Check out my new homepage:
here
-
If you want to work with me as a Grad Student, please follow
these
directions.
Note I will ignore any email that does not include a mini-research proposal.
- We are also looking for PostDocs, in various areas;
check out
ad.
- My "greiner@cs.ualberta.ca" email address has been de-activated.
Please use rgreiner-@-ualberta-.-ca address instead.
- I no longer have an office phone.
Use email, or if very timely, contact our Admin at 780 492-4828.
- To understand the difference between
Association Studies (common in biostatistics)
vs
Prediction Studies (common in machine learning),
see
Youtube lecture.
Short Bio
- Interview in
"How AI is revolutionizing medical science …
Predicting Schizophrenia"
Folio
(18/Apr/2018)
- Mentioned in
"How
AI is reshaping our lives"
Folio
(16/Apr/2018)
- Quoted in
article, in
MacLean's (16/Nov/2017).
-
Over 50 teams, from around the world, competed in the
Prostate Cancer DREAM
Challenge,
to predict the survival and toxicity of docetaxel treatment in patients with
metastatic castrate resistant prostate cancer.
Our team, PC LEARN, tied for 1st in one of the 2 specific subchallenges
-- see here.
See the press release
(we are "Canada" and "Israel");
and the article.
-
Our team (led by Mina Gheiratmand [UofA] and Irina Rish [IBM])
just published an
article
(in Nature Schizophrenia) describing a
way to diagnose schizophrenia!
- In the midst of all of the well-deserved excitment about DeepMind Alberta,
I was honored to be (briefly) in the 8/July/2017
CBC
article!
- Quoted in Toronto Star (9/May/2016):
How
artificial intelligence could transform the medical world
- PhD student, Junfeng Wen, awarded prestiguous AI-TF Fellowship, from 2016!
- Quoted in Faculty of Science news (1 Apr 2016):
Statisticians step up to aid neurological health research
- HQP Accomplishments (Dec 2015)
-
Junfeng Wen: selected as a runner-up for this year's CS department's ``Early
Achievement Award (MSc) 2015''
- Siamak Ravansbakhsh: selected as a runner-up to this year's CS
department's ``Outstanding Thesis Award (PhD) 2015''
- Felicity Allen: nominated for Best Thesis prize (PhD)
- Mina Gheiratmand: awarded a prestigious AIHS Fellowship!
-
Our recently-launched "Computational Psychiatry" direction was recently
presented on Global News (14 Oct 2015); see here.
-
Our fMRI project is included as an example of UofA's collaboration with IBM
CAS,
in news release.
- Our team, led by PhD student Siamak Ravansbakhsh, developed the
Bayesil system
for automatically identifying and quantifying metabolites using 1D 1H NMR spectra of
ultra-filtered plasma, serum or cerebrospinal fluid.
Our article is described on
GlobalTV
and elsewhere (May,June 2015).
- PhD student Siamak Ravansbakhsh won a Best Thesis Prize (2015)
- PhD student Felicity Allen's CFM-ID system won the
CASMI [Critical Assessment of Small Molecule Identification]
competition (finding the chemical structure of an ESI-MS/MS spectrum)
(2014)
- Press coverage of MITACS
Globalink Students
(July/2014)
- Press coverage of Accurately Predicting Estrogen Receptor Status
(2/Dec/2013)
-
O Schulte,
G. Frigo,
R. Greiner,
and H. Khosravi,
"
The IMAP Hybrid Method for Learning Gaussian Bayes Nets"
won the
Best Paper Prize
at the
2010 Canadian Conference on Artificial Intelligence.
-
Killam
Annual Professorship, 2007.
- Fellow of
AAAI
(Association for the Advancement of
Artificial Intelligence), 2007
- Faculty Research Award, UofA CS (March 2007)
-
ASTech Award
(to AICML) for
"Outstanding Leadership in Technology",
Oct 2006
- McCalla
Professorship,
2005-06
-
Conference Chair,
Int'l Conf. on Machine Learning (ICML'06)
Pittsburgh
-
Y Guo,
R. Greiner,
D Schuurmans;
"
Learning Coordinate Classifiers"
won a
"Distinguished Paper" award
at
IJCAI'2005.
-
Program Co-Chair,
Int'l Conf. on Machine Learning (ICML'04)
Banff, Alberta
-
T Zhu,
R. Greiner,
G Haeubl;
"Learning a Model of a Web User's Interests"
won the
2003 James Chen Best
Student Paper Award at
UM'2003.
- J.Cheng and R. Greiner,
"Learning Bayesian Belief Network Classifiers: Algorithms and System"
was RunnerUp for Best Paper,
Fourteenth Canadian Conference on Artificial Intelligence (CSCSI'01)
I am interested in building algorithms that learn from experience, to be able to
perform their tasks better.
Some of my work has
- an application pull
-- i.e., is motivated by very specific tasks;
-
a technology push -- typically extending standard learning algorithms and analyses, to produce more robust and more effective learning systems
These systems have been successfully used to address
a number of real-world challenges.
For an overview of some medical application for machine learning, see the
video,
or
website.
See
Earlier Visits
Thought for the day:
First things first; second things never.
I plan to update this daily... but that is not the highest
priority.
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