جست‌وجوی استاد و پژوهشگر

Rebecca Bennettمشاهده پروفایل

استاد

• Bioethics
• Genetic Screening Ethics
• Reproductive Ethics
• +۸ مورد دیگر

Rebecca Bennett is a Professor of Bioethics at the University of Manchester , affiliated with the School of Law . She currently serves as Associate Dean for Teaching and Learning (Distance Learning) in the Faculty of Humanities. Her academic career spans over three decades, specializing in bioethics and medical law, with a focus on reproductive ethics, genetic screening, and disability rights. Professor Bennett pioneered the LLM/MA/PGCert/PGDiploma in Healthcare Ethics and Law as one of the first fully online distance learning programs in the university. She also leads a CPD course in Medical Decision-Making: Ethics and Law , accredited by the Royal College of Physicians. Her teaching excellence was recognized with a 2022 award for Educational Leadership and Digital Delivery. Research Interests include the ethics of prenatal testing (e.g., NIPT for Down’s syndrome), newborn bloodspot screening, transgender healthcare ethics, and critiques of Procreative Beneficence. She advocates for ethical terminology reform to reduce stigma around disability and opposes eugenic interpretations of genetic screening. Her recent work includes a 2025 book The Welfare of Future Children: Reproductive Ethics and Disability Screening and articles challenging bioethical language ( Cambridge Quarterly ) and supporting gene editing ( American Journal of Bioethics ). She co-led a project on antenatal information for newborn screening (2013–2016) and advises media on ethical issues, including the BBC documentary A World Without Down’s Syndrome? She chairs a healthcare ethics discussion group on Facebook and contributes to free MOOCs on disability screening ethics. Her work aligns with UN Sustainable Development Goals addressing health equity and ethical governance.

• Stochastic Optimization
• Robust Optimization
• Data-Driven Decision-Making
• +۳ مورد دیگر

Nam Ho-Nguyen is a Senior Lecturer in the Discipline of Business Analytics at the University of Sydney Business School. He holds a PhD from the Tepper School of Business at Carnegie Mellon University and an Honours degree in Mathematics from the Australian National University. His research focuses on stochastic, robust, and data-driven optimization techniques for decision-making under uncertainty, combining methods from statistics, machine learning, and traditional optimization. Education: PhD in Operations Research, Carnegie Mellon University (2019) Bachelor of Science (Honours) in Mathematics, Australian National University Research Interests: Nam’s work addresses decision-making in uncertain environments through optimization frameworks. He explores applications in finance, healthcare, transportation, and technology, emphasizing scalability and computational efficiency. Key areas include bilevel optimization, adversarial machine learning, and distributionally robust chance-constrained programming. Awards: INFORMS Optimization Society Young Researchers Prize (2022) Gerald L. Thompson Doctoral Dissertation Award (2019) Australian Research Council Discovery Early Career Researcher Award (DECRA) ($470K AUD) Teaching & Grants: Teaches QBUS1040, QBUS2310, BUSS4932, and QBUS6820 courses. Recipient of ARC grants for research on multi-stage optimization with contextual data and forecast reconciliation ($625K AUD). Supervises research students on scheduling problems and multi-agent systems. Lab/Team: His work involves collaborations on optimization tools for choice modeling, electronic trading, and solution design in IT services, supported by interdisciplinary teams at the University of Sydney and international institutions.

Mahyar Amirgholyمشاهده پروفایل

استادیار

• Cooperative Control of Connected Automated Vehicles
• Transportation Electrification and Decarbonization
• Machine Learning and Reinforcement Learning
• +۳ مورد دیگر

Dr. Mahyar Amirgholy is an Assistant Professor at Kennesaw State University (KSU) within the Southern Polytechnic College of Engineering and Engineering Technology, and a Research Affiliate at Lawrence Berkeley National Laboratory. His research focuses on transportation systems, with expertise in cooperative control of connected automated vehicles, transportation electrification, and machine learning applications. Education: Ph.D. in Civil and Environmental Engineering (Transportation Systems) from the University of Massachusetts Amherst Postdoctoral Scholar at Cornell University’s Civil and Environmental Engineering Department M.S. from Rutgers University (Transportation Engineering) and Sharif University of Technology (Transportation Planning) B.S. in Civil Engineering from Amirkabir University of Technology Research Interests: Dr. Amirgholy’s work emphasizes optimizing large-scale transportation systems through machine learning, network modeling, and energy efficiency strategies. Key areas include decarbonization of transportation networks, cooperative control of automated vehicles, and traffic flow optimization in signal-free urban environments. Publications: His recent work addresses challenges in mixed traffic management, signal-free networks, and energy-efficient vehicle operations. Themes include leveraging AI for dynamic traffic modeling and predicting emissions from EV charging infrastructure. Awards: Recipient of the CUTC Milton Pikarsky Award for his doctoral dissertation on transportation systems. Advising & Students: Supervises a diverse team of doctoral, master’s, and undergraduate researchers focusing on smart transportation systems. Notable projects include developing the Atlanta Energy and Emission Modeling and Analysis Tool (AEEMAT). Labs & Collaborations: Leads the Futra Lab, which collaborates with entities like the Atlanta Regional Commission to advance sustainable mobility solutions. Current initiatives include predictive energy modeling for EV adoption and equitable infrastructure design.

