A Statement of Research Interests

1. Statement of Research Interests
Theory and Applications of Machine Learning
Muhammad Adil Raja∗
December 22, 2014
My research interests fall in the general area of theory and applications of machine learn-
ing. Machine learning is a sub-field of artificial intelligence that is concerned with design and
development of algorithms that allow computers to automatically create solutions to user
specified problems based on empirical data [Mitchell, 1997]. More specifically, my research
falls under two themes: (a) to develop a thorough understanding of the theoretical concepts
of various methods, algorithms and sub-domains of machine learning; and (b) to effectively
apply these ideas and concepts for solving various real-world scientific problems. Machine
learning offers solutions to many complex, computationally hard real-world problems that
are otherwise difficult to solve. As more intricate scientific and engineering problems have
emerged the need to develop sophisticated machine learning algorithms, as well as to re-
fine, the existing algorithms has also increased. To this end, I have an insatiable desire for
addressing the first objective stated earlier. I wish to learn more about various domains
and algorithms of machine learning that primarily include genetic algorithms and program-
ming, support vector machines, clustering techniques, Bayesian networks, Markov modeling,
reinforcement learning, unsupervised learning, combinatorial optimization and also the the-
oretical concepts of statistical and of computational learning theories [Vapnik, 1995]. In
parallel to this I wish to apply my knowledge of machine learning to real-world problems.
The rest of this document is organized as follows: section 1 gives a small introduction to
my previous research endeavors. Sections 2 to 10 present various areas in which I would like
to work on availability of funds and resources. Section 11 presents some thoughts about a
research methodology that I would like to adopt for a hypothetical research project spanning
a duration of two to three years. Section 12 presents a timeline for accomplishment of various
research objectives.
I have to mention here that I have been suggested by my peers (and specially by my
PhD supervisor, Dr. Colin Flanagan) that this document is rather too long. One of the
consequences of this, as I have been suggested, is that it may reflect a lack of focus on my
part about my future research objectives. I have to confess here that the intention behind
writing this long document, however, was to give the reader (possibly the potential employer)
a feeling of as to what type of problems I would like to address as a scientist. I am currently
working on dividing this document into multiple shorter, audience specific, drafts as well
∗
Statement of Research Interests by Muhammad Adil Raja is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. cbnd
1

2. as on embellishing them with more concrete evidence, ideas and open problems. I would
also like to mention here that I wanted to write a rather more general statement of research
interests encompassing areas in which I have an interest to work in the future, but of which
I do not possess adequate experience. These areas are from diverse problem domains such as
number theory, cryptography and astronomy etc. I anticipate that this, however, is going to
require a lot more time and effort.
1 Previous Research
I started my PhD studies in February, 2005 in University of Limerick, Ireland under the
supervision of Dr. Colin Flanagan and I was awarded the PhD degree in August, 2008. It
took me precisely three years and three months to accomplish my PhD studies. After that
I also had a chance to work as a postdoctoral researcher with Orange Labs, France Telecom
R&D, Lannion, France. The topic I have been studying and addressing is the speech quality
estimation problem. Speech quality, as perceived by the users of Voice over Internet Protocol
(VoIP) telephony, is critically important to the uptake of this service. VoIP quality can be de-
graded by network layer problems (delay, jitter, packet loss). The research presented methods
for real-time, non-intrusive speech quality estimation for VoIP that emulated the subjective
listening quality measures based on Mean Opinion Scores (MOS). MOS provides the numer-
ical indication of perceived quality of speech. A Genetic Programming (GP) [Koza, 1992]
based symbolic regression approach was employed to derive speech quality estimation models.
The results compared favorably with various standards proposed by International Telecom-
munications Union-Telecommunication Standardization (ITU-T). Moreover, the models are
suitable for real-time speech quality estimation of VoIP. The research resulted in numer-
ous outstanding publications [Raja et al., 2006, Raja et al., 2007, Raja and Flanagan, 2008,
Raja et al., 2008]. Among these, [Raja et al., 2007] was also nominated for best paper award
by the EuroGP 2007 conference committee 1
. Honorable mentions were made twice for the
2007 and 2008 Hummies awards for human-competitive results produced by genetic and evo-
lutionary computation 2
. Moreover, during my PhD studies I also had a chance to collaborate
with a center of excellence, namely, Biocomputing and Developmental Systems (BDS) Group
headed by Dr. Conor Ryan 3
in University of Limerick, Ireland. The group specializes in
the theory and applications of evolutionary algorithms. Recently, during my postdoctoral
research in Orange Labs I again had a chance to build industry academia liaison between
Orange Labs and the BDS group. In Orange Labs I also found an excellent match for my
research work in the industry.
