Jeffrey Zhang is a Research Fellow at the Yale School of Medicine , affiliated with the Department of Biomedical Informatics and Data Science . Supported by an NLM T15 training grant , he works in Dr. Hua Xu's lab , applying large language models to biomedical challenges. Education: PhD in Operations Research and Financial Engineering , Princeton University (2020) BA in Computer Science, Economics, and Mathematics , Yale University (2014) His research spans biomedical informatics , data science , and optimization algorithms , with a focus on machine learning applications in clinical contexts and mental health analysis . Recent publications investigate higher-order Newton methods , computational complexity , and biomedical relation extraction using AI models. Scientific Awards: NLM T15 Training Grant Jeffrey collaborates with Dr. Hua Xu , Dr. Kalpana Raja , and Dr. Qingyu Chen , among others, and contributes to interdisciplinary projects at the intersection of immunology , engineering , and AI .
Marika Kieferova is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), and a researcher at the UTS Centre for Quantum Software and Information (QSI). She previously held a postdoctoral position at UTS and earned her PhD in Physics and Astronomy from the University of Waterloo (2019) with a cotutelle from Macquarie University. Research Interests Her work spans quantum computing, quantum simulation, and quantum information theory. Key areas include developing quantum algorithms for Hamiltonian simulation, error mitigation strategies, and entanglement-induced optimization challenges in quantum neural networks. She explores non-Abelian anyon braiding, engineered dissipation for correlated states, and bound states of interacting photons in superconducting qubit arrays. Article Trends Her recent publications focus on quantum dynamics in many-body systems, error suppression techniques, and algorithmic advancements. Topics include phase transitions in random circuits, superdiffusive quantum transport, and randomized multi-product formulas for efficient simulation. These works highlight her contributions to quantum chemistry, topological quantum computing, and NISQ-era applications. Scientific Awards QIP Best Poster Award (2020) IQC Achievement Award (2019) Grants and Leadership She leads the QB-suite grant for quantum algorithm design (2024-2027) and contributes to defense quantum optimization projects (2021-2024). She serves as an associate editor for Quantum Science and Technology and participates in peer review for Physical Review A.
Susana Costa Ramalho serves as Assistant Professor at the Faculty of Human Sciences of the Catholic University of Portugal (FCH-UCP), where she has taught since 2017 and currently acts as Coordinator of the Institute of Family Sciences. As a researcher at the Católica Research Centre for Psychological, Family and Social Wellbeing, she leads the "Invisible Pillars of the Family" project and contributes to CUIDAR, a university research and extension initiative focused on child protection. Dr. Costa Ramalho earned her PhD in Educational Psychology through a joint program between the University of Lisbon and the University of Coimbra. Her 2013 research visit to NLA University College Bergen in Norway expanded her international scholarly perspective. She maintains dual professional identity as both academic researcher and clinical practitioner, holding specialist certifications in Clinical and Health Psychology and Community Psychology from the Portuguese Psychologists Order. Her research program centers on relational dynamics within families and couples, with particular emphasis on well-being promotion across the lifespan. She has developed multiple assessment instruments including the "4Ever Family Questionnaire" and scales measuring romantic love myths and couple generativity. Her scholarly work bridges theoretical frameworks with practical applications, addressing both clinical interventions and community-based approaches to strengthen family systems. Dr. Costa Ramalho teaches across multiple programs including the Undergraduate Psychology Program, Master in Psychology of Wellbeing and Health Promotion, and Advanced Program in Family Reception, Orientation, and Referral. Her course portfolio spans family development, clinical interventions with adults, psychopathology, and mental health across the lifespan, reflecting her integrative approach to psychological science and practice. As Coordinator of the Institute of Family Sciences, she oversees research initiatives examining family dynamics from multiple perspectives. Her leadership in the "Invisible Pillars of the Family" project represents a significant contribution to understanding foundational family experiences. Her clinical expertise in Cognitive-Behavioral and Emotion-Focused Psychotherapy informs both her teaching and research, creating a synergistic relationship between academic and applied work.
