جستجوی استاد راهنمای دانشگاهی

Jennifer Lachowiecمشاهده پروفایل

دانشیار

• genetic interactions
• gene regulatory networks
• plant robustness
• +۷ مورد دیگر

Jennifer Lachowiec serves as Associate Professor in the Department of Plant Sciences and Plant Pathology at Montana State University's College of Agriculture. Her research program focuses on understanding genetic robustness in plants through interdisciplinary approaches spanning quantitative genetics, genomics, and molecular biology. She teaches foundational courses in biometry and research design while leading a technology-driven laboratory that utilizes unoccupied aerial systems for large-scale phenotyping. Educational background: Ph.D. in Molecular and Cellular Biology, University of Washington (2014) B.S. in Genetics and Anthropology, University of Wisconsin - Madison (2008) Her research centers on predicting phenotypes from genetic sequences with emphasis on plant robustness—the capacity to maintain phenotypes despite genetic mutations and environmental fluctuations. The Lachowiec Lab integrates quantitative genetics, molecular biology, and evolutionary approaches to study gene regulatory networks and genetic interactions. A key innovation involves deploying UAVs and automated imaging systems to quantify robustness across plant populations, enabling high-throughput analysis of crop resiliency traits. This work bridges fundamental genetic mechanisms with practical agricultural applications for developing climate-resilient crops. Recent publications (2022-2025) reveal consistent focus on UAV-based phenotyping, polyploid genomics, and crop stress adaptation. Key trends include development of robust phenomic databases, genetic dissection of barley malting traits, herbicide resistance mechanisms in aquatic weeds, and metabolic responses to environmental stress. The work demonstrates strong translational impact through USDA-funded projects addressing nitrogen-efficient camelina production and heat stress tolerance in cereal crops. Scientific recognition includes: Nominated for USDA Teaching Award (2023) Phi Kappa Phi Anna K. Fridley Award (2023) College of Agriculture Teaching Award of Merit (2023) Transformative Teaching Award (2022) Open Education Resources Adoption Award (2021) Research is supported by multiple USDA NIFA and DOE grants including 'Enhancing Camelina Oilseed Production with Minimum Nitrogen Fertilization,' 'Uncovering developmental mechanisms to sustain grain number during heat stress,' and 'Genetic controls for crop microbiome recruitment.' Dr. Lachowiec serves on the North American Plant Phenotyping Network DEI Committee and has organized outreach events including MSU Family Science Day. Her laboratory, located in the Plant Bioscience Building on traditional Amskapii Pikani, Apsaalooke, Tsétsêhéstâhese, and Seliš lands, maintains specialized facilities for UAV-based phenotyping and growth room imaging systems to advance plant robustness research.

Kimin Leeمشاهده پروفایل

استادیار

• Safe and capable decision-making agents
• Physical AI
• Reinforcement Learning from Human Feedback
• +۹ مورد دیگر

Kimin Lee is an assistant professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where he focuses on developing safe and capable decision-making agents. His research spans multiple aspects of artificial intelligence with a strong emphasis on safety and reliability. Dr. Lee completed his educational journey at KAIST, earning a Ph.D. in Electrical Engineering with a focus on Machine/Deep Learning (2015-2020), advised by Professor Jinwoo Shin. He also holds a Master's degree in Electrical Engineering (Wireless Communication Networks, 2013-2015) and a Bachelor's degree in Electrical Engineering (2009-2013), both from KAIST. His primary research interests include: Physical AI - developing AI systems that can interact safely and effectively with the physical world Alignment - particularly reinforcement learning from human feedback (RLHF) and scalable oversight techniques Monitoring - safety evaluation frameworks and benchmarking for AI systems LLM Agents - enhancing the capabilities and safety of large language model-based agents Dr. Lee's recent publications reveal a strong trajectory toward addressing critical challenges in AI safety. His work consistently bridges theoretical advances with practical applications, particularly in the areas of reinforcement learning, computer vision, and natural language processing. A notable trend in his research is the development of methods to evaluate and enhance the safety of AI systems, especially large language models and diffusion models, while maintaining or improving their capabilities. As an active member of the academic community, Dr. Lee serves as an area chair for major conferences including NeurIPS, ICLR, and ICML, and regularly reviews for top-tier AI venues. He has also organized workshops focused on safe and trustworthy AI agents. Dr. Lee's research group at KAIST appears to focus on AI safety and decision-making, with research projects spanning from theoretical foundations to practical implementations of safe AI systems. His collaborative work with institutions like UC Berkeley and Google Research demonstrates the interdisciplinary nature of his research approach.

