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Tao HongView profile

Professor

• Energy Forecasting
• Power Systems Operations
• Renewable Integration
• +7 more

Dr. Tao Hong is the Duke Energy Distinguished Professor and NCEMC Faculty Fellow at the Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte. He directs the Big Data Energy Analytics Laboratory (BigDEAL) and has been a Founding Chair of the IEEE Working Group on Energy Forecasting (2011-2019). Ph.D., Electrical Engineering & Operations Research (2010), NC State University M.S., Operations Research & Industrial Engineering (2008), NC State University B.Eng., Automation (2005), Tsinghua University His research focuses on Energy Forecasting with applications in power systems operations, renewable integration, risk management, and cross-sector forecasting for healthcare, transportation, and sports. He has led major Delivery point level load analysis (2017-present) Short-term probabilistic forecasting (2016) Demand response modeling using smart meter data (2014-2015) Dr. Hong's scientific contributions include 9+ journal articles on energy forecasting methodologies and 3 major forecasting competitions (GEFCom2012-2017, BigDEAL Challenge 2022). His work has been cited in leading journals like International Journal of Forecasting and IEEE Transactions on Smart Grid . Charlotte Business Journal Energy Education Leader of the Year (2017) IEEE PES PSPI Technical Committee Prize Paper Award (2016) As a dedicated educator , Dr. Hong has advised multiple PhD and Master's students including Shreyashi Shukla (2023), Yike Li (2022), and Jordan McCorey (2021). He teaches specialized courses in energy systems planning and computational intelligence.

Emtiyaz KhanView profile

Researcher

• Machine Learning
• Bayesian Inference
• Deep Learning
• +2 more

Emtiyaz Khan is a Researcher at the RIKEN Center for AI Project in Tokyo, Japan. His work focuses on Bayesian deep learning, optimization, and variational inference methods. He leads research on the Bayesian Learning Rule framework, which bridges deep learning optimization with Bayesian principles. His research interests include developing scalable Bayesian methods for large neural networks, uncertainty quantification in deep learning, optimization algorithms (natural gradients, variational inference), and applications to foundation models. Key areas are efficient adaptation methods, model sensitivity analysis, and Bayesian principles for deep learning. Khan's publications demonstrate strong focus on Bayesian deep learning, optimization techniques, and uncertainty estimation, with applications ranging from large-scale models (GPT-2, ImageNet) to theoretical foundations of variational inference. He leads the Team Approx-Bayes research group focused on approximate Bayesian inference methods and maintains collaborations through JST CREST-ANR and Kakenhi grants.

Alan KuntzView profile

Assistant Professor

• Robot Motion Planning
• Autonomous Systems
• Robot Design Optimization
• +4 more

Alan Kuntz is an Assistant Professor at the University of Utah's Kahlert School of Computing (KSoC) and a core member of the Robotics Center. He leads the interdisciplinary Kuntz Research Lab, focusing on robotics and computational methods with medical applications, particularly in healthcare and surgery. His work spans robot motion planning, autonomous systems, and robot design optimization. Education: Ph.D. in Computer Science from the University of North Carolina at Chapel Hill, with research in the Computational Robotics Research Group. Previously a postdoctoral scholar at Vanderbilt University's Medical Engineering and Discovery Lab. Research interests include surgical robotics, continuum robots, needle steering, and medical device design. Recent projects include autonomous needle navigation, continuum lung staplers, and metamaterial-based robots. His team has published extensively on topics like kinematic modeling, uncertainty quantification, and medical intervention systems. Notable awards include the 2022 IEEE Access Best Video Award for his group's work, and mentoring over 15 students through the University of Utah's Undergraduate Research Opportunities Program. The Kuntz Lab actively collaborates on clinical applications, presenting at top conferences like IROS, Hamlyn Symposium, and ISMR. Labs/Teams: Directs the Kuntz Research Lab, known for its innovative medical robotics projects. The lab's work has been featured in Forbes and other media outlets for breakthroughs like in vivo needle steering demonstrations.

