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Computational Mathematics, Learning, and Data Science 

Joint phd program unipv - usi - fbk.

2024 a dmission ( 40 ^o cycle) :   see Admission Site

deadline July 1, 2024

Computational Mathematics

Machine Learning, Deep Learning

Health Data Science and Statistical Learning

Computational Physics

Models, mathematics, statistics, data and algorithms are the foundations of our digital world. Computational methods and mathematical analysis are increasingly used for simulation, prediction, data evaluation, analysis, risk assessment, development and optimization, in virtually every field of Science,  including Chemistry, Medicine and Biology .

The PhD in “ Computational Mathematics, Learning and Data Science ” aims at offering graduate students solid skills in Computational Mathematics, Computational Physics, Scientific Computing, Statistics, Optimization, Machine Learning, Data Sciences as well as an interdisciplinary view of Applied Mathematics, Physics and decision processes under uncertainty. The acquired methodological skills will be also used to answer scientific questions in the real world data. The program gives a solid theoretical and computational grounding for high level research in universities, international research institutions and industry.

This international PhD program is organized jointly with the Euler Institute of the Università della Svizzera Italiana (USI) and the Fondazione Bruno Kessler (FBK). It is also a multidisciplinary PhD program with Faculty from the Departments of Mathematics, Physics, Political Sciences, Brain and Behavioral Sciences of UniPV and the Fondazione Mondino IRCCS, the IMATI-CNR Institute and the National Institute of Nuclear Physics (INFN).

• Offerta Formativa
• Bando 40° ciclo
• Informazioni dottorandi

Phd in Computational Mathematics and Decision Sciences

University of Pavia (UniPV) – Universita` della Svizzera Italiana (USI)

Models, mathematics, statistics, data and algorithms are the foundations of our digital world. Computational methods and mathematical analysis are increasingly used for simulation, prediction, data evaluation, analysis, risk assessment, development and optimization, in virtually every field of Science.

The PhD in “ Computational Mathematics and Decision Sciences”  aims at offering graduate students solid skills in Computational Mathematics, Computational Physics, Scientific Computing, Statistics, Decision Sciences, Optimization, Machine Learning, as well as an interdisciplinary view of Applied Mathematics, Physics and decision processes under uncertainty. The program gives a solid theoretical and computational grounding for high level research in universities, international research institutions and industry.

This international PhD program is organized jointly with the Institute of Computational Sciences (ICS) of the Universita` della Svizzera Italiana (USI). It is also a multidisciplinary PhD program with Faculty from the Departments of Mathematics, Physics, Computer Engineering, Political Sciences of UniPV and the Fondazione Mondino IRCCS, the IMATI-CNR Institute and the National Institute of Nuclear Physics (INFN).

Prospective PhD students with strong mathematical background and a desire for theoretical and applied research in areas of Computational Mathematics and Physics, Scientific Computing, Decision Sciences and Operations Research, Statistics, Optimization,  are encouraged to apply.

Research areas

• Computational Mathematics: theoretical and computational tools for the applied Sciences and Engineering
• Scientific Computing: algorithms for continuous and discrete mathematical models, parallel and distributed computing, numerical simulation
• Numerical Analysis: numerical methods for ordinary and partial differential equations, approximation of data and functions, numerical linear algebra
• Mathematical Analysis and Modeling: ordinary and partial differential equations, functional analysis, variational models and methods, calculus of variations
• Biomathematics: mathematical and numerical modeling in Biology, Physiology and Neurosciences
• Mathematical Physics: kinetic theory, granular media, statistical mechanics, diffusion equations, hyperbolic systems, socio-economic modeling
• Computational Physics: mathematical and computer methods in Elementary Particle and High Energy Physics, Monte Carlo methods, quantum computing
• Biophysics: biophysical models and Monte Carlo methods in Radiotherapy and Hadrotherapy
• Computational Statistics: multivariate Statistics, data analysis, Bayesian Statistics, analytical models for Big Data and Data Sciences
• Decision Sciences: decision models, decision under uncertainty, risk theory, game theory
• Optimization and Operational Research: Optimization methods and algorithms, convex, integer, quadratic, and nonlinear programming, Control Theory
• Machine and Statistical Learning, Deep Learning
• Big Data Analytics, Bio Data Science

The 3-year PhD program is organized into one year and a half of courses and seminars, followed by one year and a half of research activities leading to the doctoral thesis. Multidisciplinary interchange with other graduate programs in the PhD schools of Pavia, USI Lugano and the other participating Institutes and  international partners is also encouraged.

Perspective students follow a personalized program by selecting both core and specialized courses in their field of interest and conducting original research under the supervision of a faculty member.

