Entropy-Regularized Optimization Professors and Supervisors | ApplySharif

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Panos ParpasView profile

Associate Professor

Imperial College LondonUnited Kingdom

Panos Parpas is a Reader in Computational Mathematics at the Department of Computing, Imperial College London. His research focuses on mathematical optimization algorithms, computational mathematics, and scientific computing, with applications in science, engineering, and finance. He previously held roles as a postdoctoral fellow at MIT and a quant at Credit Suisse. Key research interests include dynamical systems, numerical methods for optimization, and stochastic processes. He teaches courses such as Computational Optimization and Computational Finance with C++. His work bridges theoretical optimization with practical applications in areas like water distribution networks, financial modeling, and machine learning. Panos has authored numerous publications on topics like multilevel optimization methods, stochastic mirror descent algorithms, and the analysis of saddle points using Witten Laplacians. His research emphasizes algorithmic efficiency and scalability, particularly in high-dimensional and distributed settings. Notable collaborations include projects on optimizing water distribution systems (e.g., pump scheduling and pressure management) and developing robust algorithms for financial and engineering systems. His contributions span journals like Automatica , SIAM Journal on Scientific Computing , and Journal of Optimization Theory and Applications .

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Cesare AlippiView profile

Professor

University of Italian SwitzerlandSwitzerland

Cesare Alippi is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), and also holds a professorship at Politecnico di Milano, Italy. He serves as a visiting Professor at Guangdong University of Technology (China) and Consultant Professor at Northwestern Polytechnic of Xi'an (China). His academic leadership extends to multiple international institutions where he has served as a visiting researcher including UCL (UK), MIT (USA), ESPCI (France), CASIA (China), A*STAR (Singapore), and University of Kobe (Japan). Professor Alippi's research interests center around graph-based learning, adaptation and learning in non-stationary environments, and intelligence for embedded, cyber-physical systems and IoT. His work bridges theoretical foundations with practical applications in sensor networks, environmental monitoring, and industrial processes. He has established significant research infrastructure including the Wireless Embedded Systems (WEmSy) Lab and the Internet of Things Lab, with notable deployments for marine environment monitoring in Queensland, Australia and the Fiji Islands, as well as rockfall and landslide monitoring systems across Italy and Switzerland. His research output shows a clear evolution toward graph-based deep learning approaches for time series analysis, anomaly detection, and spatiotemporal forecasting, reflecting the growing importance of graph neural networks in handling complex relational data in non-stationary environments. Major Awards: IEEE CIS Enrique Ruspini Meritorious Service Award (2024) IEEE CIS Outstanding Computational Intelligence Magazine Paper Award (2018) Gabor Award from International Neural Network Society (2016) IBM Faculty Award (2013) IEEE Instrumentation and Measurement Society Young Engineer Award (2004) Professor Alippi has held significant leadership roles including Past Board of Governors member of the International Neural Network Society, Past member of the Administrative Committee of the IEEE Computational Intelligence Society, and Past Vice-President for Education of the IEEE Computational Intelligence Society. He has served as Associate Editor for Proceedings of IEEE and several other prestigious journals. His research has been supported through numerous grants including an IBM Faculty Award in 2013 specifically for research on Intelligent Embedded Systems working in non-stationary environments. His research infrastructure includes the Wireless Embedded Systems (WEmSy) Lab and the Internet of Things Lab, with notable deployments including a sophisticated automatic, adaptive, sustainable and reliable wireless monitoring system for marine environments deployed in Queensland, Australia (2007) and under deployment at the Fiji Islands (2014-2015). He has also led several top-world deployments for rockfall and landslide monitoring across Italy and Switzerland since 2010, demonstrating the practical impact of his research in real-world harsh environments.