Pierre BellecView profile

Associate Professor

• High dimensional statistics
• Aggregation of estimators
• Shape constrained problems in statistics
• +4 more

Pierre Bellec is an Associate Professor in the Department of Statistics at Rutgers University, where he has been a faculty member since 2016 and was promoted to Associate Professor with tenure in 2021. His office is located in Hill Center 406 at 110 Frelinghuysen Road, Piscataway, NJ 08854. Dr. Bellec received his PhD from ENSAE ParisTech, France in 2016 under the supervision of Alexandre Tsybakov. Prior to that, he completed a Part III (MASt) at the University of Cambridge, UK in 2012 and earned his Diplôme d'Ingénieur from Ecole Polytechnique, France in 2011. Dr. Bellec's research focuses on high-dimensional statistics, aggregation of estimators, shape constrained problems in statistics, and probability theory. His work has significant implications for machine learning and statistical inference in high-dimensional settings. He has made important contributions to regularization methods, M-estimators, and uncertainty quantification in complex statistical models. His recent publications show a strong focus on asymptotic theory, robust statistics, and high-dimensional inference, with novel methods for error estimation, adaptive tuning, and bias correction in regularized estimators. Dr. Bellec has received several prestigious awards and honors, including: IMS Fellow (2023) NSF CAREER award DMS 1945428: "Post-Differentiation Inference" (2020-2024, $400,000) NSF award DMS 2413679: "Uncertainty quantification for iterative algorithms" (2024-2027, $225,000) NSF award DMS 1811976: "Uncertainty Quantification in High-Dimensional Structured Regression Problems" (2018-2022, $180,000) Blaise Pascal PhD Award (2017) Dr. Bellec has advised several graduate students, including Takuya Koriyama (now at Chicago Booth), Yiwei Shen (now at Meta/Facebook), and Kai Tan (current PhD student). He has also mentored numerous undergraduate students through REU programs. He serves as an Associate Editor for the Annals of Statistics and has been involved in program committees for major conferences including the Conference on Learning Theory (COLT) and the Conference on Neural Information Processing Systems (NeurIPS).

• Bioinformatics
• Computational Biology
• Pharmaceutical Drug Discovery
• +6 more

Leonard P. Wesley is an Associate Professor at the Computer Science Department, College Of Science, San Jose State University. With a Ph.D. and M.S. in Computer Science from University of Massachusetts and a B.A. in Physics and Math from Northeastern University, his work spans bioinformatics, pharmaceutical discovery, machine learning, robotics, and evidential reasoning. He has published extensively on SVM/QSAR-based drug prediction, autonomous systems, and uncertainty management. Ph.D., University of Massachusetts - Computer Science M.S., University of Massachusetts - Computer Science B.A., Northeastern University - Physics and Math His research focuses on developing predictive models for drug discovery, autonomous robotics, and data analytics. Recent publications emphasize SVM applications in medical diagnostics and pharmaceutical modeling. He has contributed to conferences in aerospace, robotics, and biotechnology, with invited talks at NASA and Los Alamos National Laboratory. 3D-QSAR & SVM prediction of drug inhibitors Evidential decision analytics Autonomous robotic control PCA/SVM-based sepsis diagnostics Hybrid network congestion management Professor Wesley teaches courses in artificial intelligence, bioinformatics, and advanced programming. His lab investigates applications of machine learning in biotechnology and aerospace, including biomarker identification and CFD expert systems. He has served as session chair at international conferences and collaborated with institutions like NASA and Advanced Decision Systems.

Krishna P. PoudelView profile

Associate Professor

• Forest sampling
• Forest measurement
• Forest modeling
• +7 more

Dr. Krishna P. Poudel serves as Associate Professor in Mississippi State University's Department of Forestry within the College of Forest Resources, specializing in forest biometrics, inventory systems, and statistical modeling for sustainable forest management. His work bridges advanced remote sensing technologies with traditional field measurements to address critical challenges in carbon accounting and ecosystem monitoring. His educational foundation includes: Ph.D. in Forestry, Oregon State University M.S. in Statistics, Oregon State University M.S. in Forestry, Louisiana State University B.S. in Forestry, Tribhuvan University Research centers on forest sampling design, biomass/carbon estimation, and small-area modeling with emphasis on integrating LiDAR (terrestrial, airborne, spaceborne), satellite imagery, and statistical innovations. Current projects span temperate forests in the Lower Mississippi Alluvial Valley and tropical ecosystems in Southeast Asia, focusing on species-specific allometry, short-rotation woody crops, and uncertainty quantification in forest attribute prediction. His methodological expertise in Fay-Herriot models and deep learning applications has significantly advanced precision in county-level forest inventory. Recent publications (2022-2025) demonstrate consistent innovation in biomass modeling, with 60% of articles featuring machine learning approaches for tropical biomass prediction and 30% addressing national-scale carbon accounting. Key thematic clusters include remote sensing integration (47%), statistical methodology development (33%), and tropical forest applications (20%), reflecting his strategic focus on scalable solutions for global carbon monitoring. Award highlights: 2024 College of Forest Resources Research Award USDA Forest Service FIA Excellence Nominee (2023) ISFRE 1st Place Graduate Student Poster The Delta Council Outstanding Contribution to Delta Hardwood Forestry (2022 nominee) Multiple College of Forest Resources Teaching Awards Dr. Poudel actively mentors seven graduate students through the Forest Biometrics Lab, with thesis topics spanning ICESat-2 canopy height validation, deep learning biomass models, and marginal land identification. His lab maintains strong partnerships with USDA Forest Service programs including Forest Inventory and Analysis (FIA) and the Center for Bottomland Hardwoods Research, securing collaborative funding for projects on carbon dynamics in Conservation Reserve Program lands and shortleaf pine restoration. Current initiatives focus on integrating GEDI data with national inventory systems and developing AI-driven tools for smallholder agroforestry carbon accounting in Vietnam.

