جست‌وجوی استاد و پژوهشگر

Shahar Kovalskyمشاهده پروفایل

استادیار

• Optimization
• Geometry
• Computer Graphics
• +۴ مورد دیگر

Shahar Kovalsky is an Assistant Professor of Mathematics at the University of North Carolina at Chapel Hill (UNC-CH), with secondary appointments in the School of Data Science and Society and an adjunct role in the Department of Computer Science (both within the College of Arts and Sciences). He holds a Ph.D. in Computer Science and Applied Mathematics from the Weizmann Institute of Science, and B.Sc./M.Sc. degrees in Mathematics and Electrical Engineering from Ben-Gurion University. His research bridges optimization, geometry, computer graphics, machine learning, and their applications in biology and medicine. Kovalsky's work includes advancements in geometric modeling, medical imaging diagnostics, and evolutionary biology analysis. He has been recognized with awards such as the Günter Enderle Best Paper Award (Eurographics 2016) and the SGP 2015 Best Paper Award. Education: Ph.D., Computer Science & Applied Mathematics, Weizmann Institute of Science, Israel B.Sc./M.Sc., Mathematics & Electrical Engineering, Ben-Gurion University, Israel Research Interests: His work focuses on geometric optimization, machine learning for medical diagnostics (e.g., thyroid cytopathology), and computational methods in evolutionary biology. He develops algorithms for injectivity-preserving parameterizations, medical image analysis, and Gaussian process landmarking for morphometric studies. Key Contributions: Deep learning models for thyroid cancer prediction from smartphone images Geometric algorithms for surface parameterization and injectivity Applications of Gaussian processes in evolutionary shape analysis Awards: Günter Enderle Best Paper Award (Eurographics 2016) SGP 2015 Best Paper Award Teaching & Mentoring: Kovalsky advises graduate and undergraduate students (e.g., Fengyu Yang, Maddy Vinal) and teaches courses like Optimization in Machine Learning at UNC-CH. He also co-advises students in interdisciplinary projects with Caroline Moosmüller and Jeremy Marzuola. Labs & Affiliations: Member of the Carolina Center for Interdisciplinary Applied Mathematics (CCIAM) and previously a Phillip Griffiths Assistant Research Professor at Duke University (2017-2020).

Michael J. Jacobson Jr.مشاهده پروفایل

استاد

• Computational Number Theory
• Public-Key Cryptography
• Cryptographic Protocols
• +۳ مورد دیگر

Michael J. Jacobson Jr. is a Professor in the Department of Computer Science at the University of Calgary. He also serves as Deputy Director of ISPIA and Director of the Information Security Program. His research focuses on computational number theory and its applications to public-key cryptography, including the development of efficient algorithms for arithmetic in class groups of number fields and algebraic curves. His work aims to enhance cryptographic protocol efficiency while improving security through algorithmic advancements and benchmarking. His research interests span cryptographic protocol design, discrete logarithm problem analysis, and the interplay between number theory and cryptography. Notable contributions include studies on divisor arithmetic on hyperelliptic curves, class group computations in quadratic fields, and the security implications of cryptographic systems. Recent publications highlight his interdisciplinary work in both cryptography and medical informatics, including studies on clinical trial methodologies for dermatological conditions. He has also contributed to foundational research in computational algebraic geometry and algorithm optimization for cryptographic applications. Michael has received no publicly listed scientific awards in the provided texts. His advising and grants include collaborations on topics ranging from cryptographic infrastructure to clinical trial design. He leads the Information Security Program at the University of Calgary, fostering interdisciplinary research in cybersecurity and algorithmic security.

Yahya Fathiمشاهده پروفایل

استاد

• Linear Programming
• Nonlinear Programming
• Integer Programming
• +۴ مورد دیگر

Professor Yahya Fathi specializes in optimization and operations research at North Carolina State University. His research includes mathematical programming, production systems, and quality engineering, with applications in manufacturing and data analytics. Awarded multiple teaching excellence honors.