Ghislain Fournyمشاهده پروفایل

مدرس

• Quantum Mechanics
• Game Theory
• Database Systems
• +۶ مورد دیگر

Ghislain Fourny is a Lecturer and senior scientist in the Department of Computer Science at ETH Zurich. His research spans quantum foundations, game theory, and database systems. He develops deterministic models merging game theory into quantum physics, proposing non-Nashian solution concepts to address quantum paradoxes. He co-designed the JSONiq query language and leads the RumbleDB project for large-scale data querying. A recipient of multiple teaching awards including the Golden Owl and ETH Medal, he advises numerous students on topics ranging from quantum theory to database systems. His work bridges theoretical physics, computer science, and mathematics, aiming to reconcile determinism with quantum mechanics through game-theoretic models. Education : Holds a Master's in Quantum Information (2007) and a PhD in Computer Science (2011). His early career included pioneering work on XBRL standards and XQuery implementations. Research Themes : Non-Nashian game theory: Fixpoint equilibria avoiding Grandfather paradoxes, Pareto optimality. Quantum foundations: Deterministic models challenging free-choice assumption, spacetime games integrating special relativity. Database systems: JSONiq language design, RumbleDB for scalable querying of heterogeneous data. Teaching Contributions : Courses include Big Data for Engineers, Information Retrieval, and Information Systems for Engineers. His open-access Big Data textbook is widely used in universities. Recognition : Over 20 awards including the 2023 VIS Teaching Award, ETH Medal (2000), and multiple top ranks in international mathematics competitions since 1993.

Alec Smithمشاهده پروفایل

دانشیار

• Behavioral Economics
• Experimental Economics
• Neuroeconomics
• +۷ مورد دیگر

Alec Smith is an Associate Professor in the Department of Economics at Virginia Tech, with interdisciplinary affiliations as a Faculty Affiliate in the School of Neuroscience and the Kellogg Center for Philosophy, Politics, and Economics. He serves as Co-Director of the Virginia Tech Economics Laboratory, where he leads experimental research on human decision-making. His research lies at the intersection of behavioral economics, experimental economics, and neuroeconomics, focusing on how cognitive and emotional processes shape economic decisions. Key interests include risk perception, social emotions (e.g., guilt, frustration), cognitive load, neural correlates of decision-making, and market dynamics such as bubbles. His work often integrates neuroimaging, transcranial stimulation, and eye-tracking with economic experiments. The most recent publications reflect a strong trend toward methodological rigor, reproducibility, and the application of machine learning to behavioral data. Themes include replication studies, variability in neuroimaging analysis, and modeling of bargaining and social preferences. His research has been published in high-impact journals such as Nature , Proceedings of the National Academy of Sciences , Management Science , and Games and Economic Behavior . Smith has collaborated with leading scholars including Colin Camerer, P. Read Montague, and Antonio Rangel, underscoring his integration into the forefront of neuroeconomic research. While no formal awards are listed, his publication record and leadership roles suggest significant recognition in the field. He advises students and researchers through his lab and departmental roles, contributing to training in experimental and neuroeconomic methods. His work is supported by institutional infrastructure such as the Virginia Tech Economics Laboratory, which facilitates controlled behavioral experiments. Future research directions likely include expanding the use of AI in behavioral prediction, studying pandemic-related behavioral shifts, and further exploring the neural underpinnings of social preferences.