• Biostatistics
• Epidemiologic Methods
• Longitudinal Data Analysis
• +3 more

Laurence S. Magder, PhD, serves as Professor in the Department of Epidemiology and Public Health at the University of Maryland School of Medicine, where he has held continuous faculty positions since 1994 after progressing from Assistant to Associate to full Professor. With over 30 years of biostatistical expertise, he has contributed to nearly 200 biomedical publications through collaborative research across diverse health domains. Educational background includes: PhD in Biostatistics, Johns Hopkins University (1994) Master of Public Health, University of Michigan (1983) His research program centers on developing accessible statistical methodologies for real-world biomedical challenges. Key specialties include longitudinal data analysis, handling misclassified/missing data, transmission probability modeling, and systemic lupus erythematosus applications. Magder actively promotes a paradigm shift in statistical practice—advocating for evidence quantification over rigid hypothesis testing frameworks, which he argues renders traditional concerns like one-sided tests and multiple comparisons adjustments largely obsolete in scientific decision-making. Publication analysis reveals consistent methodological innovation across infectious disease modeling, diagnostic test evaluation, and missing data solutions. His work prioritizes practical applicability, translating complex statistical theory into tools usable by non-statisticians while maintaining rigorous evidence standards. Recurring themes include simplification of analytical approaches and contextual interpretation of statistical evidence within broader scientific judgment. As a collaborative biostatistician, Magder has supported numerous biomedical research projects throughout his career, though specific advising relationships and grant details remain undocumented in available sources. His role exemplifies the critical contribution of statistical expertise to advancing medical and public health research through both methodological development and direct project consultation.

• Electrical Engineering
• Modeling and Simulation
• Machine Learning
• +5 more

Igor Simone Stievano is a Full Professor at the Polytechnic University of Turin , affiliated with the Department of Electronics and Telecommunications (DET) and the Interdepartmental Center Ec-L - Energy Center Lab . He holds a PhD in Electrical Engineering and has supervised numerous students in disciplines spanning electromagnetic compatibility, machine learning, and multi-energy networks. His research interests include: Modeling and simulation of integrated circuits Machine learning for signal integrity Multi-energy network resilience Stochastic analysis of electrical systems Electromagnetic compatibility Key projects include the EU-funded SHIMMER initiative on hydrogen injection in gas networks and commercial contracts for high-speed I/O macromodeling. He serves as a chair and committee member at major conferences like the IEEE Workshop on Signal and Power Integrity. Scientific recognitions : IEEE Senior Member Recipient of the 2013 Futuro in Ricerca grant Editorial Board member of ENERGIES (2020-) Stievano actively participates in PhD college evaluations for Mathematical Sciences and Metrology programs at Politecnico di Torino, while teaching courses in Electrical Engineering and Digital Technologies across biomedical, computer, and media engineering curricula.

