Pure Shift Methods Professors and Supervisors | ApplySharif

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Paul LarsonView profile

Professor

Miami UniversityUnited States

Paul Larson is a Professor of Mathematics at Miami University. His research focuses on set theory, topology, and model theory, with particular expertise in forcing axioms, descriptive set theory, and infinitary logic. He holds a Ph.D. in Mathematics from the University of California, Berkeley. His work bridges foundational mathematical logic with applications in topology and combinatorics. Key contributions include studies on canonical models under fragments of the Axiom of Choice, polar forcings, and cardinal characteristics. Larson has collaborated extensively with leading researchers such as Saharon Shelah and Jindřich Zapletal. His publications span prestigious journals like the Annals of Pure and Applied Logic and Transactions of the American Mathematical Society. Beyond research, he contributes to the academic community through editorial work and expository writings on historical developments in determinacy theory. Education: Ph.D., Mathematics, University of California, Berkeley Research interests emphasize foundational questions in set theory with applications to topology and model theory. His recent work explores advanced forcing techniques, square principles in Pmax extensions, and combinatorial properties of cardinal invariants. Publications reflect interdisciplinary engagement, including crystal structure prediction in high-pressure chemistry and operator theory in functional analysis. Despite an extensive publication record, no specific scientific awards are documented here. His advising and grant activities remain unspecified in the provided texts. Collaborations span international institutions, reflecting his role as a central figure in contemporary set theory research.

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Gil KalaiView profile

Professor

Graz University of TechnologyAustria

Gil Kalai is a Professor of Mathematics at the Hebrew University of Jerusalem since 1992, where he holds the Henry and Manya Noskwith Chair. He also serves as an Adjunct Professor of Mathematics and Computer Science at Yale University since 2004 in a long-term part-time visiting position. His academic career includes visiting positions at prestigious institutions including MIT, Cornell, IAS Princeton, Berkeley, Bell-labs, IBM, and Microsoft. Professor Kalai's research spans multiple areas within mathematics and theoretical computer science. His work in combinatorics encompasses geometric, probabilistic, and topological approaches. He has made significant contributions to the study of convex sets and polytopes, linear programming, and theoretical computer science. His influential 1988 paper with Kahn and Linial on Boolean functions pioneered applications of Fourier analysis in theoretical computer science. Kalai's research has evolved to include the application of Fourier analysis to thresholds, influences, symmetries, noise, percolation, and social choice. He has developed theories in algebraic shifting and studied face-numbers and other combinatorial invariants of polytopes. His work on the diameter of polytopes and randomized simplex algorithms has been influential in optimization theory. In 1993, his collaboration with Kahn produced a groundbreaking counterexample to Borsuk's Conjecture in 1325 dimensions. Professor Kalai's publications reveal a consistent focus on the intersection of combinatorics, geometry, and theoretical computer science. His work shows a progression from foundational combinatorial geometry to increasingly sophisticated applications of harmonic analysis in discrete mathematics. The recurring themes across his 30+ year career include Boolean functions, polytope theory, and probabilistic methods in combinatorics, demonstrating remarkable coherence in his research trajectory. 2016 European congress of Mathematics, plenary speaker 2013 ERC advanced grant 2012 Rothschild Prize 1994 International Congress of Mathematicians invited section talk, Zurich 1994 Fulkerson Prize 1993 Erdos Prize 1992 Polya Prize Though specific details of his advising are not provided in the source material, Kalai has written over 70 scientific papers and maintains an active research blog entitled "Combinatorics and More." His 2013 ERC advanced grant indicates significant research funding for his work. His extensive collaborations with researchers across multiple institutions suggest a robust research program with numerous PhD students and postdoctoral researchers, though specific names are not mentioned in the provided texts. Professor Kalai maintains active research connections across multiple institutions including Hebrew University, Yale, and various research centers worldwide. His work bridges pure mathematics and theoretical computer science, creating a unique interdisciplinary research environment that influences both fields.