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Blair D. SullivanView profile

Professor

University of UtahUnited States

Dr. Blair D. Sullivan is a Professor in the School of Computing at the University of Utah. She holds a Ph.D. in Mathematics from Princeton University and a B.S. in Computer Science and Applied Mathematics from Georgia Tech. Prior to Utah, she was an Associate Professor at NC State University (2013–2019) and a Research Scientist at Oak Ridge National Laboratory (2008–2013). Research Interests: Parameterized algorithms, structural graph theory, applied discrete mathematics, random graphs, and combinatorial scientific computing. Current work focuses on integrating structural graph theory into scalable network analysis tools, with applications in robotics, fairness in algorithms, and biomedical networks. Awards & Honors: Fulbright U.S. Scholar to France (AY2025–26) Collegium de Lyon Fellow (2025–2026) Moore Investigator in Data-Driven Discovery (2014–2024) Inaugural Pathway of Progress Alumnae Honoree (Georgia Tech) Key Projects: Developed software tools like CONCUSS , spacegraphcats , and BEAVr for graph analytics and network analysis. Explores edge augmentation for fairness in social networks and robotic motion planning using parameterized algorithms. Grants: NSF-funded projects on fairness in network information flow ($1.5M). NIH grant for network-based drug repositioning algorithms. Labs & Collaborations: Active in the Laboratoire de l'Informatique du Parallélisme (LIP) at ENS de Lyon during her 2025–2026 sabbatical. Collaborates with SIAM and ICERM on workshops and conferences.

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Chongchun ZengView profile

Professor

Georgia Institute of TechnologyUnited States

Chongchun Zeng is a Professor in the School of Mathematics at Georgia Institute of Technology since 2009, previously holding roles at the University of Virginia and New York University's Courant Institute. His research focuses on applied dynamical systems and nonlinear PDEs, particularly fluid dynamics, nonlinear waves, and invariant manifold theory. Education PhD in Mathematics (1997), Brigham Young University Bachelor of Mathematics (1993), Nankai University, China Research Interests Explores regularity and dynamics of nonlinear PDEs, invariant manifolds in semiflows, and applications to fluid equations (e.g., water waves) and Schrödinger-type systems. Recent work addresses stability of traveling waves, spectral analysis of linearized operators, and inviscid damping phenomena. Awards & Grants Sloan Fellowship (2004) AMS Fellow (2019) NSF grants (2003–present, including DMS-2350115 for fluid dynamics research) Grants & Collaborations Analysis of Fluids and Nonlinear Waves (NSF DMS-2350115, 2024–2027) Studies on gravity water waves and nonlinear Schrödinger equations Lab/Team Affiliation Active in Georgia Tech's School of Mathematics research groups focused on PDEs and dynamical systems.

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Denis BelomestnyView profile

Professor

University of Duisburg-EssenGermany

Denis Belomestny is a Professor of Applied Stochastics at the Department of Mathematics, University of Duisburg-Essen. His academic journey includes a PhD from Lomonosov Moscow State University (2002), postdoctoral work at the University of Bonn, and research positions at WIAS Berlin and Humboldt University Berlin. He currently leads research at the intersection of stochastic processes and financial mathematics. PhD in Mathematics, Lomonosov Moscow State University (2002) W3 Professorship in Applied Stochastics, University of Duisburg-Essen (2011–present) His research focuses on statistics of stochastic processes , optimal stopping/control , and Monte Carlo methods , with applications in financial mathematics and machine learning. Key collaborations include work with John Schoenmakers on multilevel approximation algorithms and Alexey Naumov on variance reduction techniques. Recent publications (2025–2023) explore deep neural networks for SDEs , generative adversarial networks , and nonparametric estimation in complex stochastic models. His work spans stochastic differential equations , financial derivatives pricing , and machine learning-driven statistical inference . He supervises doctoral students, including Sascha Nolte (research: robust optimal stopping without reference models). Current projects involve McKean-Vlasov SDEs , gamma-driven processes , and reinforced optimal control .

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Rupak ChatterjeeView profile

Assistant Professor

New York UniversityUnited States

Rupak Chatterjee is an Industry Assistant Professor in the Department of Applied Physics at New York University's Tandon School of Engineering. His research bridges quantum information/computation and mathematical physics, with a focus on quantum algorithms for machine learning, optimization, operator algebras, and quantum mechanical systems. Education: Postdoctoral Scholar, Physics (James Franck Institute, University of Chicago) Ph.D. & M.S., Physics (Stony Brook University) M.Math., Mathematics (University of Waterloo) B.Sc., Physics (University of Calgary) Chatterjee's research explores quantum systems for machine learning, quantum optimization protocols, and mathematical frameworks like C∗-algebras and supersymmetric quantum mechanics. His work integrates theoretical rigor with applications in quantum computing and complex physical systems. His recent publications (2019–2024) demonstrate a strong emphasis on quantum entanglement, chaos in optomechanical systems, adiabatic quantum optimization, and relativistic quantum mechanics. Several publications also apply quantum methods to finance and diffusion modeling. No awards, student advising, or grant information is documented in the provided text.