• Databases
• Information Systems
• Data Management
• +۹ مورد دیگر

Johann Eder is a full professor for Information and Communication Systems at the Department of Informatics Systems, University of Klagenfurt, Austria, and currently serves as Deputy Head of Department. He previously held positions at the Universities of Linz, Hamburg, Vienna, and Klagenfurt, and was Vice President of the Austrian Science Funds (FWF) from 2005-2013. He was also a visiting scholar at AT&T Shannon Labs, NJ. Educational Background: Diplom-Ingenieur, University of Linz Doctor of Technical Sciences, University of Linz Johann Eder's research focuses on databases, information systems, and data management for medical research, including temporal information modeling, process evolution, and workflow systems. His work spans temporal data warehousing, exception handling in workflows, and application interoperability, with a particular emphasis on privacy and quality in medical data lakes and federated biobanks. Recent publications highlight trends in temporal reasoning for business processes, medical data quality, and service composition optimization. His editorial roles include positions at ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering . He leads the Information and Communication Systems Research Group at AAU, contributing to projects like federated biobank integration and blockchain-based smart contracts.

Nadia Figueroaمشاهده پروفایل

استادیار

• Robotics
• Human-Robot Interaction
• Machine Learning
• +۴ مورد دیگر

Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania . She holds secondary appointments in Computer and Information Science (CIS) and Electrical and Systems Engineering (ESE) , and is a core faculty member at the General Robotics, Automation, Sensing & Perception (GRASP) Laboratory . Before joining Penn, she was a Postdoctoral Associate at MIT's CSAIL under Prof. Julie A. Shah and earned her Ph.D. at EPFL with Prof. Aude Billard. Her academic journey includes research roles at DLR and NYU Abu Dhabi , along with degrees from Monterrey Tech (B.Sc.) and TU Dortmund (M.Sc.) . Education: Ph.D. in Robotics, Control and Intelligent Systems, EPFL (2019) M.Sc. in Automation and Robotics, TU Dortmund B.Sc. in Mechatronics, Monterrey Tech Her research focuses on adaptive intelligence for robots to learn from and interact with humans, emphasizing fluid collaboration in safety-critical applications. Key areas include reactive control algorithms , human-robot co-manipulation , and real-time navigation . Techniques integrate machine learning , control theory , and perception to ensure stability, safety, and robustness in dynamic environments. Recent work trends highlight reactive motion policies for imitation learning, dynamical systems modulation with non-convex obstacles, and EEG-based intent detection for assistive robotics. She also explores soft robotics with MORF systems and SE(3) control for end-effector precision. Her publications reflect interdisciplinary approaches at the intersection of robotics, AI, and human biomechanics . She has taught MEAM-520 Introduction to Robotics at Penn and served as Head Teaching Assistant at EPFL for courses like MICRO-401 Machine Learning Programming . Her Figueroa (Human-Centered) Robotics Lab , established in 2022, collaborates with institutions like MIT and EPFL to advance fluid human-robot autonomy.

Divya Mahajanمشاهده پروفایل

استادیار

• Computer Architecture
• Distributed Systems
• Machine Learning Systems
• +۲ مورد دیگر

Divya Mahajan is an Assistant Professor holding dual appointments in the College of Computing and College of Engineering at Georgia Institute of Technology. She directs the Systems Infrastructure and Architecture Research Lab, focusing on sustainable computing platforms for large-scale AI, machine learning, and data storage systems. Her research integrates computer architecture, distributed systems, and database technologies to optimize end-to-end data pipelines. Mahajan earned her PhD from Georgia Tech and Bachelor's from IIT Ropar (President's Gold Medal). Prior to academia, she was a Senior Researcher at Microsoft Azure, leading communication collective designs for distributed DNN training. Awards include NCWIT Collegiate Award (2017) and HPCA Distinguished Paper Award (2016). Research spans hardware-software co-design for ML systems, efficient recommendation architectures, federated learning optimization, and PIM/NPU acceleration. Recent projects address challenges in large-scale model serving, edge-cloud integration, and sustainable computing. Student advisees include PhD and MS researchers in systems and architecture. Publications demonstrate consistent innovation in accelerating ML workloads through novel hardware/software techniques, with work appearing in ISCA, MICRO, ASPLOS, NeurIPS, and VLDB. Current projects explore energy-efficient AI infrastructure and domain-specialized systems for emerging economies.