While working on speech quality estimation I also performed a thorough literature review
of various other related fields which include speech processing, speech coding, speech synthe-
sis, automatic speech recognition, auditory scene analysis and the various machine learning
methods that may be applied to solve problems in these areas. I also had to develop and hone
expertise about various topics in packet based networks. To this end, I had to study and
understand various aspects of communication networks such as design and implementation of
1
http://www.informatik.uni-trier.de/ ley/db/conf/eurogp/eurogp2007.html
2
http://www.genetic-programming.org/hc2007/cfe2007.html
3
http://bds.ul.ie
2

3. various network protocols for fixed, wireline, and wireless networks. I also came across some
peripheral topics in the applications of bio-inspired computing such as, evolutionary design
of digital hardware, evolutionary art and music. I also studied other elementary subjects
such as digital signal processing, statistics, linear algebra and differential calculus during my
PhD studies. As part of my research I was also heavily involved in software development at
different phases of my studies. I learned various programming languages and acquired new
techniques for handling large amounts of data. I also wrote a few rather huge applications for
the sake of fun and to satiate my curiosity about my understanding of the working and im-
plementation of algorithms. This includes: development of a genetic algorithms software for
numerical optimization, development of a genetic programming system for symbolic regres-
sion, development of a grammatical evolution system for optimization. I also gained hands
on experience about working with, and development of, various speech processing tools used
in problems such as speech coding and recognition.
Apart from this, over the past years, I have been avidly reading about various other
researchable questions in the general area of machine learning or its applications. These
include topics from diverse problem domains such as communication networks, digital hard-
ware design, computational neuroscience, digital signal processing, artificial reality, artificial
consciousness, psychology and computer aided diagnosis. At some stage I would like to ac-
tively pursue some problems in these domains. Some of the projects I would like to work on
are listed below.
Overall my personal experience about being a researcher, or of doing research, has been
a very enjoyable one. Doing research has ever since been more of a hobby for me than a
professional choice. For me it is the best way to address my euphoria and curiosity about
the world’s body of knowledge.
2 Theory and Applications of Hyper-Heuristics
A recent interest in the field of machine learning has been to devise and use hyper-heuristics
[Ross, 2005, pp. 529–556]. Where the search space of a meta-heuristic algorithm is composed
of all the possible candidate solutions of the problem at hand, hyper-heuristics operate over a
search space of heuristics [¨Ozcan et al., 2008]. During the search process the most appropri-
ate heuristic is determined and applied for solving the underlying problem at each step of the
search process. To this end, hyper-heuristics are also assumed to be problem independent.
To date, hyper-heuristics have been applied to several real world problems such as edu-
cational timetabling problems [Bader-El-Den et al., 2009], and the vehicle routing problem
[Ochi et al., 1998].
A contemporary approach to devising hyper-heuristics is the use of genetic program-
ming (GP) [Koza, 1992]. Where alternative approaches utilize a set of human designed
heuristics, GP aims to generate new heuristics from a set of potential heuristic components
[Burke et al., 2009]. To this end, GP not only tests and validates the performance of various
heuristics for a given problem, it also generates new ones.
The goal of my research would be to explore and apply the promise lent by GP in devising
hyper-heuristics. To this end, the aim would be to initially study the theoretical aspects
concerning hyper-heuristics. The objective of this would be to enhance one of the higher
3

4. performing methodologies, such as GP or support vector machines (SVMs). It would also be
very interesting to follow it up with an application of the devised scheme to a computationally
hard real-world problem.
