presentation will delve into the core principles of evolutionary algorithms and their diverse applications. We’ll explore how these algorithms are used for optimization, symbolic regression, and evolving intelligent opponents in video games.
Bio Sketch: https://webpages.uidaho.edu/tsoule/index.html#
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.
September 16, 2024: Hasan Jamil, University of Idaho.
Title: Smart Science Needs Linked Open Data with a Dash of Large Language Models and Extended Relations
Abstract: Quality scientific inquiries depend on access to data distributed over the entire globe. Linked open data (LOD) and FAIRness play major roles in ensuring access to data that scientists need to answer interesting questions. However, a data model and a query language to compute responses to complex scientific inquiries remain outstanding. As the recent emergence of large language models (LLM) reshape how we interact with machines, an intriguing prospect of posing scientific inquiries to smart machines suddenly appears realizable in which a natural language ChatBot is empowered with a LOD knowledgebase as its data source. In this paper, we introduce a model for an LLM interpreter, called ProAb, that aims to answer natural language scientific queries using a structured query language called Needle in which the LOD is viewed as a set of tables. We discuss the contours of ProAb, present its preliminary and experimental design, and highlight its salient features using an illustrative example. It should be apparent that a full automation of ProAb is feasible with further research.
Bio Sketch: https://sites.google.com/view/hasans-home-page/home
Format: Zoom ink available via email announcement. This is a UI presentation, please use the UI Zoom link.
September 23, 2024: Alex Maas, University of Idaho.
Title: Why everyone should be an economist
Abstract:
Bio Sketch: Dr. Alexander Maas is an Applied Economist whose research primarily focuses on environmental and resource economics, with particular emphasis on the intersection of human behavior and natural resource management. His work often involves complex geospatial and panel datasets to analyze environmental policy impacts, water resource management, and land use.
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.
September 30, 2024: Hunter Clark, University of Idaho.
Title: Mixing up Gemini and AST in ExplainS for Authentic SQL Tutoring
Abstract: Mastering SQL is a key data science competence. While most large language models are able to translate natural language queries to SQL, their ability to tutor learners and authentically assess student assignments are at the least fragile. In this paper, we introduce ExplainS as an experimental prototype. In this web-based system, we augment Gemini with abstract syntax tree (AST) to enhance Gemini's semantic analysis power to be able to assist and tutor students better. This edition of ExplainS provides a collection of exercises with varying difficulty levels, covering core SQL concepts. Users interact with a dynamic schema display, and their queries are validated against carefully crafted solutions. To provide context-aware personalized feedback, ExplainS leverages Gemini and the SQLglot library to analyze query AST differences between user queries and correct solutions, pinpointing the root cause of errors. This emerging research is part of a wider Data Science effort, and in this paper, we only focus on the meaningful feedback generation component of the ExplainS system.
Bio Sketch: Hunter is currently a graduate student at the University of Idaho pursuing an M.S. in Computer Science degree. Along with pursuing a graduate degree he works full time as a data engineer building pipelines and assisting data science teams with feature generation. He graduated from Boise State University with a degree in Mathematics. Prior to this he served 6 years in the United States Navy as a rescue swimmer and a tactical helicopter aircrewman. This presentation if based on his research article to be published in IEEE International Conference on Teaching, Assessment, and Learning for Engineering to be held in Bangaluru, India on December 9-12, 2024.
Format: Zoom ink available via email announcement. This is a UI presentation, please use the UI Zoom link.
October 7, 2024: Suren Byna, The Ohio State University.
Title: Towards Autonomous Data Management for Science
Abstract: Scientific applications have been using decades old interfaces and complex I/O software layers. These layers in the parallel I/O software stack have strict consistency semantics and interdependent performance characteristics that leave the burden of performance tuning on application developers. We are now in the exascale era; however, the storage and I/O software pose familiar problems with performance. With the trends of heterogeneity of computing devices, application landscape with AI and experimental / observational data analyses, and deep and distributed memory and storage hierarchies, we need to rethink the designs of data storage and I/O on HPC systems. In this talk, I will present a few solutions we developed to cope with scalable I/O using existing libraries and then present a vision for future storage and I/O systems aiming automatic data management.
