Posters

The Poster presentation and reception will be on Tuesday, November 12, starting at 5:30 PM
The maximum area available for each poster is 3 feet wide and 4 feet tall.
The following posters have been accepted for presentation at CASCON 2024.

Results of the CASCON 2024 Best Poster Competition:

• Best Poster:
  • Db2une: Tuning Under Pressure via Deep Learning
    Alexander Bianchi, Andrew Chai, Vincent Corvinelli, Parke Godfrey, Jarek Szlichta and Calisto Zuzarte
• Runner Ups:
  • A Functional Event-Driven Framework for Simplified Concurrent Applications
    Christopher Schankula, Christopher Anand and Spencer Smith
  • GreenStream: Enabling Sustainable LLM Inference in Stream Processing
    Md. Monzurul Amin Ifath and Israat Haque

The following posters have been accepted for presentation at CASCON 2024:

• 2JITsu: A Comparative Analysis of JAX and Pytorch across Modern Machine Learning Workloads
  Ayrton Chilibeck, Danila Seliayeu, Jose Nelson Amaral and Andy Patterson
• A blockchain based hybrid access control for industrial internet of things using blockchain
  Muhammad Shahzad Sarfraz and Muhammad Usman
• A Functional Event-Driven Framework for Simplified Concurrent Applications
  Christopher Schankula, Christopher Anand and Spencer Smith
• A Multi-Table Approach to Floating-Point Function Approximation
  Lucas Dutton, Christopher K. Anand, Robert Enenkel and Silvia Melitta Müller
• A multi-tier federated learning approach to pre-impact fall detection using wearable devices
  Seyed Alireza Rahimi Azghadi, Hung Truong Thanh Nguyen, René Richard, Hélène Fournier, Francis Palma and Hung Cao
• A Study on future emerging technologies and trend detection algorithms using patent big data and AI
  Minki Kim
• Addressing Noise in Multiple Instance Learning
  Ahmed Al Dallal, James Miller and Shaikh Quader
• AI Assistants for Incident Lifecycle in a Microservice Environment: An Empirical Review
  Dahlia Ziqi Zhou and Marios Fokaefs
• AI-Assisted Pipeline for Cytometry of Reaction Rate Constant
  Shai Ginsburg, Patil Korkeen, Jessica Latimer, Vasilij Koshkin, Vadim Elisseev and Sergey Krylov
• An Energy-Efficient Long-Range IoT-Based Water Monitoring System: Challenges and Use Cases
  Seyed Alireza Rahimi Azghadi, Kamyab Aghajamali, Francis Palma and Hung Cao
• An Improved MicroJIT for Templated Compilation in OpenJ9
  Harpreet Kaur, Scott Young, Kenneth Kent and Marius Pirvu
• Automated Configuration Parameter Tuning in Distributed Messaging Systems
  Emmanuel Etti, Md. Monzurul Amin Ifath and Israat Haque
• Automated Detection of Anomalous Patterns in Time Series Logs Using Silhouette Scores
  Wejdene Haouari and Marios Fokaefs
• Bringing Profile Guided Binary Layout Optimization to the IBM AIX Platform
  Dhanrajbir Singh Hira and J. Nelson Amaral
• Cloud-Boosted JIT: Towards Enhancing Compilation Decision-Making in Containerized Environments
  Ryan Liu, Shreya Shinde, Abhith Krishna, Ladan Tahvildari, Mark Stoodley, Vijay Sundaresan and Marius Pirvu
• Db2une: Tuning Under Pressure via Deep Learning
  Alexander Bianchi, Andrew Chai, Vincent Corvinelli, Parke Godfrey, Jarek Szlichta and Calisto Zuzarte
• DMBench: Load Testing and Benchmarking Tool for Data Migration
  Fares Hamouda, Marios Fokaefs and Dariusz Jania
• DMML: A Machine-learning Performance Model for Data Migration
  Hasti Ghaneshirazi, Marios Fokaefs, Fares Hamouda, Wejdene Haouari and Dariusz Jania
• Edge-based Application for On-device COVID-19 Test interpretation
  Shreya Vaghani and Marios Fokaeks
• Enhancing Epidemiological Analysis through Data Provenance
  Sarina Hamedani and Michalis Famelis
• Evaluating retrieval-augmented generation (RAG) results
  Sarah Packowski, Jenifer Schlotfeldt and Inge Halilovic
• Exploring Workforce Needs in Korea's Secondary Battery Industry: Insights and Implications for Talent Development
  Junwoo Yu
• FADE: Focused and Attention-Based Detector of Errors
  Omar Al-Shamali, James Miller and Shaikh Quader
• GreenStream: Enabling Sustainable LLM Inference in Stream Processing
  Md. Monzurul Amin Ifath and Israat Haque
• Identifying and Classifying the Centre of Interests of Non-Verbal Autistic Children in Cartoon Videos
  Roya Moeini and Sylvie Ratté
• Inspiring AI-Infused Innovations in Adoptium AQAvit and Openj9
  Gias Uddin, Afif Mamun, Longyu Zhang and Lan Xia
• Mixture of Shared Experts
  Danila Seliayeu, Quinn Pham, Prasanth Chatarasi and José Nelson Amaral
• Modelling Performance and Energy Efficiency of AI and HPC Workloads in Heterogeneous Environments
  Vadim Elisseev, Edward Taylor, Mark Birmingham and James Sexton
• Online Classification with Reordering Data Migration Framework
  Zhongxin Hu, Kaiyu Li, Xingjian Mao, Jingfeng Pan, Yunfei Peng, Aijun An, Xiaohui Yu and Dariusz Jania
• On-premise z/OS to IBM Cloud: A reference architecture
  Sanjay Ghanathey
• OOPredictor: Predicting Object-Oriented Accesses using Static Analysis
  Hassan Arafat, David Bremner, Kenneth Kent and Julian Wang
• Optimizing knowledge base content for retrieval-augmented generation (RAG)
  Inge Halilovic, Sarah Packowski and Jenifer Schlotfeldt
• Performance Assessment of AI Inference in Edge Devices
  Ghazal Sobhani, Tushar Sharma and Israat Haque
• Privacy and Security in Distributed Graph Analytics
  Bin Guo and Emil Sekerinski
• Proximity-DRL: A GPU Job Allocator Using Deep Reinforcement Learning
  Junjie Deng, Aijun An, Hajer Ayadi, Yiming Shao, Hossein Pourmodheji, Hao Zhou and Michael Feiman
• Rapid prototyping of natural language interfaces
  Sarah Packowski, Ashley Zhao, Kelly Xiang and Sara Elsharawy
• RISC-V Analyzer: verify assembly code compliance to register and procedure calling conventions.
  Rajan Maghera, Nathan Ulmer and J. Nelson Amaral
• Syntactic and Semantic Analysis of APIs to Assess and Compare their Linguistic Design Quality
  Krishno Dey, Hung Cao and Francis Palma
• Towards Energy-Efficient CUDA Kernels: A Predictive
  Saurabhsingh Rajput, Alexander Brandt and Tushar Sharma
• Towards Rapid Design of Compartmental Models Modeling Approach
  Zahra Fiyouzisabah, Jessie Galasso, Marios-Eleftherios Fokaefs and Michalis Famelis
• Triton vs. Halide: Exploring Coupled and Decoupled Machine-Learning Kernel Languages
  Quinn Pham, Danila Seliayeu, Prasanth Chatarasi and José Nelson Amaral