3 High Performance Computing for Artificial Intelli-
gence Applied to Finance (or to a Similar Hypothet-
ical Classification or Regression Problem)
A notable application domain of machine learning has been to apply various algorithms to
solve problems in the area of finance. For instance, numerous attempts have been made in the
past to develop classifier systems for stock market forecasting [Grosan et al., 2005]. Artificial
neural networks (ANNs) have by far been the most widely used. Various types of neural net-
works that have been used include Time Delay neural networks [W. Kreesuradej and Lane, 1994],
probabilistic neural networks [Tan et al., 1995], recurrent neural networks [E. Saad and Wunsch, 1996].
Apart from neural networks, Bayesian Belief networks [Wolfe, 1998], evolutionary algorithms
[Kanoudan, 2000, Kim, 2000, Allen and Karjalainen, 1999, Kaboudan, 2000, Wang, 2000],
classifier systems [Schulenburg and Ross, 2001] and fuzzy sets [Castillo and Melin, 2001] have
also been employed.
There has also been an interest in hybridizing various machine learning algorithms to
find solutions to various financial problems. These include hybrids of ANNs and genetic
algorithms (GAs) [Kim and Shin, 2007], a hybrid of a rule-based technique and an ANN
[Tsaih et al., 1998], a genetic algorithm and support vector machines (SVM) [Choudhry and Garg, 2008].
The work reported in [Choudhry and Garg, 2008] is of particular interest where they have
used a GA to select optimal set of features from the input space to be supplied to a SVM as
inputs. The GA has been used to serve as a dimensionality reduction algorithm and SVM
has been used for optimization. The goal was to develop a two-class classifier to be used for
prediction of stock market direction. Similarly, [Charles W. Richter et al., 1999] used genetic
programming (GP) and finite state automata to compute bidding strategies for competitive
auction markets.
Given the high computational requirements posed by algorithms like GP, or possibly
hyper-heuristics, one may also leverage from parallel processing systems proposed by various
research studies. In [Dracopoulos and Kent, 1996] Kent and Dracopoulos have proposed a
parallel implementation of GP using bulk synchronous parallel programming (BSP). They
propose to use a master-slave architecture in which the master node distributes subpopu-
lations of individuals to multiple slave nodes for fitness evaluation, which is normally the
most compute intensive process in GP. Similarly, in [Fernandez et al., 2000] Fernandez et
al. have proposed a distributed computing environment for GP using MPI in which each
of a number of slave nodes is assigned a GP population and to perform genetic evolution
over them, whereas a master node is utilized to perform migrations within the populations
held by the slave nodes. Their system also allows to easily model various communication
topologies such as the mesh and ring topologies. Their results show that partitioning sub-
populations on to multiple slave nodes results in faster convergence to the optimal solution.
However, they also argue that further partitioning the subpopulations on to multiple nodes
4

5. is not beneficial beyond a certain limit. [Oussaid`ene et al., 1997] have also proposed another
parallel implementation of GP particularly for a trading model induction problem. In their
scheme they present a scalable parallel implementation of GP on distributed memory ma-
chines. The system runs multiple master-slave instances each mapped on all the allocated
nodes. Asynchronous migration of individuals is performed among processes to avoid pre-
mature convergence to a locally optimal solution. Considerable speedups have been claimed
for problems of large enough sizes.
One may also benefit from work proposed by Keijzer to speed up GP on a single CPU.
For instance, in [Keijzer, 2004a] has proposed a number of subtree caching mechanisms that
are capable of adapting during the course of a GP run while maintaining a fixed size cache
of already evaluated subtrees. This is known to speed up a GP system as the whole trees do
not need to be evaluated. Where ever a subtree is found in the cache its evaluation is used
instead of re-evaluating it. Keijzer has also proposed to use vectorized evaluation of GP trees
as opposed to standard case-by-case evaluation method for interpreted GP.
Apart from other methods one may also benefit from hybridizing a GP system with linear
scaling as proposed by Keijzer [Keijzer, 2004b]. In this scheme Keijzer proposes to further
optimize a GP tree to the target data in a least squared sense. He proposes to find a slope
and an intercept value that may be attached to a GP tree. The benefits of such a scheme are
numerous and are cited elsewhere in the literature. One of the major benefits is that it forms
a good hybrid algorithm with only a small computational overhead. It was found beneficial
over hybrids of other techniques by Raja et al. [Raja and Flanagan, 2008]. Another benefit
that can be foreseen is to perform online optimization of a given GP tree in real-time. To
this end, it is thought that once a befitting GP tree has been found for any hypothetical
classification or regression problem along with its slope and intercept using linear scaling.