The Faculty of the PhD in “ Computational Mathematics and Decision Sciences”   includes scholars internationally well-acknowledged in the fields of Computational Mathematics, Physics, Statistics and Decision Sciences.

Career opportunities

The main career opportunities offered by the PhD in Computational Mathematics and Decision Sciences are careers at universities and at public and private research institutions, both in Italy and abroad; employment in industries to carry out research, modeling, data analysis, etc.; employment with consulting and information technology companies; statistical and financial consulting activities; banks and insurance companies.

Coordinator:  Prof. Luca Pavarino Department of Mathematics “Felice Casorati” e-mail:  [email protected]

Sito web:  http://compmat.unipv.it/

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The Department of Mathematics is one of seven departments making up Purdue’s College of Science. The Department has an international reputation as an outstanding center for mathematics.

The Department offers Bachelor of Science and Doctor of Philosophy degrees. Bachelor of Science programs include Actuarial Science, Applied Mathematics, Core Mathematics, Data Science, Honors Mathematics, Mathematics and Computer Science, Mathematics for Business, Mathematics Education, and Mathematics and Statistics.

The Department’s 70 professors are actively involved in current developments in many major areas of mathematics. Faculty research interests can be found in the Faculty Research Areas list in our  Faculty directory .

Please explore our website or contact us directly (see below) for more information about our undergraduate or graduate programs, faculty, research, job opportunities or information on how to support us. The annual department newsletter,  PUrview , is a good place to read about Departmental news.

Department of Mathematics

Contact Information

The Department’s Main Office (Room 835 of the MATH building) is open from 8am to 5pm on all weekdays, except University holidays. The main office is closed from 12 to 1 p.m. in the summer.  A phone number for the department is (765) 494-1901. The full address is:

Department of Mathematics Purdue University 150 North University Street West Lafayette, Indiana 47907-2067

Undergraduate Programs

Undergraduate Mathematics Information

Admissions Information  (Includes application forms and online application)

Graduate Information

For Graduate Information please see Mathematics Graduate Program Information    .

• Actuarial Science Honors, BS
• Actuarial Science, BS
• Applied Mathematics, BS
• Data Science, BS (Mathematics)
• Mathematics Education, BS
• Mathematics Honors, BS
• Mathematics, BS
• Mathematics/Business, BS
• Mathematics/Computer Science, BS
• Mathematics/Statistics, BS
• Mathematics Minor
• Data Science First Year (MA)
• MA I am requesting a sp - History Of Mathematics
• MA 1530N - College Algebra
• MA 1540N - Trigonometry
• MA 1590N - Precalculus
• MA 1601N - Brief Survey Of Calculus I
• MA 1710N - Multidimensional Mathematics
• MA 2610N - Multivariate Calculus
• MA 2660N - Ordinary Differential Equations
• MA 3510N - Elementary Linear Algebra
• MA 5110N - Linear Algebra With Applications
• MA 5230N - Introduction To Partial Differential Equations
• MA 5280N - Advanced Mathematics For Engineering And Physics II
• MA 10800 - Mathematics As A Profession And A Discipline
• MA 13700 - Mathematics For Elementary Teachers I
• MA 13800 - Mathematics For Elementary Teachers II
• MA 13900 - Mathematics For Elementary Teachers III
• MA 15300 - College Algebra
• MA 15555 - Quantitative Reasoning
• MA 15800 - Precalculus - Functions And Trigonometry
• MA 16010 - Applied Calculus I
• MA 16019 - Applied Calculus I For Technology
• MA 16020 - Applied Calculus II
• MA 16100 - Plane Analytic Geometry And Calculus I
• MA 16200 - Plane Analytic Geometry And Calculus II
• MA 16290 - Data Science Labs: Calculus
• MA 16500 - Analytic Geometry And Calculus I
• MA 16600 - Analytic Geometry And Calculus II
• MA 17000 - Introduction To Actuarial Science
• MA 18300 - Professional Practicum I
• MA 19000 - Topics In Mathematics For Undergraduates
• MA 25000 - Problem Solving In Probability
• MA 26100 - Multivariate Calculus
• MA 26190 - Data Science Labs: Multivariate Calculus
• MA 26200 - Linear Algebra And Differential Equations
• MA 26500 - Linear Algebra
• MA 26600 - Ordinary Differential Equations
• MA 27101 - Honors Multivariate Calculus
• MA 27300 - Introduction To Financial Mathematics
• MA 27900 - Modern Mathematics In Science And Society
• MA 29000 - Topics In Mathematics For Undergraduates
• MA 29199 - Cooperative Experience I
• MA 29299 - Cooperative Experience II
• MA 30100 - An Introduction To Proof Through Real Analysis
• MA 30300 - Differential Equations And Partial Differential Equations For Engineering And The Sciences
• MA 32500 - History Of Mathematics
• MA 34100 - Foundations Of Analysis
• MA 34900 - Signals And Systems For Mathematicians
• MA 34990 - Data Science Labs: Signals And Systems
• MA 35100 - Elementary Linear Algebra
• MA 35301 - Linear Algebra II
• MA 36200 - Topics In Vector Calculus
• MA 36600 - Ordinary Differential Equations
• MA 37300 - Financial Mathematics
• MA 37400 - Mathematical Foundations For Machine Learning
• MA 37500 - Introduction To Discrete Mathematics
• MA 38500 - Introduction To Logic
• MA 38600 - Professional Practicum IV
• MA 39000 - Topics In Mathematics For Undergraduates
• MA 39399 - Cooperative Experience III
• MA 39499 - Extensive Cooperative Experience IV
• MA 39599 - Extensive Cooperative Experience V
• MA 41600 - Probability
• MA 41690 - Data Science Labs: Probability
• MA 42100 - Linear Programming And Optimization Techniques
• MA 42500 - Elements Of Complex Analysis
• MA 42800 - Introduction To Fourier Analysis
• MA 43200 - Elementary Stochastic Processes
• MA 44000 - Honors Real Analysis I
• MA 44200 - Honors Real Analysis II
• MA 45000 - Algebra Honors
• MA 45300 - Elements Of Algebra I
• MA 45401 - Galois Theory Honors
• MA 46000 - Geometry
• MA 46200 - Elementary Differential Geometry
• MA 47201 - Fundamental Long Term Actuarial Mathematics
• MA 48100 - Advanced Problem-Solving Seminar
• MA 48200 - Advanced Short Term Actuarial Mathematics
• MA 48400 - Seminar On Teaching College Algebra And Trigonometry
• MA 48700 - Professional Practicum V
• MA 49000 - Topics In Mathematics For Undergraduates
• MA 49500 - Advanced Topics In Mathematics For Undergraduates

