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Gerome MiklauView profile

Professor

• Data Privacy
• Database Systems
• Differential Privacy
• +3 more

Gerome Miklau is a Professor of Computer Science at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences (CICS). He leads the DREAM Lab and focuses on privacy, security, and equitable data management, particularly in differential privacy and fair data analysis. His work includes designing algorithms for private data synthesis, privacy-preserving SQL engines, and auditing systems like AuditGuard. He co-founded Tumult Labs to commercialize privacy technology and advised the U.S. Census Bureau on privacy for the 2020 decennial census. Education: Ph.D. in Computer Science from the University of Washington (2005), B.S. in Mathematics and Rhetoric from UC Berkeley (1995). Research Interests: Differential privacy, secure data management, fairness in algorithms, privacy-preserving data synthesis, and forensic database analysis. His lab develops tools like Ektelo and PrivateSQL, addressing challenges in privacy-accurate tradeoffs and scalable private data processing. Awards: 2006 ACM SIGMOD Dissertation Award, 2007 NSF CAREER Award, 2013 ICDT Best Paper Award, and two ACM PODS Test-of-Time Awards (2020 and 2012). Grants & Service: Co-chair of OpenDP Advisory Board, steering committee member for TPDP workshops, and organizer of the 'Data, Responsibly' Dagstuhl workshop. His service includes program committees for SIGMOD, ICML, and FAT*. He teaches courses on databases, privacy, and programming. Labs/Teams: DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and collaborations with the Center for Data Science and Cybersecurity Institute at UMass Amherst.

Monika NedaView profile

Professor

• Computational Fluid Dynamics
• Turbulence
• Numerical Methods for Partial Differential Equations
• +4 more

Monika Neda is a Professor in the Department of Mathematical Sciences and Associate Dean for Research at the University of Nevada, Las Vegas (UNLV). She holds a Ph.D. in Mathematics from the University of Pittsburgh (2007) and a B.S. in Mechanical Engineering from the Technical Faculty Mihajlo Pupin in Serbia (2001). Her research focuses on theoretical and computational fluid dynamics, turbulence modeling, and numerical methods for partial differential equations, with particular emphasis on Navier-Stokes equations and sensitivity analysis. Her work explores model derivation, numerical stability, finite element error estimates, and computational simulations of fluid flow problems. She also investigates educational strategies to improve STEM students' foundational math skills through game-based and activity-based instruction. Neda is affiliated with UNLV's Center for Applied Math & Statistics and has contributed to interdisciplinary projects involving engineering education and photovoltaic panel efficiency. Recent research trends highlight advancements in stabilization techniques for fluid models (e.g., time relaxation, deconvolution operators) and innovative pedagogical approaches leveraging technology platforms like Canvas. Her publications span computational mathematics, turbulence closure models, and education-focused interventions. Neda's academic leadership includes roles in curriculum development and institutional research, reflecting her dual commitment to scholarship and academic administration.

Enno MammenView profile

Professor

• Nonparametric Statistics
• Bootstrap Methods
• Additive Models
• +6 more

Enno Mammen is a Professor of Mathematical Statistics at Heidelberg University, leading the Institute for Applied Mathematics. His career includes roles as Chair for Mathematical Statistics at Heidelberg (2014–present), Chair for Statistics at the University of Mannheim (2003–2014), and various academic positions since 1986. He holds a PhD (1983) and habilitation (1992) from Heidelberg University. Research interests focus on nonparametric statistics, bootstrap methods, additive models, high-dimensional data, and statistical theory. Key contributions include foundational work on the wild bootstrap, penalized nonparametric estimators, and nonparametric diffusion models. He has authored over 150 papers in top journals like the Annals of Statistics and Biometrika. Current research spans Hawkes processes, neural network statistics, and non-Euclidean data analysis. He has supervised 12 PhD students since 2010, with many progressing to academic roles. Awards include the Heinz Maier Leibnitz Prize (1989) and IMS Fellowship (1998). Active in editorial roles for journals like the Annals of Statistics and Bernoulli. Major funding includes leadership of the DFG-funded Research Training Group 'Statistical Modeling of Complex Systems' (2013–2022) and collaborations with Russian institutions on stochastic differential equations.