• Game Theory
• Decision Theory
• Mathematical Economics
• +۴ مورد دیگر

Cesarino Bertini, Ph.D., is an Associate Professor in the Department of Management at the University of Bergamo. His academic work focuses on Mathematical Methods of Economics, Finance and Actuarial Sciences (SSD: STAT-04/A). He holds a Ph.D. in Economic Sciences (quantitative methods) from AGH University of Krakow and a Degree in Economics from the University of Bergamo. His research primarily centers on game theory and strategic decision-making, with applications spanning economics, finance, and the social sciences. Bertini's work explores power indices, cooperative games, and quantum-inspired computational approaches. His publications appear in prestigious journals including Operations Research and Decisions , Entropy , and International Journal of Theoretical Physics . His recent publications demonstrate a strong focus on sub-coalitional solidarity values, quantum entanglement applications, and measuring voting power in complex corporate structures. His work bridges theoretical game theory with practical applications in finance and corporate governance, showing particular expertise in analyzing indirect control mechanisms within complex shareholding networks. Professor Bertini teaches courses including "Elementi di Matematica" and "Teoria dei Giochi e delle Decisioni" at the University of Bergamo, contributing to both undergraduate and graduate education in mathematical economics and decision theory.

Joseph Chowمشاهده پروفایل

دانشیار

• Transportation Systems
• Multimodal Networks
• Behavioral Urban Logistics
• +۷ مورد دیگر

Dr. Joseph Chow is an Institute Associate Professor in the Department of Civil and Urban Engineering at New York University (NYU) Tandon School of Engineering. He serves as the Deputy Director of C2SMARTER , a U.S. Department of Transportation Tier-1 University Transportation Center, and leads the BUILT@NYU (Behavioral Urban Informatics, Logistics, and Transport) laboratory. His research focuses on transportation systems with emphasis on multimodal networks , behavioral urban logistics , smart cities , and transport economics . PhD in Transportation Engineering from University of California, Irvine (2010) M.Eng. and B.S. in Civil Engineering from Cornell University (2001, 2000) Dr. Chow's research integrates operations research and agent-based modeling to address urban mobility challenges. He has developed equity-aware decision support tools using synthetic population data to evaluate transportation investments, and pioneered modular vehicle (MV) routing algorithms for energy-efficient transit systems. His work spans microtransit optimization , e-scooter deployment , and EV charging infrastructure planning . His article trends reveal expertise in stochastic optimization for ride-pooling systems , synthetic data modeling for equity analysis , and reinforcement learning applications in urban logistics . Key methodologies include MILP routing models , deep real options analysis , and multi-agent simulations . NSF CAREER Award (2017-2022) Canada Research Chair (2013-2016) Eisenhower Fellow and Eno Fellow Dr. Chow has secured NSF grants for urban transport network design , NSERC funding for smart logistics systems , and USDOT support for C2SMARTER research. He serves as Associate Editor for International Journal of Transportation Science & Technology and Transportation Research Record .

Wayne Prattمشاهده پروفایل

استاد

• Behavioral Neuroscience
• Neuropharmacology
• Motivation and Reward Systems
• +۳ مورد دیگر

Wayne Pratt is a Professor of Psychology at Wake Forest University, specializing in the neuropharmacological mechanisms underlying feeding behavior, motivation, and reward processing. His research focuses on the interactions between neurotransmitter systems (dopamine, serotonin, opioids) and brain regions (nucleus accumbens, ventral tegmentum) that regulate food intake, binge eating, and effort-based decision-making. Key Research Areas: Behavioral neuroscience, neuropharmacology, reward circuitry, obesity neurobiology Methodological Expertise: Rat models, receptor stimulation/blockade, effort-based choice tasks, reinstatement paradigms Recent work examines how mu opioid and GLP-1 receptors modulate binge-like consumption, while earlier studies characterized serotonin receptor subtypes (5-HT2C, 5-HT6) in reward valuation. His findings contribute to understanding the neural basis of eating disorders and obesity. Selected Collaborations: Investigated pharmacological agents (lorcaserin, D-fenfluramine) for anti-obesity properties, and explored corticostriatal-hypothalamic integration of energy balance and reward.

Muhammad Hamad Alizaiمشاهده پروفایل

دانشیار

• Internet of Things
• Cyber-Physical Systems
• Embedded AI
• +۷ مورد دیگر

Muhammad Hamad Alizai is an Associate Professor in the Department of Computer Science at the School of Science and Engineering (SBASSE), Lahore University of Management Sciences (LUMS), Pakistan. His research spans the Internet of Things (IoT), Cyber-Physical Systems, Embedded AI, and intermittent computing, with a focus on sustainable and accessible technologies for developing regions. His research interests include energy harvesting, batteryless computing, GenAI for IoT, wireless sensor networks, and distributed systems . He explores how generative AI can democratize IoT access in low- and middle-income countries and addresses societal challenges through computational solutions. His work is supported by grants from HEC, LUMS, DAAD, and NCBC. Recent publications reveal a strong trend in intermittent computing, energy-efficient IoT, and AI-driven system optimization , with high-impact papers in ACM SenSys, IPSN, BuildSys, and CACM. His team develops innovative systems like CheckMate, Glitch in Time, and Guardian Angel, tackling reliability, security, and accessibility in transiently powered devices. Scientific recognitions include: Best Paper Candidate at ACM SenSys 2019 Audience Choice Award at ACM BuildSys 2017 Best Abstract Award at ACM SenSys 2010 He has advised numerous graduate students, including PhDs like Saad Ahmed and Samar Abbas, and leads pedagogical innovation as head of the LUMS Learning Institute , promoting teaching excellence and AI integration in education. He has served on technical committees for ACM CoNEXT, IPSN, MobiSys, and BuildSys, and organized key workshops in transient computing. His lab focuses on building privacy-preserving, energy-neutral, and AI-augmented embedded systems , particularly for applications in water, energy, and transportation in developing regions.