Eugene Bagdasarianمشاهده پروفایل

استادیار

• Artificial Intelligence Security
• Privacy-Preserving Technologies
• Federated Learning
• +۴ مورد دیگر

Eugene Bagdasarian is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst. His research focuses on security and privacy in AI systems, particularly adversarial machine learning, federated learning vulnerabilities, and contextual integrity frameworks. He holds a PhD from Cornell Tech and a prior engineering degree from Bauman University, with industry experience at Cisco as a software engineer. Research interests include backdoor attacks in federated learning (developing frameworks like Backdoors101 and Mithridates), privacy-preserving technologies such as AirGapAgent and Ancile, and mitigating vulnerabilities in multi-modal systems. His work has addressed instruction injections, adversarial illusions, and bias in generative models, with coverage in media like The Economist and VentureBeat. Notable achievements include the Usenix Security Distinguished Paper Award, Apple Scholars in AI/ML Fellowship, and Digital Life Initiative Fellowship. His contributions span technical domains like differential privacy, secure aggregation, and policy-based federated learning frameworks. Advising and grants: No specific advising/grants listed. However, his research collaborations include developing systems like Ancile (use-based privacy enforcement) and Mithridates (backdoor auditing). Current work trends focus on advancing security in LLM agents, dynamic network firewalls, and ethical AI governance.

Lamine M. Miliمشاهده پروفایل

استاد

• Power Systems
• Signal Processing
• Robust Estimation Theory
• +۷ مورد دیگر

Lamine M. Mili is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His expertise spans power systems, signal processing, and robust estimation theory. He holds an IEEE Fellowship (2016) for contributions to robust state estimation in power systems. Mili's research focuses on advancing methodologies for power system reliability, control, and integration of renewable energy sources. His work includes studies on dynamic state estimation, nonlinear dynamics, bifurcation theory, and quantum computing applications. He has contributed extensively to resilience engineering and computational social science in power systems. Mili’s recent articles address challenges in smart grids, quantum circuit error prediction, and multifractal signal analysis in EEG. His research often combines advanced statistical techniques with real-world grid data, emphasizing robustness and adaptability in dynamic environments. Education: Ph.D., University of Liège, 1987 M.S., University of Tunis, 1983 B.S., Swiss Federal Institute of Technology, Lausanne, 1976 Research Interests: Power system stability and control State estimation and robust filtering Quantum computing for power systems Resilience and cyber-physical-social systems Nonlinear dynamics and bifurcation analysis His recent publications reflect a focus on hybrid power systems, probabilistic methods, and data-driven approaches for grid optimization. The 2025 articles highlight advancements in photovoltaic state estimation, quantum error prediction, and robust modulation techniques. Mili’s work often bridges theoretical models with practical grid applications, emphasizing uncertainty quantification and real-time monitoring.

Marco Magginiمشاهده پروفایل

استاد

• machine learning
• neural networks
• kernel machines
• +۶ مورد دیگر

Marco Maggini is a Full Professor in the Department of Information Engineering and Mathematics at the University of Siena, a position he has held since joining the university in 1996. His academic career spans over 25 years with foundational expertise in computer engineering and artificial intelligence, focusing on theoretical and applied machine learning research. His educational background includes: Laurea degree (cum laude) in Electronics Engineering from the University of Florence (1991) Ph.D. in Computer Engineering and Control Systems from the University of Florence (1995) Prof. Maggini's research encompasses machine learning, neural networks, kernel machines, and the integration of symbolic and sub-symbolic knowledge systems. He extends these foundations into practical applications including web mining, search engine technology, pattern recognition, natural language processing, and computer vision. This interdisciplinary approach bridges theoretical computer science with real-world implementation challenges across multiple domains. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on multilingual NLP applications, particularly educational puzzle generation for low-resource languages (Italian, Arabic, Persian, Turkish) using LLMs. His work demonstrates consistent innovation in named entity recognition, commonsense reasoning evaluation, and cross-lingual adaptation techniques. Secondary research threads include medical imaging segmentation, molecular property prediction, and AI security vulnerabilities, reflecting his broad technical mastery across computer vision, bioinformatics, and adversarial machine learning. No specific scientific awards were mentioned in the provided documentation, though his editorial roles indicate peer recognition within the academic community. While student mentoring details are absent from the source material, his position as Full Professor and leadership of SAILab imply active graduate supervision. His extensive publication record (120+ papers) and editorial service suggest significant research grant involvement, though specific funding sources remain undocumented. He directs the Siena Artificial Intelligence Laboratory (SAILab), which serves as an interdisciplinary hub for advancing machine learning theory and applications. The lab's current projects emphasize educational technology, multilingual NLP systems, and the integration of symbolic reasoning with neural architectures, maintaining strong industry and international academic collaborations.