Emporia State School of Science + Mathematics

Graduate Certificate in Applied Statistics and Data Analysis

Level up your mathematical skills.

Emporia State University’s Graduate Certificate in Applied Statistics and Data Analysis program is flexible so you can reach your personal goals in the way that will best fit your needs. Our classes are available on-campus and online, allowing you to decide when and how you take your classes.

Additional information

Students must take

MA 532: Mathematical Statistics I, 3 hours

Then 15 hours in Statistics and Applied Mathematics courses.

To see the full list of courses available, see the degree requirements on the Master of Science in Mathematics .

A grade point average of not less than 2.5 in the last 60 semester hours of study or an overall grade point average of no less than 3.0 for a completed master’s degree.

Applicants who have completed 24 credit hours of undergraduate mathematics, including at least two semesters of Calculus and at least one course where writing mathematical proofs is a significant part of the content, are viewed favorably.

An undergraduate degree in mathematics, mathematics education, or an area with a significant mathematics requirement is preferred.

In general, success in our programs requires familiarity with the content of the first two semesters of a typical Calculus sequence as well as experience in writing mathematical proofs.

Graduate School Application

Upon filling out the application, one has the choice to choose a concentration to supplement their Master's Degree. If one does not desire to choose a concentration, then leave that section blank on the application.

1. Official transcripts from all colleges/universities attended.

2. Copy of government-issued ID.

Submitting Application Materials

The following items must be provided/completed at the time of application submission through the Application Portal :

*Unofficial bachelor's degree transcript

*Copy of government-issued ID

Official transcripts

Students must submit official bachelor’s degree transcripts containing at least 60 credit hours of coursework and final grades. Any additional transcripts from college credit accumulated after the bachelor’s degree MUST be submitted if you will be using these credits for transfer credit or for last 60 GPA. Transcripts are considered official when they arrive in the Graduate Office in a sealed envelope from the issuing institution or are received through a secure electronic transcript service to [email protected] .

If the transcripts need to be mailed, send to:

Emporia State University Graduate School Campus Box 4003 Emporia State University 1 Kellogg Circle Emporia, KS 66801-5415.

Why choose ESU?

At ESU, we know it’s your future, emphasis on the ‘your.’ So tell us what you want to do with your certificate, and we’ll show you an educational track that sets you up for success.

Take action

Apply to the Emporia State University Graduate School.

Request more information

Request more information today. To contact the department, see the contact information at the bottom of this page.

Scholarships

Scholarships are available for Mathematics students at ESU.