Rohan Thekkemarickal Money is a Postdoctoral Fellow at Simula, affiliated with the Department of Signal and Information Processing for Intelligent Systems within the Simula Metropolitan Centre. His research focuses on advanced signal processing, graph theory, and machine learning applications to dynamic networks and topological data analysis. Recent research trends include developing simplicial vector autoregressive models for network dynamics, online topology identification for higher-order systems, and privacy-aware learning frameworks . His work addresses challenges in time-series forecasting, edge flow backcasting, and capacity estimation for lithium-ion batteries using graph-based methodologies. Rohan collaborates with researchers like Dr. B. Beferull-Lozano and Dr. E. Isufi, contributing to interdisciplinary projects at the intersection of signal processing , network science , and topological data analysis . His publications demonstrate expertise in handling nonlinear systems with sparse data and designing scalable algorithms for real-time applications.
Mark Bowen is a Professor in the Faculty of Science and Engineering at Waseda University, where he serves as Chair of the Major in Mathematical Sciences. He has been with Waseda University since 2011, first as an Associate Professor (2011-2022) and then as a Professor (2022-present). Prior to joining Waseda, he held academic positions at The University of Tokyo and the University of Nottingham, establishing an international research profile. Professor Bowen's research focuses on thin-film theory and mathematical analysis of degenerate parabolic equations. His work employs a combination of analytical methods (perturbation theory, bifurcation theory, dynamical systems, self-similarity) and numerical techniques (shooting methods, boundary value solvers, ADI computations) to study phenomena such as rupture, moving contact lines, and the effects of surface topography on liquid motion. His expertise extends to fluid mechanics, asymptotic analysis, and computational mathematics. His publication record demonstrates sustained research productivity over two decades, with recent work focusing on singularity formation in thin film equations, Cauchy-Dirichlet problems, and dipole solutions. The research spans theoretical mathematical analysis and practical fluid dynamics applications, connecting pure mathematics with real-world phenomena involving thin liquid films in nature and industry. American Physical Society (APS) Society for Industrial and Applied Mathematics (SIAM) Japanese Society for Industrial and Applied Mathematics (JSIAM) Mathematical Society of Japan Japan Society of Fluid Mechanics American Mathematical Society (AMS) Professor Bowen received the President's Award for Teaching from Waseda University in Spring Semester 2015. He supervises Master's theses and research projects, with current work including analysis of thin film equations on two-dimensional domains and Cauchy-Dirichlet problems for the porous medium equation through international collaborations. He leads the Bowen Laboratory, which currently includes undergraduate researchers Matthew Widjaja, Katerina Allegracia, Yaxin Luo, and Sameer Sharma. His research group has produced significant contributions to understanding thin film dynamics, rupture phenomena, and mathematical modeling techniques applicable across various scientific domains.
Kenichiro Tamaki is an Associate Professor at the School of Political Science and Economics, Waseda University. He holds appointments in both the Faculty of Political Science and Economics and the Graduate School of Economics, with additional sub-affiliation in the Faculty of Social Sciences. His research focuses on statistical methods applied to financial time series analysis. 2008.04 - Present: Associate Professor, Faculty of Political Science and Economics, Waseda University 2005.04 - 2008.03: Research Associate, Faculty of Science and Engineering, Waseda University Dr. Tamaki earned his Doctor of Science from Waseda University. His research interests center on Statistical Finance and Time Series Analysis, with particular emphasis on developing asymptotic theory for financial applications. His work bridges theoretical statistics with practical financial engineering problems, focusing on non-Gaussian processes, locally stationary time series, and higher-order asymptotic methods. His publication record shows consistent research focused on applying advanced statistical techniques to financial problems, particularly in option pricing, bond valuation, and time series modeling with non-Gaussian innovations. His work demonstrates progression from foundational time series asymptotics to more applied financial engineering problems while maintaining rigorous statistical methodology. Dr. Tamaki is an active member of several professional organizations: Mathematical Society of Japan Japanese Association of Financial Econometrics and Engineering Japan Statistical Society International Society for Mathematical Sciences He has led research projects including 'Generalized empirical likelihood for time series' (2013-2017) and 'Bayes approach to time series models' (2007-2009), with his research resulting in 11 publications with 28 citations and an h-index of 4 (Scopus data as of September 2025). Dr. Tamaki teaches graduate courses including Statistical Finance and Research Guidance on Statistical Finance, as well as undergraduate courses in mathematics for economics and advanced econometrics.
