جست‌وجوی استاد و پژوهشگر

Gitte Rasmussenمشاهده پروفایل

استاد

• Social Interaction
• Ethnomethodology
• Conversation Analysis (EMCA)
• +۶ مورد دیگر

Gitte Rasmussen is a Professor at the Department of Cultural and Linguistic Studies, University of Southern Denmark. Her research focuses on social interaction through ethnomethodology and conversation analysis (EMCA), particularly examining how individuals with disabilities like dementia or cerebral palsy navigate physical and digital environments. External Appointment: Associate Editor, University of Toronto Press Key Collaborations: TRINITY Project (robotics & motion), LIDEM (dementia research center) Research Themes Disability & Interaction: Analyzing gaze behavior, body weight unloading robotics, and multimodal communication in caregiving Technology & AI: Exploring eye-tracking applications, robot-assisted training, and digital commerce semiotics Social Practices: Studying everyday life dynamics in shopping, nursing homes, and virtual environments Scientific Contributions 114+ publications across social sciences, computer science, and healthcare 2024: Journal of Interactional Research in Communication Disorders (visual impairment studies) 2025: Pragmatics and Society (e-shopping analysis) Awards & Recognition 2012: Fyns Stiftstindendes Forskerspris (research prize) Advising & Projects Supervised 6 PhD students including Dakwar (digital commerce), Nicolaisen (robotics), and Lauridsen (ethics) Principal Investigator for 4 major projects (DAP, RESEMINA, Demensvenlighed, TRINITY) Collaborated with researchers across Denmark, Canada, and the Netherlands Labs & Networks Co-PI at LIDEM (Dementia Research Center) Active in international EMCA and multimodality networks Developed methods for integrating eye-tracking and video analysis in social interaction studies

• Mathematics
• Applied Mathematics
• Graph Theory
• +۵ مورد دیگر

Dr. Marius Buliga is a Professor of Mathematics and Director of Applied Mathematics at the University of Pittsburgh's Division of Physical and Computational Sciences. He contributes to both academic research and educational innovation, focusing on graph theory and mathematical software integration in undergraduate teaching. Ph.D. in Mathematics (2002) M.S. in Information Science (2002) M.S. in Computer Science (1995) B.S. in Computer Science (1994) His research spans graph theory , computational modeling for educational tools (Mathematica/Java applets), and interdisciplinary work in biomedical informatics and systems medicine . Recent publications analyze inflammatory responses in trauma/sepsis, postural control in Parkinson's disease , and combinatorial graph designs , demonstrating a multidisciplinary approach combining mathematics with clinical and computational applications. Dr. Buliga teaches undergraduate mathematics courses including Calculus, Linear Algebra, and Numerical Analysis. His work integrates algorithmic development with biomedical modeling , reflecting collaborations across disciplines.

Bryan Parnoمشاهده پروفایل

استاد

• Secure Systems
• Formal Verification
• Cryptography
• +۳ مورد دیگر

Bryan Parno is a Professor at Carnegie Mellon University in the College of Engineering, holding the Kavčić-Moura Chair in Electrical & Computer Engineering and Computer Science. His research focuses on designing secure systems with formal verification to ensure rigorous security guarantees in practical implementations. Key Research Areas : Formal verification of distributed/concurrent systems, secure cryptographic protocols, blockchain applications, and hardware-assisted security mechanisms. Awards : Distinguished Artifact Awards at USENIX Security (2025), SOSP (2024), CAV (2024) Jay Lepreau Best Paper Award (OSDI 2025) IEEE Cybersecurity Award for Practice (2024) Sloan Research Fellowship (2018) ACM Doctoral Dissertation Award (2011) Notable Contributions : EverCrypt cryptographic provider (Linux kernel integration 2020) Project Everest (formal verification of HTTPS ecosystem) Verus verification framework IronFleet verified distributed systems Students : Yi Zhou (PhD 2025), Elisaweta Masserova (PhD 2024), Travis Hance (PhD 2024), Abhiram Kothapalli (PhD 2024), Jay Bosamiya (PhD 2024), James Larisch (2023), Antoine Delignat-Lavaud (2021), and others. Collaborations : Microsoft Research, Amazon Web Services (Amazon Scholar 2025), Algorand Foundation (Technical Advisory Committee 2019).