Aritra Mitraمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Control Theory
• +۶ مورد دیگر

Aritra Mitra is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from Purdue University (2020), an M.Tech. from IIT Kanpur (2015), and a B.E. from Jadavpur University (2013). Before joining NC State, he was a postdoctoral researcher at the University of Pennsylvania. His research focuses on enabling reliable, efficient learning and decision-making in large-scale distributed systems, addressing challenges like computation, communication constraints, and adversarial robustness. Key areas include control theory, machine learning, signal processing, and network science. Education: Ph.D., Electrical and Computer Engineering, Purdue University (2020) M.Tech., Electrical Engineering, Indian Institute of Technology Kanpur (2015) B.E., Electrical Engineering, Jadavpur University (2013) Research Interests: Dr. Mitra’s work bridges theoretical foundations with practical applications in distributed systems. He designs algorithms for federated learning, reinforcement learning, and adversarial robustness, with applications in control systems and networked environments. Recent efforts emphasize finite-time analysis of TD learning, heterogeneous federated systems, and resilient control under communication constraints. His contributions often integrate tools from stochastic approximation, optimization, and signal processing. Publications: His articles explore cutting-edge topics like federated TD learning, robust system identification under heavy-tailed noise, and distributed multi-agent optimization. Recent trends highlight advancements in asynchronous algorithms, delay-adaptive systems, and model-free control under communication bottlenecks. Grants & Labs: While specific grants are not detailed, his research aligns with themes in distributed computing and control, suggesting potential involvement in NSF or industry-funded projects. No lab-specific details are provided in the text.

Chanan Singhمشاهده پروفایل

استاد

• Reliability of Electric Power Systems
• Integration of Renewable Energy Sources
• Cyber-Physical Systems Reliability
• +۳ مورد دیگر

Chanan Singh is a distinguished academic serving as a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. He holds the Irma Runyon Chair and is a Regents Professor. His affiliations include the College of Engineering and a Guest Professorship at Tsinghua University's Department of Electrical Engineering (2010–2015). Dr. Singh earned his Ph.D. in Electrical Engineering from the University of Saskatchewan, alongside M.S. and B.S. degrees from the same institution and Punjab Engineering College, respectively. His research focuses on reliability and security of electric power systems , including renewable energy integration and cyber-physical systems resilience. He pioneered methodologies for hurricane impact analysis, cyber-malfunction modeling, and wind farm optimization. Key achievements include the IEEE-PES Roy Billinton Award (2010), PMAPS Merit Award (2008), and Fellow of IEEE (1991). His work has been recognized globally, including through over 20 major awards and fellowships. Dr. Singh advises students like Hangtian Lei and leads funded projects on power system resilience. He is affiliated with the Electric Power System Group , advancing interdisciplinary research in energy systems and reliability engineering.

Chao Tianمشاهده پروفایل

دانشیار

• Information Theory
• Distributed Data Storage
• Coding Theory
• +۶ مورد دیگر

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Peter Winklerمشاهده پروفایل

استاد

• Discrete Mathematics
• Probability Theory
• Combinatorics
• +۲ مورد دیگر

Peter Winkler is William Morrill Professor of Mathematics and Computer Science at Dartmouth College, conducting research in discrete mathematics, probability, and theoretical computer science. His work connects combinatorial problems with statistical physics and algorithmic complexity. Key research areas include: Probabilistic methods in combinatorics and game theory Phase transitions in discrete structures Geometric probability and optimization Mathematical puzzles and paradoxes Winkler's publications resolve fundamental questions in pursuit-evasion theory, geometric set optimization, and combinatorial phase transitions. His work on mathematical puzzles has influenced both academic research and popular mathematics. Current projects explore limit permutations, abelian networks, and new puzzle collections. Honored with the Mathematical Association of America's Lester R. Ford Award and David P. Robbins Prize, Winkler has held visiting positions at the Institute for Advanced Study and Mathematical Sciences Research Institute.