Contact the Mathematics Department

620-341-5281

[email protected]

Science Hall Room 141

Campus Box 4027 1 Kellogg Circle Emporia, KS 66801

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FestivEli Celebrates Eli Upfal, His Career, And His Collaborations

• Posted by Jesse Polhemus
• on June 6, 2024

Brown University ’s Eli Upfal , Rush C. Hawkins Professor of Computer Science, turned seventy in 2024. To celebrate the occasion, four of his former PhD students invited his many collaborators and colleagues over the years to FestivEli, a series of talks and informal chats about the topics most dear to Eli, and where his contributions have been long-lasting and often trendsetting. Held on May 20-21, 2024, the organizers were Gopal Pandurangan (PhD ‘02, now Professor of Computer Science at the University of Houston, TX), Aris Anagnostopoulos (PhD ‘06, now Professor of Computer Engineering at Sapienza University of Rome, Italy), Matteo Riondato (PhD ‘14, now Associate Professor of Computer Science at Amherst College, MA), and Lorenzo De Stefani (PhD ‘20, now Lecturer of Computer Science at Brown), with support from Brown CS Administration and Finance Manager Lisa Manekofsky.

During Eli's long career, the organizers note, his interests moved through many different areas of computer science, from parallel algorithms to networks, to computational biology, to data science, with the guiding thread of randomization and probabilistic analysis. Eli had impactful contributions in many such areas, resulting in honors that include ACM and IEEE Fellowships, the 2020 ACM Paris Kanellakis Award, the 2023 RECOMB Test of Time Award, and the 2016 ACM KDD Best Student Paper Award.

Matteo opened the event by thanking Brown CS for its support and paying tribute to Eli and the community of “amazing collaborators, relationships, and friendships” that he’s built.

“Over the next two days,” Riondato urged the attendees filling CIT 368, “let’s learn from each other and create connections.”

Next at the podium, Roberto Tamassia (Chair of the Department of Computer Science and James A. and Julie N. Brown Professor of Computer Science) said he was truly grateful not just for Eli’s prominent research visibility but also for his academic leadership, pointing to his service as department chair and his contributions to the Center for Computational Molecular Biology,  the Data Science Institute, and the Institute for Computational and Experimental Research in Mathematics. He praised Eli’s scholarship, which has continually found new applications, and cheered Eli’s “distinct appreciation for the fine things in life”.

“Eli has truly embodied the departmental ethos,” Roberto said, “of theory with profound impact on practice.”

Sixteen talks followed, notable for their diversity of topics (everything from MCMC mean estimation to learning with unknown distribution drift to private streaming graph algorithms with low space) as well as their moments of profound esteem and friendship. Andrei Broder of Google Research opened his talk with memories of days when he and Eli were at Stanford University and there were fewer than a hundred computer science students at the department, drawn together as part of the Algorithms for Lunch Bunch. This section of the talk was warmly illustrated with Andrei’s handwritten records of splendid home-cooked meals shared with Eli: figs in champagne, balcony tomatoes, tongue with capers and couscous. Andrea Pietracaprina and Geppino Pucci of the University of Padova spoke of Eli’s lasting ties to Italy, presenting him with a University of Padova octocentenary sweatshirt and sharing their deep gratitude to him as a friend, mentor and source of inspiration.

“But beware Eli’s ideas and intuitions,” joked Geppino, “because they will keep you working for decades!”  

At an open-mic session at the end of the first day, Aris gratefully remembered Eli setting “the rules of the game” for being one of his PhD students: “You won’t be one of the PhDs who disappear.”

Anna Karlin of the University of Washington noted that Upfal was an inspiration and someone who had played a seminal role as her “unofficial” doctoral advisor: “He was scary, so smart…he’s mellowed over the years!” 

Traveling all the way from Australia to attend, Brown CS PhD alum Olya Ohrimenko of the University of Melbourne described Eli as an “academic uncle”, the kind of person that she would turn to in a crisis even before calling her own parents. “He taught me,” she said, “about research and work/life balance, how to have fun in research…Thank you for the time spent on me, thanks for always being available.” 

Eli’s son, Ilan Upfal, an electrical engineer, had attendees laughing with stories of father and son co-piloting a remote control helicopter and eating gelato together in Italy as a nine-year-old who couldn’t make much of discussions of balls and boxes. One thing stood out, Ilan said, from their many conversations over the years: “A great intellectual wrestling…how to think, how to question.”