Bio Sketch: Suren Byna is a Professor in the Department of Computer Science and Engineering (CSE) at The Ohio State University. He is also a Visiting Faculty Scientist at Lawrence Berkeley National Lab (LBNL). Prior to joining OSU in 2023, he was a Senior Scientist in the Scientific Data Division at Lawrence Berkeley National Lab (LBNL). He has led or leads several projects in scientific data management, including the ExaIO and ExaHDF5 projects, Proactive Data Containers (PDC), Securing Self-describing Data (S2-D2), etc. More details are available on his homepage.
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.
October 14, 2024: Manu Manrao, VP, Reliable Medical, Tennessee.
Title: From Grad School to the C-Suite: Navigating a Career in IT Leadership
Abstract: In this presentation, Manu Manrao, Vice President of IT at Reliable Medical, will share his career journey from a graduate student in Computer Science to becoming a leader in the IT industry. Through personal insights and professional experiences, Manu will highlight key milestones, challenges, and the strategic decisions that shaped his career path. He will offer practical advice for future IT leaders, focusing on how to leverage both technical and business skills to succeed in today’s rapidly evolving technology landscape. This presentation will also explore opportunities for Computer Science graduates, emphasizing the importance of adaptability, continuous learning, and building strong relationships. Attendees will leave with actionable takeaways for navigating their own career paths in IT leadership and making an impact in the tech industry.
Bio Sketch: Manu Manrao is the Vice President of IT at Reliable Medical, where he leads digital transformation initiatives and oversees critical business operations across IT infrastructure, software development, and data analytics. With over 17 years of experience in IT, Manu has successfully driven high-impact projects, including process automation, cloud adoption, and IT security enhancements. He holds an MS in Computer Science from Wayne State University and an MBA from Vanderbilt University, blending his deep technical expertise with strong business acumen. Manu is passionate about mentoring future IT leaders and helping them bridge the gap between technology and business strategy.
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.
October 21, 2024: Ashwin Ravichandran, NASA.
Title: Advancing Materials Discovery with Machine Learning and Generative AI: Current State and Future Perspectives
Abstract: The advent of machine learning (ML) and generative AI (GenAI) models has created unprecedented opportunities for the discovery and design of novel synthetic and biomaterials. This talk will highlight recent advancements in these technologies, particularly their applications in in-silico design of materials for sustainable energy, therapeutic development, and point-of-care diagnostic technologies. We will also examine how these models are being leveraged to accelerate the synthesis of these materials with tailored properties, significantly reducing the time and cost of material development. For computer science graduate students, this talk will provide a high-level perspective on the integration of ML and GenAI in material sciences. The future potential of generative models to streamline technology development in sustainable energy and healthcare will be explored, offering insights into the potential directions of AI in material science.
Bio Sketch: Ashwin Ravichandran is a Senior Physicist (Physicist-III) at the Computational Materials Group, NASA Ames Research Center-KBR. He has over ten years of experience in molecular simulations and thermodynamic modeling of synthetic materials and biomolecular systems. His research is focused on developing computational methodologies for fast prediction of material properties and designing novel materials in silico, combining physics-based methods and data driven techniques.
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.
October 28, 2024: Jiyin Zhang, University of Idaho.
Title: Integration of Large Language Model Agents and Geoscience Data Analysis Workflow
Abstract: Recent research on large language models (LLMs) demonstrates fruitful applications in addressing data analysis tasks and exceeds the scope of natural language processing. In this presentation, an LLM agent-driven workflow will be introduced in the context of geoscience data analysis. The proposed work features streamlining the utilization of LLMs in processing mineral datasets from Mindat, a famous mineral database, and automated visualization plotting. The proposed workflow provides reliable and scalable LLM-driven data analysis results using prompt engineering and demonstrates the directions of future work.
Bio Sketch: Jiyin Zhang is a Ph.D. student in Computer Science at the University of Idaho, specializing in applying Large Language Models and Knowledge Graphs for geoscience data analysis. He holds a Bachelor’s degree in Geology from the China University of Geosciences.
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.
November 4, 2024: Yakin Rubaiat, University of Idaho.