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Benjamin SteinbergView profile

Professor

City University of New YorkUnited States

Benjamin Steinberg is a Professor in the Mathematics Department at the City College of New York (CCNY) and the CUNY Graduate Center. He holds a Ph.D. from the University of California, Berkeley (1998) under John Rhodes and has held positions at the University of Porto (Portugal) and Carleton University (Canada). His research focuses on algebra, including semigroups, geometric group theory, algebraic combinatorics, representation theory, and automata theory, with notable work on etale groupoids, inverse semigroups, and ring theory. He is the author of several books, including *The q-theory of Finite Semigroups* and *Representation Theory of Finite Monoids*. Steinberg serves as Managing Editor of the *International Journal of Algebra and Computation* and has organized conferences such as the International Conference on Semigroups and Groups in Honor of John Rhodes. Research interests include the interplay between algebraic structures and their applications, such as in automata theory and Markov chains. His work bridges pure mathematics with combinatorial and geometric approaches, often involving categorical and topological methods. Recent articles explore topics like Nekrashevych algebras, twisted Steinberg algebras, and Lyndon's identity theorem for monoids. He has contributed to the study of profinite groups and their connections to symbolic dynamics. Steinberg’s editorial roles and conference organization reflect his leadership in the mathematical community. Despite his defunct blog, his academic contributions remain prolific, with ongoing editorial work and research in algebraic combinatorics and representation theory.

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Shima AbdullateefView profile

Research Fellow

University of EdinburghUnited Kingdom

Shima Abdullateef is a Postdoctoral Research Fellow at the Centre for Medical Informatics within the Usher Institute, College of Medicine and Veterinary Medicine at the University of Edinburgh. Her work bridges biomedical engineering and clinical medicine through computational modeling and data science applications. Education: PhD in Biomedical Engineering, Brunel University London (2016-2020) MSc in Biomedical Engineering, University of Surrey (2014-2015) BSc in Biomedical Engineering (Bioelectrics), Science and Research IA University (awarded 2013) Research Focus: Dr. Abdullateef specializes in two interconnected domains: computational hemodynamics modeling arterial wave propagation and reflection phenomena, and machine learning-driven seizure detection using minimal-density EEG montages. Her arterial research investigates how vascular geometry impacts blood pressure dynamics, while her neuroscience work develops practical clinical tools for critical care seizure monitoring that reduce electrode requirements by 50-75% compared to standard EEG setups. Publication Trends: Her 15 most recent publications (2018-2025) reveal a strategic shift from pure cardiovascular modeling toward integrated neurological applications, with 60% focusing on seizure detection algorithms. The work consistently applies one-dimensional computational models and phase-synchrony analysis to solve clinical monitoring challenges, particularly in resource-constrained pediatric intensive care settings. Active Projects: A Window in the Brain: Developing a novel seizure detection tool for pediatric critical care (since 2020), funded through University of Edinburgh research channels Collaborative Environment: She operates within the Centre for Medical Informatics' interdisciplinary ecosystem, collaborating with clinicians from Edinburgh BioQuarter and data scientists to translate engineering solutions into clinical practice, with particular emphasis on making neurocritical care monitoring more accessible through reduced-sensor EEG technology.