David Wagnerمشاهده پروفایل

استاد

• Computer Security
• Security for AI (Large Language Models)
• AI for Security
• +۷ مورد دیگر

David Wagner is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He holds the Carl J. Penther Chair in Engineering and leads the Wagner Research Group, which focuses on computer security, AI security, and systems security. Education Ph.D., Computer Science, UC Berkeley (2000) M.S., Computer Science, UC Berkeley (1999) A.B., Mathematics, Princeton University (1995) Research Interests Wagner's work spans computer security, with a particular focus on security for AI systems (especially large language models) and security for wearable/smartphone devices . He has extensive experience in wireless security , sensor network security , applied cryptography , and software security , including electronic voting systems and program analysis for security. His research group develops tools like OpenCount for election auditing, AuditBear for voting machine analysis, and PrimeVul for vulnerability detection in code. Scientific Awards ACM SIGSAC Test of Time Award (2022) IEEE Symposium on Security and Privacy Test of Time Award (2020) USENIX Test of Time Award (2019) Google Faculty Research Award (2018) Internet Defense Prize (2017) ACM SIGSAC Outstanding Innovation Award (2016) IBM Pat Goldberg Memorial Best Paper Award (2008) UC Berkeley Distinguished Teaching Award (2006) Sloan Research Fellow (2003) NSF CAREER Award (2001) Advising & Grants Wagner has advised numerous graduate students including Sizhe Chen , Zhanhao Hu , and Dennis Jacob . His research has been supported by grants from NSF (CAREER, ITR), Microsoft, Intel, and the Okawa Foundation. Labs & Teams He co-directs the Berkeley Security Research Group , participates in the ACTION Institute , and leads the KACST-UCB Joint Center on Cybersecurity. His team develops open-source security tools and datasets like DiverseVul for vulnerability detection.

Dan Crisanمشاهده پروفایل

استاد

• Stochastic Analysis
• Fluid Dynamics
• Stochastic Partial Differential Equations (SPDEs)
• +۶ مورد دیگر

Professor Dan Crisan is a Professor of Mathematics at Imperial College London and Director of the EPSRC Centre for Doctoral Training in the Mathematics of Planet Earth. He holds a PhD in Mathematics from the University of Edinburgh and an MSci in Mathematics from the University of Bucharest. His research focuses on Stochastic Analysis, Stochastic PDEs, Fluid Dynamics, Nonlinear Filtering, and Data Assimilation. He leads the Stochastic Transport in Upper Ocean Dynamics (STUOD) project, supported by an ERC Synergy Grant, exploring stochastic models in geophysical fluid dynamics. His academic roles include directing the MPE CDT and teaching advanced courses like Stochastic Calculus with Applications to Non-Linear Filtering. He collaborates extensively with researchers globally on topics including stochastic fluid dynamics, particle methods, and Bayesian inference. His work bridges theoretical advancements with applications in climate modeling and environmental systems. Education: PhD (University of Edinburgh, 1996), MSci (University of Bucharest, 1992). Professional roles include Director of MPE CDT (2013–present), Professor at Imperial College (2011–present), and prior academic appointments at the University of Cambridge and Imperial College. His research emphasizes stochastic processes in fluid dynamics, with projects funded by EPSRC and EU grants. Current PhD supervision opportunities are available in areas like stochastic fluid dynamics and data assimilation. Key achievements include foundational contributions to particle filtering, numerical methods for stochastic PDEs, and stochastic transport models. His work on the Camassa-Holm equation and rotating shallow water models demonstrates expertise in nonlinear wave dynamics and stochastic parametrization. He actively engages in international collaborations and serves on editorial boards for leading journals in stochastic analysis.

• Cyber-Physical Systems
• Machine Learning
• Autonomous Systems
• +۵ مورد دیگر

Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.