When the time came for Eli to step up to the podium, he thanked the organizers and all the attendees. Moving through a brief summary of his career, Upfal stressed the importance of working hard and studying hard, but acknowledged the profound impact of luck. He paid tribute to his advisor, Eli Shamir, as an inspiration for not just how to understand problems but how to work with students, then moved forward through time to his arrival at Brown, his term as Brown CS department chair, and the days since. 

Eli smiled broadly as he brought up a slide with a word cloud featuring the names of the many collaborators that he’s had the good fortune to come to know as close friends. “You, all of you, are a major part of my life,” he said, turning in the last moments of his comments toward a shared future. “Your ideas, your work…we are still working together.”

For more information, please click the link that follows to contact Brown CS Communications Manager Jesse C. Polhemus .

Computer Science at Brown University Providence, Rhode Island 02912 USA Phone: 401-863-7600 Map & Directions / Contact Us

Two Chemistry Alums Awarded 2024 NSF Graduate Research Fellowships

Nsf fellowships honor outstanding graduate students in science, technology, engineering and mathematics..

Nine current students and recent alums of the University of Maryland’s College of Computer, Mathematical, and Natural Sciences (CMNS) received prestigious National Science Foundation (NSF) Graduate Research Fellowships , which recognize outstanding graduate students in science, technology, engineering, and mathematics.

Across the university, 22 current students and recent alums were among the 2024 fellowship winners announced by the NSF. 

The recipients included Kaitlyn Dold (B.S. ’22, chemistry) and Katharina Krstic (B.S. ’22, chemistry). Dold is a chemistry Ph.D. student at UC Irvine and Krstic is a Ph.D. student in the organic chemistry program at MIT. NSF fellows receive three years of support, including a $37,000 annual stipend, a $16,000 cost-of-education allowance for tuition and fees, and access to opportunities for professional development.

The NSF Graduate Research Fellowship Program helps ensure the vitality of the human resource base of science and engineering in the United States and reinforces its diversity. The program recognizes and supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines who are pursuing research-based master’s and doctoral degrees at accredited U.S. institutions.

Since 1952, NSF has funded more than 60,000 Graduate Research Fellowships out of more than 500,000 applicants. At least 42 fellows have gone on to become Nobel laureates and more than 450 have become members of the National Academy of Sciences.

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Doctoral Program in Mathematical Sciences

Corso di dottorato in scienze matematiche, recent theses (mathematics).

List of the PhD theses prepared within the Mathematics Curriculum in the last years. (The name of the supervisor is enclosed in brackets. Most of the theses can be downloaded from PaDUA@research , the Digital Archive of the University of Padova: just click on the thesis' title).

XXXII Cycle

• Davide Barco, A topological approach to the Fourier transform of an elementary D-module (Andrea D'Agnolo)
• Federico Campanini,  Weak forms of the Krull-Schmidt theorem and Prüfer rings in distinguished construction s (Alberto Facchni)
• Maria Teresa Chiri, Structural properties of solutions, approximation and control for conservation laws with discontinuous flux and bioinspired PDE models (Fabio Ancona)
• Giacomo Graziani, Modular sheaves of de Rham classes on Hilbert formal modular schemes for unramified primes (Adrian Iovita)
• Maria Giovanna Le Gros, Minimal approximations for cotorsion pairs generated by modules of projective dimension at most one over commutative rings (Silvana Bazzoni)
• Federico Venturelli, The Alexander polynomial of certain classes of non-symmetric line arrangements (Remke N. Kloosterman)
• Roberto Bramati, Geometric integral inequalities on homogeneous spaces (Paolo Ciatti)
• Sebastiano Don, Functions of bounded variation in Carnot-Carathéodory spaces (Davide Vittone)
• Simone Giovannini, On some examples in higher Auslander-Reiten theory (Silvana Bazzoni)
• Yan Hu, Rationality of darmon points over genus fields of nonmaximal orders (Matteo Longo)
• Paolo Luzzini, Regularizing properties of the double layer heat potential and shape analysis of a periodic proble m (Massimo Lanza de Cristoforis)
• Roman Pukhtaievych, Periodic and hypercomplex potentials. Properties and applications (Massimo Lanza de Cristoforis)
• Marco Tarantino, Recollements from exact model structures and heart constructions in triangulated categories (Silvana Bazzoni)
• Anna Tovo, Mathematical Modelling and Statistics of Biodiversity (Marco Favretti)
• Ferraresso Francesco, On the spectral stability of polyharmonic operators on singularly perturbed domains (Pier Domenico Lamberti)
• Aigul Myrzagaliyeva, On pointwise multipliers in some function spaces   (Massimo Lanza de Cristoforis)
• Leone Cesare Cimetta, Some properties of zeta functions associated to profinite groups (Andrea Lucchini)
• Laura Cossu, Factorizations of invertible matrices into products of elementary matrices and of singular matrices into products of idempotent matrices (Paolo Zanardo)
• Ismail Abdelsheed Gad Ameen, Fractional calculus: numerical methods and SIR models (Paolo Novati)