Jamaal Ahmad serves as an External Lecturer in the Department of Mathematical Sciences at the Faculty of Science, University of Copenhagen, while concurrently working as an Actuary at Sampension in the Actuarial Finance department. His professional focus encompasses valuation and estimation within life, disability, and pension insurance systems, bridging academic research with industry practice. Education: PhD in Actuarial Mathematics, University of Copenhagen (awarded March 12, 2023) Master in Actuarial Mathematics, University of Copenhagen (awarded September 18, 2019) Bachelor in Actuarial Mathematics, University of Copenhagen (awarded July 3, 2017) Research Interests: Ahmad's scholarly work centers on applied probability and life insurance mathematics, with particular emphasis on modeling, estimation, and market-consistent valuation in life insurance and pensions. His research integrates advanced probability methods with classic actuarial mathematics to develop simulation-based projection models and matrix-oriented approaches for multi-state life insurance systems. This work addresses complex valuation challenges including path dependencies in cash flow projections and bonus computation mechanisms. Publication Trends: His recent publications (2022-2024) demonstrate concentrated advancement in aggregate Markov modeling for life insurance applications. Key themes include EM algorithm estimation techniques, absorption time distribution analysis, phase-type representations for stochastic interest rates, and multivariate moment calculations. These works collectively enhance theoretical frameworks while providing practical valuation tools for multi-state insurance models. Scientific Awards: Best Paper Award within Life/IAALS at the 32nd International Congress of Actuaries (ICA2023) Advising and Teaching: Ahmad has supervised nine master's theses at the University of Copenhagen, covering topics from disability insurance with public benefits to stochastic behavioral rates. He teaches core actuarial courses including Basic Life Insurance Mathematics (Liv1) and Topics in Life Insurance (Liv2), and develops professional training for the Danish Society of Actuaries on matrix methods and projection modeling. Research Group: As a member of the Actuarial Science research group within the Department of Mathematical Sciences, he collaborates extensively with Professors Mogens Bladt and Mogens Steffensen, and maintains international connections through a 2022 visiting position at the University of Lausanne's Actuarial Science department.
Giles Reger is a Senior Lecturer in the School of Computer Science at the University of Manchester , affiliated with the Formal Methods Group . His academic journey includes a BA in Computer Science from the University of Cambridge (2009), an MSc in Advanced Computer Science (University of Manchester, 2010) with the Highest Achiever of the Year Award , and a PhD (University of Manchester, 2014) on runtime verification. Research Interests: Theorem Proving (via Vampire system) and Runtime Verification (via MarQ and VyPR tools). Collaborations: Projects with University of Oxford, ARM, AWS, CERN, and SnT Luxembourg. Recent Work: Giles' publications span 2019-2016, focusing on Vampire's higher-order reasoning, symmetry avoidance in finite model finding, neural guidance in theorem proving, and runtime verification for Python web services (VyPR2). Trends include integrating machine learning with formal methods and advancing logic-based verification tools. Scientific Awards: Highest Achiever of the Year Award (MSc, University of Manchester, 2010) Vampire's multiple trophies at CASC and SMT-COMP competitions Advising: Supervises PhD students Michael Rawson, Ahmed Bhayat, and Joshua Dawes. Labs/Teams: Contributes to the Vampire team and the VyPR project.