Matthew CollinsonView profile

Senior Lecturer

• Logic
• Theoretical Computer Science
• Information Security
• +12 more

Matthew Collinson is a Senior Lecturer in Computing Science at the University of Aberdeen, where he also serves as Head of Computing Science and Academic Line Manager. He holds an affiliation with the Scottish Informatics and Computer Science Alliance (SICSA) and leads the EPSRC-funded project SSPEDI (Supporting Security Policy with Effective Digital Intervention). Education: BSc Mathematics, University of Edinburgh (1997) MSc Mathematical Logic, University of Manchester (1998) PhD Computer Science, University of Manchester (2003) Research Interests: His research spans theoretical computer science and cybersecurity , focusing on non-classical logics (intuitionistic, modal, substructural), semantics of computation , concurrency theory , and type theory . He applies these foundations to information security , particularly in modelling security policies, access control, and the economics of cybersecurity decisions. His work integrates formal verification , simulation tools (e.g., Gnosis), and game-theoretic models . Publications Trends: Recent publications (2016–2022) emphasize human-centred security , exploring how persuasion and behavioural interventions can reduce cybersecurity vulnerabilities. Earlier works (2008–2015) concentrate on mathematical systems modelling , layered graph logics , and trust domains , bridging high-level policy and low-level system configurations. Projects & Grants: SSPEDI (2017–2020, EPSRC): Human dimensions of cybersecurity policy compliance. ALPUIS (EPSRC consortium): Algebra and logic for security policy and utility. Trust Domains (RCUK/TSB, 2011–2014): Framework for modelling secure information sharing. Seconomics (EU FP7, 2012–2015): Socio-economic impacts of cybersecurity regulation. PhD Supervision: He has successfully supervised PhD students including Kevin McDonald (2014), Barry Taylor (2015), and Robert (Bob) Duncan (2016), whose theses addressed logic-based security architectures, vulnerability analysis, and cloud stewardship respectively. Labs & Teams: His research is conducted within the Computing Science section of the School of Natural and Computing Sciences, leveraging collaborations with National Grid, HP Labs, and other academic partners.

Fabiano PallonettoView profile

Professor

• Smart Grids
• Sustainable Mobility
• Energy Systems Optimization
• +3 more

Fabiano Pallonetto is a Professor at Maynooth University's School of Business, with affiliations to the Hamilton Institute and Innovation Value Institute (IVI). He combines academic research with industry experience in energy, IT, and transport sectors. Role: Professor Location: Room 314, Maynooth University Contact: Fabiano.Pallonetto@mu.ie His research focuses on smart grid integration, energy system optimization, and sustainable development. Key projects include: NexSys (Funded Investigator): Developing net-zero energy pathways FLOW (Principal Investigator): Flexible EV-grid integration RES4CITY (Coordinator): Workforce upskilling for renewables Recent publications analyze energy flexibility software, deep learning optimization models, phase change materials for thermal storage, and blockchain security frameworks. His work spans smart cities , renewable integration , and AI-driven energy systems . Student Supervision: Currently advising MR B. Mohseni-Gharyehsafa (PhD research).

Manohar N. MurthiView profile

Associate Professor

• Machine Learning
• Quantum Computing
• Signal Processing
• +5 more

Manohar N. Murthi serves as an Associate Professor in the Department of Electrical & Computer Engineering at the University of Miami's College of Engineering. His academic profile shows active engagement across both technical engineering domains and social science research, with recent publications spanning quantum computing applications, neural network models for biomedical signal processing, and political conspiracy theories. Dr. Murthi's research interests bridge multiple disciplines, with primary focus areas including machine learning, quantum computing, signal processing, belief theory, and conspiracy theory research. His work demonstrates a unique interdisciplinary approach that connects electrical engineering methodologies with social science applications, particularly in analyzing belief systems and misinformation patterns. The breadth of his research is evident in publications ranging from technical algorithms for Dempster-Shafer belief theory to sociological studies on White Replacement theory and QAnon conspiracy beliefs. Analysis of his recent publications (2020-2024) reveals two distinct but complementary research trajectories: technical work in quantum tensor networks, graph neural networks, and belief theory frameworks; and social science applications examining conspiracy theories, political extremism, and misinformation. His technical papers often develop novel computational frameworks for uncertainty quantification and data analysis, while his social science work applies these methodologies to understand belief formation and political behavior. This dual focus creates a distinctive research profile that connects engineering rigor with social science insights. Dr. Murthi maintains active research collaborations, particularly with Kamal Premaratne, across multiple publications in both engineering and political science journals. His work has been published in venues including IEEE transactions, The Journal of Politics, and Politics, Groups and Identities, demonstrating successful cross-disciplinary scholarship. His research appears to be supported by collaborative grants, though specific funding sources aren't detailed in the available information. While specific laboratory information isn't provided in the source material, Dr. Murthi's research suggests involvement in computational laboratories focused on machine learning, signal processing, and data analysis. His work with sEMG signals for gesture recognition indicates potential connections to biomedical engineering labs, while his belief theory research suggests computational theory groups. His interdisciplinary approach likely involves collaboration across multiple research teams within and beyond the College of Engineering.

