Find Academic Supervisors

Daisy Zhe WangView profile

Professor

• Database Systems
• Data Science
• Machine Learning
• +4 more

Dr. Daisy Zhe Wang is a Professor in the Department of Computer & Information Science & Engineering at the University of Florida, affiliated with the Herbert Wertheim College of Engineering. She directs the Data Science Research (DSR) Lab, focusing on advanced data analysis systems using machine learning and probabilistic methods. Her work spans databases, data science, and informatics, with specializations in probabilistic knowledge graphs, multimodal fusion, and healthcare analytics. Education: PhD in Computer Science from the University of California, Berkeley (2011). Awards include the Arnold and Lisa Goldberg Rising Star Professorship in Computer Science (2019-2021) and the UF Term Professorship (2018). Her lab's current projects include developing systems for tree species classification using remote sensing and improving surgical risk prediction algorithms like MySurgeryRisk. Research emphasizes bridging data science with domain-specific applications, including electronic health records (EHR) analysis, environmental remote sensing, and knowledge graph-driven decision systems. She has pioneered frameworks like RAMQA for multimodal QA and M3 for multi-hop retrieval, advancing both theoretical and applied aspects of data science. Grants and collaborations include large-scale data competitions and partnerships with healthcare institutions. The DSR Lab maintains active research in AI ethics, explainable AI, and scalable data management systems, with ongoing work on neuro-symbolic architectures and multimodal learning.

David Simchi-LeviView profile

Professor

• Supply Chain Management
• Business Analytics
• Operations Research
• +3 more

David Simchi-Levi is the William Barton Rogers Professor of Engineering Systems at MIT and serves as the head of the MIT Data Science Lab. He is a leading expert in supply chain management and business analytics, with significant contributions to risk mitigation in global supply chains. His academic leadership roles include serving as Editor-in-Chief of Management Science and Operations Research. He has advised PhD students who have secured faculty positions at top universities like Berkeley, Carnegie Mellon, and Harvard. His research focuses on dynamic pricing, inventory control, and operations management in shared economies. Notable awards include the INFORMS Impact Prize (2020), National Academy of Engineering membership (2023), and multiple industry accolades. His industry ventures include founding LogicTools (acquired by IBM), OPS Rules (acquired by Accenture), and Opalytics (acquired by Accenture Applied Intelligence). Key articles in 2025-2024 explore topics like feature-based pricing, supply chain resilience, and reinforcement learning applications. His work bridges academic rigor with practical solutions for modern business challenges.

Tian ZhaoView profile

Associate Professor

• Type systems
• Type inference
• Static analysis
• +4 more

Tian Zhao is an Associate Professor in the Department of Computer Science at the University of Wisconsin-Milwaukee, affiliated with the College of Engineering & Mathematical Sciences. Currently on sabbatical for the Fall 2024 semester, Dr. Zhao earned a PhD from Purdue University in 2002. PhD, Purdue University, 2002 Dr. Zhao's research spans type systems, type inference, static analysis, and domain-specific programming. Their work bridges theoretical foundations with practical applications in programming languages and software engineering. Key publication trends highlight expertise in JavaScript, asynchronous programming, logical frameworks, semantic web technologies, and memory management. Articles emphasize static analysis, formal methods, and domain-specific language design.

• Electrical Engineering Education
• Fuzzy Logic Systems
• Power Systems Analysis
• +3 more

Cristina Vatavu is a Lecturer in the Department of Electrical Engineering at Gheorghe Asachi Technical University of Iași's Faculty of Electrical Engineering. Her research spans electrical engineering education innovations, fuzzy logic systems, power analysis, and signal processing. She actively participates in student scientific communications sessions and coordinates competitions in theoretical electrical engineering applications. Research interests focus on pedagogical improvements through simulation tools (OrCAD), nonlinear device analysis, and practical training methodologies. Publications demonstrate strong emphasis on optimizing educational approaches and control systems through fuzzy logic algorithms. Articles reveal consistent focus on engineering education enhancement (2018-2019), alongside deeper technical work in fuzzy logic applications (2002-2005) and signal/power analysis (2005-2016).