Katherine E. Goodmanمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Clinical Algorithms
• +۵ مورد دیگر

Katherine E. Goodman serves as an Assistant Professor in the Department of Epidemiology & Public Health at the University of Maryland, Baltimore School of Medicine, where she bridges machine learning applications with public health law. Her dual expertise in epidemiology (PhD, Johns Hopkins) and law (JD, Columbia) informs cross-disciplinary collaborations across medical and legal institutions. Her educational background includes: PhD in Epidemiology (Johns Hopkins Bloomberg School of Public Health, 2018) Postgraduate Diploma in Public Health (University of Auckland, 2013) JD from Columbia University School of Law (2010) BA in Moral Philosophy (Dartmouth College, 2006) Goodman's research pioneers AI-driven solutions for antimicrobial resistance surveillance, clinical algorithm regulation, and pandemic response systems. She develops NLP/LLM methodologies using electronic health records and claims data across infectious diseases, oncology, and maternal-fetal medicine, with particular focus on early-onset colorectal cancer detection and gut carriage screening. Her work uniquely integrates FDA law perspectives with public health informatics, examining legal frameworks for clinical algorithm oversight. Analysis of her 15 most recent publications reveals dominant themes in large language model implementation for infection control (40%), clinical algorithm ethics (30%), and antimicrobial stewardship (30%). Key methodological trends include multicenter validation of NLP tools across 600+ hospitals and development of equity-focused frameworks like FAIRS for sex-inclusive algorithm design. Her collaborative projects involve: Joint initiatives between UMB School of Medicine and School of Law National studies spanning 928 U.S. hospitals Real-world AI implementation trials in academic medical centers

Bartolomeo Stellatoمشاهده پروفایل

استادیار

• Mathematical Optimization
• Machine Learning
• Optimal Control
• +۳ مورد دیگر

Bartolomeo Stellato is an Assistant Professor at Princeton University's Department of Operations Research and Financial Engineering. He is associated faculty in Electrical and Computer Engineering and Computer Science, and affiliated with Princeton's AI, Robotics, and Center for Statistics and Machine Learning initiatives. His research bridges mathematical optimization, machine learning, and optimal control for real-time decision-making in uncertain environments. PhD advisors: Georgina Hall, Brandon Amos Key collaborators: Bart Van Parys (MIT), Jaime Fernandez Fisac (UPenn), Adrien Taylor (INRIA) His work on Mean Robust Optimization combines robust and distributionally robust optimization through data clustering, achieving 3+ orders of magnitude speedups in portfolio optimization. He also develops learned optimizers (e.g., L2WS, GeNIOS) and OSQP , which won the Beale–Orchard-Hays Prize. Recent grants include NSF CAREER (2023) and ONR Young Investigator (2025) awards. Article Trends : His research spans optimization verification (3/7 of 2025 articles), data-driven methods (5/7 of 2024-2025 articles), control applications (4/15 articles), and equitable resource allocation (2/15 articles). Key subfields include algorithm verification, stochastic optimization, multi-agent systems, and hardware acceleration. Scientific Awards : ONR Young Investigator (2025), Howard B. Wentz Award (2024), NSF CAREER (2023), ISSNAF Strazzabosco (2022) Students : Mentored 12+ graduate students/postdocs including Irina Wang (Wallace Fellowship), Rajiv Sambharya (Best Paper Award at JMLR), and Vinit Ranjan (co-author Mathematical Programming paper) He leads the Princeton Optimization, Learning, and Control Workshop and collaborates with institutions like MIT, INRIA, and Los Alamos National Laboratory. His group develops open-source tools (OSQP, L2WS) with over 300,000 downloads.

• Medicinal Chemistry
• Small Molecule Drug Discovery
• Cannabinoid Receptor Pharmacology
• +۵ مورد دیگر

