Find Academic Supervisors

Donald McGinnView profile

Associate Professor

• Analytic Number Theory
• Algebraic Number Theory
• Combinatorics
• +4 more

Dr. Donald McGinn is an Associate Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is based in Building 4, Room 339, and can be reached at dmcginn@uwf.edu. He teaches Calculus II and contributes to undergraduate mathematical outreach through programs such as the AMC 8 and AMC 10/12. Ph.D. in Pure Mathematics, University of Nevada, Las Vegas M.S. in Mathematics, University of Florida B.A. in Mathematics, University of North Florida Dr. McGinn's research focuses on core areas of pure mathematics, particularly Analytic and Algebraic Number Theory and Combinatorics . His work explores deep problems such as solutions to Markoff equations, structural properties of graphs via friendly index sets, and combinatorial challenges in constructing and counting Latin squares—objects of significance in design theory and cryptography. His research bridges theoretical inquiry with discrete mathematical structures. Although no recent publications are listed in the provided text, his keywords suggest sustained engagement with number-theoretic Diophantine problems and combinatorial configurations. This indicates a research trajectory centered on discrete algebraic systems and their symmetries. There are no scientific awards mentioned in the available information. Dr. McGinn advises students through his teaching and departmental involvement, particularly in undergraduate education and competition mathematics. While formal advisees are not listed, he supports academic growth via course instruction and extracurricular mathematical activities. There is no mention of external grants or funded research projects in the provided content. There is no information available about laboratories, research teams, or collaborative research groups led by Dr. McGinn.

Patric ÖstergårdView profile

Professor

• Discrete Mathematics
• Information Theory
• Coding Theory
• +4 more

Patric Östergård is a Professor at Aalto University's Department of Information and Communications Engineering. His research focuses on fundamental problems in discrete mathematics and information theory, utilizing combinatorial algorithms and massive computations to study existence and classification of mathematical structures with applications in ICT. Department: Information and Communications Engineering Institution: Aalto University His key research areas include coding theory, design theory, graph theory, and Shannon theory. He leads a high-performance computing cluster Medusa for computational work. His research is supported by the Academy of Finland's project 'Construction and Classification of Discrete Mathematical Structures' (2015–2019). Notable trends: Steiner triple systems, Hadamard matrices, and error-correcting codes Collaboration: Active in international partnerships, particularly in combinatorics and coding theory Scientific Awards Kirkman Medal (1997) for contributions to combinatorial research Doctor et Professor Honoris Causa from University of Pécs, Hungary (2013) He has supervised 7 doctoral theses and actively participates in academic service through editorial board memberships, conference committee roles, and hosting visiting scholars.

Guillaume LandryView profile

Associate Professor

• Image Guided Radiation Therapy
• Artificial Intelligence in Radiotherapy
• Deep Learning
• +12 more

Guillaume Landry is an Associate Professor of Image Guided Radiation Therapy at the Department of Radiation Oncology, University Hospital of Ludwig Maximilian University (LMU) in Munich, Germany, a position he has held since 2019. He is affiliated with the Faculty of Medicine and also contributes to teaching in the Faculty of Physics at LMU. He leads the IGRT research group, focusing on the integration of artificial intelligence in radiotherapy. His research interests include: Image Guided Radiation Therapy (IGRT) Artificial Intelligence and Deep Learning in Radiotherapy MR-guided Radiotherapy (MRgRT) Online Adaptive Radiotherapy Motion Management and Real-time Target Localization Auto-segmentation and Dose Accumulation CBCT and Proton Therapy Imaging His work emphasizes the application of deep learning to problems such as synthetic CT generation, motion prediction, and treatment adaptation, particularly in lung and abdominal cancers. Recent publications show a strong trend toward developing and validating AI models for clinical implementation in radiotherapy, including major contributions to guidelines and grand challenges like SynthRAD2025 and TrackRAD2025. Scientific awards received include: phiRO frequent reviewer award Emerging Talent Award ("Prix de la relève") Dr. Landry is actively involved in research mentoring and grant-funded projects such as the DFG-funded AIM-CBCT-ART and the BMBF-funded INSIGHT project. His work bridges medical physics, oncology, and AI, aiming to improve precision and personalization in radiation therapy. He is a key contributor to international collaborations and clinical guideline development in AI for radiation oncology. He is associated with the LMU ART Lab, a research group dedicated to advancing image-guided and adaptive radiotherapy through innovative physics and AI solutions.