Xavier Alameda-Pinedaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Audio Processing
• +۷ مورد دیگر

Xavier Alameda-Pineda is a Research Director at Inria Grenoble Rhône-Alpes, where he leads the RobotLearn Team. He is affiliated with Université Grenoble Alpes and has been a key member of the Perception team. His work integrates machine learning, computer vision, and audio processing for scene understanding and human-robot interaction. Research Interests: His research lies at the intersection of multimodal machine learning and social behavior analysis. He focuses on developing algorithms for understanding human behavior in natural settings using audio-visual signals, with applications in robotics and AI companions. His work emphasizes real-world challenges such as noisy data, missing modalities, and dynamic environments. Publication Trends: His recent publications reflect a consistent focus on multimodal fusion, particularly combining vision and audio for social scene analysis. Themes include group behavior recognition, sound source separation, and cross-modal learning, often applied in robotics contexts. Scientific Awards: SIGMM Rising Star Award 2018 IEEE TMM Outstanding Associate Editor Award 2022 ACM TOMM Best Paper Award 2020 Best Paper Award, ACM MM 2015 Best Scientific Paper Award, ICPR 2016 Best Student Paper Award, IEEE WASPAA 2015 Outstanding Paper Award, ICMI 2011 Novel Technology Paper Award Finalist, IROS 2017 Advising and Grants: Xavier has mentored students and early-career researchers, evidenced by co-authored student papers. He coordinated the H2020 SPRING project on socially pertinent robots in gerontological healthcare and co-leads an AI chair on audio-visual perception for companion robots, indicating leadership in funded research initiatives. Labs and Teams: He is the leader of the RobotLearn Team at Inria and was previously part of the Perception team. He has also collaborated with the Multimodal and Human Understanding Group at the University of Trento.

Jianqiang Chengمشاهده پروفایل

دانشیار

• Stochastic Programming
• Robust Optimization
• Distributionally Robust Optimization
• +۷ مورد دیگر

Dr. Jianqiang Cheng is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering. He is also a member of the Graduate Faculty and affiliated with the Applied Mathematics and Statistics Graduate Interdisciplinary Programs. His research is centered on optimization under uncertainty with applications in energy systems and logistics. Research Interests: His primary research areas include stochastic programming, robust optimization, distributionally robust optimization, semidefinite programming, and chance-constrained optimization. He applies these methodologies to challenges in power systems, renewable energy integration, microgrid design, and resilient supply chains. The recent publications (2020–2022) reflect a strong trend toward data-driven and computationally efficient methods in optimization. Key themes include distributionally robust optimization under moment and Wasserstein ambiguity, chance-constrained AC optimal power flow, and resilient supply chain modeling under disruptions such as the COVID-19 pandemic. His work frequently appears in top journals like INFORMS Journal on Computing , IEEE Transactions on Power Systems , and European Journal of Operational Research . Scientific Awards: Best Short Paper Award, INFORMS Workshop on Data Science (Fall 2022) NSF CAREER Award, National Science Foundation (Spring 2022) Science Foundation Arizona's 2017 Bisgrove Scholar (Spring 2017) Dr. Cheng has secured significant research funding, including the NSF CAREER Award, supporting his work in data-driven optimization. He collaborates extensively with researchers in energy systems and operations research, including K. Pan, M. Cheramin, A. M. Fathabad, and A. Lisser. While specific advisees are not listed, his role as a member of the Graduate Faculty indicates active supervision of graduate students in systems engineering, applied mathematics, and statistics. His research contributes to the development of advanced optimization models for real-world systems affected by uncertainty, particularly in energy and logistics. Though no specific lab is mentioned, his work implies involvement in computational optimization and energy systems modeling research groups within the College of Engineering.