One may iteratively re-compute the slope and the intercept terms as the new data arrives.
This may be done as a function of past N input/output tuples, where N may be an arbitrarily
small or large number.
Apart from the above stated mainly meta-heuristic algorithms, numerous numerical algo-
rithms like Gauss-Newton or Levenberg-Marquardt can be used for nonlinear optimization.
Nonetheless, meta-heuristic algorithms have well known advantages over the numerical algo-
rithms. These include, but are not limited to, being able to find globally optimal solutions as
opposed to locally optimal ones, their ease of understanding and use, and their applicability to
a wider range of problems. One may also wish to develop hybrid algorithms using one or more
of such numerical algorithms with GP, as in [Topchy and Punch, 2001]. In such a scheme,
GP tries to find the optimum program, from the program space, for the underlying problem,
whereas, the numerical optimization algorithm attempts at fine-tuning the coefficients of any
given program. One may also benefit from more sophisticated approaches lent by coevolu-
tionary algorithms [Ficici, 2004], evolutionary game theory [Vincent and Brown, 2005] and
from ideas in agent based computational economics [Tesfatsion and Judd, 2006].
4 Social Simulation
A slightly different approach to solving various real-world problems is to employ an agent-
based modeling system [Gou, 2006]. In [Epstein and Axtell, 1996] Epstein and Axtel have
5

6. proposed to use a so-called sugar-scape model to simulate an artificial society. The artificial
society they propose contains agents, an environment made of a two dimensional grid, and
rules that govern the interaction of the agents with each other and with the environment.
Agents can move on this grid with each other to gather resources (sugar), mate with each
other, give rise to offspring and eventually die. It is believed that such an artificial society can
serve as a good laboratory for simulating various real world phenomena. The model has been
celebrated well among economists and may have applications in other domains particularly in
social science. In social science one can leverage from the ideas in creating artificial societies
to learn about social issues such as epidemiology of disease, spread of negative emotions such
as fear and anxiety, and spread of wealth in a society [Tesfatsion and Judd, 2006]. Agent
based models can also be used to study information diffusion patterns, epidemic models for
the spread of ideas, interactions between network traffic and structure dynamics, and to
explain the emergence of viral bursts of attention.
5 Application of Machine Learning to Selected Prob-
lems in Communication Networks
Recently there has been a growing interest in applying machine learning methods to problems
in communication networks. My own PhD thesis was an application of machine learning.
Besides, many studies have applied machine learning methods for solving computationally
hard problems. Network coding is one such domain [Kim, 2008]. Similarly, other examples
could be topology optimization of a cellular network [Li et al., 2009], data mining for net-
work intrusion detection [Wilson, 2008] etc. and to more basic problems such as routing as in
[Murgu et al., 1994]. A few application domains to which machine learning has been success-
fully applied are network analysis and design, routing protocols, transport protocols, network
protection systems, load balancing, quality-of-service provisioning, mobile ad hoc networks,
sensor networks, network robotics and sensor-actor networks, distributed inference and coop-
erative communication systems, distributed search and computing in peer-to-peer networks,
parallel and distributed optimization algorithms, grid computing, distributed data mining.
To this end, it may be worthwhile to identify an open problem in the area of communication
networks and to solve it using machine learning.
6 Wind Farm Engineering
Over the previous years there has been a growing worldwide demand for wind energy. How-
ever, there are still challenging issues that need to be addressed so as to optimize the wind
technology. Numerous problems in wind farm engineering require optimization of a certain
user defined metric. These could be the optimization of turbine specific factors such as power
factor and power output, wind specific parameters such as wind speed prediction or simula-
tion and emulation of wind energy systems. Machine learning methods have been used in the
past to solve various problems. For instance [Charhate et al., 2008] have used GP and ANNs
to derive wind parameters from measured waves by employing an inverse modeling approach.
Similarly [Kusiak and Verma, 2011] have employed GP to monitor blade pitch faults in wind
6

7. turbines. [Ebner, 2003] employed an evolution strategy to evolve a wind turbine. I would be
interested in working on a viable project in the domain of wind farm engineering.