XXVIII Cycle

• Marta Zoppello, Controllability and optimization of deformable bodies in fluids: from biology to robotics (Franco Cardin)
• Luigi Provenzano, On mass distribution and concentration phenomena for linear elliptic partial differential operators (Pier Domenico Lamberti)
• Daria Ghilli, Some Results in Nonlinear PDEs: Large Deviations Problems, Nonlocal Operators, and Stability for Some Isoperimetric Problems (Martino Bardi)
• Valentina Franceschi, Sharp and Quantitative Isoperimetric Inequalities in Carnot-Carath é odory spaces (Roberto Monti)

XXVII Cycle

• Zhanar Taspaganbetova, Boundedness and compactness of matrix operators in weighted spaces of sequences and their applications (Massimo Lanza de Cristoforis)
• Guhanvenkat Harikumar, Darmon cycles and the Kohnen - Shintani lifting (Matteo Longo)
• Nguyen Khanh Tung, Cyclically presented modules, automorphism-invariant modules and poor modules (Alberto Facchini)
• Francesco Mattiello , New trends in tilting theory (Riccardo Colpi)
• Genaro Hernandez Mada, Monodromy Criterion for the Good Reduction of Surfaces (Bruno Chiarellotto )
• DavideBuoso, Shape sensitivity analysis of the eigenvalues of polyharmonic operators and elliptic systems (Pier Domenico Lamberti )
• Joao Henrique Branco Meireles, Singular Perturbations and Ergodic Problems for degenerate parabolic Bellman PDEs in R^m with Unbounded Data (Martino Bardi)
• Velibor Bojkovic, Finite morphism of p-adic curves (Francesco Baldassarri)
• Federico Bambozzi, On a generalization of affinoid varieties (Francesco Baldassarri)
• Marco Cirant, Nonlinear PDEs in ergodic control, Mean Field Games and prescribed curvature problems (Martino Bardi)
• Ly Kim Ha, On two approaches for partial differential equations in several complex variables (Giuseppe Zampieri)
• Nguyen Luong Van , On regular and singular points of the minimum time function (Giovanni Colombo)
• Dau The Phiet, Pseudoconvex domains: Diederich - Fornaess index and the invariant metrics near the boundary points (Giuseppe Zampieri)
• Martino Garonzi, Coverings of Groups by Subgroups (Andrea Lucchini)
• Alice Pavarin, Equivalences of additive categories (Silvana Bazzoni)
• Cecilia De Zan, Some new results on reaction-diffusion equations and geometric flows (Pierpaolo Soravia)
• Daniele Fontanari, Quantum manifestations of the adiabatic chaos of perturbed superintegrable Hamiltonian systems (Francesco Fassò, Dmitri í Sadovski í )
• Nicola Girardi, Regular biproduct decompositions of objects (Alberto Facchini)
• Nurgul Kydyrmina, Operators in Sobolev Morrey spaces (Viktor Burenkov)
• Paolo Musolino, Singular perturbation and homogenization problems in a periodically perforated domain. A functional analytic approach (Massimo Lanza de Cristoforis)
• Simone Vazzoler, Semiclassical limit: coherent states, quasimodes and WKB approximation (Franco Cardin)