Michael A. NewtonView profile

Professor

• Biostatistics
• Genomics
• Computational Statistics
• +5 more

Michael A. Newton is a Professor and Chair of the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, School of Medicine and Public Health. His research focuses on statistical methodologies for high-dimensional biomedical data, including cancer biology, immunology, and genomics. He is renowned for developing empirical Bayesian methods, stochastic models, and computational tools for analyzing molecular data. His work integrates statistical theory with interdisciplinary collaborations, contributing to advancements in translational biomedicine. Newton has held prestigious awards, including the Mortimer Spiegelman Award (2003) and the COPSS Presidents' Award (2004). He is an elected Fellow of the American Statistical Association and an elected Member of the International Statistical Institute. He leads the Biostatistics and Epidemiology Research and Design (BERD) core at the Institute for Clinical and Translational Research and is affiliated with the Carbone Comprehensive Cancer Center and the Center for Genome Science and Innovation. His teaching includes advanced courses in computational statistics, Bayesian analysis, and statistical methods in molecular biology. Newton directs graduate programs in Statistics and Biomedical Data Science, emphasizing interdisciplinary training.

• Environmental Chemistry
• Mass Spectrometry
• Non-Targeted Analysis
• +5 more

Dr. Saer Samanipour is a Visiting Professor at the Van 't Hoff Institute for Molecular Sciences, part of the Faculty of Science at the University of Amsterdam. His research focuses on advanced analytical techniques for environmental and biomedical applications, with a strong emphasis on non-targeted analysis, mass spectrometry, and machine learning integration. He leads efforts in developing open-source tools like GcDUO and jHRMSToolBox to enhance data interpretation in complex chemical datasets. Key areas include environmental contaminant detection, chemical exposure assessment via wastewater-based epidemiology, and proteomic analysis of snake venoms. His work bridges computational methods with experimental chemistry to address global challenges in environmental health and toxicology. Primary affiliation: Van 't Hoff Institute for Molecular Sciences Research themes: Non-targeted LC-HRMS workflows, machine learning applications in analytical chemistry, PFAS analysis, and exposome research Software contributions: GcDUO (GC×GC-MS), jHRMSToolBox (HRMS data processing) His publications highlight innovations in data-driven approaches for compound prioritization, toxicity prediction, and method optimization. Recent work explores chemical space exploration and chemometric strategies for complex mixture analysis, with applications to environmental monitoring and forensic science.

Alexander J. StewartView profile

Associate Professor

• Mathematical Biology
• Computational Social Science
• Cultural Evolution
• +7 more

Alexander J. Stewart is an Associate Professor of Informatics at Indiana University Bloomington and an Honorary Professor of Mathematics at the University of St Andrews. He holds a PhD in Mathematical Biology from University College London (2010), and earlier degrees in Physics (Imperial College London) and Mathematical Biology (University College London). His research focuses on mathematical and computational models of social learning in online ecosystems, addressing topics such as misinformation, polarization, and cooperative behavior through lenses of game theory and evolutionary dynamics. Key themes include cultural evolution's interaction with algorithms, social network structures' impact on collective decisions, and cognitive mechanisms shaping social interactions. His work has been published in high-impact journals like Nature , PNAS , and Science Advances , addressing topics ranging from information gerrymandering to the evolution of stereotyping. He leads projects funded by the John Templeton Foundation, exploring cultural evolution and online social contracts. Advising over 7 graduate students and postdocs, he has mentored researchers now in academic and industry roles globally. His teaching includes advanced courses on game theory and social media dynamics. Media contributions include analyses on polarization's societal impacts and the dangers of algorithmic misinformation. Current research initiatives include modeling behavioral motivations for cooperation and investigating eco-evolutionary trade-offs in pathogen competition. His labs focus on interdisciplinary approaches to understanding complex social and biological systems.