7 Research in Speech Technologies
As mentioned earlier, during my PhD studies I was actively involved with various speech
technologies and I had an opportunity to not only perform a thorough literature review but
also to interact with various open source software that are used in several fields. To this end,
I developed familiarity with processing, recognition, coding, synthesis, transcription, quality
estimation of natural human speech. I also developed familiarity with various related and
diverse subjects such as speaker identification, music genre classification, natural language
processing and auditory scene analysis. The most challenging and interesting aspects in these
subjects are to develop an understanding of the various algorithms and to implement them.
Although most of these fields are saturated in the sense that a lot of research has already
been done, some of them, such as speech recognition, are considered as insurmountable
opportunities. Specifically, in fields such as speech recognition and synthesis there is a great
need for improvement on aspects such as naturalness, expressiveness and fluency. I am also
very keen about enhancing my knowledge about (and applying it to) the applications of
evolutionary algorithms and biologically inspired techniques to musicology. I am particularly
interested in fields such as visual art and music generation, analysis, and interpretation,
sound synthesis, architecture, video; poetry, design, source separation of multi-channel audio
and other creative tasks.
8 Computer aided diagnosis
Computer aided diagnosis (CAD) is a relatively young field that leverages from the subjects
of machine learning, digital image processing, computer vision and 3D image analysis for de-
velopment of algorithms and models for diagnosing clinical illnesses. CAD has normally been
used in diagnosis of various types of tumors (breast cancer, lung cancer, prostrate cancer, to
name a few). CAD heavily relies on pattern recognition techniques for detection of presence
of a disease. For instance, in mammography CAD highlights micro calcification clusters in the
soft tissue. The main idea is that the CAD software would assist the radiologist in diagnosis
of the disease [Howard et al., 2008]. One of the open problems in CAD involves developing
methods to analyze breast density and parenchymal tissue patterns from Digital Mammog-
raphy (DM) and Digital Breast Tomosynthesis (DBT) and estimating their predictive value
to determine a womans risk of breast cancer and breast cancer screening outcomes. Another
interesting application can be the development and use of high resolution adaptive optics
instrumentation for the study of retinal structure and function. Another quite interesting
problem is to work on developing methods for image-guided real-time tumor targeting.
7

8. 9 Applications of Machine Learning to Bioinformatics
and Computational Biology.
In the past few years enormous growth has been observed in the amount of biomedical
data. Particularly, sequencing of human genome and of a few other organisms has generated
complete genomic sequences of novel and groundbreaking number and size. Consequently,
this has also resulted in tremendous other relevant types of data including protein sequences,
data about genomic and proteomic experiments. Interpretation of such data has recently
been the focus of research in biomedical computing and informatics. The ultimate goal of
such research endeavors is to develop insight into the fundamental biology of organisms so as
to enhance the standard of life of human beings. More specifically research seeks to uncover
the mechanisms underlying disease. The wealth of data cannot be dealt with manually but
requires advanced computational tools that mimic some manual information gleaning process
but are rather much faster. Machine Learning is a natural choice for this as it is concerned
with acquisition of models from data, as well as the usage of such models for automatic
prediction of some user expected outcome. To this end, machine learning methods have
tremendous potential in modeling complex biological systems and in predicting or inferring
the roles of genes and proteins within these systems. Although I shall be open to addressing
a random question in the area of computational biology, I would naturally be bent towards
working in the field of HIV research and evolutionary biology.
10 Brain Research
Over the past two years, I have been trying to develop an understanding on as to how human
mind and brain function. To this end, I have been involved in reading books from notable
authors such as Anthony Robins [Robbins, 2001] 4
, Marvin Minsky and Kay Redfield Jami-
son5
and also through other web sources such as MIT’s McGovern institute for brain research
6
and Scientific American Mind 7
. One of my keen interests is to study the basis of aspects
like perception, cognition, attention, short/long term memory and action in human beings.
Currently I am also attending online lectures by Marvin Minsky on ”The Society of Mind”
8
. The subject, and his book with the same name, present great insights into the subject of
machine consciousness. I am also particularly interested in developing computational models
that may aid in estimating and treating mental illnesses such as bipolar disorder, autism,
epilepsy etc. I am also keen about developing methods monitoring of human behaviors in
natural environment and providing behavior-enhancing biofeedback.