XXIII Cycle

• Khai Tien Nguyen, The regularity of the minimum time function via nonsmooth analysis and geometric measure theory   (Giovanni Colombo)
• Gabriele Fusacchia, Injective modules over semistar Noetherian domains (Luigi Salce)
• Massimiliano Patassini, On the Dirichlet polynomial of the simple groups of Lie type (Andrea Lucchini)
• Marco Perone, Direct sum decompositions and weak Krull-Schmidt Theorems (Alberto Facchini)
• Valentina Settimi, On some additive problems with primes and powers of a fixed integer (Alessandro Languasco)
• Heer Zhao, On 1-Motives with torsion and their l-ADIC realisations (Barbieri Viale)
• Saltanat Rakhimova, The oscillation properties of half-linear second order and higher order differential equations (Ryskul Oinarov, Massimo Lanza de Cristoforis)
• Lyailya Zhapsarbayeva, Regularity and approximation properties of the solutions of second order degenerate and nonlinear elliptic systems (Otelbayev Mukhtarbay, Massimo Lanza de Cristoforis)
• Alice Ciccioni, On the Bloch-Ogus formalism for the rigid syntomic cohomology (Bruno Chiarellotto)
• Valentina Colombo, Some Properties Of The Moebius Function In The Subgroup Lattice Of The Alternating And Symmetric Groups (Andrea Lucchini)
• Raffaele Marigo, Separate analyticity and holomorphic sectors (Giuseppe Zampieri)
• Ermal Feleqi, Spectral stability estimates for the eigenfunctions of second order elliptic operators (Burenkov)
• Vu Khanh Tran, A general method of weights in the d-bar-Neumann problem (Giuseppe Zampieri)
• Crestani Eleonora, Monotone 2-Groups (Federico Menegazzo)
• Andrea Pavan, Computing Arithmetic Subgroups of Affine Algebraic Groups (Federico Menegazzo)
• Marco Formentin, Two problems concerning interacting systems: 1. On the purity of the free boundary condition Potts measure on Galton-Watson trees 2. Uniform propagation of chaos in some spin-flip models (Paolo Dai Prà)
• Matteo Dalla Riva, Potential theoretic methods for the analysis of singularly perturbed problems in linearized elasticity   (Massimo Lanza de Cristoforis)
• Giovanni Cerulli Irelli, Structural theory of rank three cluster algebras of affine type (Alberto Tonolo)
• Paola Zizzi, From Quantum Metalanguage to the Logic of Qubits (Giovanni Sambin, Pieralberto Marchetti)
• Gabriele Terrone, Singular Perturbation and Homogenization Problems in Control Theory, Differential Games and fully nonlinear Partial Differential Equations (Martino Bardi)
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Computer engineering graduate student invited to Heidelberg Laureate Forum

Graduate student Dina Hussein has been selected to participate in the Heidelberg Laureate Forum where she will have the chance to meet the world’s top researchers in computer science and math.

The week-long event is a networking conference in which 200 math and computer science student researchers from around the world spend a week interacting with recipients of the top prizes in those fields, such as the Abel Prize, ACM A.M. Turing Award, ACM Prize in Computing, Fields Medal and the Nevanlinna Prize. The event will be held in Heidelberg, Germany in September.

“I feel incredibly honored and excited to have been asked to participate in the Heidelberg Laureate Forum,” said Hussein, a PhD student in WSU’s School of Electrical Engineering and Computer Science (EECS). “It’s a once-in-a-lifetime opportunity to engage with laureates who have made groundbreaking contributions to mathematics and computer science.”

Hussein said she is particularly excited about getting the opportunity to discuss her research with experts in the field, receiving feedback, and learning from their experiences.

“The first question I would like to ask the laureates would be, ‘What advice would you give to young researchers like me who are just starting their journey in research,’” she said.

A graduate student at WSU since 2021, Hussein is studying computer engineering. Her research is in machine learning algorithms for wearable device applications.  She has received several awards at WSU, including being named an outstanding graduate research assistant in 2024 and an outstanding teaching assistant in 2022. She took second place this year in Voiland College’s three-minute thesis competition. She also received an award at the Association for Computing Machinery’s Special Interest Group on Embedded Systems (SIGBED) student research competition in 2023. After completing her PhD, she hopes to conduct research in academia or in a research lab.

“Dina is an outstanding student in the School of EECS with wide breadth and depth of publications in design automation, embedded systems, and AI venues. She is also working on problems with social impact,” said Ganapati Bhat, Raymond and Beverly Lorenz Distinguished Assistant Professor in Electrical and Computer Engineering and Hussein’s advisor. “Her contributions have been well recognized, including through the School of EECS, the 3-minute thesis competition, and ACM student research contributions.”

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Understanding bushfire evacuations through a mathematics PhD

5 Minute read

Life after graduate research: Dr Felicia Eng

Dr Felicia Eng’s PhD in mathematics has helped her build a career at IBM. Her PhD research used game theory to explain how people make decisions when evacuating from a bushfire. The connections she built were a highlight of her University of Melbourne experience and a key to many opportunities she has had in her career.

“I think a lot of people don't realise that PhDs don't have to go into academia. And I think in my mind I was always keen on contributing to industry,” says Dr Felicia Eng.

Dr Eng graduated from the University of Melbourne with a PhD in Science . She now works at IBM , a multinational computer and information technology company.

There are spaces and places in industry where having a PhD and knowing how research works is extremely important. Dr Felicia Eng

“But I've discovered that even when you don't need a PhD, the skills that you learn from going through a PhD translate so well into industry. Those skills are things like being able to formulate a really clear argument,” Dr Eng says.

As a Customer Success Manager, Dr Eng acts as the interface between IBM’s technology and its customers. She still uses her technical skills in coding and mathematics.