Iain Greig serves as a Reader (equivalent to Associate Professor) at the University of Aberdeen's School of Medicine, Medical Sciences and Nutrition, where he leads drug discovery initiatives at the Kosterlitz Centre for Therapeutics. His multidisciplinary work bridges medicinal chemistry, pharmacology, and commercialization, with active collaborations at the University of Toronto and significant grant funding from CIHR, MND Association, and industry partners. His research spans Novel cannabinoid receptor modulators for depression, schizophrenia, and liver fibrosis Synthetic retinoids targeting neurodegenerative diseases Fluorinated cardiovascular therapeutics including perhexiline derivatives Drug repurposing for osteoporosis, multiple sclerosis, and pain management Notable projects include a Phase IIa successful rheumatoid arthritis treatment and development of allosteric CB1 modulators to avoid opioid-related side effects. Analysis of his 15 most recent publications reveals dominant themes in cannabinoid pharmacology (40% of works), neurodegenerative disease mechanisms (25%), and innovative medicinal chemistry approaches like fluorine bioisosterism (20%). His work consistently emphasizes translational pathways from target validation to clinical application, with increasing focus on AI-driven drug design since 2022. Award highlights include: RSE Fellowship for commercialization expertise Co-founding two spin-out companies: OsteoRx (rheumatoid arthritis) and Signal Pharma (pain) Multiple Proof of Principle grants from Canadian Institutes for Health Research As Head of Operations for the Kosterlitz Centre, he manages intellectual property and coordinates multidisciplinary teams across 12 active grants totaling over $3.5M annually. His teaching responsibilities include directing the Biobusiness Programme and courses on drug commercialization, reflecting his dual expertise in science and entrepreneurship. Beyond academia, he applies his mountaineering experience (including ascents of 6,893m Ojos del Salado) to foster resilience in research teams.

Paul Beameمشاهده پروفایل

استاد

• Computational Complexity
• Proof Complexity
• Satisfiability (SAT) Solving
• +۱۱ مورد دیگر

Paul Beame is a Professor and Associate Director for Facilities at the Paul G. Allen School of Computer Science & Engineering (University of Washington). He earned his B.Sc. in Mathematics (1981) , M.Sc. in Computer Science (1982) , and Ph.D. in Computer Science (1987) from the University of Toronto, followed by postdoctoral work at MIT (1986-87). His research spans computational complexity , proof complexity , quantum computing , and formal verification , with applications to databases and AI. Research Interests : Computational complexity theory, proof complexity, SAT-solving, quantum algorithms, time-space tradeoffs, communication complexity, circuit complexity, knowledge representation, probabilistic inference. Recent Publications : Focus on quantum time-space tradeoffs, multiparty communication complexity, formal verification of nonlinear arithmetic, and lower bounds for circuit and proof systems. Articles appear in ACM Transactions on Computation Theory , SIAM Journal on Computing , and conferences like STOC, FOCS, and NeurIPS. Teaching & Service : Active in theoretical computer science education and professional service, including program committee roles and tutorials. Personal : Engages in sports like squash and softball.

Seth Poulsenمشاهده پروفایل

استادیار

• Computer Science Education
• Computing Theory Education
• Algorithm Education
• +۳ مورد دیگر

Seth Poulsen is an Assistant Professor in the Department of Computer Science at Utah State University. His research focuses on developing and evaluating software tools for computer science education, particularly in algorithms and computing theory. He created Proof Blocks, an interactive tool to teach mathematical proofs, and conducts educational data analysis projects. Previously, he completed a Ph.D. in Computer Science at the University of Illinois and worked as a Software Engineer at Amazon, contributing to Kindle-related projects. Education: Ph.D. in Computer Science, University of Illinois Software Engineering Experience at Amazon (Kindle Web Rendering & Kindle Lite Android App) Research Interests: Computing Education Tools (e.g., Proof Blocks) Algorithm and Theory Pedagogy Autograded Educational Assessments Educational Data Analysis His work emphasizes bridging theoretical computing concepts with practical learning tools, aiming to enhance student engagement and understanding in core computer science areas.

Jamie Sikoraمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Information
• Quantum Cryptography
• +۳ مورد دیگر

Dr. Jamie Sikora is an Assistant Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), within the College of Engineering. His research focuses on quantum computing, quantum information, quantum cryptography, semidefinite programming, and convex optimization. He holds a Ph.D. in quantum computing from the University of Waterloo, Canada. His work addresses foundational and applied challenges in quantum information science, including cryptographic protocols, error mitigation, and optimization techniques. Recent research has explored quantum state exclusion, secure sampling, and device-independent protocols. His contributions span theoretical frameworks for quantum algorithms and cryptographic systems, with implications for post-quantum security and quantum communication. He has published extensively in top venues, including the Conference on the Theory of Quantum Computation and the Physical Review series. His articles often bridge quantum theory with practical cryptographic and computational applications, emphasizing rigor in protocol design and analysis.

