Eduardo Coelho De Lima
Lecturer
Department of Computer Science
Golisano College of Computing and Information Sciences
585-475-6133
Office Location
Eduardo Coelho De Lima
Lecturer
Department of Computer Science
Golisano College of Computing and Information Sciences
Select Scholarship
Published Conference Proceedings
Assogba, Kevin, et al. "PredictDDL: Reusable Workload Performance Prediction for Distributed Deep Learning." Proceedings of the 2023 IEEE International Conference on Cluster Computing (CLUSTER). Ed. IEEE. Santa Fe, NM, USA, NM: IEEE, 2023. Print.
Alshangiti, Moayad, et al. "Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviews." Proceedings of the ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Ed. Abhik Roychoudhury, Cristian Cadar, and Miryung Kim. Singapore, Singapore: Association for Computing Machinery, 2022. Web.
Lad, Vivek Govind, Eduardo Lima, and Xumin Liu. "HSG-CDM: A Heterogeneous Service Graph Contextual Deep Model for Web Service Classification." Proceedings of the 2022 IEEE International Conference on Services Computing (SCC). Ed. Carl K Chang. Barcelona, Spain: IEEE, 2022. Web.
Lima, Eduardo and Xumin Liu. "A Structure Alignment Deep Graph Model for Mashup Recommendation." Proceedings of the ICSOC 2021: Service-Oriented Computing. Ed. Hakim Hacid, et al. Virtual Event, Virtual Event: Springer, Cham, 2021. Web.
Currently Teaching
CSCI-141
Computer Science I
4 Credits
This course serves as an introduction to computational thinking using a problem-centered approach. Specific topics covered include: expression of algorithms in pseudo code and a programming language; functional and imperative programming techniques; control structures; problem solving using recursion; basic searching and sorting; elementary data structures such as lists, trees, and graphs; and correctness, testing and debugging. Assignments (both in class and for homework) requiring a pseudo code solution and an implementation are an integral part of the course. An end-of-term project is also required.
CSCI-335
Machine Learning
3 Credits
An introduction to both foundational and modern machine learning theories and algorithms, and their application in classification and regression. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), Bayesian decision theory, parametric and non-parameteric classification models (e.g., SVMs and Nearest Neighbor models) and neural network models (e.g. Convolutional, Recurrent, and Deep Neural Networks). Programming assignments are required.
CSCI-605
Advanced Object-Oriented Programming Concepts
3 Credits
This course focuses on identifying advanced object-oriented programming concepts and implementing them in the context of specific problems. This course covers advanced concepts such as event-driven programming, design patterns, distributed and concurrent programming, and the use, design and implementation of applications. Assignments (both in class and as homework) requiring a solution to a problem and an implementation in code are an integral part of the course. Note: This course serves as a bridge course for graduate students and cannot be taken by undergraduate students without permission from the CS Undergraduate Program Coordinator.
CSCI-635
Introduction to Machine Learning
3 Credits
This course offers an introduction to supervised machine learning theories and algorithms, and their application to classification and regression tasks. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), neural models (e.g. Convolutional Neural Networks, Recurrent Neural Networks), probabilistic graphical models (e.g. Bayesian networks, Markov models), and reinforcement learning. Programming assignments are required.