Sarah Alice GagglView profile

Researcher

• Abstract Argumentation
• Answer Set Programming (ASP)
• Knowledge Representation
• +5 more

Dr. Sarah Alice Gaggl is a Researcher at the International Center for Computational Logic (ICCL) within the Technische Universität Dresden , where she has served as Group Leader for Logical Programming and Argumentation since October 2020. She also leads the BMBF-funded project NAVAS - Navigation in the Solution Space of Answer Sets and was a Principal Investigator in the Collaborative Research Center 248 (CPEC) from 2019 to 2022. Her research centers on Abstract Argumentation , Answer Set Programming (ASP) , and Knowledge Representation . Doctorate in Computer Science, Vienna University of Technology, March 2013 Her work bridges Answer Set Programming with practical applications in Knowledge Representation and Nonmonotonic Reasoning , focusing on navigation in plan spaces, efficient algorithms for ASP, and multi-criteria answer set selection. She has supervised numerous theses on topics like Multi-Shot ASP , Abstract Argumentation Frameworks , and Reinforcement Learning in game environments. Recent publications highlight trends in Answer Set Navigation (e.g., PlanPilot , IASCAR ), Algorithm Refinement (e.g., Winning Snake ), and Rule-Based Argumentation (e.g., Grounding Rule-Based Argumentation ). Her contributions span Computational Logic , Automated Reasoning , and Software Systems . She has held editorial and organizational roles for journals and conferences, including the Argument & Computation journal, KR , IJCAI , and ICLP . Dr. Gaggl leads the Logical Programming and Argumentation group and has been affiliated with the Computational Logic Group at TU Dresden since 2013. Her teaching includes courses like Theoretical Computer Science & Logic and Advanced Problem Solving .

Jonathan HermonView profile

Assistant Professor

• Discrete Probability
• Mixing Times of Markov Chains
• Cutoff Phenomenon
• +4 more

Jonathan Hermon is an Assistant Professor at the Department of Mathematics within the University of British Columbia . He previously served as a Research Associate at the Statistical Laboratory (DPMMS, University of Cambridge, 2017-2020) and completed his PhD in Statistics at UC Berkeley (2016) under Allan Sly , following an M.Sc. in Mathematics at the Weizmann Institute of Science advised by Itai Benjamini . Education M.Sc. in Mathematics, Weizmann Institute of Science Ph.D., UC Berkeley (Statistics) Hermon’s research focuses on discrete probability theory , particularly the mixing times of Markov chains , cutoff phenomenon , particle systems , and dependent percolation . His work explores how geometric properties of graphs influence random walks and percolation dynamics, often combining analytical techniques with probabilistic reasoning. His recent publications (2022-2024) emphasize the cutoff phenomenon across diverse graph structures (community-based, nilpotent groups, random matchings) and information concentration on discrete groups. Earlier works (2016-2021) address epidemic models , hypergraph mixing , and recurrence properties of Markov chain traces. Hermon has held postdoctoral positions at the Weizmann Institute and University of Cambridge , and he teaches advanced topics in probability, including mixing times of Markov chains. He collaborates extensively with researchers like Perla Sousi and Justin Salez , contributing to fields such as random graphs, statistical mechanics, and functional inequalities.