But a PhD, she says, is also a sign of someone who can find new ways forward when they come across blockers.

“In industry, that's going to happen a lot. You may have objections. You may have questions or problems that don't appear to be solvable on the surface. But with some out-of-the-box thinking, you can find a way. And when you can help clients with that, you become their trusted advisor,” Dr Eng says.

How game theory can help evacuate people during disasters

Dr Eng’s PhD research examined how people make evacuation decisions during disasters. As a mathematician, she collaborated with researchers in computer sciences and geospatial sciences, including researchers at RMIT University and the Australian National University.

As the planet warms, we’re likely to experience more disasters like bushfires and floods . How can we make sure that as many people as possible get to safety?

“Understanding human behaviour is really critical,” says Dr Eng.

Dr Eng looked at people’s evacuation decisions during the 2009 Black Saturday bushfires in Yarra Glen. There were only four roads out of town – and one was blocked by the advancing fire.

If you were leaving a town threatened by a bushfire, would you follow where everyone else is going? Using game theory , Dr Eng could describe how people’s decisions interacted.

“In game theory, what I choose to do will affect what you choose to do. Therefore, what you choose to do will then affect what I will choose to do. There's this feedback loop,” she says.

Understanding why people make decisions and based on what information can guide future evacuations. It will help policymakers understand how to help people evacuate as fast as possible.

“It's not an easy space to write policy for, because sometimes there are ethical consequences that need to be considered. You can't just tell one person, ‘You should go a longer way. Even though it may be worse for you, it's better for everyone as a whole,’” Dr Eng says.

Learn more about our graduate research options

Colouring breaks and juggling responsibilities: PhD life at the University of Melbourne

Dr Eng grew close with her PhD cohort.

“We would do those adult colouring books for half an hour to take a break. Or there would be other days where one of us would be asleep on the floor under the desk, because it had just been that kind of day,” she reminisces.

We'd all be there for each other, and that is something that I will always treasure. Dr Felicia Eng

For four years of her PhD, Dr Eng worked as a residential tutor at St Hilda’s College . St Hilda’s is a residential college for undergraduate students at the University of Melbourne.

As a tutor, Dr Eng got to experience college life on her own terms. She did her PhD work during the day and tutored students in economics and statistics in the evening. She had dinner with students and attended college events.

“I loved it for myself. But I also loved to see the impact that the college and its people had on the students,” she says.

Dr Eng was also already working at IBM Research during her PhD. She contributed her expertise to a project updating disaster management communication architecture.

Working at St Hilda’s helped Dr Eng juggle all her responsibilities.

“It took some of the burden off my day-to-day life, like cooking or cleaning. I felt like I could focus on the tutoring or my work, or the PhD,” she says.

“I'm still heavily involved with St Hilda’s. I've been on their board of directors since 2023.”

Connecting with people has opened doors for Dr Eng.

“The more people you talk to, and the more you share your story, the more people will remember you. And they will preference somebody that they know over somebody that they don't,” she says.

Dr Eng got both her jobs at St Hilda’s and at IBM Research through her networks.

How the University of Melbourne supports PhD researchers

Yet Dr Eng also enjoyed the autonomy she had as a PhD researcher. She could confidently explore and try new things, knowing she could always go to her supervisors for help.

“I felt really comfortable asking for help. My supervisors made it easy. They were always open, they were always understanding. They were always collaborative,” she says.

Before her PhD, Dr Eng had completed a Bachelor of Commerce (Honours) and a Bachelor of Science at the University of Melbourne.

“I didn't want to do a PhD because it was the next thing expected of me or because I didn't have anything better to do. I needed it to be something that I was going to care a lot about,” she says.

Dr Eng spent two years teaching in the economics department before she started her PhD. She started in economics but switched to mathematics. The University of Melbourne supported her decision and made the transition between faculties smooth.

Dr Eng had an Australian Commonwealth government-supported PhD scholarship that she could take between faculties. But the funding ended before she could have finished her new project. The Faculty of Science provided her with scholarships to help her complete the PhD.

“It wasn't clear to me that I was going to complete the PhD. But with the support that I received from the people in the School of Mathematics and Statistics – and the scholarships from the Faculty of Science – I got there, and I'm so happy that I got there.”

Learn more about a PhD in Science

First published on 5 June 2024.

Keep reading

World environment day 2024.

Find out how the University is committing to solve the climate and biodiversity crisis across this year’s themes.

Why research with us

Explore the benefits of undertaking your graduate research at the University of Melbourne.

Your research options

Explore your options as a graduate researcher at the University of Melbourne.

How to apply

Find out how to apply for graduate research at the University of Melbourne.

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