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Wuchen LiView profile

Assistant Professor

University of South CarolinaUnited States

Wuchen Li is an Assistant Professor in Mathematics at the University of South Carolina , specializing in Transport information geometry and its applications across Complex Dynamical systems, PDEs, Statistics, Optimization, Control and Games, Mathematical Data science, Graphs and Neural networks , and Scientific Computations . His work bridges theoretical mathematics with practical algorithms for machine learning, Bayesian inference, and optimal transport problems. His research explores geometric frameworks for probability spaces, including Wasserstein-2 metrics , Onsager gradient flows , and primal-dual hybrid gradient algorithms . Recent publications focus on accelerated sampling methods, mean field control systems, and novel applications of optimal transport in high-dimensional settings. 2022 : Air Force Office of Scientific Research YIP award for Transport Information Geometric Computations Key article trends include stochastic differential equations (37%), Wasserstein gradient flows (42%), Markov chain Monte Carlo (28%), and Hamilton-Jacobi-Bellman equations (33%). Subfields span accelerated optimization , nonlinear mobility metrics , generative modeling , and reaction-diffusion systems .

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Ana DjurdjevacView profile

Assistant Professor

Free University of BerlinGermany

Ana Djurdjevac is an Assistant Professor in the Department of Numerical Analysis and Stochastics at the Freie Universität Berlin , within the Department of Mathematics and Computer Science. Her research focuses on numerical analysis, stochastic processes, and partial differential equations, with particular emphasis on uncertainty quantification and mathematical modeling in evolving domains. She teaches advanced courses such as Numerical Methods for Stochastic Differential Equations and Stochastik I , reflecting her expertise in computational methods and probabilistic frameworks. Her work integrates theoretical analysis with practical numerical techniques, addressing challenges in domains such as fluid dynamics, quantum systems, and biological surface fluctuations. Recent contributions include studies on hybrid algorithms for particle systems, rough homogenization in stochastic dynamics, and synchronization mechanisms in conservation laws. Djurdjevac actively participates in academic events, including the 2025 SIAM Conference on Computational Science and Engineering, and collaborates on projects involving quasi-Monte Carlo methods for Bayesian inversion and domain decomposition techniques. Professional activities highlight her role in shaping emerging fields like stochastic PDEs on evolving domains and feedback loops in agent-based models. While no specific awards are listed, her prolific publication record and teaching roles underscore her contributions to computational science and applied mathematics.

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Ayfer OzgurView profile

Associate Professor

Stanford UniversityUnited States

Ayfer Ozgur is an Associate Professor of Electrical Engineering at Stanford University and a Chambers Faculty Scholar in the School of Engineering. She holds a Ph.D. from EPFL (2009) and B.S./M.S. degrees from Middle East Technical University. Her research focuses on information theory, wireless communication, distributed estimation, and machine learning, with notable contributions to federated learning, privacy-aware systems, and energy-efficient networks. Education: Ph.D., EPFL (2009); M.S., Middle East Technical University (2005); B.S. in Electrical Engineering and Physics (2001). Research Interests: Information Theory and Geometry Wireless Networks and Communication Federated Learning and Privacy Energy Harvesting Systems Distributed Estimation and Learning Publications Trends: Ozgur's work emphasizes theoretical foundations of decentralized data science, privacy-utility trade-offs, and geometric approaches to information theory. Recent articles explore over-the-air statistical estimation, federated learning with secure aggregation, and optimal compression under privacy constraints. Awards: Recipient of the IEEE CTTC Early Achievement Award (2018), Okawa Foundation Award (2018), and NSF Career Award (2013). Advising & Grants: Supervises doctoral and master's students in distributed systems and machine learning. Current NSF projects include 'Foundations of Decentralized Data Science' and 'Geometric Network Information Theory.' Labs/Teams: Leads a research group with active members in theoretical and applied communication systems, collaborating with industry partners like Google and META.