PD Dr. Sigrun Ortleb is an Associate Professor at the Institute of Mathematics, University of Kassel , Germany. Her academic affiliation spans over two decades at the same institution. Current Position: Associate Professor (Privatdozentin) since 2021 Academic Background: Habilitation (2021), PhD (2011), Diplom (2006) from TU Braunschweig Research Focus: Sigrun Ortleb specializes in advanced numerical methods for partial differential equations, with particular emphasis on: Discontinuous Galerkin (DG) methods for conservation/balance equations (Euler, Navier-Stokes, shallow water) Positivity-preserving time integration techniques Summation-by-parts operators for high-order accuracy IMEX and multi-rate time integration Efficient shock filters for DG processes Image processing methods for numerical solution post-processing Publication Trends: Her work primarily addresses computational fluid dynamics challenges through: Development of stable, high-order DG schemes IMEX time integration for stiff systems Geometric conservation laws in FSI Adaptive filtering techniques Positivity preservation in shallow water equations Summation-by-parts operators for conservation Teaching Contributions: Dr. Ortleb has taught advanced mathematics courses for mechanical engineers since 2007, including: Higher Mathematics I & II Numerics of Stiff Problems Fluid-Structure Interaction Numerical Analysis of ODEs/PDEs Mathematical Modeling Discontinuous Galerkin Methods Key Collaborations: She has collaborated with experts in: Computational mechanics (J. Boungard, J. Wackerfuß) Exponential integrators (V. Straub, P. Birken, A. Meister) Geometric conservation laws (S. Bremicker-Trübelhorn)
Sam Tobin-Hochstadt is an Assistant Professor at the School of Informatics & Computing, Indiana University, with a focus on programming languages and systems. He is affiliated with the Department of Computer Science and actively contributes to the Racket and JavaScript language ecosystems. Research: Design and implementation of programming systems, particularly languages enabling software evolution (e.g., Racket, Typed Racket, JavaScript). Teaching: Courses like C211, P632, and honors sections of CS 2510. Collaborations: Mozilla Research, Sun Labs Programming Language Research Group. His research spans gradual typing , DSL implementation , compiler design , and parallel programming , with recent work on build systems and probabilistic programming. While specific scientific awards aren't listed, his contributions to PLDI, POPL, and other program committees highlight his field prominence. He mentors Ph.D. students at Indiana University and has organized academic events like IFL 2014. Personal interests include Ultimate and outdoor activities, alongside his wife Katie Edmonds' post-doc work in chemistry.
Joyce Poon is a Professor in the Electrical and Computer Engineering Department at the University of Toronto , with affiliations as Director of the Max Planck Institute for Microstructure Physics and Honorary Professor at the Technical University of Berlin. Her research focuses on integrated photonic devices for communications and neurotechnology , leveraging silicon photonics for applications in visible light systems and neural interfacing. Education: PhD and M.S. in Electrical Engineering from Caltech (2007, 2003); BASc in Engineering Science (Physics) from the University of Toronto (2002) Her work spans visible-light silicon photonics , optical phased arrays , and implantable neural probes , with recent advancements in 3D-printed scaffolds for neural tissue engineering and thermally tunable photonic devices. She has pioneered programmable photonic circuits and hybrid integration techniques for high-efficiency systems. Key trends in her publications include visible-light silicon nitride waveguides , MEMS-based optical switching , and neurophotonic probes for deep brain optogenetics. Collaborative efforts extend to AI-assisted photonic design and biomedical applications of photonic integrated circuits. Scientific Honors : IEEE Fellow (2022) Fellow of Optica (2018) Mit TR35 (2012) Canada Research Chair in Integrated Photonic Devices (Tier 2, 2012–present) Milton and Francis Clauser Doctoral Thesis Prize (Caltech, 2007) She contributes to professional communities through editorial roles (e.g., Optics Express) and leadership in conferences like OFC and IEEE Group IV Photonics. Her lab develops nanophotonic neural probes for brain imaging and stimulation, and she is affiliated with the Krembil Research Institute (University Health Network).