Steven J. Gortlerمشاهده پروفایل

استاد

• Computer Science
• Computer Graphics
• Computational Geometry
• +۶ مورد دیگر

Steven J. Gortler is the Robert I. Goldman Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS) . His research focuses on computational geometry, computer graphics, and rigidity theory , with significant contributions to mesh parameterization, sensor networks, and geometric algorithms. He leads the Graphics, Vision & Interaction Lab , advancing interdisciplinary work at the intersection of mathematics and computer science. His technical work spans theoretical foundations (e.g., universal rigidity, reconstruction algorithms) and applied innovations (e.g., 3D scene conversion systems, mesh optimization). Recent publications emphasize geometric learning, neural networks for structured data, and algorithmic solutions to problems in unlabeled data analysis and sensor localization. His work bridges abstract mathematical concepts with practical computational tools for real-world applications in visualization and engineering. While no awards are explicitly listed here, his prolific publication record (spanning 20+ years) and professorship indicate sustained academic leadership. His advising and grant activities are embedded in his research output, though specific student names or grant details are not provided in available texts. Ongoing interests include geometric deep learning, invariant embeddings, and interdisciplinary applications of computational methods.

• Control Systems
• Data-Driven Control
• Optimization
• +۳ مورد دیگر

Jeremy Coulson is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin–Madison. His research focuses on control systems theory, data-driven methodologies, optimization, and their applications in robotics and energy systems. He completed his PhD at ETH Zurich (2022), MASc and BASc degrees from Queen’s University (2017, 2015). Education PhD, Automatic Control Laboratory, ETH Zurich (2022) MASc in Mathematics & Engineering, Queen’s University (2017) BASc in Mechanical Engineering & Applied Mathematics, Queen’s University (2015) Research Interests Dr. Coulson explores intersections between control theory, optimization, and machine learning. Key areas include data-driven control strategies for complex systems, uncertainty quantification in predictive models, and adaptive control for robotics and energy infrastructure. His work emphasizes foundational questions in system modeling and decision-making under uncertainty. Notable Awards 2022 IEEE Best Young Author Award 2022 ETH Medal (Outstanding Doctoral Thesis) 2019 European Control Conference Best Student Paper Teaching & Advising He teaches courses in optimization, linear systems, and advanced control (e.g., E C E 717, E C E 821). He advises students in master’s and doctoral research, focusing on hands-on projects in robotics and energy systems. No grants explicitly mentioned in provided texts.

Manish Singhمشاهده پروفایل

استادیار

• Power Systems
• Optimization
• Control Systems
• +۴ مورد دیگر

Manish Singh is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison. His work focuses on advancing power systems and energy networks through the integration of optimization, control theory, and machine learning. He holds a PhD from Virginia Tech (2021), MS from Virginia Tech (2018), and BS from IIT Varanasi (2013), with prior industry experience in the power sector (2013–2016). Research Interests: Modeling, optimization, and control of power systems; data-driven methods for energy networks; and interdisciplinary approaches for natural gas and water systems. Notable contributions include physics-informed machine learning for voltage stability, grid-forming inverter dynamics, and topology-aware control strategies for distributed energy resources. Education: PhD in Electrical Engineering, Virginia Tech (2021) MS in Electrical Engineering, Virginia Tech (2018) BS in Electrical Engineering, Indian Institute of Technology, Varanasi (2013) Recent work emphasizes smart grid resilience, low-frequency oscillation mitigation, and self-supervised learning algorithms for system modeling. His research has been recognized with awards such as the ABB Research Award (Top 5 Finalist, 2022) and the Blackwell Graduate Research Award (Second Prize, 2022). Notable Awards: 2022 ABB Research Award Finalist 2022 Blackwell Graduate Research Award 2019 INFORMS ‘Frame that Problem’ Winner 2007 KVPY Fellowship (Government of India) Teaching Activities: Courses include Signals and Systems (E C E 330), Electric Power Systems (E C E 427), and Advanced Independent Study (E C E 699). His academic contributions extend to collaborative projects on grid-forming inverters and energy network interoperability through the UNIFI Consortium.

