Talks about Science and Research in the Computer Science and Engineering domain. The scientific foundations and methods of computer science and computer engineering.

1. Computer Science and Engineering
Research
Priyadarsan Patra, Professor
Part 1

2. Foundations of Computer Science & Engineering
Prof. P. Patra 2
Research in Computer Science and Engineering
Religion, Art, Culture, the Unknown etc.
Humanities
Social Sciences
Natural
Sciences
Math
Math
Logic
Paradigm
shift
with
omni
data

3. Scientific Method
What is science?
❑ A process that systematically builds and advances knowledge by explaining,
predicting, or altering phenomena/operations in the real (or the artificial!)
world
The Way of ‘Doing Science’
❑ Something happens in the world around you; Observe and learn about it
❑ Form a hypothesis that explains/predicts/alters it
❑ Gather data through experimentation*
❑ Test hypothesis against the collected “hard cases or benchmarks”
❑ If successful, use it as a model and explain more
◼ Meetings, publish, share
 Value of skepticism, criticism, peer review, challenge
◼ Become part of the scientific literature – you made a “dent”
else ‘Rinse and repeat’ (Until no known counterexample disproves it)
◼ New hypotheses, new experiments to retest ideas
 Multiple conditions, times, people -> well-supported hypothesis -> model ->
theory
➢ The Scientific Method is a way of thinking, not a formula
Prof. P. Patra Research in Computer Science and Engineering 3

4. CSE is a Young Science
There is science in Computer Science (CS) although not all of CS is
conventional science
◼ It is the study of computing and computing engines
CS combines science, engineering, and math
◼ Hypothesis formulation, Algorithm Design and Performance Testing
◼ Design and implementation of computer systems
◼ Proofs and analysis of algorithms and information transformation
◼ Computer Science, and Computer Engineering are often clubbed
together to be called CSE: Designing a computing or communication
chip…
CS has been combined with nearly every other field
◼ CSE has found itself at the cornerstone of interdisciplinary research
◼ Nearly endless combinations leading to nearly endless interesting
questions to explore
◼ Has a foundation and transformative role at the interfaces of disciplines
Prof. P. Patra Research in Computer Science and Engineering 4

5. The “Science” in Computer Science
Some think that computer science is not a science in the same sense
as biology or chemistry
◼ Interdisciplinary nature of computer science has made it hard to classify
◼ Discovery vs Invention/innovation
• Researchers in Biologists, physicists, chemists, psychology… are discoverers
• Computer scientists, researchers in nanotech or in process engineering or in
industrial engineering… are inventors
Computer science is the study of computation and the machinery
◼ Involves all aspects of problem solving and machine building, including
 design and analysis of algorithms
 formalization of algorithms as programs
 development of computational devices for executing programs and ‘learning’
 theoretical study of the power and limitations of computing and computer
◼ In short, computer science represents a rigorous approach to
understanding complex phenomena and problem solving!
Prof. P. Patra Research in Computer Science and Engineering 5

6. Scientific Method (in CSE)
The process developed by the scientific community for examining
observations and events is known as the scientific method
Many activities carried out by computer scientists follow the scientific method
◼ e.g., designing and implementing a large database system requires
hypothesizing about its behavior under various conditioning, experimenting to
test those hypotheses, analyzing the results, and possibly redesigning
◼ e.g., debugging a complex program requires forming hypotheses about where an
error might be occurring, experimenting to test those hypotheses, analyzing the
results, and fixing the bugs
◼ Specifying, Architecting, Modeling, Designing, Simulating, Verifying,
Manufacturing, then Validating the resultants (with loopbacks at various stages!)
Prof. P. Patra Research in Computer Science and Engineering 6

7. Computer Science in science
Examples
• Bioinformatics
• Experimental particle physics
• Quantum computing
• DNA computing
• Political and social science
• Protein folding
• Public health informatics
…
Scholars in information technology, software engineering, and
computer science study the technical and computational attributes of
digital technology
Scholars in behavioral, cognitive, and psychological sciences study
individuals’ exposure, use, appropriation, and general behaviors [within
digital technology domains].
Scholars in organizational science, management, and business study
how corporate environments shape, and are shaped by digital technology.
Economists study the large-scale effects of digital technology diffusion and
innovation on organizations, markets, and societies.
Prof. P. Patra Research in Computer Science and Engineering 7