• Theoretical Computer Science
• Algorithms and Data Structures
• Computational Geometry
• +۵ مورد دیگر

Raimund Seidel is a Professor in the Department of Computer Science at Universität des Saarlandes, leading the Chair of Theoretical Computer Science. He is actively involved in research and teaching, focusing on foundational aspects of algorithms and data structures, particularly in computational geometry. His primary research interests include theoretical computer science , design and analysis of efficient algorithms , geometric data structures , randomized algorithms , and combinatorial geometry . His work addresses fundamental problems such as planar point location, convex hull computation, and efficient encoding of triangulations. He also investigates geometric algorithms under the transdichotomous model, leveraging word-level parallelism. The selected publications reflect a long-standing contribution to computational geometry and data structure theory , with a focus on randomized methods and exact complexity analysis. His research combines theoretical rigor with practical implications for algorithm design. Award or honor not found in the provided text. Prof. Seidel has advised several students, including Alexander Malkis , Ralf Osbild , Udo Adamy , Christian Sohler , and others, many of whom have gone on to academic and research careers. No explicit information about grants or funding is available in the text. He leads a research group within the Department of Computer Science at Universität des Saarlandes, mentoring current staff such as László Kozma , Giorgi Nadiradze , and Lavinia Dinu . The group maintains active research in theoretical computer science and computational geometry.

• VLSI Design
• Built-In Self-Test (BIST)
• Test Data Compression
• +۷ مورد دیگر

Chr. Kavousianos is an Assistant Professor in the Department of Informatics at the University of Ioannina, Greece. He is actively engaged in research and teaching in the fields of VLSI design, testability, and low-power testing. He has been involved in major national and international research programs such as Heracleitus II, Pythagoras, and NSF-SRC (USA). Research Interests: His research focuses on advanced techniques in built-in self-test (BIST), test data compression, scan-based testing, fault tolerance, and embedded control architectures. He explores methods to reduce test data volume, power consumption during testing, and improve defect coverage in integrated circuits. Publication Trends: His recent publications (2004–2011) show a strong trend toward defect-aware testing, power-efficient test compression, and multicore SoC testing. He frequently collaborates with leading researchers, including Prof. Krishnendu Chakrabarty (Duke University). His work on multilevel Huffman coding and reseeding techniques has been highly influential, with one paper among the most accessed in 2004. Special Distinction for Excellent Academic Performance from TEE, 1996 Doctoral Scholarship 'In Memory of Professor Maritsa', 1999 Postdoctoral Scholarship from IKY, 2002 Advising and Grants: He has supervised multiple postdoctoral researchers, PhD candidates (e.g., Vasilis Tenentes, Emmanouil Kalligeros), and master’s students. He has led research projects such as 'Embedded Control Architectures' (Heracleitus II) and 'Design Techniques for Embedded Self-Control Circuits' (Pythagoras II). His international collaboration with Duke University included a postdoctoral research role in 2009. Labs and Teams: He leads a research group at the University of Ioannina with postdocs, PhD students, and visiting professors, including Prof. Krishnendu Chakrabarty. His team works on cutting-edge VLSI testing and design methodologies.

• Computational Chemistry
• Computer-Aided Drug Design
• Cheminformatics
• +۱۲ مورد دیگر