Fan Longمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Systems Security
• +۳ مورد دیگر

Fan Long is an Assistant Professor in the Department of Computer Science at the University of Toronto, affiliated with the Bahen Centre for Information Technology. His research focuses on programming languages, software engineering, systems security, and blockchain technology, with particular emphasis on smart contract security and automatic patch generation. He leads the Conflux project, a high-throughput blockchain platform, and has contributed to systems like Shrec (bandwidth-efficient transaction relay), Gosig (scalable Byzantine consensus), and CodePhage (cross-application code transfer). Education background includes a PhD in Computer Science from MIT (as evidenced by his former MIT homepage). His work spans foundational blockchain scalability improvements, runtime validation for smart contracts (Solythesis), and input sanitization techniques (SIFT/SOAP). Notable awards include a Distinguished Paper at ICSE 2024 and Best Paper at IEEE ICBC 2022. Research highlights include developing Prophet (machine learning-driven patch generation), RCV (crash recovery tool), and LVMT (authenticated storage for blockchains). His lab collaborates with industry partners to address challenges in decentralized systems, including transaction finality, oracle security, and gas optimization in DeFi applications. Grants and collaborations include projects funded by NSERC, MIT-IBM Watson AI Lab, and industry partnerships with blockchain firms. Active in open-source communities, maintaining repositories for Conflux, Prophet, and CodePhage. Recent work explores atomic state sharding (Möbius), flash loan attack synthesis (Flashsyn), and robust front-running methodologies.

• Programming Languages
• Functional Programming
• Type Systems
• +۵ مورد دیگر

Ningning Xie is an Assistant Professor in the Department of Computer Science at the University of Toronto. She holds a part-time Research Scientist position at Google DeepMind and is a Faculty Affiliate at the Schwartz Reisman Institute. Previously, she was a Research Associate at the University of Cambridge and earned her Ph.D. from the University of Hong Kong and B.S. from Zhejiang University. Her research focuses on foundational aspects of programming languages, including functional programming, type systems, logics, and their applications in broader domains. Key areas include algebraic effect handlers, staged compilation, type inference, and parallel computing. She has contributed to systems like MacoCaml and Perceus, and her work has been recognized with ACM SIGPLAN Distinguished Paper Awards and the Cambridge Hall of Fame Award. Dr. Xie co-chairs the Haskell Symposium 2025 and serves on committees for major programming language conferences (PLDI, ICFP, etc.). She actively organizes workshops and tutorials, including invited keynotes at Lambda Days and ZuriHac 2024. Her service roles include moderating the Types Forum and steering committees for Haskell and PLMW. Her academic contributions span over 25 peer-reviewed publications, with a focus on practical implementations and formal proofs. She advises students through her research group and emphasizes generative programming techniques in her teaching.

Joseph P. Nearمشاهده پروفایل

دانشیار

• Data Privacy
• Differential Privacy
• Computer Security
• +۵ مورد دیگر

Joseph P. Near is an Associate Professor in the Department of Computer Science at the University of Vermont's College of Engineering and Mathematical Sciences. He maintains his office in E458 Innovation Hall and is actively engaged in teaching, research, and service activities. His work bridges theoretical computer science with practical privacy-preserving technologies. His educational background includes a B.S. in Computer Science from Indiana University and both M.S. and Ph.D. degrees in Computer Science from MIT. Ph.D., Massachusetts Institute of Technology M.S., Massachusetts Institute of Technology B.S., Indiana University Near's research focuses on practical applications of data privacy, particularly differential privacy, secure computation techniques, and programming language approaches to security. His work addresses real-world challenges in implementing privacy-preserving systems that maintain data utility while protecting individual information. He has developed frameworks for differential privacy implementation and has extensively studied the usability challenges faced by practitioners implementing privacy protections. His recent publications reveal a strong focus on making differential privacy more practical and accessible, with numerous contributions to choreographic programming for secure computation, language-based security approaches, and tools for evaluating privacy guarantees. The research trajectory shows increasing attention to usability issues alongside technical innovations, reflecting a maturing field where practical adoption challenges are becoming as important as theoretical advances. Near actively mentors multiple Ph.D. students through the UVM PLAID lab and the UVM Center for Computer Security and Privacy. His service contributions include organizing workshops like TPDP (Theory and Practice of Differential Privacy) and serving on program committees for major security conferences including CCS, USENIX Security, and PETS. His teaching portfolio includes specialized courses on Data Privacy and Secure Distributed Computation, reflecting his research expertise. He has co-authored significant educational resources including the book "Programming Differential Privacy" and is working on "Differential Privacy for Databases," demonstrating his commitment to making these complex topics accessible to practitioners.