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K uanysh Mura t A sta na, Kaza kh sta n | +7 776 686 70 36 | ku anysh .d rk @ gm ail. c o m | Te le g ra m : @Kuka 24 E D UC ATIO N N aza rb aye v Univ e rs it y - Asta n a B ach elo r’s Deg re e: Com pute r Scie n ce Sen io r R ele va nt Cours e w ork : ● Gen era l purp ose an d sy ste m s pro g ra m min g : Pyth o n ● Adva n ce d pro g ra m min g : OOP, data str u ctu re s ● Data str u ctu re s an d Alg orit h m s, Adva n ce d Alg orit h m ic pro b le m so lv in g W ORK EX PER IE N CE C ++ Pro g ra m min g an d DSA Te a ch er | Ja n uary 20 24 to May 20 24 ● Ta u g ht a gro up of 9 stu d ents C++, OOP, Data str u ctu re s an d alg orit h m s fro m th e Naza rb aye v Univ e rs it y fo und atio n ye ar pro g ra m . ● Built a co m pre h en siv e co urs e pro g ra m co ve rin g Com pute r Scie n ce basic s, C/C ++ pro g ra m min g , OOP in C++, C++ STL, va rio us data str u ctu re s (A rra y, Matr ix , Lin ear co nta in ers , Set, Map , Prio rit y queu e, Ran g e query ), an d alg orit h m s (S ortin g , Div id e an d Conq uer, 2 poin te rs meth o d , Gre e d y, DP, Gra p h alg orit h m s, Ran d om iz a tio n). ● Cond ucte d 3 le ctu re s per week, each 2 ho urs lo ng . ● Desig ned an d ad m in is te re d exa m s, co nte sts , an d ho m ew ork assig nm ents wit h th o ro ug h fe ed b ack. ● Succe ssfu lly guid ed 7 stu d ents th ro ug h th e co urs e , all of who m re ce iv e d gra nts fo r th e Com pute r Scie n ce bach elo r's pro g ra m . R ese a rc h Assis ta n ts h ip at Naza rb aye v Univ e rs it y | Dece m ber 20 23 to Dece m ber 20 24 ● Contr ib ute d to a re se arc h pro je ct fo cu se d on su b li n ear alg orit h m s, pro p erty te stin g , an d sta tis tic a l le arn in g th eo ry . ● Im ple m ente d pro p erty te stin g an d dis ta n ce ap p ro xim atio n alg orit h m s in C++ fo r 2D an d 3D data , pro vin g th eir sp eed an d acc u ra cy. ● Utiliz e d Pyth o n lib ra rie s lik e su b pro ce ss an d pan d as fo r co nd uctin g ben ch m ark s an d co ll e ctin g re su lt s . M ach in e Le a rn in g Ju nio r Eng in eer at Yu rt Te ch | Dece m ber 20 24 to April 20 25 ● Deve lo p ed a sm art ap pro ach fo r co ll e ctin g tra in in g data usin g 3D ske le to n data fro m Kin ect devic e s an d pro je ctio ns wit h Num Py, gen era tin g ove r 10 0,0 00 tra in in g exa m ple s. ● Im pro ve d fa ll dete ctio n acc u ra cy fro m 62% to 93% by build in g a new se rv ic e usin g Fla sk an d aio -p ik a wit h an RN N mod el. ● Cre ate d a hum an dete ctio n se rv ic e to filt e r vid eo tra ffi c, re d ucin g ir re le va nt vid eo s by 40 % . ● Im ple m ente d sc e n e cla ssifi catio n usin g In te rn V L VLM to bett er org an iz e vid eo data b ase s. ● Optim iz e d mod els lik e YO LO an d In te rn V L usin g OpenV IN O an d quantiz a tio n, boo stin g perfo rm an ce on se rv e rs by ove r 25 tim es. ● Fu lly la b ele d a data se t of ap pro xim ate ly 10 ,0 00 KZ lic e n se pla te s usin g YO LO , Easy O CR, an d Te sse ra ctO CR fo r fu tu re mod el tra in in g . S K IL LS / TO OLS ● Pro g ra m min g La n g uag es: Pyth o n, C/C ++ ● Data Scie n ce /M ach in e Le a rn in g : Num Py, Pan d as, Matp lo tlib , Scik it - le arn , PyTo rc h , Te n so rF lo w , YO LO , In te rn V L VLM ● Backe n d Fra m ew ork s: Fla sk ● Opera tin g Syste m s: Lin ux/B ash