James Forbesمشاهده پروفایل

دانشیار

• Dynamics and Control
• Robotics
• Nonlinear Control
• +۵ مورد دیگر

James Forbes is an Associate Professor in the Department of Mechanical Engineering at McGill University. He holds the title of William Dawson Scholar and is affiliated with the Dynamics Estimation & Control of Aerospace & Robotics Systems research group. His primary research focus is on Dynamics and Control, with emphasis on navigation, guidance, and control (GNC) techniques for robotic systems. He teaches courses such as MECH 309 (Numerical Methods), MECH 412 (System Dynamics), and advanced topics in control systems. Forbes earned his Ph.D. in Aerospace Science and Engineering from the University of Toronto, following an M.A.Sc. from the same institution and a B.A.Sc. in Mechanical Engineering from the University of Waterloo. His research interests include nonlinear state estimation (batch methods, filtering), control synthesis via optimization (LQR, LMI approaches), and data-driven modeling using Koopman operator techniques. Applications span unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and SLAM systems. He has developed the navlie Python package for state estimation on Lie groups. Notable awards include the William Dawson Scholar distinction. His recent work focuses on multi-UAV localization, robust control algorithms, and sensor fusion techniques. He collaborates on projects involving UWB-based positioning and inertial navigation systems.

• Computational Process Engineering
• Pharmaceutical Manufacturing
• Process Optimization and Planning
• +۳ مورد دیگر

Gintaras Reklaitis is the Burton and Kathryn Gedge Distinguished Professor of Chemical Engineering at Purdue University's Davidson School of Chemical Engineering. He joined Purdue in 1970 and holds a B.S. from Illinois Institute of Technology (1965) and M.S./Ph.D. from Stanford University (1969). His research focuses on applying computing and systems technology to optimize processing systems, particularly in pharmaceutical manufacturing and integrated energy systems. Key areas include batch process design, enterprise-wide planning, and robust scheduling under uncertainty. Professor Reklaitis has been recognized with prestigious awards, including National Academy of Engineering membership (2007) and the Professional Achievement Award from Illinois Institute of Technology (2006). He has co-advised multiple graduate students, including Megha Das, Zachary Hillman, Shrivatsa Korde, and Dalton Yu. His work appears in journals like Computers & Chemical Engineering and Journal of Pharmaceutical Sciences . Research highlights include developing frameworks for real-time quality assurance in pharmaceutical manufacturing, integrating data management systems, and advancing continuous manufacturing technologies. His contributions also span editorial roles, including Editor-in-chief of Computers & Chemical Engineering (1986–2008). Current projects emphasize digital design tools, techno-economic analysis, and sensor-driven process monitoring.

Ping Wangمشاهده پروفایل

استاد

• Chinese Intellectual History
• Literature
• Medieval Philology
• +۳ مورد دیگر

Ping Wang is a Professor in the Department of Asian Languages & Literature at the University of Washington, serving as Graduate Program Coordinator and Graduate Admissions & Education Committee Chair. They hold a Ph.D. in Chinese Language and Literature from the University of Washington (2006). Their research focuses on Chinese Intellectual History, Medieval Philology, Poetry and Poetics, and Translation Studies. Recent research trends include interdisciplinary approaches to classical texts and modern translation methodologies. The listed articles highlight contributions to computational linguistics and AI, though primary disciplinary focus remains humanities. No specific awards are listed, though the department's 'Awards & Honors' page may contain institutional recognitions. Advising emphasizes graduate education coordination rather than individual student mentorship. Active in departmental administration and curriculum development. Research affiliations include the Bilingual and Biliteracy Research Lab and the Early Buddhist Manuscript Project. Office located in GWN M246 with regular Thursday office hours.

Ben Ward-Cherrierمشاهده پروفایل

مدرس ارشد

• Robotics
• Computational Neuroscience
• Prosthetics
• +۲ مورد دیگر

Ben Ward-Cherrier is a Senior Lecturer in Robotics at the University of Bristol's School of Engineering Mathematics and Technology. His research focuses on biomimetic tactile sensing, neuromorphic systems for robotics, and haptic interfaces. He develops artificial tactile systems inspired by biological sensory mechanisms for applications in prosthetics, robotic manipulation, and human-robot interaction. Key research areas include neuromorphic tactile sensors that mimic biological afferents, real-time texture and edge classification algorithms, incipient slip detection for stable grasping, and vibrotactile feedback systems. Recent work integrates spiking neural networks with tactile hardware for efficient sensory processing. Publications demonstrate advancement in tactile sensing capabilities, including braille recognition in noisy environments, psychophysics-inspired benchmarking, multi-modal texture/velocity classification, and industrial applications like composite defect detection. Research bridges computational neuroscience with practical robotic systems.