Andrea Volkamer is a computational chemist and active principal investigator in the field of computer-aided drug design (CADD), with a focus on kinase targets, druggability prediction, and machine learning applications. She has published extensively in journals such as the Journal of Chemical Information and Modeling and Journal of Medicinal Chemistry , with recent work up to 2025 indicating an ongoing academic research program. Her research group, referred to as 'volkamerlab,' develops open-source tools including DoGSite, KiSSim, KinFragLib, and TeachOpenCADD, which are widely used in both academic and industrial drug discovery settings. Her research interests span computational drug discovery , structural bioinformatics , kinase inhibitor design , off-target and polypharmacology prediction , and educational platforms for CADD . She emphasizes open science and reproducibility, particularly through the TeachOpenCADD initiative, which provides interactive Jupyter Notebooks and KNIME workflows for teaching cheminformatics concepts. The 15 most recent publications reflect a strong trend toward integrating machine learning and deep learning (e.g., transformers, graph neural networks) with structure-based methods such as molecular docking, free energy calculations, and binding site comparison. Her work increasingly addresses real-world challenges in drug discovery, including kinase mutation resistance, selectivity optimization, and in vivo toxicity prediction using conformal and hybrid models. Scientific Contributions and Awards: Development of key computational tools: DoGSite, KiSSim, KinFragLib, TeachOpenCADD. Leadership in open-source and open-education initiatives in cheminformatics. Active publication record in top-tier journals with interdisciplinary impact. Advising and Grants: While specific student names and grant details are not mentioned in the provided text, her role as a corresponding author on numerous publications and the existence of a dedicated research lab ('volkamerlab') imply that she mentors students and postdoctoral researchers. She likely secures competitive funding to support her research in computational drug discovery and method development. Labs and Teams: She leads the Volkamer Lab ('volkamerlab'), which focuses on developing and applying computational methods for drug discovery. The lab collaborates with both academic and pharmaceutical partners and emphasizes open-source software development and educational outreach.

Zahra Mobiniمشاهده پروفایل

استادیار

• Operations Management
• Healthcare Operations
• Behavioral Operations
• +۵ مورد دیگر

Zahra Mobini is an Assistant Professor of Operations Management at the Scheller College of Business, Georgia Institute of Technology. Her research focuses on human-centric solutions in operations, particularly in healthcare settings, using empirical and analytical methods to study behavior and incentive alignment. Research Interests: Her work centers on behavioral and human-centric operations management, healthcare operations, and health analytics. She investigates how technology, regulations, and clinical protocols shape provider and patient behavior, aiming to optimize outcomes through better system design. Scientific Awards and Recognition: WAIM Research Fellowship (NSF Future of Work at the Human-Technology Frontier) INFORMS Decision Analysis Society recognition POMS College of Healthcare Operations recognition Education: Ph.D. in Management Science - Operations Management, Jindal School of Management, University of Texas at Dallas George Family Foundation Postdoctoral Fellow, Industrial and Systems Engineering, Georgia Tech Advising and Grants: Her research has been supported by the WAIM Research Fellowship with funding from the NSF. While no students are currently listed, she is expected to advise graduate students in operations management and healthcare analytics. Labs and Research Teams: Though not explicitly named, her postdoctoral fellowship at Georgia Tech’s ISyE and her current role suggest active collaboration with interdisciplinary research groups focused on intelligent systems and healthcare operations.

John Griecoمشاهده پروفایل

استاد پژوهشی

• Ecology of Vector-Borne Diseases
• Medical Entomology
• Vector Ecology
• +۱۰ مورد دیگر