Soha HassounView profile

Professor

• Machine Learning
• Systems Biology
• Metabolic Engineering
• +5 more

Soha Hassoun is a Professor at Tufts University with primary appointment in the Department of Computer Science and secondary appointments in the Department of Electrical and Computer Engineering and the Department of Chemical and Biological Engineering. Her interdisciplinary work bridges computer science, engineering, and biology to develop computational tools for biological systems analysis and design. Education: Ph.D. from University of Washington (1997) M.S. from Massachusetts Institute of Technology (1988) B.S. from South Dakota State University (1986) Soha Hassoun's research focuses on the intersection of Machine Learning and Biology, with particular emphasis on developing ANALYSIS + DESIGN tools to advance (RE-)DESIGNING BIOLOGY. Her work spans Systems Biology, Metabolic Engineering, and computer-aided design for integrated circuits. She develops MACHINE LEARNING models specifically tailored for biological data, drawing inspiration from her prior experience in circuit design and design automation for electronic systems where she contributed seminal ideas for FinFET devices and power grids for 3D integrated circuits. Her recent publications demonstrate a strong trend toward applying advanced machine learning techniques, particularly graph neural networks and transformer models, to biological problems including metabolite annotation, enzyme-substrate interaction prediction, and metabolic network analysis. Her work increasingly focuses on creating standardized benchmarks and frameworks for evaluating computational methods in metabolomics. Soha leads the Hassoun Lab, which is dedicated to developing computational tools that provide insight into complex biological systems and enable building novel biological components to produce useful chemicals and therapeutics. The lab's research spans from fundamental machine learning algorithm development to practical applications in biotechnology.

• Epistemology
• Legal Philosophy
• Social Philosophy
• +10 more

Georgi Gardiner is an Associate Professor of Philosophy and Gender and Sexuality Studies (GESS) at Tulane University. Previously, she held faculty positions at the University of Tennessee and fellowships at Oxford University, the Denbo Center for the Humanities, and the American Council of Learned Societies (ACLS). Her research spans epistemology, legal philosophy, meta-philosophy, and the philosophy of gender and sexuality. She explores topics like evidence law, legal proof, rape accusations, virtue epistemology, and the philosophy of attention. Her work also engages with unconventional subjects such as circus, risk, and the occult. Education: PhD in Philosophy from Rutgers University (2017), MSc and MA in Philosophy from the University of Edinburgh (2011 and 2009). Research Interests: Epistemology: focusing on evidence, skepticism, and virtue epistemology Legal Philosophy: legal proof standards, evidence admissibility, and ethical implications Social Philosophy: gender, sexuality, and the ethics of belief Innovative methodologies: using tarot, art, and interactive philosophy to enhance inquiry Awards and Recognition: Royal Institute of Philosophy annual essay prize (2020), ACLS fellowship, Denbo Center fellowship, and Oxford University fellowship. Philosophical Initiatives: Co-editing The Philosophy of Sexual Violence , a multidisciplinary collection combining art and scholarship. Innovates teaching through tarot-based sessions, Lego Serious Play, and community-focused events like the Festival of Creative Community.

Peng WangView profile

Lecturer

• Human-robot Interaction
• Autonomous Driving
• Mobile Robots Navigation
• +8 more

