جست‌وجوی استاد و پژوهشگر

Asim Ansariمشاهده پروفایل

استاد

• Internet Recommendation Systems
• Digital Customization
• Social Network Modeling
• +۵ مورد دیگر

Asim Ansari is the William T. Dillard Professor of Marketing at Columbia University’s Columbia Business School. His research focuses on Internet Recommendation Systems, Digital Customization, Social Network Modeling, and Bayesian Methods for Customer Data. He holds affiliations with the Data, Media and Society; Financial and Business Analytics; and Foundations of Data Science centers. Professor Ansari has been recognized with the Paul Green Award (1994) and the Dean’s Award for Teaching Excellence (2009). His work has been nominated for prestigious awards including the O’Dell Award and Long Term Impact Award. He serves as an Associate Editor for Management Science and Quantitative Marketing and Economics , and is on the editorial boards of Marketing Science and the Journal of Marketing Research . His research explores cutting-edge topics such as generative models for consumer behavior, probabilistic machine learning applications in marketing, and the dynamics of consumer choices. His work bridges statistical methods with real-world marketing challenges, emphasizing data-driven strategies for businesses and consumer insights.

Zhiwei Steven Wuمشاهده پروفایل

استادیار

• Machine Learning
• Algorithms
• Differential Privacy
• +۶ مورد دیگر

Steven Wu is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, with a primary appointment in the Software and Societal Systems Department and affiliations in the Machine Learning Department, Human-Computer Interaction Institute (HCII), CyLab, and the Theory Group. His research focuses on algorithms and machine learning, particularly in responsible AI, privacy, bias, and uncertainty. He has received funding from NSF CAREER, Okawa Foundation, Amazon, Google, and others. Research Interests: Foundations of responsible AI (privacy, bias, uncertainty) Interactive learning (imitation, reinforcement learning) Causal inference, game theory, econometrics, and language modeling Grants and Awards: NSF CAREER Award Okawa Foundation Award Amazon Research Award Google Faculty Research Award J.P. Morgan Faculty Awards Advising: Supervises PhD, master's, and undergraduate students across multiple programs, with notable alumni now at Amazon, Stanford, Tsinghua, and others. Leads the Tartan Federer team, which won all four tracks of The Vector Institute's MIDST challenge in 2025. Labs and Collaborations: Active in CyLab (CMU's cybersecurity institute) and the Theory Group, focusing on privacy-preserving machine learning and algorithmic fairness.

Dimitri Bertsekasمشاهده پروفایل

استاد

• Optimization
• Game Theory
• Systems
• +۳ مورد دیگر

Dimitri Bertsekas is the Jerry Mcafee (1940) Professor in Engineering at the Massachusetts Institute of Technology. His research focuses on optimization, game theory, systems, networking, control, and autonomy. He works within the Laboratory for Information and Systems Decisions. His recent publications demonstrate a strong emphasis on reinforcement learning, dynamic programming, and algorithmic solutions for complex systems. Work spans applications in robotics, transportation optimization, computer vision, game AI, and knowledge systems. Common themes include multi-agent coordination, real-time decision-making under uncertainty, and scalable computational methods. Bertsekas contributes to both theoretical frameworks and practical implementations, with innovations in auction algorithms, rollout methods, and model predictive control integration.

• Artificial Intelligence
• Reinforcement Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Daniel Jarne Ornia is a Researcher at the University of Oxford's Department of Computer Science, located in Room 213 of the Wolfson Building on Parks Road, Oxford. His work focuses on artificial intelligence, multi-agent systems, and reinforcement learning, with particular emphasis on risk-aware decision-making, robust learning algorithms, and autonomous agent coordination. Research interests include developing methods for efficient and reliable coordination in multi-agent environments, exploring bounded rationality in resource-constrained systems, and advancing techniques for predictable reinforcement learning dynamics. His recent publications address topics such as contingency planning with multi-modal predictions, entropy rate minimization in learning systems, and navigation strategies for robotic swarms. No academic awards or grants are explicitly mentioned in the provided information. Daniel’s research often intersects with robotics, distributed control systems, and theoretical foundations of autonomous decision-making.