Alessio MansuttiView profile

Assistant Professor

• Logic in Computer Science
• Automated Verification
• Formal Methods
• +12 more

Alessio Mansutti is an Assistant Professor at IMDEA Software Institute, Madrid, where he conducts research in logic and formal methods in computer science. Prior to this, he was a Research Associate in the Automated Verification Group at the University of Oxford. His research focuses on decision procedures for arithmetic theories, separation logic, modal logics, and proof theory. Key areas include Presburger arithmetic with non-linear operations (exponentiation, GCD), quantifier elimination, complexity analysis, and logical expressiveness. He has made significant contributions to the decidability and complexity of extended arithmetic and spatial logics. The recent publications show a strong trend in developing quantifier elimination techniques for linear-exponential and counting extensions of arithmetic, analyzing reachability in separation logic, and designing internal calculi for modal and spatial logics. His work bridges theoretical logic with practical verification and optimization problems. Scientific Awards : None mentioned in the text. Advising and Grants : Alessio Mansutti is currently leading independent research funded by the Madrid Regional Government under the César Nombela grant 2023-T1/COM-29001. There is no mention of formal students or advisees, suggesting he may be early in his independent career. He was previously involved in the ERC project ARiAT (2020–2024) led by Christoph Haase, focusing on advanced reasoning in arithmetic theories. Labs and Teams : He is affiliated with the IMDEA Software Institute and was part of the Automated Verification Group at the University of Oxford. His research is deeply collaborative within the formal methods and logic communities, particularly in decision procedures and logical foundations for program verification.

Luke FredericksView profile

Visiting Assistant Professor

• Number Theory
• Elliptic Curves
• Statistics Education
• +5 more

Luke Fredericks is a Visiting Assistant Professor of Mathematics in the Department of Mathematics and Computer Science at Berry College. He teaches a range of undergraduate mathematics courses including Precalculus, Calculus, Abstract Algebra, and Elementary Statistics. His research focuses on number theory, particularly the theory of elliptic curves over finite fields. He is also deeply interested in innovative pedagogical approaches such as inquiry-based learning and mastery-based grading, with a strong emphasis on statistics education. Dr. Fredericks entered college at the pre-algebra level and worked his way up to becoming a mathematics major. He earned a BS in Mathematics and a BA in French from California State University, Chico, and completed his Ph.D. at the University of California, Irvine, where his dissertation involved counting problems for elliptic curves over finite fields. There are no recent publications listed, but his academic trajectory and teaching philosophy suggest a strong commitment to both mathematical research and student-centered learning. Scientific Awards: No awards listed in the provided text. Advising and Grants: There is no information available regarding student advising or research grants. Labs and Research Teams: No specific labs, research teams, or collaborative groups are mentioned in the text.

Brian BeeckenView profile

Professor

• Physics
• Engineering
• Spacecraft Charging
• +6 more

Brian Beecken is a Professor in the Department of Physics & Engineering within the College of Arts and Sciences at Bethel University, where he has been a faculty member since 1988. His work bridges physics and engineering, with a strong emphasis on applied research in space and defense technologies. Education: B.A. in Mathematics and Physics, Elmhurst College, 1980 M.S. in Physics, University of Minnesota, 1984 Ph.D. in Physics, University of Minnesota, 1986 Brian Beecken's research is centered on the physical and engineering challenges of spacecraft systems, particularly the modeling of dielectric charging in space environments and the development and analysis of optical and infrared detectors. His work involves advanced statistical and computational modeling to understand noise in focal plane arrays and limitations in infrared detection systems. These interests are deeply applied, with real-world implications for satellite reliability and remote sensing technologies. His publication record, though modest in volume (around a dozen papers), reflects sustained engagement with high-impact research problems in aerospace and defense. The articles span topics from spacecraft charging to infrared imaging, showing a consistent focus on detector physics and space environment interactions. The keywords across these works highlight expertise in Physics , Engineering , Optics , and Space Science , with subfields including noise modeling , infrared detection , statistical analysis , and spacecraft surface charging . Scientific Awards: Bethel Excellence in Scholarship Award Brian Beecken has secured significant external funding for his research, demonstrating strong national recognition. His work has been supported by 5 NASA fellowships , 1 NASA grant , 2 grants from the Air Force Office of Scientific Research (AFOSR) , 4 AFOSR fellowships , 4 fellowships from the American Society for Engineering Education , 1 grant from Calspan Corp. , and is currently funded by a 2-year grant from the Air Force Research Laboratory . These grants support both his research and likely the training of student researchers, although specific advisees are not listed. His collaborations with NASA's Jet Propulsion Laboratory (JPL), Langley Research Center, and multiple Air Force labs underscore his integration into national research networks. While no formal lab or research team is explicitly named, his extensive collaborative projects with federal agencies suggest leadership in a research group focused on applied physics and engineering at Bethel. His work on test facility development at Eglin Air Force Base further indicates project management and instrumentation development experience.

