As a grad­u­ate engi­neer from École des Ponts Paris­Tech (ENSP), Thier­ry Denoeux pur­sued a PhD the­sis on “the reli­a­bil­i­ty of rain fore­casts by weath­er radar” in a lab­o­ra­to­ry ded­i­cat­ed to the envi­ron­ment at ENPC. He is inter­est­ed, among oth­er things, in com­put­er sci­ence, pat­tern recog­ni­tion and image pro­cess­ing. “The goal was to process radar images to analyse and extrap­o­late the move­ment of rain cells for quan­ti­ta­tive fore­cast­ing in the very short term (one to two hours). These pre­dic­tions were used to opti­mize the man­age­ment of large sew­er sys­tems in order to lim­it flood­ing in the event of a storm,” he explains.

An inter­est that nat­u­ral­ly led him, after his PhD, to join the Lab­o­ra­toire d’In­for­ma­tique Avancée de Com­piègne (LIAC), Lyon­naise des Eaux, which has since become Suez, as a research engi­neer. He stayed there for three years and worked on Euro­pean projects with lec­tur­er cum-research sci­en­tists work­ing at UTC, in the ear­ly 1990s, when arti­fi­cial intel­li­gence (AI) was already arous­ing a lot of inter­est with the devel­op­ment of expert systems.

He joined UTC Com­pieg­ne in 1992 as a con­tract lec­tur­er-cum-research sin­guli­er sci­en­tist at Heudi­asyc before being appoint­ed as full pro­fes­sor in 1999. Sev­er­al respon­si­bil­i­ties fol­lowed suit: direc­tor of a joint lab­o­ra­to­ry with Suez, deputy direc­tor of Heudi­asyc, vice-pres­i­dent of the sci­en­tif­ic coun­cil of the UTC, sci­en­tif­ic coor­di­na­tor, before tak­ing over as direc­tor in Jan­u­ary 2019, of the Labex Maîtrise des Sys­tèmes de Sys­tèmes Tech­nologiques (MS2T) — a tenyear project — which, as part of the Gov­ern­ment Incen­tive Pro­gramme Invest­ments for the Future (PIA), run­ning until 2021. Also in Jan­u­ary 2019, he took over the man­age­ment of the SHIC¹ research fed­er­a­tion, a CNRS struc­ture ini­tial­ly group­ing togeth­er the mixed Heudi­asyc, BMBI and Rober­val units, which were recent­ly joined by the Costech unit. This fed­er­a­tion pro­vid­ed impe­tus to a new dynam­ic for inter­dis­ci­pli­nary tech­no­log­i­cal research with­in the UTC. 

At UTC-Heudi­asyc, Thier­ry Denoeux is part of the CID team in charge of arti­fi­cial intel­li­gence, struc­tured around two main research areas. The first con­cerns knowl­edge and data pro­cess­ing with themes such as knowl­edge mod­el­ling, machine learn­ing and uncer­tain­ty man­age­ment, a major chal­lenge in both arti­fi­cial intel­li­gence and sta­tis­tics. “Indeed, how can we mod­el uncer­tain­ty, rea­son and make deci­sions know­ing that we do not dis­pose of all the infor­ma­tion need­ed ?” he says. The sec­ond area of research is con­cerned with cus­tomized adap­tive sys­tems. In oth­er words, every­thing that relates to the inter­ac­tion between humans and sys­tems with the idea of design­ing sys­tems that can auto­mat­i­cal­ly and dynam­i­cal­ly adapt to the user and the con­text of use. 

Thier­ry Denoeux works main­ly on the first pri­or­i­ty theme. “I work essen­tial­ly on the mod­el­ling of uncer­tain­ties in intel­li­gent sys­tems, a theme that lies at the inter­face between arti­fi­cial intel­li­gence and sta­tis­tics. I am par­tic­u­lar­ly inter­est­ed in the the­o­ry of belief func­tions, a the­o­ry of uncer­tain­ty that allows us to rea­son and make deci­sions in the pres­ence of uncer­tain­ties. It is a gen­er­al the­o­ry, which encom­pass­es prob­a­bil­i­ty the­o­ry, and has many appli­ca­tions because uncer­tain­ties are ubiq­ui­tous. Research in this area is mul­ti­dis­ci­pli­nary and involves econ­o­mists, AI spe­cial­ists, sta­tis­ti­cians and oth­ers,” he explains. 