Dr. Peng Wang is a Lecturer in the Department of Computing and Mathematics at Manchester Metropolitan University. Previously, he held postdoctoral positions at the University of Sheffield (UK) and University of Technology of Compiègne (France), and served as Deputy Director at a joint laboratory between University of Science and Technology of China and E-THINK. His research spans robotics, machine learning, and sustainability applications. Research Focus: Dr. Wang's work centers on three core areas: Autonomy : Human-robot interaction, autonomous driving in uncertain environments, and LiDAR/vision-based navigation systems Sustainability : Scalable ML models (Gaussian Processes, Deep Learning) for traffic forecasting and air quality monitoring Manufacturing : Digital twins for collaborative manufacturing and IoT solutions for industrial processes Publication Trends: His recent articles (2019-2021) predominantly focus on uncertainty quantification in machine learning, autonomous vehicle localization, robotics navigation, and industrial applications. Key methodologies include Gaussian processes, Bayesian neural networks, and sensor fusion techniques. Projects & Supervision: Currently leads two projects: 3D human modeling for human-robot collaboration and environmental monitoring systems. Accepts student researchers across robotics, ML, and human-robot interaction domains. Teaches graduate courses in Artificial Intelligence Principles and Robotic Science.

Mohsen RakhshanView profile

Assistant Professor

• Brain-machine interfaces
• Neural prostheses
• Computational neuroscience
• +5 more

Mohsen Rakhshan serves as an Assistant Professor in the Department of Electrical and Computer Engineering within the College of Engineering at the University of Central Florida. He leads the Laboratory for Interaction of Machine and Brain (LIMB) in the Disability, Aging, and Technology Research Cluster, focusing on ethical technological innovations to improve functional independence for older adults and individuals with nervous system injuries. His research spans Brain-Machine Interfaces , Neural Prostheses , and Computational Neuroscience , with specific projects including non-invasive sensory restoration for upper limb amputees, neuromorphic encoding for sensory compression, and intuitive robotic hand control. Current initiatives integrate Control Theory and Robotics to develop scalable tactile sensing systems. Analysis of his 15 most recent publications reveals a trajectory from theoretical control systems (2016-2019) toward increasingly neuroscience-focused work (2020-2024), particularly investigating sensory integration mechanisms in neuroprosthetics and neural correlates of decision-making under uncertainty. His computational approaches bridge engineering precision with biological plausibility. Honors include: BME Distinguished Fellowship from John Hopkins University E. E. Just Fellowship from Dartmouth College As a Technical Reviewer for IEEE journals and PLOS Computational Biology, he contributes to fields spanning neural networks, cybernetics, and cognitive systems. His laboratory emphasizes interdisciplinary collaboration between neuroscience and engineering.

Leila KosseimView profile

Professor

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics
• +3 more

Leila Kosseim is a Professor in the Department of Computer Science and Software Engineering at Concordia University, co-directing the CLaC (Computational Linguistics @Concordia) lab. She holds a PhD from the University of Montreal (1995) and has conducted postdoctoral research at Druide informatique inc. and the RALI group. Her academic leadership roles include Vice-President (2017-2019), President (2019-2021), and Past-President (2021-2023) of the Canadian AI Association (CAIAC), and current Graduate Program Director for the PhD Programs in CSSE. Her research focuses on Natural Language Processing (NLP) and Artificial Intelligence , with particular emphasis on discourse analysis, computational linguistics, and text generation. Notable projects include automated analysis of argumentative essays, clinical trial inference, and meme persuasion detection. Her work integrates deep learning, neural networks, and transformer models to address challenges in text coherence, discourse parsing, and multilingual NLP. Leila has supervised 8 PhD and over 20 Master’s students, contributing to the next generation of computational linguists. Her CLaC lab has participated in multiple international challenges (SemEval, TREC, DEFT) and produced influential tools like TIMBERT and the Concordia NLG Surface Realizer.

