There are several major closed-form challenges in Computer Science, such as
However, the hottest topics are broad and intentionally defined with
some vagueness, to encourage out-of-the-box thinking. For such topics,
zooming in on the right questions often marks significant progress in
itself.
Here’s my list for 2015.
Abundant-data applications, algorithms, and architectures are a
meta-topic that includes research avenues such as data mining (quickly
finding relatively simple patterns in massive amounts of loosely
structured data, evaluating and labeling data, etc), machine learning
(building mathematical models that represent structure and statistical
trends in data, with good predictive properties), hardware architectures
to process more data than is possible today.
Artificial intelligence and robotics - broadly, figuring out
how to formalize human capabilities, which currently appear beyond the
reach of computers and robots, then make computers and robots more
efficient at it. Self-driving cars and swarms of search-and-rescue
robots are a good illustration. In the past, once good model were found
for something (such as computer-aided design of electronic circuits),
this research moves into a different field – the design of efficient
algorithms, statistical models, computing hardware, etc.
Bio-informatics and other uses of CS in biology, biomedical engineering, and medicine, including systems biology (modeling interactions of multiple systems in a living organism, including immune systems and cancer development), computational biophysics (modeling and understanding mechanical, electrical, and molecular-level interactions inside an organism), computational neurobiology
(understanding how organisms process incoming information and react to
it, control their bodies, store information, and think). There is a very
large gap between what is known about brain structure and the
functional capabilities of a living brain – closing this gap is one of
the grand challenged in modern science and engineering. DNA analysis and
genetics have also become computer-based in the last 20 years.
Biomedical engineering is another major area of growth, where
microprocessor-based systems can monitor vital signs, and even
administer life-saving medications without waiting for a doctor.
Computer-aided design of prosthetics is very promising.
Computer-assisted education, especially at the high-school
level. Even for CS, few high schools offer competent curriculum, even in
developed countries. Cheat-proof automated support for exams and
testing, essay grading, generation of multiple-choice questions. Support
for learning specific skills, such as programming (immediate feedback
on simple mistakes and suggestions on how to fix them, peer grading,
style analysis).
Databases, data centers, information retrieval, and natural-language processing:
collecting and storing massive collections of data and making them
easily available (indexing, search), helping computers understand
(structure in) human-generated documents and artifacts of all kinds
(speech, video, text, motion, biometrics) and helping people search for
the information they need when they need it. There are many interactions
with abundant-data applications here, as well as with human-computer
interaction, as well as with networking.
Emerging technologies for computing hardware, communication, and sensing:
new models of computation (such as optical and quantum computing) and
figuring out what they are [not] good for. Best uses for
three-dimensional integrated circuits and a variety of new memory chips.
Modeling and using new types of electronic switches (memristors,
devices using carbon nano-tubes, etc), quantum communication and
cryptography, and a lot more.
Human-computer interaction covers human-computer interface
design and focused techniques that allow computers to understand people
(detect emotions, intent, level of skill), as well as the design of
human-facing software (social networks) and hardware (talking
smart-phones and self-driving cars).
Large-scale networking: high-performance hardware for data
centers, mobile networking, support for more efficient multicast,
multimedia, and high-level user-facing services (social networks),
networking services for developing countries (without permanent
high-bandwidth connections), various policy issues (who should run the
Internet and whether the governments should control it). Outer-space
communication networks. Network security (which I also listed under
Security) is also a big deal.
Limits of computation and communication at the level of
problem types (some problems cannot be solved in principle!), algorithms
(sometimes an efficient algorithm is unlikely to exist) and physical
resources, especially space, time, energy and materials. This topic
covers Complexity Theory from Theoretical CS, but also the practical
obstacles faced by the designers of modern electronic systems, hinting
at limits that have not yet been formalized.
Multimedia: graphics, audio (speech, music, ambient sound),
video – analysis, compression, generation, playback, multi-channel
communication etc. Both hardware and software are involved. Specific
questions include scene analysis (describing what’s on the picture),
comprehending movement, synthesizing realistic multimedia, etc.
Programming languages and environments: automated analysis of
programs in terms of correctness and resource requirements, comparisons
between languages, software support for languages (i.e., compilation),
program optimization, support for parallel programming, domain-specific
languages, interactions between languages, systems that assist
programmers by inferring their intent.
Security of computer systems and support for digital democracy,
including network-level security (intrusion detection and defense),
OS-level security (anti-virus SW) and physical security (biometrics,
tamper-proof packaging, trusted computing on untrusted platforms),
support for personal privacy (efficient and user-friendly encryption),
free speech (file sharing, circumventing sensors and network
restrictions by oppressive regimes), as well as issues related to
electronic polls and voting. Security is also a major issue in the use
of embedded systems and the Internet of Things (IoT).
Verification, proofs, and automated debugging of hardware
designs, software, networking protocols, mathematical theorems, etc.
This includes formal reasoning (proof systems and new types of logical
arguments), finding bugs efficiently and diagnosing them, finding bug
fixes, and confirming the absence of bugs (usually by means of automated
theorem-proving).
If something is not listed, it may still be a very worthwhile topic,
but not necessarily “hot” right now, or perhaps lurking in my blind
spot.
Now that you have a long answer, let’s revisit the question! Hotness
usually refers to how easy it is to make impact in the field and how
impactful the field is likely to be in the broader sense. For example,
solving P vs. NP would be impactful and outright awesome, but also
extremely unlikely to happen any time soon. So, new researchers are
advised to stay away from such an established challenge. Quantum computing
is roughly in the same category, although apparently the media and the
masses have not realized this. On the positive side, applied physicists
are building interesting new devices, producing results that are
worthwhile by themselves. So, quantum information processing is a hot
area in applied physics, but not in computer design.