• Millimeter wave antenna design
• Electromagnetic wave propagation and radiation
• Numerical modeling of electromagnetic problems
• +7 more

Sérgio de Almeida Matos is an Associate Professor with Habilitation at the Department of Information Science and Technology, School of Technology and Architecture, Instituto Universitário de Lisboa (ISCTE-IUL). He is also Deputy Director and Integrated Researcher at the Institute of Telecommunications – IUL (IT-IUL), where he leads research within the Antenna and Propagation Group. His educational background includes a Bachelor's, Master's, and Ph.D. in Electrical and Computer Engineering from Instituto Superior Técnico (IST), University of Lisbon, completed in 2004, 2006, and 2010, respectively. In 2022, he obtained the Aggregation in Science and Communication Technologies from ISCTE-IUL, confirming his qualification for the Associate Professor rank. His primary research interests lie in the design of millimeter-wave antennas for satellite, 5G, and 6G applications. He specializes in numerical modeling of electromagnetic problems, electromagnetic wave propagation in metamaterials, and a coordinate-independent approach to electromagnetic waves. His work has significant impact in national and international satellite communication projects, particularly in the Ka-band. He has authored or co-authored over 110 technical papers, with high citation counts across Web of Science, Scopus, and Google Scholar. His research outputs span journal articles and conference proceedings, focusing on advanced antenna technologies and electromagnetic theory, though specific titles are not listed in the source text. IEEE Senior Member (since 2019) He actively supervises graduate students, currently advising two doctoral candidates and one master’s student. His guidance has led to the successful completion of two master’s theses on metallic antenna design for 5G and 3D-printed dielectric lenses. He has been consistently involved in teaching core engineering courses such as Electromagnetism and Fundamentals of Guided and Wireless Transmission at the undergraduate level. He also previously taught 'Propagation and Radiation of Electromagnetic Waves'. He is a key member of the Antenna and Propagation Group at IT-IUL and contributes to national and international academic leadership, including serving as Chair of the Scientific Committee B (Fields and Waves) of the Portuguese URSI Committee and as a member of the EuCAP Steering Committee. His work integrates theoretical modeling with practical antenna development, often leveraging 3D printing technologies for next-generation wireless systems.