Dawn HolmesView profile

Teaching Professor

• Bayesian Networks
• Machine Learning
• Maximum Entropy Formalism
• +4 more

Dr. Dawn E. Holmes is a Teaching Professor at the University of California, Santa Barbara (UCSB), affiliated with the Department of Statistics and Applied Probability and the College of Letters and Science. She holds dual office locations: South Hall 5506 (primary) and Cheadle Hall 1117F. Her work focuses on Bayesian Networks, Maximum Entropy Formalism, Machine Learning, and statistical education innovation. She received the Academic Senate Distinguished Teaching Award 2007-2008 for her contributions to pedagogy. Research Interests: Holmes' research spans foundational topics in Bayesianism, data mining, and intuitionistic mathematics. She has pioneered methods for handling incomplete information in causal networks using entropy optimization and has contributed to probabilistic decision support systems. Her work bridges theoretical advancements with practical applications in education and healthcare. Editorial Contributions: Holmes serves on editorial boards for journals such as The Reasoner , Journal of Supercomputing , and International Journal of Knowledge-Based and Intelligent Information Systems . She has guest-edited special issues on maximum entropy applications and authored/co-edited influential books in data mining and Bayesian networks. Teaching and Mentorship: Holmes teaches both undergraduate and graduate courses in statistics, stochastic processes, and Bayesian methods. She has supervised PhD students and internships, emphasizing statistical computing and pedagogical innovation. Her courses include PSTAT 5A (Statistics), PSTAT 160A/B (Stochastic Processes), and specialized topics like Bayesian Networks (PSTAT 262BN). Awards and Recognition: Beyond her teaching award, Holmes has published over 50 peer-reviewed articles and authored/co-edited eight books. Her work appears in venues like Statistical Methods in Medical Research and Bayesian Inference and Maximum Entropy Methods . Labs and Teams: While no specific lab is named, her research collaborations involve interdisciplinary teams addressing challenges in data mining, healthcare analytics, and educational technology. Her editorial roles reflect active participation in global academic networks.

Nathan KallusView profile

Associate Professor

• Machine Learning
• Causal Inference
• Operations Research
• +3 more

Nathan Kallus is an Associate Professor at Cornell Tech and Cornell University, affiliated with the Department of Operations Research and Information Engineering (ORIE), as well as Computer Science (CS), Economics, Statistics, and Computational Applied Mathematics (CAM). His research focuses on data-driven decision-making, causal inference, optimization, and machine learning. Kallus holds a PhD from MIT and undergraduate degrees from UC Berkeley. He leads the Netflix Machine Learning & Inference Research team and advises students in topics like reinforcement learning, causal ML, and policy evaluation. His work bridges theory and practical applications, with contributions to A/B testing, off-policy evaluation, and spatiotemporal causal inference. Education: PhD in Operations Research (MIT), BA in Pure Mathematics, BS in Computer Science (UC Berkeley). Current research emphasizes causal inference powered by ML, distributional RL for LLM post-training, and efficient sequential decision-making. Recent projects include GST-UNet for spatiotemporal data, Value-Guided Search for reasoning, and nonparametric IV inference. His work has been recognized for its methodological rigor and impact on fields like healthcare, digital platforms, and public policy. Students and Collaborators : Advises PhD students (e.g., Antonia Oprescu, Kaiwen Wang) and alumni in academia and industry roles. Collaborates on projects spanning causal ML, fair AI, and large-scale experimentation. Recruits motivated PhD candidates for interdisciplinary research. Labs and Teams : Research Director at Netflix’s Machine Learning & Inference group, leading work on decision rules, recommendation systems, and causal analysis. Active in Cornell’s ORIE department and affiliated with interdisciplinary initiatives in AI and statistics.

Wulfram GerstnerView profile

Professor

• Computational Neuroscience
• Theoretical Neuroscience
• Spiking Neurons
• +8 more

Wulfram Gerstner is a Full Professor at EPFL, with double appointments in the School of Computer and Communication Sciences and School of Life Sciences . He directs the Computational Neuroscience Laboratory , focusing on spiking neuron models, synaptic plasticity, and learning mechanisms. His research spans: Models of spiking neurons and spike-timing dependent plasticity Neuronal coding in single neurons and populations Linking biologically plausible learning to behavioral outcomes High-dimensional neural dynamics and chaotic networks Recent Publications (2024–2025) explore: Emergent rate-based dynamics in spiking networks Context selectivity and lifelong learning Optogenetic validation of cortical models Energy-efficient deep spiking networks Awards : Valentino Braitenberg Award (2018) Member, Academy of Sciences and Literature Mainz (Germany) Teaching includes courses on Brain-style learning in Neural Networks and Computational Neuroscience: Neuronal Dynamics , targeting interdisciplinary students in NeuroX, Physics, Computer Science, and Life Sciences.