Rachel Childersمشاهده پروفایل

استادیار مهمان

• Econometrics
• Machine Learning
• Macroeconomics
• +۳ مورد دیگر

Rachel Childers is a Visiting Assistant Professor of Economics at Bowdoin College. She holds a PhD in Economics from Yale University and a BSFS in International Economics from Georgetown University. Her research focuses on combining econometrics, machine learning, and high-dimensional statistics to develop methods for decision-making in dynamic environments, with applications to macroeconomics and causal inference. She hosts weekly global econometrics office hours and is currently on the academic job market. Education : PhD in Economics, Yale University (2016) BSFS in International Economics, Georgetown University Research Interests : Dr. Childers specializes in computational economics, causal inference, and the application of machine learning to macroeconomic problems. Her work emphasizes methodological innovation in high-dimensional settings and has led to contributions in areas like efficient causal effect estimation and automated heterogeneous agent model solutions. Teaching : Econ 2557: Economic Statistics Econ 2001: Economic Policy Econ 3360: Economic Forecasting Grants & Advising : No explicit grants or advisees listed, but her work reflects collaborative research with institutions like NBER. She actively engages students via office hours and course materials.

Nicola Ivoryمشاهده پروفایل

مدرس

• Addictive Behaviors
• Neurobiological Models of Personality
• Drug and Alcohol Use
• +۳ مورد دیگر

Dr. Nicola Ivory is a Lecturer in the School of Psychology at Charles Sturt University, where she has been a faculty member since 2022. She holds a PhD from Deakin University, focusing on Reinforcement Sensitivity Theory and coping behaviors in alcohol use. Her teaching portfolio includes research methods, statistics, and foundational psychology courses such as personality and cognitive psychology. Dr. Ivory’s research interests center on drug and alcohol use, addictive behaviors, and neurobiological models of personality, particularly leveraging Reinforcement Sensitivity Theory. Her work frequently intersects with interdisciplinary studies, including collaborations on global childhood communication and rural health disparities. Notable contributions include scoping reviews on professional identity in social work and innovative placement models in education. She has received several accolades, including the Charles Sturt “Research Excellence Through Partnership Award” (2022) and the ANZSWWER Research Achievement Awards (2023). Her research has addressed topics like rural mental health staff wellbeing, global child voice projects, and disaster preparedness in social work practice. Dr. Ivory’s grants include funding for rural student research during the pandemic and a pilot study on non-governmental mental health staff retention. She actively participates in collaborative networks, such as the Psychosocial Well-being of Rural and Regional Australians Research Group and the Children’s Voices Centre, advancing community-focused initiatives.

Maryam Parsaمشاهده پروفایل

استادیار

• Neuromorphic Computing
• Neural Architecture Search
• Bayesian Optimization
• +۶ مورد دیگر

Maryam Parsa is a Tenure-Track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University. Her research focuses on neuromorphic computing, Bayesian optimization, and algorithm-hardware co-design, aiming to develop energy-efficient, secure, and resilient AI systems for edge computing applications. She holds a PhD from Purdue University, supported by an Intel/SRC fellowship, and previously worked at Oak Ridge National Lab. Education: PhD in Electrical and Computer Engineering, Purdue University (2020) MS in Civil Engineering, Purdue University (Year unknown) MS in Electrical and Computer Engineering, University of Ottawa (Year unknown) BS in Electrical and Computer Engineering, Khaje Nasir Toosi University of Technology (Year unknown) Research Interests: Neuromorphic learning and bio-inspired robotics Bayesian optimization for materials discovery Privacy-preserving spiking neural networks Edge AI and real-time embedded systems Major Achievements: Lead a $2.4M 3-year project on 3D chip creation (2023) Recipient of Intel/SRC PhD Fellowship Current Work: Developing neuromorphic architectures for smart healthcare and cyber-physical systems Pioneering causal machine learning for materials innovation Advancing privacy & security in neuromorphic systems Labs/Teams: Active in Mason's neuromorphic computing research group with collaborations in national labs and industry partners.

Luís Paulo Santosمشاهده پروفایل

استادیار

• Computer Graphics
• Quantum Computing
• Parallel Computing
• +۳ مورد دیگر

Luís Paulo Santos is an Assistant Professor at the Department of Informatics, Universidade do Minho, and a Senior Researcher at CSIG, INESC-TEC. His research focuses on rendering, global illumination, and quantum algorithms, particularly in optimizing computational efficiency through heterogeneous parallel computing and quantum techniques. He has authored a book on Bayesian Monte Carlo Rendering and contributed to international conferences as a committee member. His work bridges quantum computing with computer graphics and reinforcement learning. Research interests include quantum ray tracing, variational quantum algorithms, and the application of quantum mechanics to solve complex computational problems. He has organized six international conferences in Portugal and served as Associate Editor of Computers & Graphics and President of the Portuguese Group of Computer Graphics (2017-2018). Publications emphasize quantum computing advancements in rendering and reinforcement learning, exploring topics like Hessian recycling in quantum algorithms and gradient optimization challenges. His work highlights the potential of quantum methods to reduce computational complexity in graphics and machine learning. Advising includes guiding students in quantum optimization and reinforcement learning, with notable supervised theses on quantum algorithms and applications. He has held leadership roles in academic administration, including Vice Director of the Department and Director of the Doctoral Programme on Informatics. Current affiliations include the High-Assurance Software Centre at INESC-TEC and collaboration with the United Nations University on electronic governance initiatives.