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Robert CalderbankView profile

Professor

Duke UniversityUnited States

Robert Calderbank is a distinguished academic and researcher at Duke University, holding professorships in Computer Science, Electrical and Computer Engineering, and Mathematics. He serves as Director of the Information Initiative at Duke and is affiliated with the Duke Quantum Center. His interdisciplinary work bridges information theory, quantum computing, and biomedical applications. Education: Ph.D. from California Institute of Technology (1980) Previous Institution: Princeton University (Distinguished Professor) Calderbank's research spans wireless communications, distributed storage systems, machine learning, and quantum information theory. Recent work focuses on two-dimensional magnetic recording, quantum error correction, and biomedical imaging with pump-probe microscopy. His publications demonstrate sustained innovation in constrained coding, matrix completion, and subspace classification. Scientific contributions include grants from the National Science Foundation and collaborative projects with the University of Maryland. Awards include Fellowships from the Royal Society, IEEE, and AAAS. His teaching and mentorship at Duke and Princeton have shaped next-generation signal processing and computer science research.

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Pengcheng LiuView profile

Senior Lecturer

University of YorkUnited Kingdom

Pengcheng Liu is an Associate Professor (Senior Lecturer) in Robotics and Applied Control at the Department of Computer Science, University of York, UK. He has previously held academic positions at Cardiff Metropolitan University, University of Lincoln, Bournemouth University, and visiting roles in China. He is a member of IEEE and IFAC, and serves on several IEEE Technical Committees related to robotics and automation. PhD in Robotics and Control MSc in Control Theory and Control Engineering BEng in Measurement Control and Instrumentation His research focuses on the convergence of robotics and biology, particularly in bio-inspired design, soft robotics, human-robot collaboration, rehabilitation, and embodied AI. He explores how biological systems can inform adaptive, resilient, and energy-efficient robotic systems. His work spans modeling animal/human motion, sensory-motor learning, morphological design, and control optimization. The 15 most recent publications reflect a strong interdisciplinary trend combining robotics, control theory, machine learning, and biomedical applications. Key themes include soft robotics, bio-inspired actuators, motion planning, rehabilitation systems, and wireless communication for robotic systems. The research integrates simulation, hardware prototyping, and intelligent control, often using ROS and Gazebo. Global Peer Review Awards — Top 1% in Cross-Field (2019) Global Peer Review Awards — Top 1% in Engineering (2019) Outstanding Associate Editor of 2020, IEEE Access Outstanding Contribution Awards, Elsevier (2017) Dr Liu has extensive experience in securing and managing research grants from EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, and NSFC. He serves as a regular reviewer for around 30 journals and 8 flagship conferences in robotics, AI, and control. He is an Associate Editor for IEEE Access and Academic Editor for PeerJ Computer Science, and has contributed to special issues on computational learning for robotics. He leads research in bio-inspired robotics and advises PhD students in areas such as lifelong learning machine intelligence, soft robotics, motion planning, and cyber-physical systems. He is actively recruiting motivated PhD candidates with backgrounds in engineering, computer science, or applied mathematics.

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Dan MikulincerView profile

Assistant Professor

University of California, San DiegoUnited States

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.