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Uwe HartmannView profile

Professor

Saarland UniversityGermany

Prof. Dr. Uwe Hartmann is a faculty member and Chair Holder at Saarland University, affiliated with the Institute of Experimental Physics within the Faculty of Natural Sciences and Technology. His research group specializes in nanostructure research and nanotechnology, with a strong focus on instrumentation, nanofabrication, and quantum systems. He is based in Building C6.3, Saarbrücken, Germany. His research interests lie at the intersection of physics and engineering, particularly in nanotechnology , scanning probe microscopy , quantum device characterization , and nanofabrication . His work spans fundamental investigations of nanostructures to applied developments in microsystem technology and industrial nanomanufacturing. He has contributed significantly to cleanroom technologies, surface physics, and the transition from micro- to nanoscale systems. The recent publications highlight a strong thematic focus on enabling technologies for nanoscale science, including instrumentation (e.g., atomic force microscopy), fabrication methods, and analysis of quantum and metallic nanostructures. The research demonstrates a consistent trajectory toward understanding and manipulating matter at the nanoscale for both scientific and industrial applications. Scientific Awards: No awards explicitly mentioned in the provided text. Prof. Hartmann actively supervises doctoral and diploma students, indicating a strong commitment to academic mentoring. His group includes PhD students and scientific staff, suggesting ongoing research projects and potential grant funding, although specific grants are not listed. He has led a long-standing research group with technical staff supporting experimental work, indicating a well-established laboratory infrastructure focused on experimental physics and nanotechnology development. Laboratory and Team: The working group includes scientific staff (e.g., Dr. Haibin Gao), PhD and diploma students, and technical staff (electronics engineers, mechanics, workshop heads), forming a multidisciplinary team capable of both theoretical and hands-on experimental research in nanotechnology. The presence of a dedicated workshop and technical personnel underscores the practical, device-oriented nature of the research.

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Mohammad RostamiView profile

Academic

University of PennsylvaniaUnited States

Mohammad Rostami is a Research Assistant Professor at the University of Southern California (USC) in the Department of Computer Science and Electrical and Computer Engineering, with a joint appointment at the USC Information Sciences Institute (ISI). He holds a PhD in Electrical and Systems Engineering from the University of Pennsylvania and additional degrees in Robotics, Philosophy, Electrical Engineering, and Pure Mathematics from prestigious institutions including the University of Waterloo and Sharif University of Technology. His research focuses on machine learning in data-scarce environments, particularly transfer learning, domain adaptation, low-shot learning, and improving learning efficiency through continual and collective learning. He incorporates symbolic logic and neuro-symbolic approaches to address challenges in catastrophic forgetting and knowledge retention. Applications span medical imaging, computer vision, and explainable AI. Rostami has received several accolades including the UPenn Best PhD Dissertation Award, IJCAI Distinguished Student Paper Award, and University of Waterloo Outstanding Achievement Award. His work bridges theoretical advancements with practical implementations, emphasizing real-world applications in healthcare and autonomous systems. He teaches graduate courses in applied natural language processing and knowledge graph construction. Rostami advises students at all academic levels and collaborates with remote researchers, emphasizing motivated, long-term project commitments.

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Marta CasanellasView profile

Professor

Polytechnic University of CataloniaSpain

Marta Casanellas is a full professor in the Department of Mathematics at the Polytechnic University of Catalonia (UPC) and a researcher at the Centre de Recerca Matemàtica. She teaches at the Faculty of Mathematics and Statistics, ETSEIB, and FIB. She earned her PhD in mathematics from the University of Barcelona under RM Miró-Roig, focusing on algebraic geometry and liaison theory. After completing a postdoc at UC Berkeley with a Fulbright Scholarship, she shifted her research to applications of algebraic geometry in computational biology, particularly phylogenetics. Her educational background includes a PhD from the University of Barcelona (2002), followed by a postdoctoral fellowship at UC Berkeley (2002-2003) supported by a Fulbright Scholarship. She obtained a prestigious Ramón y Cajal contract at UPC in 2003, which marked her transition to interdisciplinary research at the intersection of mathematics and biology. Casanellas' research focuses on applying algebraic and geometric techniques to phylogenetics, with particular emphasis on evolutionary models, phylogenetic invariants, and computational methods for genomic data analysis. Her work bridges pure mathematics (particularly algebraic geometry) with biological applications, developing mathematical frameworks to reconstruct evolutionary histories and understand genomic relationships. She has published extensively in both mathematics journals like Advances in Mathematics and biology journals like Molecular Biology and Evolution. Her recent publications demonstrate a consistent focus on developing algebraic methods for phylogenetic analysis, with increasing attention to heterogeneous evolutionary processes across lineages, time-reversible models, and computational implementations of theoretical results. The trend shows progression from theoretical foundations in algebraic geometry toward increasingly sophisticated and applicable computational methods for biological data. Fulbright Scholarship for postdoctoral research at UC Berkeley Ramón y Cajal contract (2003) Casanellas has supervised PhD students including A. Kedzierska (co-supervised with R. Guigó of the CRG). She has served as principal investigator for three competitive Spanish government projects involving fifteen researchers each. She has held significant academic leadership roles including Deputy Director of Research of the Department of Mathematics at UPC (2015-2018), head of studies for the Degree in Data Science and Engineering at UPC (2018-2022), and currently coordinates UPC's PhD in Bioinformatics program and Bachelor's Degree in Bioinformatics. She leads the BIO-GEOMAP research group focused on applying mathematical techniques to biological problems.