Michal Valkoمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Roles & Affiliations: Michal Valko is the Chief Models Officer at a stealth startup, a tenured researcher at Inria (Inria Lille - Nord Europe), and a lecturer in the MVA Master program at École Normale Supérieure Paris-Saclay. He previously held roles including Principal Llama Engineer at Meta and Senior Staff Research Scientist at Google DeepMind. Education: Ph.D. in Machine Learning from the University of Pittsburgh (2011), Habilitation à Diriger des Recherches (HdR) from École Normale Supérieure Paris-Saclay (2016). Research Interests: Michal focuses on minimizing human supervision in AI systems, including deep reinforcement learning, bandit algorithms, self-supervised learning, and large language models (LLMs). His work emphasizes scalable test-time inference, fine-tuning, and alignment of LLMs. He also explores theoretical foundations of reinforcement learning and representation learning. Awards: Recipient of the Outstanding Paper Award at ICML 2023, Inria's Prime d'Excellence Scientifique (2014–2017 and 2018–2021), Homer Warner Best Paper Award (2010), and Google Best Paper Award (2010). Advising & Grants: Advised over 20 PhD and postdoctoral students, including notable alumni like Daniele Calandriello and Jean Tarbouriech. His research has been supported by grants from ANR (Extra-Learn, BOLD), Intel, and collaborative projects like EduBand with Carnegie Mellon. Labs & Teams: Co-founder of the SequeL team at Inria and a key contributor to the DeepMind Paris and Meta GenAI research groups. Active in organizing conferences like ICML, NeurIPS, and workshops on Graphs in Machine Learning.

Debora Slanziمشاهده پروفایل

دانشیار

• Statistics
• Bayesian Networks
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Debora Slanzi is an Associate Professor at the Department of Management , Venice School of Management , Ca' Foscari University of Venice . She operates from the San Giobbe campus (C2 building, second floor, room 212) and serves as a full internal member of the Disciplinary Committee. Email: debora.slanzi@unive.it Office Hours: Available both in-person and online via a mandatory registration form Research Interests: Her work focuses on Statistics , Bayesian Networks , High-Dimensional Data Analysis , and their applications in Medical Research , Urban Development , and Pharmaceutical Chemistry . She integrates machine learning with traditional statistical methods for problems in drug discovery, mobility sustainability, and complex systems modeling. Scientific Contributions: Recent publications highlight her expertise in: Bayesian shrinkage methods for high-dimensional regression Molecular modeling for drug discovery Bayesian network applications in healthcare and urban planning Behavioral analysis in crowdfunding and travel psychology High-dimensional data optimization via evolutionary algorithms

Jie Xuمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Stochastic Optimization
• +۶ مورد دیگر

Jie Xu is a Professor in the Department of Systems Engineering and Operations Research (SEOR) at George Mason University, with affiliations in the Department of Computer Science and Department of Statistics. His research focuses on algorithmic advancements in artificial intelligence, digital twin simulation, data science, stochastic optimization, and risk analytics, with applications in transportation, power systems, healthcare, and semiconductor manufacturing. He has led NSF-funded projects on bus fleet electrification, EV infrastructure, and human digital twin modeling. Previously, he served as President of the INFORMS Simulation Society (2022-2024) and as an Associate Editor for multiple journals. Education : PhD in Industrial Engineering (Northwestern University), MS in Computer Science (SUNY Buffalo), ME in Communications (Shanghai Jiao Tong University), BS in Electrical Engineering (Nanjing University). Research interests include digital twin-based optimization, Bayesian optimization, Monte Carlo tree search, and reinforcement learning. He has developed methodologies for cloud computing, EV grid integration, and cyber-physical systems resilience. Recent work emphasizes real-time simulation learning and DDDAS for energy systems. His NSF grants include the $1.5M CREATE project for transit electrification and FDT-BioTech for neurophysiological modeling. He teaches courses on stochastic simulation, predictive analytics, and optimization. Grants include funding from NSF, DOE, and ONR, totaling over $3M. Collaborations span universities, national labs, and industry partners.