Dan MikulincerView profile

Assistant Professor

• High-Dimensional Geometry
• Probability Theory
• Statistics
• +10 more

Dan Mikulincer is the Brian and Tiffinie Pang Assistant Professor at the University of Washington in the Department of Mathematics, College of Arts and Sciences. He previously held a postdoctoral Instructor position at MIT Mathematics and earned his Ph.D. from the Weizmann Institute of Science under Ronen Eldan. He completed his B.Sc. in Mathematics and Computer Science at Ben-Gurion University, where he also studied Cognitive Neuroscience. B.Sc.: Ben-Gurion University (Mathematics, Computer Science, Cognitive Neuroscience) Ph.D.: Weizmann Institute of Science, Faculty of Mathematics Postdoc: MIT Mathematics Current: Assistant Professor, University of Washington, Department of Mathematics His research lies at the intersection of high-dimensional geometry, probability, statistics, information theory, and data science. He is particularly focused on normal approximations, Stein's method, stochastic analysis, and dimension-free phenomena. His work explores foundational aspects of learning theory, random matrices, transportation inequalities, and neural networks, often using probabilistic and analytic tools to derive sharp, robust results in high dimensions. The recent publications reflect a consistent focus on probabilistic methods in high-dimensional settings. Key themes include normal approximation via Stein's method, optimal transport, concentration and anti-concentration inequalities, random graph models, and theoretical aspects of machine learning such as learnability and neural network expressivity. The work spans both pure mathematics (e.g., GAFA, PTRF) and top-tier computer science venues (e.g., COLT, STOC, NeurIPS), highlighting interdisciplinary impact. Although no formal scientific awards are listed in the provided text, his publications in premier journals and conferences (Annals of Probability, STOC, NeurIPS, COLT) indicate significant recognition in the theoretical community. Dan Mikulincer has advised or collaborated with several researchers including Yair Shenfeld, Max Fathi, Ronen Eldan, and Sébastien Bubeck. He has served as a TA for 18.650: Statistics for Applications at MIT and taught programming courses (Java, Python, JavaScript) at the Interdisciplinary Center Herzliya. He is also a senior lecturer at WeCode, a nonprofit providing free programming education to underrepresented youth in Israel, indicating a strong commitment to education and outreach. He has been affiliated with research groups at MIT Mathematics, Weizmann Institute, and Microsoft Research AI, where he spent the summer of 2019 hosted by Sébastien Bubeck. These collaborations span theoretical machine learning, stochastic processes, and algorithmic foundations.

J. Maurice RojasView profile

Professor

• Computational Algebraic Geometry
• Discrete Geometry
• Algebra & Combinatorics
• +5 more

J. Maurice Rojas is a Professor and Associate Head of Graduate Programs at Texas A&M University's Department of Mathematics. His research focuses on computational algebraic geometry, discrete geometry, and polynomial equation solving with applications in complexity theory and number theory. He holds a Ph.D. from the University of California, Berkeley (1995), alongside earlier degrees from Berkeley and UCLA. Rojas' work bridges theoretical and algorithmic approaches to problems in algebraic geometry, including fewnomial theory, real and p-adic root counting, and tropical geometry. His contributions address the computational complexity of polynomial systems, with recent emphasis on sparse polynomials and circuit-based algorithms. He has authored over 50 papers and contributed to NSF-funded projects on arithmetic geometry and algorithmic methods. His research has explored applications in statistical modeling of petascale data, topological data analysis, and interdisciplinary collaborations between mathematics and computer science. Notable achievements include foundational work on A-discriminants, Viro's patchworking, and the development of sub-linear algorithms for algebraic structures.

Davide Cesare VenianiView profile

Academic

• Algebraic Geometry
• K3 Surfaces
• Enriques Surfaces
• +3 more

Davide Cesare Veniani is a Postdoctoral Assistant at the University of Stuttgart, affiliated with the Institute of Discrete Structures and Symbolic Computing and the Chair of Differential Geometry. He holds a Privatdozent (Priv.-Doz.) title and completed his habilitation in Mathematics at Stuttgart in 2021. His research focuses on algebraic geometry, particularly K3 surfaces, Enriques surfaces, and their symmetries, enumerative problems, and connections to number theory. Education: PhD in Mathematics (2016), Leibniz Universität Hannover, under Prof. Matthias Schütt Postdoctoral positions at Johannes Gutenberg-Universität Mainz (2016–2019) and Universität Stuttgart (2019–present) His research interests include the geometry of algebraic surfaces, their moduli spaces, automorphisms, and applications to arithmetic geometry. He has organized events such as the 2023 FrAGe conference and contributed to international summer schools on arithmetic geometry. Teaching includes courses on mathematics for economics, advanced geometry topics, and seminars on group theory and algorithmic applications. His publications span high-impact journals, exploring subjects like elliptic fibrations, symplectic rigidity of hyperkähler manifolds, and Hensel lifting algorithms for quadratic forms.