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Luca VitaglianoView profile

Professor

University of SalernoItaly

Luca Vitagliano is a Full Professor of Geometry at the Department of Mathematics, University of Salerno. His research focuses on Differential Geometry and Mathematical Physics, with specializations in Poisson Geometry, Lie Algebroids/Groupoids, Differentiable Stacks, and Geometric Methods for PDEs. He has advised four PhD students, including Antonio Maglio (2025) and Pier Paolo La Pastina (2020). He teaches courses such as Geometry II, Homology and Cohomology, and Higher Geometry. His recent articles explore shifted contact structures, Nijenhuis integrations, and deformation cohomology. Education: Not explicitly stated in text Research Groups: Geometry Group at University of Salerno Affiliations: INdAM Intensive Period on Poisson Geometry, Poisson 2024 Conference His work bridges pure mathematics (homological methods, stack theory) with applications in mathematical physics, emphasizing geometric structures like Jacobi manifolds and coisotropic submanifolds. He actively participates in international conferences and publishes with collaborators globally.

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Mireille E. BrouckeView profile

Professor

Queen's University at KingstonCanada

Mireille E. Broucke is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, where she is a member of the Systems Control Group within the Faculty of Applied Science and Engineering. She teaches various undergraduate and graduate courses including Adaptive Control and Reinforcement Learning, Robot Modeling and Control, and Introduction to Nonlinear Systems, demonstrating her commitment to education in control systems engineering. Professor Broucke's research focuses on mathematical system theory with particular emphasis on Systems Neuroscience, Reach Control Problems, and Patterned Linear Systems. Her work bridges theoretical control theory with applications in neuroscience and robotics. She has developed theoretical frameworks for understanding neural adaptation through control theory principles and has applied reach control theory to robotics problems including motion control of quadrocopters. Her research demonstrates how control theory can provide insights into biological systems while also advancing engineering applications. Her recent publications show a clear trend toward applying control theory to neuroscience, particularly in understanding adaptive internal models in the brain. The publications span from theoretical reach control problems on simplices and polytopes to practical applications in robotics and neural systems. Her work increasingly focuses on the intersection of control theory and neuroscience, examining how the brain implements adaptive control mechanisms for motor functions. This represents a significant shift from her earlier work which was more focused on pure control theory problems. Professor Broucke has advised several PhD students including Fatima Ghadieh, Erick Mejia Uzeda, and Mohamed Hafez. Her research has been supported by various grants that enable her work in control theory and its applications to neuroscience and robotics. She maintains an active research program with numerous publications in top control theory journals including IEEE Transactions on Automatic Control, Automatica, and Systems and Control Letters.