Georg HeinView profile

Professor

• Algebraic Geometry
• Vector Bundles
• Moduli Spaces
• +2 more

Prof. Dr. Georg Hein is a Professor in the Department of Mathematics at the University of Duisburg-Essen, Campus Essen. His office is located at WSC-O-3.58, Thea-Leymann-Str. 9, 45117 Essen, with office hours held every Tuesday from 2-3 PM and by appointment. He serves as Director of the Research Group Hein, focusing on Algebraic Geometry. His research centers on Algebraic Geometry with specific expertise in vector bundles, moduli spaces, elliptic surfaces, and sheaf theory. Hein's work demonstrates deep engagement with stability conditions, Fourier-Mukai transforms, and geometric invariant theory. His publications reveal a consistent focus on foundational structures in algebraic geometry, particularly through the lens of vector bundles on curves and surfaces. Analysis of his 15 most recent publications (2017-2007) shows a strong thematic continuity in moduli space theory and vector bundle classification. His work bridges abstract algebraic constructions with computational approaches, as evidenced by algorithmic developments for elliptic surfaces and lattice-theoretic investigations. The publications collectively emphasize stability criteria across diverse geometric contexts. Hein actively supervises PhD students including Asbjørn Michelsen, with former advisees Quyet Thang Truong and Dr. Dario Weißmann. He contributes to academic outreach through the Essen Math Circle for students, organizing weekly sessions for grades 5-13 covering advanced mathematical topics beyond standard curricula. His teaching portfolio includes courses such as Mathematische Miniaturen and Topologie .

M. Hadi AminiView profile

Assistant Professor

• Distributed Machine Learning
• Federated Learning
• Interdependent Networks
• +7 more

M. Hadi Amini is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences. He founded and directs the Sustainability, Optimization, and Learning for InterDependent networks (SOLID) laboratory, focusing on cyber-physical-social systems and distributed AI applications. Ph.D., Electrical and Computer Engineering (2019), Carnegie Mellon University M.Sc., Electrical and Computer Engineering (2015), Carnegie Mellon University M.Sc. (2013), Tarbiat Modares University B.Sc. (2011), Sharif University of Technology His research spans federated learning, interdependent network optimization, and AI applications in smart cities , energy systems , and healthcare . Recent work emphasizes privacy-preserving techniques, quantum encryption, and blockchain integration for secure distributed learning. The 15 most recent publications highlight trends in large language models , edge computing , medical imaging security , and infrastructure resilience , with interdisciplinary emphasis across computer science, systems engineering, and urban planning. Best Paper Award, IEEE Conference on Computational Science & Computational Intelligence (2019) Best Journal Paper Award, Springer Nature Operations Research Forum (2021) Excellence in Teaching Award, FIU (2020) Multiple Best Reviewer Awards, IEEE Transactions NSF Travel Awards (2019) As Associate Editor for Frontiers in Communications and Networks and book series editor for Sustainable Interdependent Networks , he actively shapes research discourse. His lab has secured $3.6M in federal/state funding for AI-driven infrastructure projects.

Antonis PapapantoleonView profile

Professor

• Mathematical Finance
• Stochastic Processes
• Lévy Processes
• +5 more

Antonis Papapantoleon is a Professor of Mathematical Finance at the Delft Institute of Applied Mathematics (DIAM), part of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at TU Delft. He is also affiliated with the Delft FinTech Lab and serves as an Affiliated Researcher at the Institute of Applied and Computational Mathematics (IACM), FORTH. Previously, he held roles including Assistant Professor at the National Technical University of Athens (NTUA), Juniorprofessor at TU Berlin (2011–2017), and Deputy Professor at the University of Mannheim (2016–2017). His research focuses on stochastic systems, Lévy processes in finance, term structure/LIBOR modeling, systemic risk management, model-free methods, and applications of machine learning in finance. Key contributions include advancements in affine LIBOR models, systemic risk analysis, and copula-based risk allocation. His work has been published in top journals like Annals of Applied Probability , Mathematical Finance , and Finance and Stochastics . His research has been supported by the Hellenic Foundation for Research and Innovation (HFRI), Europlace Institute of Finance, DAAD, MATHEON, and private corporations. He has delivered invited talks globally and co-edited Advanced Modelling in Mathematical Finance (Springer, 2016). Current affiliations include leadership in the Delft FinTech Lab and active participation in international collaborations. His work bridges theoretical stochastic analysis with practical financial applications, emphasizing robust frameworks for risk measurement and derivatives pricing.