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Jörn DunkelView profile

Professor

Massachusetts Institute of TechnologyUnited States

Jörn Dunkel is the MathWorks Professor of Mathematics and Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT), where he has been faculty since 2013. He holds appointments in the Department of Mathematics, with research spanning biological physics, active matter, and nonlinear dynamics. His work integrates theoretical modeling, computational approaches, and collaborative experiments to study complex systems across scales. Education: Dr. rer. nat. in Physics, Universität Augsburg (2008) Dipl. Math & Dipl. Phys, Humboldt-Universität zu Berlin (2004-2005) Visiting Student, University of Oxford (2000-2001) Research Focus: Dunkel's group develops mathematical frameworks for biological and soft matter systems, with emphasis on: emergent order in active materials; topological methods in biophysics; nonequilibrium statistical mechanics; and hydrodynamic theories of living systems. Recent breakthroughs include programmable active matter, topological control of biological networks, and entropy dynamics in living cells. Publication Trends: His 15 most recent works (2021-2024) reveal strong focus on: 1) Mechanobiology of cellular structures (membranes, nuclei), 2) Collective dynamics in active matter and biofilms, 3) Topological analysis of biological organization, and 4) Machine learning approaches for complex systems modeling. These consistently bridge mathematical theory with experimental biophysics. Awards & Honors: Schmidt Science Polymath Award (2023) Complex Systems Scholar Award, McDonnell Foundation (2016-2020) Alfred P. Sloan Research Fellowship (2015-2017) Gustav Hertz Prize, German Physical Society (2011) Multiple MIT-specific research and teaching awards Research Infrastructure: Leads the Physical Mathematics Group at MIT, collaborating with experimental labs worldwide. Regular group meetings (Thursdays) and seminar series (Physical Mathematics Seminar Tuesdays) anchor interdisciplinary research in mathematical biophysics.

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Omar FawziView profile

Researcher

École Normale Supérieure de LyonFrance

Omar Fawzi is a Research Director (Directeur de Recherche) at Inria, heading the QInfo team at École Normale Supérieure de Lyon. His primary roles include leading research in quantum information theory and theoretical computer science, with a focus on quantum algorithms, error-correcting codes, and quantum cryptography. He has held academic positions since 2011, including teaching at McGill University and ENS Lyon. His research interests span quantum information theory, theoretical computer science, and their applications. He has contributed to foundational work on quantum channel capacities, entropy accumulation theorems, and quantum error correction codes like quantum expander codes. His work bridges theoretical insights with practical implementations, including fault-tolerant quantum computing and device-independent cryptography. Fawzi has advised numerous PhD students and postdoctoral researchers, with students such as Aadil Oufkir (now at RWTH Aachen) and Antoine Grospellier (teaching in France). He has led major grants including the ERC Starting Grant AlgoQIP and the ANR-18-CE47-0011 ACOM project. His research team focuses on advancing quantum communication protocols, quantum algorithms, and the mathematical foundations of quantum mechanics. Key contributions include the entropy accumulation theorem, variational bounds on quantum divergences, and efficient simulation methods for quantum systems. His work emphasizes interdisciplinary approaches, combining tools from computer science, mathematics, and physics to tackle fundamental quantum information challenges.

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Dimitri ShlyakhtenkoView profile

Professor

University of California, Los AngelesUnited States

Dimitri Shlyakhtenko is a Professor at the University of California, Los Angeles (UCLA) Department of Mathematics, with a distinguished career in free probability, operator algebras, and von Neumann algebras. He has held leadership roles as Department Chair (2012-2015) and Director of the Institute for Pure and Applied Mathematics (2017-). Ph.D. in Mathematics (1997) from UC Berkeley under Dan-Virgil Voiculescu . B.S. with high distinction from the University of Nevada, Reno (1993). His research spans free probability , von Neumann algebras , C*-algebras , L2-invariants , and Poisson geometry . He has pioneered connections between free probability and random matrix theory, subfactor classifications, and non-commutative transport equations. Recent work includes inequalities for free entropy dimension and duality in non-commutative optimal transport. His 15 most recent publications focus on free entropy dimension, crossed products, non-commutative transport, and classification of von Neumann algebras. Topics include non-microstates free entropy , free group factors , quasi-regular inclusions , and type B free probability , with applications to quantum information and ergodic theory. Scientific Awards : Sloan Foundation Fellowship (2001), NSF Fellowships (1994, 1998), Clay Mathematics Institute award (2002), and Bourbaki Seminar recognition (2004). Organized major conferences at Fields Institute, Oberwolfach, IPAM, and UC Berkeley on quantitative linear algebra, free probability, and von Neumann algebras. He has mentored 12 Ph.D. students, including David Jekel and Max Zhou , with research in free analysis and operator-valued distributions. His work also involves collaborations with leading mathematicians like Alain Connes and Sorin Popa .

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