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\signature{Dr. Kenneth W.\ Regan}

\begin{letter}{Faculty Recruiting Committee\\
Department of Computer Science\\
University of Alabama at Birmingham\\
University Hall 4105\\
1402 10th Ave. S.\\
Birmingham, AL 35294-124\\
Re: faculty applicant Dr.\ Tianle Ma, Ref.\ CN000026929
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\opening{\leftline{Dear Faculty Recruiting Committee:}}

\email{regan@buffalo.edu}
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\phone{(716) 645-4738}


I am delighted to provide a reference for Dr.\ Tianle Ma for a faculty position at UAB.  He would fill both the Data Science and Biomedical Applications foci of your search.

I have known Dr.\ Ma as a top-tier student in my graduate theory of computation course (at the time required for all PhDs), as a TA for a large undergraduate course, and from office visits and other contacts in the Department.  I was not a member of his PhD dissertation committee but I attended his PhD defense in December, and we have kept contact since.  In fact, last April we discussed aspects of the two positions he applied for at the Broad Institute.  The one offered was promising but a lesser fit.  Unfortunately the latter aspect has proved out with greater emphasis on software engineering and less on fundamental research.  He is therefore resuming his quest for faculty positions that emphasize interdisciplinary applications.  He is an excellent match for your initiative in Data Science.

Dr.\ Ma has continued apace with paper submissions based on his thesis work.  My main impression on viewing his papers and thesis draft is to be impressed not only at the data-centric content and relevance to healthcare but also the depth of statistical thinking.  To be sure, the papers are written with an advisor who is not only a star but has gone to NSF and thus has embraced the responsibility for skeptical insight not just promoting the work.  But Tianle is the only co-author with her on a host of the papers, and in tandem with his research statement they show he has originated a lot of the critical thinking.

What he's done in the prize-winning ICBB 2017 paper is take a clustering method called Similarity Network Fusion (from a 2014 non-Buffalo multi-author paper) and alter it to be not only more efficient and interpretable in general use but also more effective on certain highly relevant kinds of datasets.  Demonstrating these improvements takes some technically intensive analysis, including two degrees of random-walk simulations.  I wish I had anything approaching this level of intensity in my graduate seminars on my statistical chess model and the project on the whole.  It's notable that they have two further papers this year on the resulting Affinity Network paradigm.  

I see the other 2018 paper, ``Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis,'' through this lens: When data is scarce and/or expensive, experimental design is important to achieve sufficient statistical \emph{power}.  There are undergraduate and even high-school courses (Nichols School in Buffalo) on that subject.  But when the data is also partial, error-prone, and subject to bias, and when multiple selections on the data must be supported, the mathematical complexities involved in achieving the highest uniform level of power are on the frontier.  At the risk of over-simplifying the idea of ``multi-omic'' data and its particular features in biology, I view the issue in a glass-half-empty manner as a loss of independence in what ought to be separately contributing data sets.  The correlations are not given in advance but rather must be discovered by simulation and analysis.  

His combination of perspicacity and technical depth bodes well for productive research in an area where funding is particularly effective.  I clearly believe he has strong ability to originate research apart from his advisor.  His research points to not only being an applyer of new data-processing methods but a critically-aware generator of them.  His work is naturally multi-disciplinary.  It is also in a propitious area for current interest in funding, for which he is an excellent candidate.   The dissertation and papers tell me he must have great breadth in statistical knowledge, but it is the \emph{skeptical insight} that I really care about.

He was a great TA for me and often filled slack when the other TAs had things come up.  Having an EE background was no impediment to his excelling in the theory course (A-, 7th of 32).  He is very personable---indeed cheerful---and speaks English forcefully.  His writing is representative of the quality of normal conversations.  I have no hesitation in giving him my highest recommendation and will be happy to answer any further questions you may have.


\signature{Dr. Kenneth W. Regan}






\closing{Yours sincerely}

\address{Department of CSE\\
University at Buffalo\\
338 Davis Hall\\  
Buffalo,  NY  14260--2500}


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