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Lillian RatliffView profile

Associate Professor

University of WashingtonUnited States

Lillian Ratliff is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Washington, holding additional Adjunct Associate Professor positions in the Allen School of Computer Science and Engineering and the Department of Aeronautics and Astronautics. She specializes in research at the intersection of game theory, optimization, machine learning, and control theory, with a focus on decision-making in intelligent systems with learning-enabled components and strategic agents. Her research interests span game theory and economics, optimization, machine learning, and control theory. She develops theoretical frameworks for understanding how intelligent systems make decisions when interacting with strategic agents. Her work bridges theoretical foundations with practical applications in multi-agent learning systems, where she examines how learning algorithms converge to equilibria in strategic environments. She has made significant contributions to understanding the dynamics of gradient-based learning in games, Stackelberg games, and decision-dependent distributions. Her recent publications demonstrate a strong focus on theoretical foundations of game-theoretic learning, with particular attention to convergence properties, equilibrium analysis, and strategic behavior in multi-agent systems. Her work spans zero-sum games, matrix games, Stackelberg games, and decision-dependent learning scenarios, with applications across multiple domains including human-machine interaction and networked systems. NSF Graduate Research Fellowship (2009) NSF CISE Research Initiation Initiative award (2017) NSF CAREER award (2019) ONR Young Investigator award (2020) UW CoE Junior Faculty Award (2021) Invited speaker at NAE China-America Frontiers of Engineering Symposium (2019) Dhanani Endowed Faculty Fellowship (2020) Professor Ratliff's research is supported by multiple NSF grants (current: CNS-1736582, CNS-1836819, CNS-1931718, CNS-1907907, CNS-1844729, CNS-1952011; previous: CNS-1634136, CNS-1646912, CNS-1656873) and an Office of Naval Research Young Investigator award. She has mentored numerous students and collaborators across her extensive publication record, with work appearing in top venues including NeurIPS, ICML, AISTATS, and IEEE conferences.

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Thomas WeitinView profile

Professor

Technical University of DarmstadtGermany

Professor Thomas Weitin is a leading scholar at Darmstadt University of Technology's Institute of Linguistics and Literary Studies, where he heads the German Studies - Digital Literary Studies department. As Director of the LitLab, he bridges traditional literary scholarship with cutting-edge computational and cognitive science methodologies. His work positions him at the forefront of the digital humanities movement in Germany. Weitin's research focuses on the intersection of digital literary studies and cognitive science, particularly examining how humans process literary texts through methods like eye-tracking, EEG, and physiological measurements. His 'Reading at Scale' project analyzes 19th-century German novellas to explore the middle ground between close reading and distant reading approaches. Weitin argues against the simplistic 'close vs. distant reading' dichotomy, advocating instead for methodologically appropriate scale selection based on specific research questions. Analysis of Weitin's recent publications reveals a strong interdisciplinary approach combining literary theory, cognitive neuroscience, and data science. His work increasingly incorporates empirical methods to study reading processes, moving beyond purely textual analysis to investigate the physiological and cognitive dimensions of literary reception. This represents a significant methodological shift in literary studies toward evidence-based approaches to understanding how literature affects readers. Weitin maintains significant leadership roles including membership on the Editorial Board of Frontiers in Human Neuroscience, Senior Editorship of Open Library of Humanities, and Executive Board membership of the Digital Humanities Cooperation between TU Darmstadt and the University of Konstanz. He serves as a reviewer for major funding organizations including the German Research Foundation (DFG) and the Science Council. At the LitLab, Weitin oversees research infrastructure including eye-tracking equipment (both stationary and mobile glasses), EEG amplifiers, and physiological measurement devices for pulse, heart activity, and skin conductance. This sophisticated laboratory enables multimodal studies of reading that combine textual analysis with direct measurement of reader responses, representing a novel approach to literary scholarship.

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Søren EilersView profile

Professor

University of CopenhagenDenmark

Søren Eilers is a Professor at the Department of Mathematical Sciences , University of Copenhagen. His research focuses on Operator Algebras , particularly the classification of C*-algebras related to discrete and low-dimensional structures. He is a member of the FNU network 'Automorphisms and Invariants for Operator Algebras' and advocates for experimental mathematics using computational methods in pure mathematics. Education: MS in Mathematics and Computer Science, University of Copenhagen (1993) PhD in Mathematics, University of Copenhagen (1995) Research Interests: Operator Algebras K-theory Symbolic Dynamics Discrete Mathematics Experimental Mathematics Recent Publications (2016-2024) demonstrate expertise in graph C*-algebras , symbolic dynamics , and computational approaches to pure mathematics, with key collaborations in Denmark, Japan, Canada, and the U.S. Scientific Leadership: President, Danish Mathematical Society (2006-2008) Principal Investigator, Villum Fonden (2012-2016) Main Organizer, Mittag-Leffler Institute Program (2016) Advisory Roles: Supervised 28 master's theses and mentored 9 PhD students/postdocs (2003-2022) across institutions in Denmark, Canada, Japan, and the U.S.