Another of my keen interests is to work on various aspects of language processing and
communicative disorders. I am interested both in comprehension and production of language.
I am particularly interested in the production and correction of naive (or more generally
4
http://www.tonyrobbins.com/biography.php
5
http://www.hopkinsmedicine.org/psychiatry/expert team/faculty/J/Jamison.html
6
http://mcgovern.mit.edu/about-the-institute
7
http://www.scientificamerican.com/sciammind/
8
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-
spring-2007/index.htm
8

9. belligerent, hypocritical or other negative types of) rhetoric in various contexts. I am also
interested in understanding the difference between how (and what) the mind composes (in
terms of sentences or phrases) and how (and what) the tongue utters. I am also very keen
about finding ways to bringing mind and tongue in harmony so as to produce, for instance,
more sensible, polite and honest language at both (mental and lingual) levels, possibly in
real time.
I am also very keen about gaining hands on experience with novel imaging techniques such
as fMRI, EEG and PET and to apply these to research in mind and brain related topics. In the
past I was also offered to work on a two year Welcome Trust funded postdoctoral project by
NeoNatal brain research group, university college Cork, Ireland, to develop seizure detection
algorithms for neonates. I could not take up that position at that time due to some familial
obligations and priorities. One of my heartiest desires is to work on a problem concerning
mind and brain.
I am an avid reader of philosophy too and I keep on trying to educate myself about
various ideas concerning neuroscience from that point of view. Recently I came across a
rather nice but controversial book (in my point of view) by Sam Harris titled End of Faith:
Religion, Terror and the Future of Reason [Harris, 2004] written purely from an atheist’s
vantage point. Although the main theme of the book was to show that religion has been
(and still remains increasingly so) the harbinger of evil to the society over the millennia,
his writings on consciousness and meditation are very enlightening. Moreover, it has been
argued by normally every religious cult that religion is supposed to bring about peace in the
society and to the mind of the individuals and also to improve the human condition. The
author claims that this is not the case obviously because religion orchestrates an element
of animosity. One of my heartiest desires would be to study this aspect of religiosity. The
author recently completed his PhD studies in neuroscience and is an outspoken advocate
of Buddhist mediation for emotional and mental well being. I am also a keen reader of
other related topics from sociology and neuroscience such as evolutionary psychology (and
history) of religion evolutionary psychology of personality [Kirkpatrick, 1999], theory of mind
[Cohen, 1991] and artificial life [Bedau, 2003]. One of my other keen interests would be to
study the effect of meditation (such as Islamic or Zen Budhist) on human emotional wellness.
Given the availability of adequate laboratory facilities, this can be extended as an empirical
study where the subjects can be tested with equipment such as EEG or fMRI to see the effect
of various meditative practices on human mind and brain.
Machine learning can generally be applied for solving many complex engineering problems.
I am personally very open to new problem domains such as automatic speech recognition,
bioinformatics, gene regulation, information retrieval, machine perception, brain computer
interfaces, cognitive, behavioral and social sciences and I would like to work on a challenging
problem.
11 Methodology
The specific methodology that would need to be adopted in research is not laid out compre-
hensively as it would depend on the problem statement. However, it may be anticipated that
it would have the components of a typical research project. Few thoughts are as follows:
9

10. Initially, depending on the problem statement, a literature review would be required. This
would primarily be of the problem domain, and also of the computer scientific methods that
have been or may have to be used to solve the problem. A critical analysis of the benefits of
one method over the other would be important. This may follow with a data collection phase
in which problem critical data may have to be collected or created. This would follow with
an experimentation phase that may require design and execution of simulations. Specially
if compute intensive algorithms like GP are used, it may also need to be decided whether
resources for parallel processing are required. If so, issues concerning design and deployment
of software on multiple machines may also have to be addressed.
The experimentation phase is normally expected to follow with writing, reporting and
publishing of the results and findings. The expectation here would be that the work would
end up in decent scholarly publications.
12 Timeline
Given that two to three years are normally allotted for a project, following set of activities
can be foreseen:
1. Literature review – 6 months.
2. Design and evaluation of any software that may be required – 3 months.
3. Experimentation and writing (of any papers/journal articles/patents) – 6 to 12 months.
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