A field that led him, in 2010, to par­tic­i­pate in the cre­ation of a learned soci­ety Belief func­tions and Appli­ca­tions soci­ety (BFAS), an asso­ci­a­tion of which he is the Pres­i­dent. The objec­tive? In par­tic­u­lar, to pro­mote teach­ing, research, the advance­ment of knowl­edge in the field of belief func­tions and to explore the links with oth­er the­o­ries of uncer­tain­ty. Hence the launch of inter­na­tion­al con­fer­ences held every two years — the next one will be con­vened in Shang­hai in 2020 — and a the­mat­ic school for the train­ing of PhD stu­dents, the lat­est edi­tion of which was held in Octo­ber 2019 in Siena (Italy).

How­ev­er, Thier­ry Denoeux does not con­fine him­self to the the­o­ret­i­cal aspect of his research on belief func­tions, as he is also inter­est­ed in the con­crete appli­ca­tions that can derive from them. One exam­ple is auto­mat­ed postal address recog­ni­tion, which was the sub­ject of a CIFRE the­sis in part­ner­ship with Solystic, one of the world lead­ers in the pro­vi­sion of auto­mat­ed sort­ing and dis­tri­b­u­tion prepa­ra­tion solu­tions for parcels and mail. “This com­pa­ny sells machines with hand­writ­ten address recog­ni­tion soft­ware. So when the address is not rec­og­nized, the enve­lope is reject­ed and processed man­u­al­ly. The chal­lenge is to reject as few envelopes as pos­si­ble while mak­ing as few errors as pos­si­ble on those that are accept­ed. To meet these two cri­te­ria and improve machine per­for­mance, the idea was to inte­grate sev­er­al soft­ware pro­grams and com­bine the results of these sys­tems using belief func­tion the­o­ry,” he explains.

Oth­er appli­ca­tions include the work car­ried out with the French Insti­tute of Trans­port, Plan­ning and Net­work Sci­ence and Tech­nol­o­gy (IFSTTAR) and the SNCF on “Diag­no­sis of rail­way track cir­cuits”, and the ongo­ing col­lab­o­ra­tion with the Lab­o­ra­to­ry of Com­put­er Sci­ence, Infor­ma­tion Pro­cess­ing and Sys­tems (LITIS) of the Uni­ver­si­ty of Rouen on “Seg­men­ta­tion of tumours in med­ical images and prog­no­sis based on the evo­lu­tion of patient data”. The the­o­ret­i­cal cor­pus of belief func­tions is of course also of inter­est to the SyRI (Robot­ic Sys­tems in Inter­ac­tion) team, which is work­ing in par­tic­u­lar on intel­li­gent vehi­cles (IV). “One of the prob­lems in IV con­cerns per­cep­tion. IVs are full of sen­sors and the chal­lenge is to be able to process the infor­ma­tion col­lect­ed by these sen­sors to rec­og­nize objects on the road such as pedes­tri­ans, cyclists, etc. One of the prob­lems in IVs is per­cep­tion. We there­fore need to com­bine the infor­ma­tion from these dif­fer­ent sen­sors. And here again, there is a lot of uncer­tain­ty, because each sen­sor pro­vides par­tial and some­times unre­li­able infor­ma­tion about the envi­ron­ment,” adds Thier­ry Denoeux. Should we be afraid of AI? “Irra­tional fear is irrel­e­vant. How­ev­er, some appli­ca­tions of AI pose eth­i­cal prob­lems, such as gen­er­alised video sur­veil­lance with, in par­tic­u­lar, the devel­op­ment of facial recog­ni­tion. As biol­o­gists have been doing for a long time, com­put­er sci­en­tists must now be con­cerned about the eth­i­cal impli­ca­tions of their work,” he concludes.