Simon Burgessمشاهده پروفایل

استاد

• Economics of Education
• Teacher Effectiveness
• Pupil Motivation
• +۷ مورد دیگر

Professor Simon Burgess is a Professor of Economics at the School of Economics, University of Bristol . He is a Fellow of the British Academy and a Research Fellow at the Institute for the Study of Labor (IZA) . His academic career includes directing major research centers such as the Centre for Market and Public Organisation (CMPO) and the Centre for Understanding Behaviour Change (CUBeC) , with a focus on education policy and social equity. Education: MA (Cambridge), DPhil (Oxford) Research Interests : Burgess specializes in the economics of education, examining teacher effectiveness, pupil motivation, school accountability, and ethnic segregation in schools. He also explores broader topics like unemployment, poverty, and public sector incentives. Recent Publications emphasize school performance metrics, classroom time allocation, and gender gaps in STEM education. Key themes include Education Policy , Labour Market Dynamics , and Social Equity , with methodological strengths in experimental economics and statistical modeling. Scientific Awards : Fellow of the British Academy Research Fellow, IZA Advisory Roles : Burgess chairs the Somerset Challenge (2016-2017) and serves on multiple advisory boards, while also contributing to public policy discussions on education reform.

Stephanie Palmerمشاهده پروفایل

دانشیار

• Biophysics
• Neuroscience
• Evolutionary Biology
• +۳ مورد دیگر

Stephanie Palmer is an Associate Professor at the University of Chicago, holding dual appointments in the Department of Physics and the Department of Organismal Biology and Anatomy. Her research integrates statistical physics, neuroscience, and evolutionary biology to investigate how biological systems process information and evolve complex traits. She is affiliated with the Enrico Fermi Institute, James Franck Institute, and Kadanoff Center, among others. Her work spans topics such as predictive coding in neural networks, evolutionary mechanisms in butterfly vision, and the statistical mechanics of biological systems. Notably, she was awarded the Schmidt Science Polymath in 2025 for her interdisciplinary contributions. Recent studies focus on retinal processing of natural scenes, scale-free network dynamics, and the genetic basis of mate preference in Heliconius butterflies. Palmer employs advanced techniques like synchrotron imaging and computational modeling, often bridging theory and experiment. Her research highlights how biological systems optimize information transmission under evolutionary and physiological constraints.

Hadas Ritzمشاهده پروفایل

مدرس ارشد

• Engineering Education
• Solid Mechanics
• Engineering Mathematics
• +۲ مورد دیگر

Hadas Ritz is a Senior Lecturer at Cornell University's Sibley School of Mechanical and Aerospace Engineering and Faculty Teaching Fellow in the James McCormick Family Teaching Excellence Institute. She holds a Ph.D. in Mechanical Engineering from Cornell (2008) and specializes in engineering education, solid mechanics, and active learning strategies. Her pedagogical innovations focus on calculus, solid mechanics, and introductory engineering courses. Research interests include collaborative problem-solving techniques, alternative grading systems, and student agency in laboratory settings. She has developed low-cost remote laboratories and investigated the impact of COVID-19 on student motivation and engagement. Her scholarship explores conceptual retention through think-aloud interviews and effective assessment design. Honors include the ASEE National Outstanding Teaching Award (2021), Tau Beta Pi Professor of the Year (2019), and multiple College of Engineering Excellence in Teaching Awards. Her work advances evidence-based teaching practices through faculty development programs and cross-institutional educational research.