David GossetView profile

Associate Professor

• Quantum Computing
• Quantum Algorithms
• Quantum Complexity Theory
• +3 more

David Gosset is an Associate Professor in the Department of Combinatorics and Optimization at the University of Waterloo, with affiliations at the Institute for Quantum Computing (IQC) and the Perimeter Institute for Theoretical Physics. He holds a PhD in Physics from MIT (2011) under Edward Farhi and completed postdoctoral studies at IQC/Waterloo and Caltech. His research focuses on quantum computing theory, quantum algorithms, and quantum complexity theory, with contributions to classical simulation of quantum systems and quantum advantage demonstrations. Prior to academia, he was a research staff member and manager of the Theory of Quantum Algorithms group at IBM’s T.J. Watson Research Center. His work has been recognized with the Pat Goldberg Memorial Best Paper Award (2016, 2018) and the Outstanding Paper Award at TQC 2019. He is also a CIFAR Fellow in Quantum Information Science. Key research interests include quantum algorithms for optimization and simulation, entanglement theory in many-body systems, and computational complexity of quantum problems. His interdisciplinary approach bridges theoretical physics, computer science, and mathematics. Education: PhD in Physics, MIT, 2011 Bachelor’s in Physics and Mathematics, UBC Awards: CIFAR Fellow (Quantum Information Science) Pat Goldberg Memorial Award (IBM, 2016 and 2018) TQC 2019 Outstanding Paper Award His research group explores foundational questions in quantum computing, including the development of efficient classical algorithms for quantum problems and the delineation of quantum computational advantages. Notable contributions include work on shallow quantum circuits, stabilizer rank, and frustration-free spin systems.

• Combinatorics
• Computer Science
• Log-concave Polynomials
• +3 more

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo . His research focuses on log-concave polynomials and their applications in combinatorics and computer science. Education: PhD in Mathematics, UC Berkeley (2014-2019) MS in Mathematics, Texas A&M University (2010-2012) BS in Computer Engineering and Applied Math, Texas A&M University (2006-2010) Research Interests: His work centers around log-concave polynomials and their connections to combinatorics, optimization, and computer science. Key areas include: Lorentzian polynomials and their applications Polynomial capacity and optimization Sampling algorithms and combinatorial structures Representation theory and algebraic combinatorics Scientific Awards: Dirichlet Postdoctoral Fellowship (TU Berlin, 2020-2022) James H. Simons Fellowship (Simons Institute, UC Berkeley, Spring 2019) Previous Positions: Postdoc Fellowship, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoc, KTH, Stockholm (Fall 2019) Developer, Teacher Retirement System of Texas (2012-2014)

• Mathematics Education
• Probability Theory
• Statistics
• +3 more

Karaliopoulou Margarita is a Lecturer affiliated with the Department of Mathematics at the National and Kapodistrian University of Athens, within the School of Science. She holds the capacity of 'Teachers outside the department,' indicating cross-departmental academic involvement. Her work spans mathematics education, stochastic processes, and educational technology. She has contributed to emergency remote teaching strategies during the pandemic and explored student perceptions of programming environments. Her research interests prominently feature semi-Markov processes in statistical analysis and their applications to word occurrences. She also investigates technology integration in education, particularly in programming pedagogy and dual modality tools. Her earlier work (1999–2009) focused on foundational mathematics education and probabilistic modeling. Notable recent efforts include analyzing asymptotic properties in semi-Markov sequences (2022) and evaluating teaching methodologies under pandemic constraints (2021). Her articles consistently bridge theoretical mathematics with practical educational challenges.