Percy Liangمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Foundation Models
• +۴ مورد دیگر

Affiliations Associate Professor at Stanford University's Computer Science Department within the School of Engineering. Director of the Center for Research on Foundation Models (CRFM) and core member of Stanford HAI . Also affiliated with the Artificial Intelligence Lab, Natural Language Processing Group, and Machine Learning Group. Education Ph.D. in Computer Science, UC Berkeley (2011), advisors: Michael Jordan and Dan Klein MEng in Computer Science, MIT (2005), advisor: Michael Collins B.S. in Computer Science, MIT (2004) Research Focuses on foundational aspects of AI, including: Foundation models and their societal implications (e.g., copyright, data attribution) Building scalable and interpretable AI systems Reproducible research via CodaLab Worksheets Formal methods for ensuring correctness in ML systems Key Contributions Pioneered work in: Automated reasoning for Olympiad-style problems Data Programming framework for rapid dataset creation Executable papers and experiment reproducibility AI ethics and model interpretability Grants & Awards Presidential Early Career Award (2019) IJCAI Computers and Thought Award (2016) Microsoft Research Faculty Fellowship (2014) Multiple patents and industry collaborations Labs & Teams Leads the CRFM team developing open foundation models. Collaborates with industry (e.g., Google, Meta, OpenAI) and co-founded xAI initiatives. Active in open-source projects like sfig for presentation tools.

John Sheppardمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Probabilistic Models
• +۷ مورد دیگر

Dr. John Sheppard is a Professor of Computer Science at Montana State University (MSU), affiliated with the College of Engineering's School of Computing. He leads the Numerical Intelligent Systems Laboratory (NISL), focusing on AI, machine learning, and probabilistic models. His research spans Bayesian networks, evolutionary algorithms, and applications in precision agriculture and health management. He holds a PhD from Johns Hopkins University (1997) and has been recognized with awards like the Provost’s Award for Graduate Research (2022) and IEEE Fellow (2006). Education: Ph.D. (1997), M.S. (1990) from Johns Hopkins University; B.S. (1983) from Southern Methodist University. Active in IEEE standards committees and serves on boards like the Bozeman Symphony and Lutheran Campus Ministries. Research emphasizes probabilistic graphical models, explainable AI, and system-level problems like equipment health management. Recent articles address neural networks for uncertainty reduction, hyperspectral imaging, and factored evolutionary algorithms. His work bridges theory and practice, with funded projects in agriculture, energy, and diagnostics. Awards highlight contributions to research mentoring and academic leadership. Advises over 30 students, including current PhD candidates like Muhammad Arju and past graduates such as Dr. Logan Perreault. Leads grants on precision agriculture and AI ethics. NISL collaborates on tools for diagnostic reasoning and PHM standards, advancing both academic and industrial applications.

• Machine Learning
• Signal Processing
• Hidden Markov Models
• +۳ مورد دیگر

Aggelos Pikrakis is an Associate Professor in the Department of Informatics at the University of Piraeus, with research focus on Signal Processing with emphasis on Sound and Voice . He serves as an affiliate member of the Institute for Language and Speech Processing (ILSP). Specializes in machine learning algorithms for audio analysis Expertise in Hidden Markov Models and Bayesian architectures Supervises doctoral research in audio processing and machine learning His work spans music information retrieval, voice pathology classification, and mobility pattern analysis. With over 50 publications and 1150 citations (h-index=16), he has received best paper awards and first place in algorithm competitions . He has participated in research programs funded by GGET and European Union as part of research teams. Scientific Awards Best Paper Award First Place in Algorithm Competition Active on editorial boards (EURASIP Journal on Advances in Signal Processing since 2008) and conference committees, he contributes to advancing audio processing methodologies.

Marek Reformatمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Intelligence
• Fuzzy Systems
• +۷ مورد دیگر

Dr. Marek Reformat is a Professor in the Department of Electrical and Computer Engineering at the University of Alberta, Faculty of Engineering. His research focuses on developing human-like intelligent systems through the integration of computational intelligence (fuzzy systems, neural computing, evolutionary algorithms), uncertainty theories (probability, evidence theory), and semantic web technologies (RDF, ontologies). He also explores mathematical foundations like category theory and type theory for knowledge representation. His research interests include knowledge extraction via granular computing, semantic-based decision support systems, and applications in software quality analysis. He teaches courses such as ECE 220 (Programming for Electrical Engineering) and ECE 447 (Data Analytics and Machine Learning for Engineers). Dr. Reformat actively contributes to conferences like AAAI, NeurIPS, and IEEE WCCI. His work spans topics like fuzzy ontology integration, neural networks for medical imaging, and uncertainty modeling in decision-making systems. He currently advises eight graduate students and collaborates with industry partners on projects involving AI-driven solutions for complex systems.