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Michael NussbaumView profile

Professor

Cornell UniversityUnited States

Michael Nussbaum is a Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds a Ph.D. (1979) and Dr. Sc. (1990) from the Academy of Sciences Berlin (Germany). His research focuses on quantum statistics, applying mathematical statistics to analyze quantum experiments and developing asymptotic methods for hypothesis testing, equivalence theory of statistical experiments, and nonparametric models. Key contributions include work on quantum Chernoff bounds, Gaussian approximation of quantum models, and asymptotic equivalence between statistical experiments. Recent research emphasizes quantum hypothesis testing, low-rank quantum state estimation, and asymptotic equivalence with quantum Gaussian white noise. Supported by an NSF Grant (DMS-1915884), his work bridges operator algebras, quantum information, and noncommutative probability. Publications span foundational topics like nonparametric regression, spectral density estimation, and functional empirical processes. He teaches courses such as Statistical Theory and Application in the Real World and supervises graduate research. His lab explores interdisciplinary applications of asymptotic statistical methods in quantum engineering and communication technologies.

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Joni TeravainenView profile

Assistant Professor

University of CambridgeUnited Kingdom

Joni Teräväinen is an Assistant Professor at the University of Cambridge , affiliated with the Department of Pure Mathematics and Mathematical Statistics. His research is funded by an ERC Starting Grant and has been supported by prestigious fellowships including the Academy Research Fellow, Marie Curie Fellow, and von Neumann Fellow. Current Role: Assistant Professor, University of Cambridge Previous Roles: Academy Research Fellow (University of Turku), von Neumann Fellow (IAS), Titchmarsh Research Fellow (University of Oxford) His research interests lie at the intersection of analytic number theory , additive combinatorics , and ergodic theory , with a focus on multiplicative functions, prime distribution, and higher-order Fourier analysis. Recent work explores correlations of multiplicative functions, Fourier uniformity in short intervals, and applications to the Chowla and Elliott conjectures. Joni has published extensively in top journals including the Journal of the European Mathematical Society , Annals of Mathematics , and Proceedings of the London Mathematical Society . His publications reveal a trend toward quantitative bounds for Gowers uniformity, shifted exponential composites, and function field analogues of classical problems. Scientific Awards: ERC Starting Grant Academy Research Fellow Marie Curie Fellow von Neumann Fellow Academy of Finland Postdoctoral Researcher Titchmarsh Research Fellow Contact: jt945@cam.ac.uk . More details are available on his personal homepage .

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Akshay KrishnamurthyView profile

Adjunct Assistant Professor

University of Massachusetts AmherstUnited States

Akshay Krishnamurthy is an Adjunct Assistant Professor at the University of Massachusetts Amherst and a Senior Principal Research Manager at Microsoft Research NYC. His research focuses on machine learning and statistics, particularly interactive learning frameworks, reinforcement learning, and generative AI. He holds a PhD from Carnegie Mellon University and a B.Sc. from UC Berkeley. Education: PhD in Computer Science, Carnegie Mellon University (2015) B.Sc. in Electrical Engineering and Computer Science, UC Berkeley (2010) Research Interests: His work emphasizes feedback-driven data collection in machine learning, including contextual bandits, reinforcement learning, and their applications to language models and generative AI. Recent focus areas include sample-efficient exploration and decision-making under limited feedback. Awards: National Science Foundation Graduate Research Fellowship (2010) Best Student Paper Award at Asilomar Conference (2013) Grants & Collaborations: Active in interdisciplinary collaborations, including organizing workshops on foundations of post-training in generative AI. His research bridges theoretical guarantees with practical applications in reinforcement learning and decision-making systems. Labs/Teams: Leads research initiatives at Microsoft on generative AI, reinforcement learning, and scalable machine learning systems.

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