Geoff Sutcliffe is a Professor in the Department of Computer Science within the College of Arts and Sciences at the University of Miami. His research focuses on automated reasoning systems, theorem proving, and logic languages, with significant contributions to the Thousands of Problems for Theorem Provers (TPTP) infrastructure. He serves as an active researcher in formal methods and artificial intelligence, with recent work integrating cloud computing infrastructure for theorem proving systems. Sutcliffe's research interests center on automated reasoning, where he has developed foundational infrastructure for theorem proving competitions and benchmarking. His work spans classical first-order logic through higher-order logic and non-classical logics, with particular emphasis on translation methods between logical systems. He investigates how automated theorem provers can solve complex logical problems, including quantified modal logic and higher-order logical inference. His research also explores the integration of machine learning techniques, particularly reinforcement learning, to enhance theorem proving systems' performance and efficiency. Sutcliffe has received notable recognition including the 2023 Amazon Research Award for his work on Automated Theorem Proving Community Infrastructure in the AWS Cloud . This award highlights his contributions to building scalable infrastructure for the automated reasoning community using cloud technologies. His publication record shows consistent contributions to the field of automated theorem proving, with recent work focusing on system competitions (CASC), logic language frameworks, and the empirical assessment of progress in automated reasoning. Sutcliffe has been instrumental in organizing and evaluating automated theorem proving systems through annual competitions that serve as the de facto world championship for ATP systems.
Dr. Tilo Arens is a Lecturer at the Institute for Applied and Numerical Mathematics , part of the Karlsruhe Institute of Technology (KIT) . His research focuses on numerical methods for scattering problems , particularly involving electromagnetic waves , chiral scatterers , and inverse scattering problems . He actively contributes to the Working Group 4: Inverse Problems and collaborates on projects within the CRC 1173 Wave Phenomena . His teaching portfolio includes courses such as Higher Mathematics II for engineering disciplines, Optimization Theory , and Scattering Theory . He also supervises bachelor’s and master’s theses on topics like Approximation of rotation-minimizing frames using B-spline curves and Numerical methods for integral equations . Dr. Arens has authored numerous publications on inverse scattering, integral equations, and computational methods. Notable works include studies on monotonicity-based shape reconstruction , high-order numerical methods for bi-periodic structures , and electromagnetic chirality measurement . His research intersects with applied mathematics, physics, and computational science. He previously collaborated on a student seminar with Heisenberg-Gymnasium Karlsruhe to bridge university and secondary education in mathematics. His habilitation (2010) and PhD (2000) are in electromagnetic and elastic wave scattering, respectively.
Prof. Dr. Andreas Rieder is a Professor at the Institute for Applied and Numerical Mathematics of Karlsruher Institut für Technologie (KIT). He leads projects C1 and C2 within the CRC 1173 Wave Phenomena - Analysis and Numerics and serves on the advisory panel of the journal Inverse Problems . His office is located at Kollegiengebäude Mathematik (20.30) 3.040, Karlsruhe. Research Interests Inverse and Ill-posed Problems Numerical Analysis for Imaging Wavelet Methods in Signal Processing Partial Differential Equations His recent work focuses on: Full Waveform Inversion in visco-acoustic/viscoelastic regimes Generalized Radon Transforms for Seismic Imaging Microlocal Analysis of Migration Formulas Inexact Newton Regularization Techniques Tangential Cone Conditions for Wave Operators Key affiliations: Member of CRC 1173 Wave Phenomena Leader of Research Group 3: Scientific Computing Contributor to interdisciplinary projects with geophysics and medical imaging
Marc Denecker is a Professor at the Department of Computer Science, KU Leuven, affiliated with the Faculty of Engineering Science and the Declarative Languages and Artificial Intelligence (DTAI) research group. He contributes to KU Leuven's Institute for Artificial Intelligence (Leuven.AI). Current promotor for projects like A Category-Theoretic Perspective on Approximation Fixpoint Theory (2024-2028) and Proof systems for first-order logic extended with inductive definitions (2023-2027). Co-promotor for AI in Industry: Learning and Reasoning for Automation (2021-2025) and IMPULS-AI-2021 (2021-2024). His research focuses on formal logic, knowledge representation, and artificial intelligence. Recent work includes epistemic logic for decision-making under uncertainty, approximation fixpoint theory, justification theory, and symmetry-based satisfiability optimization. Publications span venues like AAAI, SAC, and LPNMR conferences, as well as journals such as Artificial Intelligence . Key subfields include logic programming, inductive definitions, model expansion, and constructive knowledge formalization. His teaching involves courses on complex systems (G0B23A/H0N05A), knowledge representation (H02C3A), automata (G0P84A), and logic for computer science (G0T42D).