Title: Online Digital Investigative Journalism using SociaLens
Abstract: Media companies witnessed a significant transformation with the rise of the internet, bigdata, machine learning (ML) and AI. Recent emergence of large language models (LLM) have added another aspect to this transformation. Researchers believe that with the help of these technologies, investigative digital journalism will enter a new era. Using a smart set of data gathering and analysis tools, journalists will be able to create data driven contents and insights in unprecedented ways. In this paper, we introduce a versatile and autonomous investigative journalism tool, called SociaLens, for identifying and extracting query specific data from online sources, responding to probing queries and drawing conclusions entailed by large volumes of data using ML analytics fully autonomously. We envision its use in investigative journalism, law enforcement and social policy planning. The proposed system capitalizes on the integration of ML technology with LLMs and advanced bigdata search techniques. We illustrate the functionality of SociaLens using a focused case study on rape incidents in a developing country and demonstrate that journalists can gain nuanced insights without requiring coding expertise they might lack. SociaLens is designed as a ChatBot that is capable of contextual conversation, find and collect data relevant to queries, initiate ML tasks to respond to queries, generate textual and visual reports, all fully autonomously within the ChatBot environment.
Bio Sketch: Yakin is a PhD student in the Smart Database Lab at the Department of Computer Science. He obtained his BS in Computer Science and Engineering from the Patuakhali Science and Technology University in Bangladesh in 2019. He has worked in the industry since his graduation until Fall 2024. During this time, he has coauthored more than six research articles. This presentation is based on an article he is scheduled to present at the 26th International Conference on Information Integration and Web Intelligence (iiWAS2024) to be held in December 2-4, 2024 in Slovakia.
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.
November 11, 2024: Sajjan G. Siva, University of Memphis.
Title: Machine Learning System Development Life Cycle – State of the Art
Abstract: Machine Learning (ML) has now taken over as the preferred method for building intelligent applications. Correspondingly, since ML systems are predominantly software applications, the software development process has transitioned from the traditional behavior-oriented process (programming) to a data-oriented process. Ideally, the ML system development workflow should follow the Software Development Life Cycle (SDLC) processes to utilize the lessons learned in traditional systems development. However, the data-centric nature of ML systems does not cater completely to this integration. This talk presents the state-of-the-art in Machine Learning System Development Lifecycle (ML SDLC) and brief descriptions of Machine Learning Operations (MLOps). Although the industry has undertaken significant measures to streamline the ML system development and deployment and several new processes are proposed by researchers, there is no accepted standard for the MLSDLC. In addition, with the current emphasis on building trustable ML/AI systems, their security mechanisms and explainability of prediction features have also become important, especially for critical applications. As such, we propose a security and explainability augmented ML system development life cycle.
Bio Sketch: Dr. Sajjan Shiva is currently First Horizon Foundation Distinguished Professor of computer science and the Director of Game Theory and Cyber Security laboratory (https://gtcs.cs.memphis.edu ) at the University of Memphis. He received the B.E (Electrical) degree from Bangalore University and MEE and PhD degrees from Auburn University. He is an IEEE Life Fellow. He served as the founding chairman of the Computer Science Department at the University of Memphis from 2002 to 2015. He has served on the Computer Science faculties of University of Alabama in Huntsville and Alabama A&M University. He also has served as the Software Quality Manager, Technical Project Manager and Senior Software Engineer in industry and has been a consultant to industry and Government since 1975. His current research spans game theory applications to cyber security, cloud security, secure software development, SCADA security, machine learning based intrusion detection and Frameworks for security and privacy assessment of cloud and Internet of Medical things. His research has been supported by NASA, NSF, U.S. Department of Defense and ONR. He has taught courses on computer architecture, software engineering, security testing of systems and software, cyber security and cloud security. He has authored four books (10 editions) on computer architecture, now used in more than 120 universities around the world.
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.
November 18, 2024: Mary Everett, University of Idaho.
Title: Artificial Intelligence Techniques for Precision Agriculture Microclimate Analysis
Abstract: Growing crops more efficiently will necessitate site-specific management to make the most efficient use of farming inputs and outputs. To that end, characterizing microclimates in a farm is very helpful to growers to understand necessary differences in management. However, this process requires a lot of data as well as time-intensive analysis. This talk explores different artificial intelligence techniques applied to characterizing microclimates from sensor data and how to use this information for making decisions. We will explore time series clustering methods and quantitative association rule mining for weather data.
Bio Sketch: Mary Everett received her PhD from the University of Idaho in 2023 and works as a Research Scientist for the Center for Intelligent Industrial Robotics at UI. She primarily researches wireless sensor networks and artificial intelligence for agriculture in manufacturing. She works with raspberry pi embedded systems as well as genetic algorithms and unsupervised learning techniques. She also assists undergraduate students with research projects and mentoring at the Coeur d'Alene campus.
Format: Zoom link available via email announcement. This is a UI presentation, please use the UI Zoom link.