8. Computer Science Themes
since computation encompasses many different types of activities, computer
science research is often difficult to classify
◼ three recurring themes define the discipline
David Reed, ISBN
0-13-046709-X
Prof. P. Patra Research in Computer Science and Engineering 8

9. A Science of the ‘Artificial’
The distinction between computer science and natural sciences like biology,
chemistry, and physics is the type of systems being studied
◼ natural sciences study naturally occurring phenomena and attempt to extract
underlying laws of nature
◼ computer science studies human-made constructs: programs, computers, and
computational modes
Herbert Simon coined the phrase "artificial science" to distinguish computer
science from the natural sciences
In Europe, computer science is commonly called "Informatics"
◼ emphasizes the role of information processing as opposed to machinery
◼ Denning: Science of information processes and their interactions with the world
The term "Algorithmics" has also been proposed
◼ emphasizes the role of algorithms and problem solving
Other related fields study computation from different perspectives
◼ computer engineering focuses on the design and construction of computers
◼ information systems management focuses on business applications
Ack: David Reed, Creighton University
Prof. P. Patra Research in Computer Science and Engineering 9

10. Basic vs. Applied in the World of CSE
Search Engines and Information Retrieval:
Applied: Code employing algorithms, data
structures, and distributed computing
Basic: PageRank algorithms, natural
language processing, and information
retrieval theory.
Machine Learning and Deep Learning:
Applied: image and speech recognition,
recommendation systems, and autonomous
vehicles are among numerous applications.
Basic: neural networks, reinforcement
learning, and transfer learning, pushing the
boundaries of AI understanding.
Cybersecurity – information and systems:
Applied: security protocols and tools to
protect computer systems from cyber threats
Basic: spawned cryptography, network
security, and cybersecurity theory
Blockchain and Cryptocurrency:
Applied: like Bitcoin involve Applied in
distributed ledger technology.
Basic: cryptography, consensus algorithms,
and decentralized systems.
Human-Computer Interaction (HCI):
Applied: user interface & experience
in/with software and hardware products
Basic: cognitive psychology, usability
testing, and user-centered design principles.
Natural Language Processing (NLP):
Applied: Building chatbots, language
translation tools, and sentiment analysis
Basic: fundamental research in syntax,
semantics, and linguistic models.
Quantum Computing:
Applied: applications such as cryptography
and search optimization problems
Basic: quantum algorithms, and quantum
information theory
Prof. P. Patra Research in Computer Science and Engineering 10

11. The Art and Science of CSE Research
The "Art":
Creativity: The ability to think outside the
box, propose new solutions, and design
innovative algorithms or systems.
Design: The artistry involved in designing
user interfaces, algorithms, architectures,
and other components of systems & devices.
Problem Solving: The creative approach to
identifying and solving complex problems,
which often requires unconventional
thinking.
Visualization: Creating visual representations
of data or systems, making complex concepts
more understandable.
User Experience (UX) Design: Crafting
interfaces and interactions that are not only
functional but also aesthetically pleasing and
user-friendly.
Innovation: Pushing the boundaries of what's
possible -- through novel concepts, ideas, and
approaches.
The ‘Science’:
Scientific Method: involves formulating hypotheses,
conducting experiments, collecting data, and drawing
conclusions based on evidence.
Empirical Analysis: Gathering and analyzing data to validate
hypotheses, makeinformed decisions, and quantify the
effectiveness of solutions.
Algorithms: Thedevelopment and analysis of algorithms
based on mathematical principles and formalproofs.
Applying Engineering Principles: such as systemdesign,
optimization, and reliability, to create practicaland efficient
solutions.
Experimental Validation: Conducting experiments,
simulations, or testing to validate theories and models in a
controlled and systematic manner.
Quantitative Analysis: Using quantitativemethods, statistics,
and metrics to measurethe performance, efficiency, and
effectiveness of systems and solutions.
Computer Science Theory: Investigating thetheoretical
foundations such as automata theory, complexity theory, and
formallanguages.
DataScience: Applying scientific methods and specialized
devices to extract meaningful insights fromdata and make
data-driven decisions
Software Engineering: systematic approaches to design,
develop, test and maintain softwaresystems.
Prof. P. Patra Research in Computer Science and Engineering 11