Ferenc HuszárView profile

Professor

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +5 more

Ferenc Huszár is a Professor of Machine Learning at the Department of Computer Science and Technology, University of Cambridge. He joined the department in 2020 after extensive industry experience in machine learning research at Twitter and Magic Pony Technology. His research spans multiple critical areas of machine learning including deep learning optimization, causal inference, probabilistic modeling, and representation learning. Huszár's work explores fundamental questions about why deep networks generalize, the role of optimization algorithms, and mathematical models of emergent behaviors in large language models. His research interests also include unsupervised representation learning, probabilistic foundations of deep learning, and causal inference with a focus on identifiability problems. Analyzing his recent publications (2023-2025), Huszár demonstrates a strong focus on theoretical foundations of machine learning with particular emphasis on causal inference, representation learning, and the mathematical understanding of large language models. His work bridges theoretical insights with practical applications, examining topics ranging from federated learning personalization to mathematical reasoning in LLMs and technical AI safety considerations. Huszár teaches advanced courses including Theory of Deep Learning (R252), Deep Learning and Neural Networks (DeepNN), and Advanced Topics in Machine Learning or Natural Language Processing (R250), where he has covered specialized subjects such as Causal Inference and AI Safety. Beyond academia, he actively contributes to the community through initiatives like Nagymaros AI Retreats for Hungarian high-school students and the Ukraine Math and Science Achievement Fund, which supports Ukrainian students displaced by war.

Matthew CookView profile

Professor

• Computer Science
• Artificial Intelligence
• Neural Networks
• +6 more

M.M. (Matthew) Cook, PhD is a Full Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence group at the Bernoulli Institute. His research focuses on theoretical computer science, neural networks, and biologically inspired computation. He holds a PhD and has extensive experience in neuromorphic engineering, cellular automata, and computational neuroscience. His work spans interdisciplinary areas including spiking neural networks for biosignal processing, self-assembly algorithms, and paradox resolution in causal inference. Key contributions include foundational studies on universality in cellular automata (e.g., Rule 110), neuromorphic hardware implementations, and neuroimaging analysis techniques for synaptic partner detection in Drosophila brain data. Recent publications highlight advancements in event-driven neural networks for epilepsy monitoring, low-power neuromorphic systems, and algorithmic exploration strategies. His research bridges theoretical computer science with practical applications in biomedical engineering and cognitive modeling.

Clayton MorrisonView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Causal Inference
• +3 more

Clayton Morrison is an Associate Professor at the University of Arizona, specializing in artificial intelligence and machine learning. His research focuses on machine learning, probabilistic modeling, causal inference, knowledge representation, and automated planning. He is affiliated with the Core Faculty and PhD Faculty in Artificial Intelligence and Machine Learning programs. His recent work emphasizes information extraction from scientific literature, legal documents, and code binaries, leveraging neural networks and generative models. Notable projects include the Eidos, INDRA, and Delphi systems for causal model recovery and the Tomcat dataset for benchmarking. His research bridges theoretical advancements with practical applications in 3D texture generation, lightweight object detection, and code synthesis. He has contributed to over 60 peer-reviewed articles spanning machine learning, computer vision, and computational linguistics. His work often addresses interdisciplinary challenges such as linking mathematical formulas to textual descriptions (MathAlign) and modeling biological event contexts in biomedical texts. Morrison's lab focuses on creating AI systems that can autonomously learn from complex data, with applications in automated model assembly (Automates) and federated learning frameworks. His research has been supported by grants exploring reinforcement learning, multi-hop inference, and explainable AI.