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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
- Db2une: Tuning Under Pressure via Deep Learning
- 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
- A Functional Event-Driven Framework for Simplified Concurrent Applications
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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