Computer Science Colloquia & Seminars are held each semester and sponsored by the Computer Science department. Faculty invite speakers from all areas of computer science, and the talks are open to all members of the RPI community.
Computer Science Colloquia :Nikos Vasilakis
Scaling out Shell Programs, Automatically
Full abstract and bio can be viewed with the link provided
Conservative Safety Monitoring of Stochastic Dynamical Systems
Ivan Ruchkin
from University of Florida
Autonomous cyber-physical and robotic systems are increasingly deployed in complex and safety-critical environments.
Human-ML Collaboration and the Role of Explainable ML
Kasun Amarasinghe
from Carnegie Mellon University
Machine Learning (ML) systems that inform real-world decisions are typically parts of larger sociotechnical systems, involve multiple human stakeholders, and rely on human-ML collaboration at different stages of the development and deployment pipelin
Signal recovery in the high-noise, high-dimensional regime
William Leeb
from University of Minnesota, Twin Cities
This talk will describe recent work on mathematical methods for signal recovery in high noise.
Data-Driven Decision Making in Adversarial Environments: Challenges and Applications
Thanh Nguyen
from University of Oregon
Many real-world problems require the creation of robust AI models that include both learning and planning for an agent (or a team of agents) in interaction with adversaries in a multi-agent environment.
Towards Behavior-Informed Machine Learning
Chien-Ju Ho
from Washington University
Machine learning (ML) has seamlessly integrated into various facets of humans' everyday lives, largely drawing from human data for its training.
Intelligent Software in the Era of Deep Learning
Yuke Wang
from University of California, Santa Barbara
With the end of Moore's Law and the rise of compute- and data-intensive deep-learning (DL) applications, the focus on arduous new processor design has shifted towards a more effective and agile approach -- Intelligent Software to maximize the perform
Program Analysis: A Journey through Traditional Methods, Emerging Data-Driven Approaches, and Machine Learning Applications
Ke Wang
from Stanford University
Program analysis, the process of analyzing source code to derive its properties, has been a prominent research area for decades.
Quantum Computing Now: A Tensor Approach
Xiao-Yang (Yanglet) Liu
from Rensselaer Polytechnic Institute
Quantum algorithms claim to outperform classical algorithms by harnessing computational and communication properties unique to quantum systems, such as superposition and entanglement, e.g., Shor's factoring algorithm and Grover's algorithm.
Bridging the Gap Between Theory and Practice: Solving Intractable Problems in a Multi-Agent Machine Learning World
Emmanouil-Vasileios (Manolis) Vlatakis Gkaragkounis
from UC Berkeley
Traditional computing sciences have made significant advances with tools like Complexity and Worst-Case Analysis.
Statistical-Computational Tradeoffs in Random Optimization Problems
Eren Kizildag
from Columbia University
Optimization problems with random objective functions are central in computer science, probability, and modern data science. Despite their ubiquity, finding efficient algorithms for solving these problems remains a major challenge.
Security of Quantum Computing Systems
Jakob Szefer
from Yale University
Quantum computer device research continues to advance rapidly to improve size and fidelity of the quantum computers.
Co-Design of Quantum Software and Hardware: The Pulse-Level Paradigm Shift
Zhiding Liang
from University of Notre Dame
In this talk, I will provide an overview of my contributions to quantum computing, specifically focusing on hardware software co-design for quantum computing by diving into the pulse level.
Types and Metaprogramming for Correct, Safe, and Performant Software Systems
Guannan Wei
from Purdue University
In this talk, I will present an overview of my research, which provides novel directions for building correct, safe, and performant software systems through the use of programming languages and compiler techniques.
ECSE/CS Joint Seminar: Intelligent Cross-Stack Co-Design of Quantum Computer Systems
Hanrui Wang, MIT Ph.D. candidate
from MIT
Quantum Computing has the potential to solve classically intractable problems with greater speed and efficiency, and recent several years have witnessed exciting advancements in this domain.
Computer Science Poster Session (2023)
Computer Science Graduate Students
Michael Lenyszyn
Advisor: Konstantin Kuzmin
Title: Author Disambiguation
Sean Patch
Advisor: Radoslav Ivanov
Title: Tree Identification and Segmentation
Computer Science Seminar - Ge Wang: AI Foundation Models in Medicine
The advent of AI foundation models promises transformative advancements in medical applications, an area central to our research.