John Grieco is a Research Professor in the Department of Biological Sciences and a joint faculty member at the Eck Institute for Global Health at the University of Notre Dame. He serves as Associate Director of the Eck Institute and co-director of the Belize Vector and Ecology Center (BVEC), where he leads a multidisciplinary research program focused on the ecology, behavior, and control of vectors of diseases such as malaria, dengue, Chagas, and Japanese encephalitis. University: University of Notre Dame School: College of Science Department: Department of Biological Sciences Position: Research Professor Joint Appointment: Eck Institute for Global Health Leadership Role: Associate Director, Eck Institute for Global Health; Co-Director, BVEC Dr. Grieco completed his Ph.D. at the Uniformed Services University, an M.S. at Texas A&M University, and earned his B.S. from the University of Notre Dame. His research spans vector behavior, spatial repellency, insecticide resistance, and disease risk modeling using GIS and remote sensing. He has led and collaborated on field studies in Belize, Thailand, Nepal, Peru, Mexico, and South Korea, and maintains partnerships with industry leaders such as BASF, SC Johnson, and Olfactor Laboratories. His recent publications highlight innovations in spatial repellent deployment, mosquito trap efficacy, and novel vector control strategies. The research consistently emphasizes translational outcomes for public health, particularly in low-resource settings. His work is supported by major initiatives including the Spatial Repellents Program, IMPACTS, AEGIS, and BRICC, often in collaboration with WHO, PAHO, and the Mayo Clinic. Dr. Grieco actively mentors undergraduate and Master of Science in Global Health students, supervising numerous capstone projects on vector-borne disease interventions. His lab, the Achee/Grieco Lab, is known for its field-based, interdisciplinary approach and commitment to training the next generation of global health scientists. Medical Entomology Vector Ecology and Behavior Vector Control Strategies Spatial Repellents and Irritants Infectious Disease Dynamics Disease Risk Mapping (GIS/Remote Sensing) Arbovirus and Malaria Transmission Scientific awards and honors are not explicitly mentioned in the provided texts. Dr. Grieco advises numerous students at both undergraduate and graduate levels, particularly through the Master of Science in Global Health program. His lab supports student research on topics including insecticide resistance, community perceptions of vector control, disease surveillance, and behavioral effects of repellents. He also leads the High-Throughput Screening System (HITSS) for evaluating vector behavioral responses. The Achee/Grieco Lab operates the Belize Vector and Ecology Center (BVEC), which conducts extensive field research, community outreach, and surveillance in Belize. BVEC collaborates with the Ministry of Health, University of Belize, and international partners. The lab also participates in global networks such as the Worldwide Insecticide Resistance Network (WIN) and the Remote Emerging Disease Intelligence NETwork (REDI-NET).

Laurie Ciaramellaمشاهده پروفایل

استادیار

• Innovation Economics
• Intellectual Property
• Markets for Technology
• +۴ مورد دیگر

Laurie Ciaramella is an Assistant Professor in Economics at Institut Polytechnique de Paris (Télécom Paris) and an Affiliated Research Fellow at the Max Planck Institute for Innovation and Competition. She also serves as an adjunct Associate Professor at the Norwegian School of Economics (NHH) and holds the ANR JCJC TaxIP grant as principal investigator. Her educational background includes a BSc in Economics from Université Paris Dauphine, an MSc in Economics of Markets and Organizations from Toulouse School of Economics, and a PhD in Economics from MINES ParisTech. Her doctoral thesis, 'Trade and Relocation of Intellectual Property: Essays on the Markets for Patents,' was supervised by Yann Ménière and Catalina Martinez and earned recognition as a Best Dissertation Award Finalist at the Academy of Management. Ciaramella's research centers on the economics of innovation, with specific expertise in intellectual property systems, tax policy implications for innovation, markets for technology, and innovation financing. She employs advanced econometric methods to investigate how firms manage intellectual property assets, how taxation affects patent relocation decisions, and how geographical constraints impact technology markets. Her work demonstrates how patent boxes influence corporate tax strategies and how intellectual property can serve as loan collateral, particularly benefiting small and financially constrained firms. Her publications reveal consistent focus on European patent systems, international knowledge flows, and the intersection of tax policy with innovation strategies. Key trends show increasing empirical sophistication in analyzing firm-level patent data, with growing attention to policy implications for international tax coordination and innovation financing mechanisms. Best Dissertation Award Finalist, Technology and Innovation Management Division, Academy of Management (2018) Best PhD Paper (Bent Dalum) Award, DRUID 17 Academy (2017) Presentation at Rising Star Session, EARIE (2017) As principal investigator of the ANR JCJC TaxIP grant, Ciaramella leads significant research on taxation and intellectual property. Her research visits to Northwestern University's Searle Center and EPFL's College of Management demonstrate international scholarly engagement. Her work bridges theoretical economics with practical policy considerations, particularly regarding how tax regimes affect corporate innovation strategies and intellectual property management. Current projects include 'Intellectual Property as Loan Collateral,' investigating how firms use IP assets to secure financing. Ciaramella maintains active research collaborations with institutions including Max Planck Institute for Innovation and Competition, CREST research center, and various European patent offices. Her methodological approach combines microeconomic analysis with legal and tax system insights to examine real-world innovation dynamics.