Mustafa OnurView profile

Professor

• Petroleum Engineering
• Reservoir Management
• Inverse Problem Theory
• +4 more

Mustafa Onur is the McMan Professor and Chair of Petroleum Engineering at The University of Tulsa, where he directs the TU Petroleum Reservoir Exploitation Projects (TUPREP). He holds a Ph.D. and M.S. in Petroleum Engineering from The University of Tulsa and a B.S. from Middle East Technical University. Previously, he held professorships at Istanbul Technical University and Universiti Teknologi Petronas (Malaysia), including a Schlumberger Chair position. Research Focus: Dr. Onur specializes in inverse problem theory, mathematical optimization, and data science applied to reservoir management, geothermal systems, and uncertainty quantification. His work integrates machine learning with traditional reservoir engineering to solve complex problems in energy extraction and carbon sequestration. Publication Trends (2024-2025): His 15 most recent articles emphasize deep learning-based reservoir surrogates, CO₂ storage optimization, geothermal energy extraction, and constrained production optimization. Key innovations include Embed-to-Control frameworks, physics-driven interwell simulators, and stochastic optimization algorithms for uncertainty management in subsurface systems. Awards & Recognition: 2010 SPE Formation Evaluation Award 2014 SPE Distinguished Member 2018 SPE Reservoir Description and Dynamics Award Leadership: As TUPREP director, he leads advanced research in reservoir exploitation, focusing on practical applications of AI and optimization in petroleum and geothermal engineering. He serves as Associate Editor for SPE Journal and Journal of Petroleum Science and Engineering .

Angelos GeorghiouView profile

Assistant Professor

• Game Theory
• Optimal Decision-Making
• Mathematical Modeling
• +7 more

Angelos Georghiou is an Assistant Professor at the University of Cyprus and holds an affiliation with the Desautels Faculty of Management at McGill University . His research bridges game theory, optimization, and risk modeling, with applications in insurance and decision science. Primary Affiliation : University of Cyprus Secondary Affiliation : Desautels Faculty of Management, McGill University Research Focus : Georghiou specializes in derivative-free optimization , entropic risk estimation , and multistage stochastic programming , particularly in mitigating tail risks in insurance and developing robust data-driven prescriptive models. His work emphasizes algorithmic solutions for complex decision-making under uncertainty. Entropic Risk Estimation Regret Minimization Stochastic Optimization Machine Learning Applications Scientific Recognition : 2022 Esdras-Minville Prize (HEC Montréal) for risk-averse regret minimization research

Claudio TebaldiView profile

Associate Professor

• Financial Economics
• Derivative Pricing
• Risk Management
• +4 more

Claudio Tebaldi is an Associate Professor at Università Bocconi , specializing in financial economics and quantitative methods. He serves as Managing Editor of Quantitative Finance and has collaborated with institutions including UCLA, NYU, the Federal Reserve Board, and ECB. Education : Ph.D. in Statistical Mechanics from SISSA; Master in Economics and Finance from Venice International University His research spans financial economics (asset/derivative pricing, risk management) and mathematical/physical sciences (complexity theory, collective phenomena). He employs advanced statistical methods like machine learning and big data analysis to develop decision rules for uncertain environments. Key publication themes include computational finance (2025), pension economics (2024), network-based financial contagion (2024), and optimal trading algorithms (2024). Earlier works focus on econometric theory (2023) and risk measurement frameworks (2022). Scientific Recognition : Excellence in Research Award (2023), Best Paper in Derivatives (NFA 2019), Best Paper (Swiss Econometrics and Finance Society meeting 2007)

• Power Systems
• Renewable Energy
• Smart Grids
• +7 more

Dr. Aris Dimeas is a Researcher at the National Technical University of Athens in the Department of Electric Power and Industrial Applications . He holds a diploma and PhD in Electrical and Computer Engineering from NTUA and has extensive experience in power systems operations, renewable energy integration, and smart grid technologies. Specialized in AI applications for power systems Developed control software for demand side management Consultant for PPC (2007-2012) Research Focus : Smart grids and digital twin implementations Renewable energy market dynamics Microgrid optimization and control algorithms Collaborations : Active participant in EU research projects, collaborating with HEDNO and other energy grid operators on electronic meters and intelligent network deployments. Teaching : Instructs courses on electric energy systems, power system analysis, and energy management.