Alan MisloveView profile

Professor

• Algorithmic Auditing
• Fairness
• Privacy
• +4 more

Alan Mislove is a Professor of Computer Science at Northeastern University and serves as Senior Associate Dean for Academic Affairs and Interim Executive Director of the Institute of Experiential Artificial Intelligence (EAI). He is also a core faculty member of the Cybersecurity and Privacy Institute and EAI at Northeastern. His research focuses on algorithmic auditing, fairness, and privacy in online systems. Notably, he has testified before Congress on algorithmic discrimination, challenged the CFAA in ACLU litigation, and contributed to landmark FTC and DOJ cases against Meta. His work has been funded by the NSF, Army Research Office, Google, and Amazon Web Services. Mislove’s academic leadership includes roles in Khoury College, where he supports over 2,000 students and faculty. He has advised numerous PhD students, including current research scientist Piotr Sapieżyński. His lab explores algorithmic transparency, PKI security, and middlebox traffic differentiation. Key awards include the 2017 ACM SIGCOMM Test of Time Award and recognition for work on QUIC protocol evaluation. He has authored over 150 publications, including seminal studies on Facebook ad discrimination and Uber’s surge pricing. His teaching spans systems, networks, and social computing courses, emphasizing experiential learning. Mislove actively serves on program committees for IMC, PETS, and other top conferences, and contributed to policy as Deputy U.S. CTO for Privacy (2023–2024).

• Complex Systems Modeling
• Computational Mathematics
• Applied Mathematics
• +7 more

Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS

Juan Felipe Carrasquilla ÁlvarezView profile

Assistant Professor

• Condensed Matter Physics
• Quantum Computing
• Machine Learning
• +3 more

Juan Felipe Carrasquilla Álvarez is an Assistant Professor in the Department of Physics at the University of Toronto. His research focuses on the intersection of condensed matter physics, quantum computing, and machine learning, emphasizing quantum many-body systems, quantum device validation, and phase identification. He holds affiliations with the Acceleration Consortium and the Centre for Quantum Information and Quantum Control at the University of Toronto, and is a Perimeter Institute Visiting Fellow. Education: PhD in Physics from SISSA (Italy), followed by postdoctoral fellowships at Georgetown University (2011-2013), the Perimeter Institute (2013-2016), and a stint as a Research Scientist at D-Wave Systems Inc. Earlier, he completed the Abdus Salam ICTP Diploma Programme (2005-2006). Research interests span quantum Monte Carlo simulations, machine learning-driven analysis of quantum systems, and applications to quantum computing validation. His work bridges theoretical physics with computational methods, addressing challenges in both classical and quantum computing paradigms. Notable contributions include developing neural network architectures for quantum state reconstruction, error mitigation in quantum simulations, and optimal control strategies for quantum thermal machines. His publications explore topics like topological order detection, shadow tomography, and hybrid quantum-classical algorithms. Awards/Fellowships: Perimeter Institute Postdoctoral Fellowship (2013-2016), Georgetown University Postdoctoral Fellowship (2011-2013), SISSA PhD Fellowship (2006-2010), and Abdus Salam ICTP Diploma Programme Fellowship (2005-2006). Advising/Grants: No formal advisee list provided. Active in interdisciplinary collaborations through affiliations with major quantum research consortia and institutions. Lab/Teams: Part of the Acceleration Consortium and the Centre for Quantum Information and Quantum Control, contributing to cutting-edge quantum computing and machine learning research.