• Financial Econometrics
• Asset Pricing
• Statistical Methods in Finance
• +۷ مورد دیگر

Paul Schneider is a Full Professor in the Faculty of Economic Sciences at the University of Italian Switzerland (USI), where he has been a faculty member since 2012. He is affiliated with the Institute of Finance (IFin) and the Euler Institute (EUL), contributing to interdisciplinary research in quantitative finance and econometrics. His research focuses on financial econometrics, asset pricing, and statistical methods in finance, with an emphasis on extracting latent market information under minimal assumptions. He integrates techniques from engineering, mathematics, and data science to develop robust models for financial markets. His work spans risk premia, ambiguity in investment decisions, nonlinear pricing, and model-free recovery methods. His recent publications (2023–2024) in journals such as Review of Finance , Management Science , and SIAM Journal on Mathematics of Data Science highlight trends in adaptive learning, empirical scenario generation, constrained likelihood estimation, and optimal investment under ambiguity . These reflect a strong focus on data-driven, computationally efficient, and theoretically sound approaches to financial modeling. Adaptive joint distribution learning Fast empirical scenarios Optimal Investment under Ambiguity Constrained polynomial likelihood Dispersion of Beliefs and Sentimental Recovery Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: While no formal list of advisees is provided, Paul Schneider has collaborated extensively with researchers such as Damir Filipovic, Fabio Trojani, and Christian Wagner, suggesting a strong mentorship and collaborative role. He has contributed to funded research projects, particularly in financial modeling and econometrics, though specific grant names are not detailed. Labs and Research Teams: He is actively involved with the Institute of Finance (IFin) and the Euler Institute at USI, which support interdisciplinary research in finance, mathematics, and data science. He has also developed computational tools such as the KDM R package for kernel density machines, indicating engagement with data science and open research practices.

• Machine Learning
• Natural Language Processing (NLP)
• Political Science
• +۴ مورد دیگر

Victor Kristof is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the INDY2 laboratory. His work spans interdisciplinary domains, combining Natural Language Processing (NLP) , Machine Learning , and Social Process Modeling to analyze legislative dynamics, vote prediction, and environmental perception. Research Focus: Kristof develops interpretable models for democratic transparency, including aligning interest group positions with parliamentary speeches. He pioneered methods for predicting legislative edit acceptance using matrix factorization and NLP. His work on Swiss referendum prediction integrates historical data with real-time analysis via the Predikon platform . Methodological Contributions: He applies Bayesian statistics , time-dynamic pairwise comparison models , and active learning algorithms to diverse problems, from carbon footprint perception to sports analytics. His War of Words framework reveals ideological patterns in EU law-making, while his Player Kernel model improves football match prediction. Labs & Collaborations: Based at EPFL's Laboratory of Dynamic Information and Networks (INDY2) , he collaborates with researchers like Matthias Grossglauser and Patrick Thiran. His datasets on legislative edits and carbon perception have advanced transparency studies.

Tianxing Heمشاهده پروفایل

استادیار

• AI Safety
• AI-driven Simulation
• Large Language Models
• +۵ مورد دیگر

Tianxing He is an Assistant Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences (IIIS), also known as the Yao Class, which he joined in September 2024. He also works part-time at Shanghai Qi Zhi Institute where he recruits for research-oriented positions and video game development roles. His academic journey includes a postdoc at the University of Washington under Yulia Tsvetkov, a PhD at MIT supervised by James Glass, and bachelor's and master's degrees at Shanghai Jiao Tong University in the ACM honored class under Prof. Kai Yu. Dr. He's research spans AI safety and AI-driven simulation with particular focus on large language models. His work examines LLM capabilities in cryptography (AICrypto benchmark), security vulnerabilities (jailbreaking techniques), multi-agent system integrity, and socially impactful applications like vaccine hesitancy simulation (VACSIM framework). He has developed visualization tools like MiniAgents-Pro using Unity for generative agent simulation and created watermarking techniques such as SemStamp and k-SemStamp for detecting machine-generated text. His publication record shows consistent contributions to top-tier conferences including ACL, EMNLP, NeurIPS, and ICLR, with recent work focusing on evaluating LLM capabilities across diverse domains. The research demonstrates sophisticated understanding of both the technical aspects of language models and their broader societal implications, particularly in security and ethical considerations. Scientific Awards: The Best Paper Award at the Efficient Natural Language and Speech Processing Workshop (NeurIPS ENLSP 2022) for "PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation" Dr. He actively mentors students and recruits research interns, PhD candidates, and post-bachelor researchers. He plans to teach an undergraduate NLP course in Fall 2025 and organizes the IIIS AI Safety Lunch, a monthly gathering in Beijing for those working on AI safety. His recruitment efforts target students interested in LLM social simulation, AI+game development, and AI safety research, emphasizing the need for strong self-motivation and research passion. He leads research initiatives including the IIIS AI Safety Lunch series and has developed multiple visualization and simulation frameworks. His work sits at the intersection of AI safety, language modeling, and practical applications, with team collaborations spanning multiple institutions including MIT, University of Washington, and Shanghai Jiao Tong University.