Kim IttonenView profile

Researcher

• Audit
• Corporate Governance
• Financial Reporting
• +3 more

Kim Ittonen is a Researcher at the Department of Accounting, Hanken School of Economics in Helsinki, Finland. They hold a Doctorate in Business Administration (Econ. dr) from the University of Vaasa (2009). Their research focuses on audit practices, corporate governance, financial reporting quality, and organizational resilience in the auditing sector. Key areas of interest include audit firm adaptation to crises (e.g., COVID-19), remote auditing challenges, auditor skill development, and ethical implications of AI in accounting. Ittonen has authored/co-authored over 35 publications since 2009, with notable work on distracted auditors' impact on earnings quality (2024), audit firms' resilience during pandemics (2024), and the evolving role of accountants as consultants (2022). They actively engage in international academic collaborations, visiting institutions like Università Bocconi in 2025. Their work has been cited 546 times, reflecting significant scholarly impact. Professional activities include serving as a reviewer for journals like Accounting Forum and International Journal of Auditing , supervising doctoral dissertations, and participating in policy discussions on audit regulation and sustainability reporting (CSRD). Recent research highlights the transformative role of technology and remote work in auditing practices while emphasizing the need for auditor skill diversification in a rapidly changing market.

Jacqueline McClearyView profile

Assistant Professor

• Astrophysics
• Cosmology
• Weak Gravitational Lensing
• +4 more

Dr. Jacqueline McCleary is an Assistant Professor of Physics at Northeastern University's College of Science, specializing in observational cosmology with a focus on galaxy clusters and dark matter. She leads research using weak gravitational lensing to study cosmic structures, collaborating on projects like the COSMOS-Web (JWST), SuperBIT (balloon telescope), and LoVoCCS surveys. Her work leverages multi-wavelength data from space, stratospheric, and ground-based observatories. Education: M.S. in Astronomy (New Mexico State University), M.S. and Ph.D. in Physics (Brown University), Postdoctoral Fellow at NASA's Jet Propulsion Laboratory. She transitioned to Northeastern as an ADVANCE Future Faculty Fellow before becoming a tenure-track faculty member in 2022. Research Interests: Dark matter interactions, galaxy cluster dynamics, gravitational lensing techniques, and next-generation observational platforms. Her team develops advanced algorithms and instrumentation for high-resolution imaging. Recent Contributions: COSMOS-Web has enabled unprecedented observations of distant galaxies using JWST, while SuperBIT's stratospheric flights provide diffraction-limited data. Key publications focus on lensing surveys, data reduction techniques, and dark matter-halo relationships. Awards: Recognized as a Northeastern ADVANCE Future Faculty Fellow. Media Engagement: Regularly comments on space exploration, asteroid risks, and cosmic phenomena for public outlets.

Yong-Bin KimView profile

Professor

• Integrated Circuit Design
• Nanoelectronics
• High-Speed System Integration
• +4 more

Yong-Bin Kim is a Professor in the Department of Electrical and Computer Engineering at Northeastern University, part of the College of Engineering. He has held prior positions at Intel Corp., Hewlett Packard Co., Sun Microsystems, and the University of Utah. His research focuses on integrated circuit design, nanoelectronics, bio-chip interfaces, and low-power VLSI systems. He has contributed to initiatives like the HPVLSI Lab and Microsystems and Electron Devices Lab. Education includes a B.S. in Electronic Engineering from Sogang University (Seoul, South Korea), an M.S. from the New Jersey Institute of Technology, and a Ph.D. in Computer Engineering from Colorado State University (1996). Research interests encompass high-speed low-power VLSI design, system-on-chip (SoC), physical VLSI CAD, and nanoelectronics. Specific areas include bio-sensor interface circuits, electronic neuron design, and adaptive robot controllers. Key projects involve compact power-efficient integrated circuits and high-speed transceiver design. Outstanding Paper Award, 2020 IEEE ISOCC South Korean Patent for Autonomous Impedance Calibration (2021) Best Paper Award, 2016 International SoC Design Conference Patent for Improved Receiver Circuit (2020) Patent for Method to Detect Trojan Circuits (2018) Advisees include graduate student Yixuan He. He has led research projects funded by Winchester Technology and Hynix Semiconductor, focusing on semi-self-calibration transceivers and tunable RF inductors. Labs include the HPVLSI Lab and Microsystems and Electron Devices Lab at Northeastern University, which focus on high-speed/low-power IC design and microfabrication technologies.